Spaces:
Running
Running
Download index.html from caustino/Dissertation: direct link, hf CLI and curl.
- Browser
- Download file 156 kB
-
https://huggingface.co/spaces/caustino/Dissertation/resolve/main/index.html
- Command line
-
hf download hf://spaces/caustino/Dissertation/index.html
-
curl -L -o index.html https://huggingface.co/spaces/caustino/Dissertation/resolve/main/index.html
156 kB
| <html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang=""> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <meta name="generator" content="pandoc" /> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> | |
| <meta name="author" content="Caustin Lee McLaughlin" /> | |
| <title>Integrating Neuroimaging, Genomic Data, and Neuroimmune Biomarkers to Characterize Executive Function Impairment</title> | |
| <style> | |
| code{white-space: pre-wrap;} | |
| span.smallcaps{font-variant: small-caps;} | |
| span.underline{text-decoration: underline;} | |
| div.column{display: inline-block; vertical-align: top; width: 50%;} | |
| div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} | |
| ul.task-list{list-style: none;} | |
| pre > code.sourceCode { white-space: pre; position: relative; } | |
| pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } | |
| pre > code.sourceCode > span:empty { height: 1.2em; } | |
| .sourceCode { overflow: visible; } | |
| code.sourceCode > span { color: inherit; text-decoration: inherit; } | |
| div.sourceCode { margin: 1em 0; } | |
| pre.sourceCode { margin: 0; } | |
| @media screen { | |
| div.sourceCode { overflow: auto; } | |
| } | |
| @media print { | |
| pre > code.sourceCode { white-space: pre-wrap; } | |
| pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; } | |
| } | |
| pre.numberSource code | |
| { counter-reset: source-line 0; } | |
| pre.numberSource code > span | |
| { position: relative; left: -4em; counter-increment: source-line; } | |
| pre.numberSource code > span > a:first-child::before | |
| { content: counter(source-line); | |
| position: relative; left: -1em; text-align: right; vertical-align: baseline; | |
| border: none; display: inline-block; | |
| -webkit-touch-callout: none; -webkit-user-select: none; | |
| -khtml-user-select: none; -moz-user-select: none; | |
| -ms-user-select: none; user-select: none; | |
| padding: 0 4px; width: 4em; | |
| color: #aaaaaa; | |
| } | |
| pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; } | |
| div.sourceCode | |
| { } | |
| @media screen { | |
| pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } | |
| } | |
| code span.al { color: #ff0000; font-weight: bold; } /* Alert */ | |
| code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */ | |
| code span.at { color: #7d9029; } /* Attribute */ | |
| code span.bn { color: #40a070; } /* BaseN */ | |
| code span.bu { color: #008000; } /* BuiltIn */ | |
| code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */ | |
| code span.ch { color: #4070a0; } /* Char */ | |
| code span.cn { color: #880000; } /* Constant */ | |
| code span.co { color: #60a0b0; font-style: italic; } /* Comment */ | |
| code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */ | |
| code span.do { color: #ba2121; font-style: italic; } /* Documentation */ | |
| code span.dt { color: #902000; } /* DataType */ | |
| code span.dv { color: #40a070; } /* DecVal */ | |
| code span.er { color: #ff0000; font-weight: bold; } /* Error */ | |
| code span.ex { } /* Extension */ | |
| code span.fl { color: #40a070; } /* Float */ | |
| code span.fu { color: #06287e; } /* Function */ | |
| code span.im { color: #008000; font-weight: bold; } /* Import */ | |
| code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */ | |
| code span.kw { color: #007020; font-weight: bold; } /* Keyword */ | |
| code span.op { color: #666666; } /* Operator */ | |
| code span.ot { color: #007020; } /* Other */ | |
| code span.pp { color: #bc7a00; } /* Preprocessor */ | |
| code span.sc { color: #4070a0; } /* SpecialChar */ | |
| code span.ss { color: #bb6688; } /* SpecialString */ | |
| code span.st { color: #4070a0; } /* String */ | |
| code span.va { color: #19177c; } /* Variable */ | |
| code span.vs { color: #4070a0; } /* VerbatimString */ | |
| code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */ | |
| .display.math{display: block; text-align: center; margin: 0.5rem auto;} | |
| </style> | |
| <link rel="stylesheet" href="style.css" /> | |
| </head> | |
| <body> | |
| <header id="title-block-header"> | |
| <h1 class="title">Integrating Neuroimaging, Genomic Data, and | |
| Neuroimmune Biomarkers to Characterize Executive Function | |
| Impairment</h1> | |
| <p class="author">Caustin Lee McLaughlin</p> | |
| <p class="date">August 2026</p> | |
| <div class="abstract"> | |
| <div class="abstract-title">Abstract</div> | |
| This dissertation develops and defends a research question on whether | |
| the integration of multimodal biomarkers — functional and structural | |
| neuroimaging, polygenic and candidate-gene genomic data, and neuroimmune | |
| laboratory markers — can meaningfully characterize executive function | |
| impairment in an individual case, and what the implications are for | |
| precision medicine, disability assessment, and genomic data privacy. The | |
| work proceeds in three layers: (i) a critical synthesis of the | |
| peer-reviewed literature on ADHD neurobiology, dopaminergic and | |
| serotonergic pathways, polygenic risk scoring, and neuroimmune | |
| interactions; (ii) a structured case-based analysis of a single adult | |
| proband, drawing on direct-to-consumer genotype data, clinical | |
| neuroimaging reports, longitudinal laboratory values, and the formal | |
| academic accommodations record; and (iii) a legal, ethical, and | |
| health-policy analysis of how multimodal biomarker evidence is, and | |
| could be, evaluated under HIPAA, GINA, the ADA, and SSA disability | |
| frameworks. Findings are presented descriptively; the work explicitly | |
| distinguishes between established science, observations from the present | |
| case, and broader implications, and avoids causal claims that the data | |
| cannot support. | |
| </div> | |
| </header> | |
| <nav id="TOC" role="doc-toc"> | |
| <ul> | |
| <li><a | |
| href="#integrating-neuroimaging-genomic-data-and-neuroimmune-biomarkers-to-characterize-executive-function-impairment-implications-for-precision-medicine-disability-assessment-and-health-data-privacy">Integrating | |
| Neuroimaging, Genomic Data, and Neuroimmune Biomarkers to Characterize | |
| Executive Function Impairment: Implications for Precision Medicine, | |
| Disability Assessment, and Health Data Privacy</a> | |
| <ul> | |
| <li><a href="#table-of-contents">Table of Contents</a></li> | |
| <li><a href="#abstract">Abstract</a></li> | |
| <li><a href="#introduction">1. Introduction</a> | |
| <ul> | |
| <li><a href="#background-and-motivation">1.1 Background and | |
| Motivation</a></li> | |
| <li><a href="#research-question-and-aims">1.2 Research Question and | |
| Aims</a></li> | |
| <li><a href="#hypotheses">1.3 Hypotheses</a></li> | |
| <li><a | |
| href="#distinguishing-established-science-case-data-and-implications">1.4 | |
| Distinguishing Established Science, Case Data, and Implications</a></li> | |
| <li><a href="#structure-of-the-dissertation">1.5 Structure of the | |
| Dissertation</a></li> | |
| </ul></li> | |
| <li><a href="#literature-review">2. Literature Review</a> | |
| <ul> | |
| <li><a href="#adhd-neurobiology-and-executive-function">2.1 ADHD | |
| Neurobiology and Executive Function</a></li> | |
| <li><a href="#functional-mri-and-structural-mri-findings">2.2 Functional | |
| MRI and Structural MRI Findings</a></li> | |
| <li><a href="#dopaminergic-pathways">2.3 Dopaminergic Pathways</a></li> | |
| <li><a href="#serotonergic-pathways">2.4 Serotonergic Pathways</a></li> | |
| <li><a href="#polygenic-risk-scores-and-psychiatric-genetics">2.5 | |
| Polygenic Risk Scores and Psychiatric Genetics</a></li> | |
| <li><a href="#neuroimmune-interactions">2.6 Neuroimmune | |
| Interactions</a></li> | |
| <li><a href="#precision-medicine-and-genomic-privacy">2.7 Precision | |
| Medicine and Genomic Privacy</a></li> | |
| <li><a href="#disability-assessment-and-health-policy">2.8 Disability | |
| Assessment and Health Policy</a></li> | |
| <li><a href="#summary">2.9 Summary</a></li> | |
| </ul></li> | |
| <li><a href="#methods">3. Methods</a> | |
| <ul> | |
| <li><a href="#study-design">3.1 Study Design</a></li> | |
| <li><a href="#datasets">3.2 Datasets</a></li> | |
| <li><a href="#bioinformatic-processing">3.3 Bioinformatic | |
| Processing</a></li> | |
| <li><a href="#statistical-methods">3.4 Statistical Methods</a></li> | |
| <li><a href="#ethical-considerations-and-privacy-protections">3.5 | |
| Ethical Considerations and Privacy Protections</a></li> | |
| <li><a href="#reproducibility">3.6 Reproducibility</a></li> | |
| </ul></li> | |
| <li><a href="#results">4. Results</a> | |
| <ul> | |
| <li><a href="#genetic-variants">4.1 Genetic Variants</a></li> | |
| <li><a href="#neuroimaging-observations">4.2 Neuroimaging | |
| Observations</a></li> | |
| <li><a href="#laboratory-values">4.3 Laboratory Values</a></li> | |
| <li><a href="#academic-accommodations-record-1">4.4 Academic | |
| Accommodations Record</a></li> | |
| <li><a href="#cross-modal-concordance-1">4.5 Cross-Modal | |
| Concordance</a></li> | |
| </ul></li> | |
| <li><a href="#discussion">5. Discussion</a> | |
| <ul> | |
| <li><a href="#agreement-with-published-literature">5.1 Agreement with | |
| Published Literature</a></li> | |
| <li><a href="#disagreement-and-open-questions">5.2 Disagreement and Open | |
| Questions</a></li> | |
| <li><a href="#limitations">5.3 Limitations</a></li> | |
| <li><a href="#clinical-significance">5.4 Clinical Significance</a></li> | |
| </ul></li> | |
| <li><a href="#legal-ethical-and-health-policy-considerations">6. Legal, | |
| Ethical, and Health Policy Considerations</a> | |
| <ul> | |
| <li><a href="#hipaa-and-the-clinical-research-boundary-1">6.1 HIPAA and | |
| the Clinical-Research Boundary</a></li> | |
| <li><a href="#gina-and-the-limits-of-anti-discrimination-law">6.2 GINA | |
| and the Limits of Anti-Discrimination Law</a></li> | |
| <li><a href="#ada-title-ii-and-reasonable-accommodation">6.3 ADA Title | |
| II and Reasonable Accommodation</a></li> | |
| <li><a | |
| href="#ssa-listings-and-the-objective-medical-evidence-standard">6.4 SSA | |
| Listings and the “Objective Medical Evidence” Standard</a></li> | |
| <li><a href="#genomic-privacy-in-the-direct-to-consumer-era">6.5 Genomic | |
| Privacy in the Direct-to-Consumer Era</a></li> | |
| <li><a href="#procedural-access-and-the-no-mans-land-problem-1">6.6 | |
| Procedural Access and the “No-Man’s Land” Problem</a></li> | |
| <li><a href="#summary-1">6.7 Summary</a></li> | |
| </ul></li> | |
| <li><a href="#future-research">7. Future Research</a> | |
| <ul> | |
| <li><a href="#validation-in-larger-cohorts">7.1 Validation in Larger | |
| Cohorts</a></li> | |
| <li><a | |
| href="#longitudinal-imaging-and-repeated-biomarker-assessment">7.2 | |
| Longitudinal Imaging and Repeated Biomarker Assessment</a></li> | |
| <li><a href="#multimodal-biomarker-modeling">7.3 Multimodal Biomarker | |
| Modeling</a></li> | |
| <li><a href="#secure-genomic-computing">7.4 Secure Genomic | |
| Computing</a></li> | |
| <li><a href="#ai-assisted-clinical-decision-support">7.5 AI-Assisted | |
| Clinical Decision Support</a></li> | |
| <li><a href="#policy-and-regulatory-research">7.6 Policy and Regulatory | |
| Research</a></li> | |
| </ul></li> | |
| <li><a href="#conclusion">8. Conclusion</a></li> | |
| <li><a href="#references">References</a></li> | |
| <li><a href="#appendices">Appendices</a> | |
| <ul> | |
| <li><a href="#appendix-a.-complete-snp-table-grch38">Appendix A. | |
| Complete SNP Table (GRCh38)</a></li> | |
| <li><a href="#appendix-b.-neuroimaging-figures">Appendix B. Neuroimaging | |
| Figures</a></li> | |
| <li><a href="#appendix-c.-statistical-outputs">Appendix C. Statistical | |
| Outputs</a></li> | |
| <li><a href="#appendix-d.-bioinformatics-workflow">Appendix D. | |
| Bioinformatics Workflow</a></li> | |
| <li><a href="#appendix-e.-r-python-analysis-scripts">Appendix E. R / | |
| Python Analysis Scripts</a></li> | |
| <li><a | |
| href="#appendix-f.-ethical-and-data-management-documentation">Appendix | |
| F. Ethical and Data Management Documentation</a></li> | |
| </ul></li> | |
| <li><a href="#acknowledgments">Acknowledgments</a></li> | |
| <li><a href="#statement-of-authorship">Statement of Authorship</a></li> | |
| </ul></li> | |
| </ul> | |
| </nav> | |
| <h1 | |
| id="integrating-neuroimaging-genomic-data-and-neuroimmune-biomarkers-to-characterize-executive-function-impairment-implications-for-precision-medicine-disability-assessment-and-health-data-privacy">Integrating | |
| Neuroimaging, Genomic Data, and Neuroimmune Biomarkers to Characterize | |
| Executive Function Impairment: Implications for Precision Medicine, | |
| Disability Assessment, and Health Data Privacy</h1> | |
| <p><strong>A Master’s Dissertation</strong></p> | |
| <p>Caustin Lee McLaughlin, B.S. (Candidate)</p> | |
| <p>August 2026</p> | |
| <hr /> | |
| <h2 id="table-of-contents">Table of Contents</h2> | |
| <table> | |
| <colgroup> | |
| <col style="width: 33%" /> | |
| <col style="width: 33%" /> | |
| <col style="width: 33%" /> | |
| </colgroup> | |
| <thead> | |
| <tr class="header"> | |
| <th>Chapter</th> | |
| <th>Title</th> | |
| <th>Page</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>Abstract</td> | |
| <td>1</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>1</td> | |
| <td>Introduction</td> | |
| <td>3</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>2</td> | |
| <td>Literature Review</td> | |
| <td>8</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>2.1 ADHD Neurobiology and Executive Function</td> | |
| <td>8</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>2.2 Functional MRI and Structural MRI Findings</td> | |
| <td>11</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>2.3 Dopaminergic Pathways</td> | |
| <td>14</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>2.4 Serotonergic Pathways</td> | |
| <td>17</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>2.5 Polygenic Risk Scores and Psychiatric Genetics</td> | |
| <td>19</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>2.6 Neuroimmune Interactions</td> | |
| <td>22</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>2.7 Precision Medicine and Genomic Privacy</td> | |
| <td>25</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>2.8 Disability Assessment and Health Policy</td> | |
| <td>28</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>3</td> | |
| <td>Methods</td> | |
| <td>32</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>3.1 Study Design</td> | |
| <td>32</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>3.2 Datasets</td> | |
| <td>32</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>3.3 Bioinformatic Processing</td> | |
| <td>35</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>3.4 Statistical Methods</td> | |
| <td>37</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>3.5 Ethical Considerations and Privacy Protections</td> | |
| <td>38</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>4</td> | |
| <td>Results</td> | |
| <td>41</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>4.1 Genetic Variants</td> | |
| <td>41</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>4.2 Neuroimaging Observations</td> | |
| <td>47</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>4.3 Laboratory Values</td> | |
| <td>51</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>4.4 Academic Accommodations Record</td> | |
| <td>53</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>4.5 Cross-Modal Concordance</td> | |
| <td>54</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>5</td> | |
| <td>Discussion</td> | |
| <td>56</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>5.1 Agreement with Published Literature</td> | |
| <td>56</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>5.2 Disagreement and Open Questions</td> | |
| <td>60</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>5.3 Limitations</td> | |
| <td>62</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>5.4 Clinical Significance</td> | |
| <td>64</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>6</td> | |
| <td>Legal, Ethical, and Health Policy Considerations</td> | |
| <td>67</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>6.1 HIPAA and the Clinical-Research Boundary</td> | |
| <td>67</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>6.2 GINA and the Limits of Anti-Discrimination Law</td> | |
| <td>69</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>6.3 ADA Title II and Reasonable Accommodation</td> | |
| <td>71</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>6.4 SSA Listings and the “Objective Medical Evidence” Standard</td> | |
| <td>73</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>6.5 Genomic Privacy in the Direct-to-Consumer Era</td> | |
| <td>75</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>7</td> | |
| <td>Future Research</td> | |
| <td>78</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>8</td> | |
| <td>Conclusion</td> | |
| <td>82</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td></td> | |
| <td>References</td> | |
| <td>85</td> | |
| </tr> | |
| <tr class="even"> | |
| <td></td> | |
| <td>Appendices</td> | |
| <td>105</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>A</td> | |
| <td>Complete SNP Table (GRCh38)</td> | |
| <td>105</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>B</td> | |
| <td>Neuroimaging Figures</td> | |
| <td>110</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>C</td> | |
| <td>Statistical Outputs</td> | |
| <td>113</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>D</td> | |
| <td>Bioinformatics Workflow</td> | |
| <td>116</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>E</td> | |
| <td>R / Python Analysis Scripts</td> | |
| <td>119</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>F</td> | |
| <td>Ethical and Data Management Documentation</td> | |
| <td>124</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <hr /> | |
| <h2 id="abstract">Abstract</h2> | |
| <p><strong>Background.</strong> Executive function impairment in adults | |
| is most commonly attributed to attention-deficit/hyperactivity disorder | |
| (ADHD), a highly heritable neurodevelopmental condition with documented | |
| polygenic architecture, structural and functional neuroimaging | |
| correlates, and emerging neuroimmune interactions. Disability assessment | |
| and precision-medicine efforts are increasingly interested in whether | |
| multimodal biomarker integration can improve diagnostic precision, | |
| prognostic stratification, and accommodation decisions.</p> | |
| <p><strong>Research question.</strong> Can a defensible, evidence-based | |
| characterization of executive function impairment be constructed by | |
| integrating neuroimaging, genomic, and neuroimmune data from a single | |
| adult proband, and what are the implications for precision medicine, | |
| disability assessment, and genomic data privacy?</p> | |
| <p><strong>Methods.</strong> A critical synthesis of peer-reviewed | |
| literature (PubMed, Google Scholar, and primary journals) was conducted | |
| across eight thematic areas: ADHD neurobiology; structural and | |
| functional MRI; dopaminergic and serotonergic pathways; polygenic risk | |
| scoring; neuroimmune interactions; precision medicine and genomic | |
| privacy; and disability assessment. A structured case-based analysis of | |
| a single adult proband was then performed using: (i) direct-to-consumer | |
| genotype data (23andMe); (ii) clinical fMRI and structural MRI reports; | |
| (iii) longitudinal laboratory values including CD4 counts, MTHFR-related | |
| methylation indices, and inflammatory markers; and (iv) the formal | |
| academic accommodations record. Bioinformatic processing followed | |
| published pipelines (PRSice-2 / LDpred2). Cross-modal concordance was | |
| assessed descriptively, without causal inference.</p> | |
| <p><strong>Findings.</strong> Established literature supports a | |
| polygenic model of ADHD in which dopaminergic and serotonergic variants | |
| (e.g., DRD2 rs1800497, COMT rs4680, SLC6A3 / DAT1 10/10 VNTR, DRD4 | |
| 7R-VNTR, FKBP5 rs1360780) modulate executive function through partially | |
| overlapping pathways. The present case exhibits a constellation of | |
| functional and structural neuroimaging findings (bilateral basal ganglia | |
| volume reduction, dorsolateral prefrontal cortex hypoactivation, | |
| anterior cingulate cortex hypoactivation, default-mode network | |
| connectivity alterations) and a functionally curated set of | |
| candidate-gene variants that are concordant with — but not diagnostic of | |
| — the published endophenotype. Polygenic risk score calculation yielded | |
| a value of 0.92 (92nd percentile) against a published ADHD reference | |
| distribution. Neuroimmune markers (CD4 count fluctuations, MTHFR C677T | |
| carrier status, IL-10 polymorphism) provide an additional layer of | |
| concordance but do not, on their own, support causal claims.</p> | |
| <p><strong>Conclusions.</strong> Multimodal biomarker integration in a | |
| single case can produce a coherent, evidence-anchored characterization | |
| of executive function impairment, but such characterizations must remain | |
| descriptive and hypothesis-generating rather than diagnostic of | |
| causality. The legal and ethical implications of using multimodal | |
| biomarkers in disability assessment — particularly the tension between | |
| clinical utility, genomic privacy (HIPAA, GINA), and the SSA’s | |
| “objective medical evidence” standard — are unresolved and warrant | |
| further empirical, regulatory, and bioethical inquiry.</p> | |
| <p><strong>Keywords:</strong> ADHD, executive function, polygenic risk | |
| score, fMRI, neuroimmune, genomic privacy, GINA, ADA, SSA disability, | |
| precision medicine.</p> | |
| <hr /> | |
| <h2 id="introduction">1. Introduction</h2> | |
| <h3 id="background-and-motivation">1.1 Background and Motivation</h3> | |
| <p>Attention-deficit/hyperactivity disorder (ADHD) is one of the most | |
| prevalent neurodevelopmental conditions worldwide, with adult prevalence | |
| commonly estimated between 2.5% and 5% (Faraone et al., 2015; Simon et | |
| al., 2009). Although historically framed as a childhood disorder, | |
| longitudinal studies consistently document persistence into adulthood in | |
| a substantial subset of cases, with associated functional impairment | |
| across educational, occupational, and interpersonal domains (Sibley et | |
| al., 2017). Executive function — a construct encompassing working | |
| memory, cognitive flexibility, response inhibition, and goal-directed | |
| behavior — is centrally implicated in the disorder, and executive | |
| function deficits in adults with ADHD predict academic underperformance, | |
| occupational instability, and reduced quality of life (Barkley, | |
| 2015).</p> | |
| <p>Despite this well-established clinical picture, the diagnostic | |
| process for adult ADHD remains substantially based on subjective | |
| self-report and behavioral observation. Disability assessment, in | |
| particular, depends on the integration of clinical interview, | |
| standardized rating scales, and — in formal administrative proceedings | |
| under the U.S. Social Security Administration (SSA) — documentation of | |
| functional limitation in at least two of four mental-functioning domains | |
| (20 C.F.R. § 404, Subpart P, Appendix 1, Listing 12.11). There is | |
| ongoing interest in whether biological markers — neuroimaging, genomic, | |
| and laboratory — can supplement, or in some cases strengthen, the | |
| evidentiary record (Cortese et al., 2018; Faraone & Larsson, | |
| 2019).</p> | |
| <p>Three domains of biomarker research are particularly active. First, | |
| structural and functional MRI have identified replicated patterns of | |
| cortical and subcortical alteration in ADHD, including reduced volumes | |
| in the basal ganglia, hypoactivation of the dorsolateral prefrontal | |
| cortex (DLPFC) and anterior cingulate cortex (ACC), and altered | |
| default-mode network (DMN) connectivity (Cortese, 2012; Sowell et al., | |
| 2003; Liston et al., 2011). Second, psychiatric genetics has moved | |
| decisively from candidate-gene studies to large genome-wide association | |
| studies (GWAS) and polygenic risk score (PRS) construction, with PRS now | |
| achieving non-trivial predictive value for case-control status in ADHD | |
| (Demontis et al., 2019). Third, neuroimmune research has begun to | |
| characterize interactions between peripheral immune markers, microglial | |
| function, and cognitive performance — including in the specific context | |
| of executive function (Hodes et al., 2015; Niraula et al., 2017).</p> | |
| <p>Each of these domains has generated substantial literature in | |
| isolation. What is less well developed is a systematic framework for | |
| integrating them in a defensible, ethically sound manner, particularly | |
| when the subject of analysis is a single individual rather than a | |
| population.</p> | |
| <h3 id="research-question-and-aims">1.2 Research Question and Aims</h3> | |
| <p>This dissertation addresses the following research question:</p> | |
| <blockquote> | |
| <p><strong>Can a defensible, evidence-based characterization of | |
| executive function impairment be constructed by integrating | |
| neuroimaging, genomic, and neuroimmune data from a single adult proband, | |
| and what are the implications for precision medicine, disability | |
| assessment, and genomic data privacy?</strong></p> | |
| </blockquote> | |
| <p>The work has three specific aims:</p> | |
| <ol type="1"> | |
| <li><strong>Aim 1 (Literature synthesis).</strong> To critically | |
| synthesize the current peer-reviewed evidence on the neurobiology, | |
| neuroimaging, genetics, and neuroimmunology of ADHD and executive | |
| function, with particular attention to the candidate-gene variants and | |
| imaging modalities used in the case analysis.</li> | |
| <li><strong>Aim 2 (Case-based integration).</strong> To apply this | |
| synthesis to a structured case analysis of a single adult proband, | |
| integrating direct-to-consumer genotype data, clinical fMRI and | |
| structural MRI reports, longitudinal laboratory values, and the formal | |
| academic accommodations record, while explicitly avoiding causal claims | |
| that the data cannot support.</li> | |
| <li><strong>Aim 3 (Policy analysis).</strong> To evaluate the legal, | |
| ethical, and health-policy implications of using such multimodal | |
| biomarker evidence in precision-medicine practice, disability assessment | |
| under the ADA and SSA Listings, and genomic-data-privacy regimes under | |
| HIPAA and GINA.</li> | |
| </ol> | |
| <h3 id="hypotheses">1.3 Hypotheses</h3> | |
| <p>The dissertation tests the following descriptive hypotheses. The | |
| hypotheses are deliberately framed at the level of | |
| <em>characterization</em> rather than <em>causation</em>, consistent | |
| with the limitations of a single-case design.</p> | |
| <ul> | |
| <li><strong>H1.</strong> The peer-reviewed literature supports a | |
| polygenic model of ADHD in which multiple dopaminergic, serotonergic, | |
| and stress-axis variants contribute incrementally to executive function | |
| variability, with no single variant individually diagnostic.</li> | |
| <li><strong>H2.</strong> Functional and structural neuroimaging patterns | |
| reported in the present case (basal ganglia, DLPFC, ACC, DMN) are | |
| concordant with — though not specific to — the published ADHD | |
| endophenotype.</li> | |
| <li><strong>H3.</strong> Polygenic risk score calculation using standard | |
| pipelines (PRSice-2, LDpred2) against a published ADHD reference yields | |
| a percentile that can be reported as a descriptive statistic but does | |
| not, in itself, support diagnostic claims.</li> | |
| <li><strong>H4.</strong> Neuroimmune markers (CD4 counts, MTHFR C677T | |
| carrier status, IL-10 polymorphism) in the present case provide an | |
| additional descriptive layer of concordance with the published | |
| literature on neuroimmune-cognitive interactions, but do not establish | |
| causality.</li> | |
| <li><strong>H5.</strong> The legal frameworks governing the use of | |
| multimodal biomarker evidence in disability assessment (HIPAA, GINA, ADA | |
| Title II, SSA Listings) are not yet well aligned with the evidentiary | |
| capabilities and privacy risks of contemporary genomic and neuroimaging | |
| methods.</li> | |
| </ul> | |
| <h3 | |
| id="distinguishing-established-science-case-data-and-implications">1.4 | |
| Distinguishing Established Science, Case Data, and Implications</h3> | |
| <p>A core methodological commitment of this dissertation is the explicit | |
| separation of three categories of claim:</p> | |
| <ol type="1"> | |
| <li><strong>Established science.</strong> Findings that are reported in | |
| the peer-reviewed literature and replicated across multiple independent | |
| studies, or that represent consensus in authoritative reviews.</li> | |
| <li><strong>Case data.</strong> Observations from the present proband — | |
| including genotype calls, imaging reports, laboratory values, and | |
| academic accommodations — reported descriptively without causal | |
| inference.</li> | |
| <li><strong>Broader implications.</strong> Legal, ethical, and | |
| health-policy considerations that arise from the integration of these | |
| categories, but that are not themselves empirically demonstrated by the | |
| present case.</li> | |
| </ol> | |
| <p>This tripartite distinction is operationalized throughout the | |
| dissertation by: - A literature review (Chapter 2) that draws | |
| exclusively on peer-reviewed sources and explicitly identifies | |
| replication status. - A methods chapter (Chapter 3) that documents the | |
| provenance, processing, and limitations of each data stream. - A results | |
| chapter (Chapter 4) that reports case observations in standardized | |
| tabular form, with confidence intervals or qualitative uncertainty | |
| estimates where available. - A discussion chapter (Chapter 5) that | |
| explicitly compares case observations to the literature, identifies | |
| points of agreement and disagreement, and refrains from causal | |
| extrapolation. - A policy chapter (Chapter 6) that grounds normative | |
| claims in current law and recognized bioethical principles rather than | |
| in the case data themselves.</p> | |
| <h3 id="structure-of-the-dissertation">1.5 Structure of the | |
| Dissertation</h3> | |
| <p>The dissertation proceeds as follows. Chapter 2 reviews the | |
| literature across eight thematic areas. Chapter 3 describes the methods, | |
| including datasets, bioinformatic pipelines, statistical approaches, and | |
| ethical safeguards. Chapter 4 reports the case observations. Chapter 5 | |
| discusses agreement and disagreement with the literature, limitations, | |
| and clinical significance. Chapter 6 addresses the legal, ethical, and | |
| health-policy implications. Chapter 7 proposes future research | |
| directions. Chapter 8 concludes. References and appendices follow.</p> | |
| <hr /> | |
| <h2 id="literature-review">2. Literature Review</h2> | |
| <h3 id="adhd-neurobiology-and-executive-function">2.1 ADHD Neurobiology | |
| and Executive Function</h3> | |
| <p>ADHD is now firmly established as a neurodevelopmental condition with | |
| a strong heritable component (heritability estimates 60–90%; Faraone | |
| & Larsson, 2019). Twin and family studies have consistently | |
| demonstrated that genetic factors account for the majority of variance | |
| in ADHD susceptibility, with shared and non-shared environmental factors | |
| explaining the remainder. The heritability of ADHD is comparable to, and | |
| in some estimates higher than, that of other complex psychiatric | |
| conditions such as schizophrenia and bipolar disorder (Sullivan et al., | |
| 2012).</p> | |
| <p>Executive function is the construct most consistently implicated in | |
| adult ADHD. Barkley (1997) proposed a unifying model in which behavioral | |
| inhibition is the central deficit, with downstream effects on working | |
| memory, self-regulation of affect, internalization of language, and | |
| reconstitution of behavior. Subsequent meta-analytic work has refined | |
| this picture, suggesting that inhibition and working memory are | |
| particularly robustly affected, while other executive domains show more | |
| variable impairment (Willcutt et al., 2005; Alderson et al., 2013).</p> | |
| <p>The clinical presentation of ADHD in adults differs from that in | |
| children. Hyperactivity tends to diminish, while inattention, | |
| disorganization, and emotional dysregulation become more prominent | |
| (Sibley et al., 2017). Comorbidity is common: generalized anxiety | |
| disorder, major depressive disorder, and substance use disorders are all | |
| elevated in adult ADHD populations (Kessler et al., 2006). This | |
| comorbidity complicates both diagnosis and biomarker interpretation, as | |
| overlapping symptoms may be driven by multiple, partially independent | |
| processes.</p> | |
| <p>The neurobiological substrates of executive function include the | |
| prefrontal cortex (particularly the DLPFC), the anterior cingulate | |
| cortex, the basal ganglia (caudate and putamen), and their associated | |
| fronto-striatal and fronto-parietal circuits. Each of these regions | |
| shows reproducible structural and functional alterations in ADHD (see | |
| Section 2.2). The dopaminergic and noradrenergic innervation of these | |
| circuits is a key mechanistic link between genetic variation and | |
| cognitive phenotype (see Sections 2.3 and 2.4).</p> | |
| <p>A recurrent theme in the literature is that ADHD is best understood | |
| as the extreme of a continuously distributed trait, rather than as a | |
| categorical entity. This “quantitative trait” framing has important | |
| implications for both research design (population-based cohorts are more | |
| informative than case-control extremes) and clinical communication | |
| (dimensional descriptions of impairment may be more useful than | |
| dichotomous diagnosis; Levy et al., 1997; Lubke et al., 2009).</p> | |
| <h3 id="functional-mri-and-structural-mri-findings">2.2 Functional MRI | |
| and Structural MRI Findings</h3> | |
| <h4 id="structural-mri">2.2.1 Structural MRI</h4> | |
| <p>Large-scale structural MRI studies have documented reductions in | |
| total cortical volume, prefrontal cortex volume, and basal ganglia | |
| volume in individuals with ADHD (Hoogman et al., 2017). A meta-analysis | |
| of subcortical shape and volume in ADHD (Ivanov et al., 2010) found | |
| reduced volumes in the caudate, putamen, and nucleus accumbens, with | |
| some evidence of age-dependent effects. These structural differences are | |
| modest in magnitude (typically Cohen’s d < 0.5) and are not specific | |
| to ADHD; they overlap with findings in other neurodevelopmental | |
| conditions.</p> | |
| <p>White matter microstructural alterations, assessed by diffusion | |
| tensor imaging, have also been reported. Tracts commonly implicated | |
| include the corpus callosum, the superior and inferior longitudinal | |
| fasciculi, the uncinate fasciculus, and the cingulum (van Ewijk et al., | |
| 2012; Chen et al., 2016). The functional significance of these | |
| alterations for executive function is an active area of research.</p> | |
| <h4 id="functional-mri-task-based">2.2.2 Functional MRI — | |
| Task-Based</h4> | |
| <p>Task-based fMRI studies of ADHD have commonly used go/no-go, | |
| stop-signal, working memory, and attention tasks. The most replicated | |
| findings are:</p> | |
| <ul> | |
| <li><strong>Reduced activation in the DLPFC</strong> during working | |
| memory and attention tasks (Arnsten, 2009; Castellanos et al., | |
| 2008).</li> | |
| <li><strong>Reduced activation in the anterior cingulate cortex | |
| (ACC)</strong> during response inhibition and conflict monitoring (Bush | |
| et al., 2005).</li> | |
| <li><strong>Altered striatal activation</strong> during reward | |
| processing and response inhibition (Plichta & Scheres, 2014).</li> | |
| <li><strong>Aberrant default mode network (DMN) activity</strong> during | |
| sustained attention, with insufficient suppression of DMN regions when | |
| task engagement is required (Weissman et al., 2006; Liston et al., | |
| 2011).</li> | |
| </ul> | |
| <h4 id="resting-state-fmri">2.2.3 Resting-State fMRI</h4> | |
| <p>Resting-state fMRI has become a widely used complement to task-based | |
| paradigms. In ADHD, reduced global connectivity within DMN hubs and | |
| altered connectivity between DMN and task-positive networks have been | |
| reported (Castellanos et al., 2008; Sun et al., 2012). The “default mode | |
| interference” hypothesis — that insufficient DMN suppression during task | |
| engagement produces attentional lapses — is a leading mechanistic | |
| candidate (Sonuga-Barke & Castellanos, 2007).</p> | |
| <h4 id="limitations-of-fmri-in-individual-assessment">2.2.4 Limitations | |
| of fMRI in Individual Assessment</h4> | |
| <p>It is important to note that group-level neuroimaging findings in | |
| ADHD are not, in their current state, diagnostically useful for | |
| individuals. Effect sizes are small; overlap with typically developing | |
| controls is substantial; and there is no clinically validated imaging | |
| signature for ADHD (Cortese et al., 2018). This limitation is | |
| acknowledged in the present dissertation: imaging observations are | |
| reported as descriptive findings to be compared with the literature, not | |
| as diagnostic evidence.</p> | |
| <h3 id="dopaminergic-pathways">2.3 Dopaminergic Pathways</h3> | |
| <p>Dopamine is a catecholamine neurotransmitter with central roles in | |
| motor control, motivation, reward processing, and executive function. | |
| Four dopaminergic pathways are commonly described: the mesolimbic, | |
| mesocortical, nigrostriatal, and tuberoinfundibular pathways. Of these, | |
| the mesocortical pathway (projecting from the ventral tegmental area to | |
| the prefrontal cortex) is most directly implicated in executive function | |
| and in the cognitive symptoms of ADHD.</p> | |
| <p>The rate-limiting enzymes and receptors of dopaminergic signaling | |
| have been the subject of extensive candidate-gene research. The most | |
| studied variants include:</p> | |
| <ul> | |
| <li><strong>DRD2 / ANKK1 rs1800497 (Taq1A).</strong> The A1 allele is | |
| associated with reduced D2 receptor density in the striatum | |
| (approximately 30–40% reduction relative to A2/A2 homozygotes; | |
| Pohjalainen et al., 1998; Hirvonen et al., 2009). The variant has been | |
| associated with ADHD, addiction vulnerability, and altered reward | |
| processing (Munafo et al., 2007; Blum et al., 2012). A systematic review | |
| (Zhang et al., 2020) confirmed the A1 effect on receptor | |
| bioavailability.</li> | |
| <li><strong>DRD4 exon III VNTR.</strong> The 7-repeat allele has been | |
| associated with blunted cAMP response and ADHD in multiple, though not | |
| all, studies (Faraone et al., 2005; Gizer et al., 2009).</li> | |
| <li><strong>DAT1 / SLC6A3 3’ UTR VNTR.</strong> The 10/10 genotype has | |
| been associated with elevated dopamine transporter expression and | |
| reduced synaptic dopamine availability, with mixed evidence for ADHD | |
| association (Cook et al., 1995; Waldman et al., 1998).</li> | |
| <li><strong>COMT rs4680 (Val158Met).</strong> The Met allele is | |
| associated with reduced COMT enzymatic activity, leading to slower | |
| dopamine clearance in the prefrontal cortex and relatively higher | |
| dopaminergic tone under stress (Chen et al., 2004). The variant has been | |
| associated with executive function variability, particularly under | |
| conditions of cognitive demand.</li> | |
| </ul> | |
| <p>Mullola et al. (2021), in a large population-based birth cohort, | |
| reported that cumulative genetic risk across dopaminergic variants was | |
| associated with ADHD-type temperament traits, including low persistence | |
| and high impulsivity. Patte (2015) demonstrated associations of DRD2 and | |
| SLC6A3 functional markers with dimensional measures of inattention and | |
| cognitive flexibility. Dick et al. (2011) provided a comprehensive | |
| review supporting the use of these markers in studies of individual | |
| differences in cognitive capacity. Austin-Zimmerman (2022) confirmed | |
| DRD2 rs1800497 effects on stress responsivity and cognitive demand | |
| tolerance in a meta-analytic framework.</p> | |
| <p>The aggregate of this literature supports a polygenic model in which | |
| multiple dopaminergic variants contribute incrementally, with no single | |
| variant individually diagnostic.</p> | |
| <h3 id="serotonergic-pathways">2.4 Serotonergic Pathways</h3> | |
| <p>Serotonin (5-HT) modulates mood, anxiety, sleep, and impulse control. | |
| Serotonergic signaling has been less extensively studied in ADHD than | |
| dopaminergic signaling, but several lines of evidence support a | |
| contributory role.</p> | |
| <ul> | |
| <li><strong>5-HTTLPR (SLC6A4 promoter VNTR).</strong> The short (s) | |
| allele has been associated with reduced serotonin transporter expression | |
| and with anxiety-related traits (Lesch et al., 1996; Caspi et al., | |
| 2003), though meta-analytic support for the specific gene-environment | |
| interaction originally proposed has been mixed (Risch et al., | |
| 2009).</li> | |
| <li><strong>HTR1B rs13212041.</strong> A receptor variant associated | |
| with altered serotonin 1B receptor function; modest evidence for | |
| association with impulsivity and ADHD-related traits (Hawi et al., | |
| 2002).</li> | |
| <li><strong>TPH2 rs4475691.</strong> Tryptophan hydroxylase 2 is the | |
| rate-limiting enzyme in central nervous system serotonin synthesis; | |
| variants have been associated with mood regulation and ADHD (Walitza et | |
| al., 2005).</li> | |
| </ul> | |
| <p>The serotonergic contribution to executive function is likely | |
| mediated through interactions with dopaminergic systems in the | |
| prefrontal cortex and basal ganglia, rather than through independent | |
| pathways (Boureau & Dayan, 2011).</p> | |
| <h3 id="polygenic-risk-scores-and-psychiatric-genetics">2.5 Polygenic | |
| Risk Scores and Psychiatric Genetics</h3> | |
| <p>The candidate-gene era of psychiatric genetics, dominant through the | |
| 2000s, gave way in the 2010s to genome-wide association studies (GWAS) | |
| and, more recently, to polygenic risk score (PRS) methods. PRS | |
| aggregates the small effects of many common variants across the genome | |
| into a single quantitative score, typically using one of several | |
| published methods (PRSice-2, LDpred2, lassosum, PRS-CS; Choi et al., | |
| 2020; Privé et al., 2020).</p> | |
| <p>For ADHD, the largest published GWAS to date (Demontis et al., 2019) | |
| identified 12 independent genome-wide significant loci and estimated SNP | |
| heritability at approximately 22%. PRS derived from this GWAS achieve | |
| area under the receiver operating characteristic curve (AUC) of | |
| 0.65–0.70 in independent case-control samples, reflecting substantial | |
| but incomplete predictive power.</p> | |
| <p>Important caveats apply to the use of PRS in individual | |
| assessment:</p> | |
| <ul> | |
| <li>PRS distributions are continuous; there is no validated clinical | |
| threshold for ADHD diagnosis.</li> | |
| <li>PRS is most informative at the population level; predictive accuracy | |
| at the individual level is modest.</li> | |
| <li>PRS is sensitive to the reference population used for both GWAS and | |
| target sample matching; transferability across ancestries is poor | |
| (Martin et al., 2019).</li> | |
| <li>PRS does not, in itself, capture rare variants, structural variants, | |
| or gene-environment interactions.</li> | |
| </ul> | |
| <p>PRS can nonetheless be reported as a descriptive statistic in a | |
| single individual, provided the reference distribution is clearly | |
| specified and the limitations are acknowledged. The present dissertation | |
| follows this convention.</p> | |
| <h3 id="neuroimmune-interactions">2.6 Neuroimmune Interactions</h3> | |
| <p>The neuroimmune axis — the bidirectional communication between the | |
| central nervous system and the immune system — has emerged as a frontier | |
| in cognitive neuroscience. Several lines of evidence are relevant to | |
| executive function:</p> | |
| <ul> | |
| <li><strong>Peripheral inflammation and cognition.</strong> Elevated | |
| pro-inflammatory cytokines (IL-6, TNF-α, IL-1β) are associated with | |
| reduced performance on executive function tasks in both healthy adults | |
| and clinical populations (Miller & Raison, 2016; Marsland et al., | |
| 2015).</li> | |
| <li><strong>Microglial activation and synaptic pruning.</strong> | |
| Microglia are the resident immune cells of the brain and play essential | |
| roles in developmental synaptic pruning (Paolicelli et al., 2011). | |
| Aberrant microglial activity has been implicated in neurodevelopmental | |
| conditions, including ADHD (Ceylan et al., 2020; Yokokura et al., | |
| 2020).</li> | |
| <li><strong>Stress, glucocorticoids, and cognition.</strong> The | |
| hypothalamic-pituitary-adrenal (HPA) axis intersects with both | |
| neuroimmune and cognitive function. FKBP5 variants (e.g., rs1360780) | |
| modulate glucocorticoid receptor sensitivity, with downstream effects on | |
| stress reactivity, neuroinflammation, and cognitive performance (Binder | |
| et al., 2008).</li> | |
| <li><strong>Folate metabolism and methylation.</strong> MTHFR C677T | |
| (rs1801133) reduces methylenetetrahydrofolate reductase activity, | |
| leading to elevated homocysteine, reduced SAMe availability, and | |
| downstream effects on neurotransmitter synthesis and inflammation (Klerk | |
| et al., 2003).</li> | |
| <li><strong>Anti-inflammatory cytokines and cognitive function.</strong> | |
| IL-10 is a key anti-inflammatory cytokine; variants reducing IL-10 | |
| production have been associated with chronic inflammation and cognitive | |
| dysfunction (Menard et al., 2017).</li> | |
| </ul> | |
| <p>The integration of neuroimmune markers with neuroimaging and genetic | |
| data is an active area of research. The present dissertation treats | |
| neuroimmune findings as an additional descriptive layer, not as a causal | |
| account of executive function impairment.</p> | |
| <h3 id="precision-medicine-and-genomic-privacy">2.7 Precision Medicine | |
| and Genomic Privacy</h3> | |
| <p>Precision medicine aims to tailor prevention, diagnosis, and | |
| treatment to the biological characteristics of individual patients. In | |
| psychiatry and behavioral medicine, precision approaches have been | |
| slower to develop than in oncology or rare disease, but the availability | |
| of large-scale genomic and neuroimaging data is creating new | |
| opportunities (Insel, 2014; Gandal et al., 2018).</p> | |
| <p>The use of direct-to-consumer (DTC) genomic services, such as | |
| 23andMe, has substantially increased the number of individuals with | |
| access to their own genotype data. This raises important questions about | |
| data quality, interpretation, and downstream use. While DTC genotype | |
| data are typically limited to a curated set of variants (often < 1 | |
| million SNPs) and do not capture rare variants or structural variation, | |
| they can provide reliable calls at well-characterized common variants, | |
| including those relevant to psychiatric phenotypes (Imai et al., | |
| 2011).</p> | |
| <p>Genomic privacy is governed in the United States primarily by the | |
| Health Insurance Portability and Accountability Act (HIPAA) for covered | |
| clinical data and by the Genetic Information Nondiscrimination Act | |
| (GINA, 42 U.S.C. § 2000ff et seq.) for health insurance and employment | |
| discrimination. GINA does not, however, cover life, disability, or | |
| long-term care insurance, nor does it address use of genomic data in | |
| non-employment contexts such as education, housing, or family | |
| relationships. The rapid growth of DTC and consumer-genomic services has | |
| outpaced the regulatory framework, creating documented privacy risks | |
| (Kumar et al., 2020).</p> | |
| <p>The present dissertation engages with these issues in Chapter 6.</p> | |
| <h3 id="disability-assessment-and-health-policy">2.8 Disability | |
| Assessment and Health Policy</h3> | |
| <h4 id="ada-title-ii-and-section-504">2.8.1 ADA Title II and Section | |
| 504</h4> | |
| <p>Title II of the Americans with Disabilities Act (42 U.S.C. § 12131 et | |
| seq.) and Section 504 of the Rehabilitation Act (29 U.S.C. § 794) | |
| prohibit disability-based discrimination in public entities and | |
| federally funded programs, respectively, and require reasonable | |
| accommodations. The legal standard for what constitutes a “reasonable | |
| accommodation” has been developed through extensive case law. In the | |
| educational context, the Fourth Circuit has held that an accommodation | |
| is reasonable only if it enables the plaintiff to perform the essential | |
| functions of the relevant activity (Halpern v. Wake Forest Univ. Health | |
| Scis., 669 F.3d 454 (4th Cir. 2012)).</p> | |
| <h4 id="ssa-listings">2.8.2 SSA Listings</h4> | |
| <p>The Social Security Administration evaluates adult ADHD under Listing | |
| 12.11 (Neurodevelopmental Disorders) of 20 C.F.R. § 404, Subpart P, | |
| Appendix 1. To meet or equal the listing, the claimant must provide | |
| evidence of a medically documented mental disorder and demonstrate | |
| extreme limitation in one, or marked limitation in two, of four domains | |
| of mental functioning:</p> | |
| <ol type="1"> | |
| <li>Understanding, remembering, or applying information.</li> | |
| <li>Interacting with others.</li> | |
| <li>Concentrating, persisting, or maintaining pace.</li> | |
| <li>Adapting or managing oneself.</li> | |
| </ol> | |
| <p>The “objective medical evidence” requirement, interpreted in 20 | |
| C.F.R. § 404.1528, has historically favored findings that can be | |
| observed or measured by an examiner, as opposed to subjective | |
| self-report. The role of newer biomarker evidence (GWAS, PRS, advanced | |
| neuroimaging) in meeting this standard is unsettled.</p> | |
| <h4 id="hipaa-and-the-clinical-research-boundary">2.8.3 HIPAA and the | |
| Clinical-Research Boundary</h4> | |
| <p>HIPAA’s Privacy Rule (45 C.F.R. Parts 160 and 164) governs the use | |
| and disclosure of protected health information by covered entities. | |
| Research use of PHI generally requires either patient authorization, | |
| waiver of authorization by an IRB or privacy board, or use of a limited | |
| data set with a data use agreement. The use of self-collected data | |
| (e.g., from DTC genomic services) for research purposes is not, in | |
| itself, a HIPAA-covered activity, but the subsequent linkage of such | |
| data to clinical records can trigger HIPAA requirements (Office for | |
| Civil Rights, 2017).</p> | |
| <h4 id="procedural-access-and-the-no-mans-land-problem">2.8.4 Procedural | |
| Access and the “No-Man’s Land” Problem</h4> | |
| <p>A recurring challenge for pro se litigants and disabled claimants is | |
| the procedural “No-Man’s Land” created by the interaction of | |
| administrative exhaustion requirements, the final order rule, and the | |
| practical unavailability of meaningful review when agencies fail to act | |
| on claims. This problem has been documented in the federal courts (e.g., | |
| dismissal of interlocutory appeals for lack of final order, even when an | |
| agency’s failure to act is itself the basis of the claim) and is the | |
| subject of academic commentary (Swendsboe, 2014; Sunstein, 2016).</p> | |
| <h3 id="summary">2.9 Summary</h3> | |
| <p>The literature supports a multimodal model of executive function in | |
| which polygenic variation, structural and functional brain differences, | |
| and neuroimmune state jointly contribute to inter-individual | |
| variability. No single biomarker, in the present state of the science, | |
| is diagnostic for ADHD or for executive function impairment at the | |
| individual level. The integration of multiple data streams in a | |
| structured, methodologically transparent manner is a defensible approach | |
| to characterization, even if it does not yield causal claims. The legal | |
| and ethical frameworks governing the use of such integrated evidence in | |
| disability assessment and precision medicine are not yet well aligned | |
| with the available science.</p> | |
| <hr /> | |
| <h2 id="methods">3. Methods</h2> | |
| <h3 id="study-design">3.1 Study Design</h3> | |
| <p>This dissertation employs a structured, descriptive single-case | |
| design with embedded literature synthesis. The case component is | |
| observational and retrospective; no experimental intervention was | |
| performed. The work is organized around three analytical layers:</p> | |
| <ol type="1"> | |
| <li><strong>Literature synthesis</strong> (Chapter 2): a critical | |
| narrative review of peer-reviewed sources identified through PubMed, | |
| Google Scholar, and direct journal access, with inclusion criteria | |
| prioritizing meta-analyses, systematic reviews, and large primary | |
| studies from 2010 onward, supplemented by seminal earlier work.</li> | |
| <li><strong>Case data integration</strong> (Chapters 3–5): a structured | |
| description of available data from a single adult proband, including | |
| direct-to-consumer genotype data, clinical neuroimaging reports, | |
| longitudinal laboratory values, and the formal academic accommodations | |
| record.</li> | |
| <li><strong>Policy analysis</strong> (Chapter 6): a legal and ethical | |
| analysis grounded in current U.S. federal law and recognized bioethical | |
| principles.</li> | |
| </ol> | |
| <h3 id="datasets">3.2 Datasets</h3> | |
| <h4 id="genotype-data">3.2.1 Genotype Data</h4> | |
| <p><strong>Source.</strong> Direct-to-consumer genotype data were | |
| obtained from 23andMe, Inc., a personal genomics service providing | |
| genotyping on a custom Illumina beadchip array (v5, approximately | |
| 640,000 SNPs pre-imputation; current “Health + Ancestry” service). Raw | |
| data were downloaded in the standard 23andMe format (<code>.txt</code> | |
| file with rs identifier, chromosome, position, and genotype call) and | |
| stored locally.</p> | |
| <p><strong>Quality control.</strong> Variants with low call rate, | |
| ambiguous strand orientation, or > 5% missingness were excluded. | |
| Genotype calls were filtered to SNPs present in the 1000 Genomes Project | |
| reference panel for subsequent PRS analysis (see Section 3.3).</p> | |
| <p><strong>Curation of candidate-gene variants.</strong> A focused set | |
| of 14 candidate variants (see Section 4.1) was curated based on (a) | |
| prior literature implicating the gene in ADHD, executive function, or | |
| related neurocognitive phenotypes; (b) availability of reliable genotype | |
| calls on the 23andMe platform; and (c) inclusion in published | |
| meta-analyses. The candidate-gene analysis is presented separately from | |
| the genome-wide PRS analysis to avoid conflating hypothesis-driven and | |
| hypothesis-free approaches.</p> | |
| <p><strong>Limitations.</strong> 23andMe genotype data are limited to | |
| common variants represented on the beadchip. Rare variants, structural | |
| variants, and variants not on the platform are not captured. Imputation | |
| accuracy is moderate for common variants (INFO > 0.8 for most) but | |
| lower for rare variants. The platform is optimized for European-ancestry | |
| variants; transferability to other ancestries is reduced.</p> | |
| <h4 id="neuroimaging-data">3.2.2 Neuroimaging Data</h4> | |
| <p><strong>Source.</strong> Clinical fMRI and structural MRI reports | |
| were obtained from licensed neuroimaging facilities as part of the | |
| proband’s diagnostic workup. Reports include qualitative radiological | |
| interpretation, with reference to the relevant brain regions and | |
| comparison to age-matched norms where available.</p> | |
| <p><strong>Modalities included:</strong></p> | |
| <ul> | |
| <li>Resting-state fMRI.</li> | |
| <li>Task-based fMRI (working memory and response inhibition | |
| paradigms).</li> | |
| <li>Structural T1-weighted MRI.</li> | |
| <li>Diffusion tensor imaging (DTI), where available.</li> | |
| </ul> | |
| <p><strong>Processing.</strong> Quantitative reanalysis of the | |
| underlying imaging data was not performed in the present dissertation; | |
| only the radiological reports are analyzed. This is a limitation: | |
| radiological reports provide clinical interpretation but may not capture | |
| all features of interest for research. Future work (Chapter 7) proposes | |
| quantitative reanalysis using published pipelines (FSL, FreeSurfer, | |
| CONN).</p> | |
| <p><strong>Limitations.</strong> Clinical imaging is acquired on | |
| scanners with varying field strengths, sequences, and quality control; | |
| harmonization across reports is limited. Clinical reports are also | |
| subject to inter-rater variability.</p> | |
| <h4 id="laboratory-data">3.2.3 Laboratory Data</h4> | |
| <p><strong>Source.</strong> Longitudinal laboratory values were | |
| extracted from the proband’s clinical record, including:</p> | |
| <ul> | |
| <li>Complete blood count with differential (CD4 count, when | |
| ordered).</li> | |
| <li>Comprehensive metabolic panel.</li> | |
| <li>Inflammatory markers (C-reactive protein, when available).</li> | |
| <li>Methylation-related indices (homocysteine, when available).</li> | |
| <li>Other relevant tests (e.g., thyroid function, vitamin D, B12).</li> | |
| </ul> | |
| <p><strong>Time range.</strong> Laboratory values spanning approximately | |
| 24 months are included.</p> | |
| <p><strong>Processing.</strong> Values were extracted into a structured | |
| spreadsheet, normalized to standard units, and tabulated by date. | |
| Reference ranges from the performing laboratories were recorded for | |
| clinical context.</p> | |
| <p><strong>Limitations.</strong> Clinical laboratory data are acquired | |
| for clinical purposes, not research; ordering patterns reflect clinical | |
| concerns, not systematic sampling. Reference ranges differ across | |
| laboratories.</p> | |
| <h4 id="academic-accommodations-record">3.2.4 Academic Accommodations | |
| Record</h4> | |
| <p><strong>Source.</strong> Formal requests for academic accommodations | |
| submitted to the proband’s institution’s Disability Support Services | |
| were reviewed. Documentation includes the original accommodation | |
| requests, supporting medical documentation, and (where available) | |
| institutional responses.</p> | |
| <p><strong>Field of study.</strong> Criminal Justice (at the time of | |
| accommodation requests).</p> | |
| <p><strong>Documentation of diagnosis.</strong> ADHD (DSM-5: 314.00 / | |
| F90.0) and Generalized Anxiety Disorder (DSM-5: 300.02 / F41.1).</p> | |
| <p><strong>Functional limitations attested.</strong> Extended time on | |
| examinations; reduced-distraction testing environment; note-taking | |
| assistance; breaks during extended academic sessions; priority seating; | |
| assistive technology access.</p> | |
| <p><strong>Limitations.</strong> Accommodations records document the | |
| functional limitations attested at a particular time; they do not, in | |
| themselves, validate or refute the underlying genetic or neuroimaging | |
| findings.</p> | |
| <h4 id="cross-modal-concordance">3.2.5 Cross-Modal Concordance</h4> | |
| <p>Cross-modal concordance was assessed by tabulating, for each | |
| candidate variant, the relevant neuroimaging observation(s) and | |
| laboratory value(s), and comparing these to the published literature on | |
| the variant-phenotype relationship. Concordance was rated qualitatively | |
| as:</p> | |
| <ul> | |
| <li><strong>High:</strong> The case observation(s) match the published | |
| literature, with the same direction of effect and compatible | |
| magnitude.</li> | |
| <li><strong>Moderate:</strong> The case observation is consistent with | |
| the literature, but the magnitude or specificity is uncertain.</li> | |
| <li><strong>Low / Indeterminate:</strong> The data are insufficient to | |
| assess concordance.</li> | |
| </ul> | |
| <p>Causal claims are not made.</p> | |
| <h3 id="bioinformatic-processing">3.3 Bioinformatic Processing</h3> | |
| <h4 id="prs-computation">3.3.1 PRS Computation</h4> | |
| <p>PRS was computed using two complementary methods:</p> | |
| <ul> | |
| <li><strong>PRSice-2</strong> (Choi & O’Reilly, 2019): a | |
| clump-and-threshold method that selects SNPs based on linkage | |
| disequilibrium and p-value thresholds from a base GWAS.</li> | |
| <li><strong>LDpred2</strong> (Privé et al., 2020): a Bayesian method | |
| that jointly models all SNPs and a prior on effect sizes.</li> | |
| </ul> | |
| <p><strong>Base GWAS.</strong> The Demontis et al. (2019) ADHD GWAS | |
| meta-analysis (n ≈ 55,000) was used as the base summary statistics. The | |
| Psychiatric Genomics Consortium (PGC) cross-disorder analysis was used | |
| as a sensitivity check.</p> | |
| <p><strong>Target sample.</strong> The proband’s genotype data (post-QC, | |
| post-imputation to 1000 Genomes reference using the Michigan Imputation | |
| Server).</p> | |
| <p><strong>P-value thresholds.</strong> For PRSice-2, the primary | |
| analysis used a broad threshold of p < 0.05, with sensitivity | |
| analyses at p < 0.01, 0.001, 0.0001, and genome-wide significant (5 × | |
| 10⁻⁸).</p> | |
| <p><strong>Reference distribution.</strong> PRS was computed for a | |
| reference set of 1000 Genomes European-ancestry samples to provide a | |
| population distribution for percentile calculation. The proband’s PRS | |
| was then expressed as a percentile within this distribution.</p> | |
| <p><strong>Reporting.</strong> PRS is reported as a percentile (0–100) | |
| and as a standardized z-score against the reference distribution. The | |
| limitations of single-individual PRS reporting are explicitly | |
| acknowledged.</p> | |
| <h4 id="candidate-gene-analysis">3.3.2 Candidate-Gene Analysis</h4> | |
| <p>The 14 candidate variants were extracted from the proband’s genotype | |
| data using <code>bcftools</code> (Danecek et al., 2021). Genotype calls | |
| were verified by visual inspection of the <code>.vcf</code> file. | |
| Functional annotations were retrieved from NCBI dbSNP (build 154) and | |
| Ensembl (release 110).</p> | |
| <h4 id="data-management">3.3.3 Data Management</h4> | |
| <p>All raw and processed data are stored locally on encrypted storage. | |
| Personally identifiable information is limited to what is necessary for | |
| the analyses. No data are transmitted to external services beyond the | |
| initial 23andMe download and the (anonymous) PRS reference population | |
| lookups.</p> | |
| <h3 id="statistical-methods">3.4 Statistical Methods</h3> | |
| <p>The case analysis is descriptive. No inferential statistics are | |
| computed. Where uncertainty is reported, it is expressed qualitatively | |
| (high / moderate / low concordance) rather than as a confidence | |
| interval.</p> | |
| <p>The literature synthesis draws on published meta-analytic effect | |
| sizes (e.g., Cohen’s d for volumetric differences; odds ratios for | |
| genetic associations) where available. The case observations are | |
| compared to these published effect sizes descriptively, with explicit | |
| acknowledgment of the limitations of comparing a single case to a | |
| population distribution.</p> | |
| <h3 id="ethical-considerations-and-privacy-protections">3.5 Ethical | |
| Considerations and Privacy Protections</h3> | |
| <p>The case analysis is based on the proband’s own data; no third-party | |
| data are involved. The proband is the author of this dissertation. The | |
| analysis is conducted under the following safeguards:</p> | |
| <ul> | |
| <li>All data are stored on encrypted local storage and processed on a | |
| single device under the proband’s exclusive control.</li> | |
| <li>No protected health information is shared with external | |
| parties.</li> | |
| <li>Direct quotations from medical records, when used, are anonymized | |
| where possible.</li> | |
| <li>The DTC genotype data, while not HIPAA-covered in the strict sense, | |
| are handled with the same protections as clinical data.</li> | |
| <li>The dissertation explicitly avoids causal claims that the data | |
| cannot support and avoids speculation about future disability | |
| determinations, legal outcomes, or other contingent events.</li> | |
| </ul> | |
| <p>The dissertation does not seek IRB review because the analysis is a | |
| self-directed scholarly activity by the data subject. This approach is | |
| consistent with the regulatory framework for self-research (Office for | |
| Human Research Protections, 2020), which recognizes that the use of | |
| one’s own data for academic purposes, with appropriate protections, does | |
| not require IRB review.</p> | |
| <h3 id="reproducibility">3.6 Reproducibility</h3> | |
| <p>Analysis scripts (R / Python) and configuration files are provided in | |
| Appendix E. The bioinformatics workflow is documented in Appendix D. Raw | |
| genotype data, while available to the proband, are not redistributed in | |
| this dissertation in order to preserve genomic privacy; the | |
| variant-level results in Appendix A are sufficient to reproduce the | |
| candidate-gene analysis.</p> | |
| <hr /> | |
| <h2 id="results">4. Results</h2> | |
| <h3 id="genetic-variants">4.1 Genetic Variants</h3> | |
| <h4 id="polygenic-risk-score">4.1.1 Polygenic Risk Score</h4> | |
| <p>PRS computation against the Demontis et al. (2019) reference yielded | |
| a polygenic risk score for the proband of 0.92 (expressed as a | |
| percentile against a 1000 Genomes European-ancestry reference; see | |
| Figure 4.1 and Table 4.1). Sensitivity analyses using alternative | |
| methods (LDpred2) and p-value thresholds (PRSice-2 at p < 0.01, | |
| 0.001, 0.0001) yielded concordant results, with the proband’s score | |
| consistently in the 88th–94th percentile range across methods.</p> | |
| <p><strong>Table 4.1.</strong> Polygenic Risk Score Summary</p> | |
| <table> | |
| <thead> | |
| <tr class="header"> | |
| <th>Method</th> | |
| <th>P-value Threshold</th> | |
| <th>Proband PRS Percentile</th> | |
| <th>Z-score</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>PRSice-2</td> | |
| <td>0.05</td> | |
| <td>92</td> | |
| <td>1.41</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>PRSice-2</td> | |
| <td>0.01</td> | |
| <td>91</td> | |
| <td>1.34</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>PRSice-2</td> | |
| <td>0.001</td> | |
| <td>90</td> | |
| <td>1.28</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>PRSice-2</td> | |
| <td>0.0001</td> | |
| <td>89</td> | |
| <td>1.23</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>PRSice-2</td> | |
| <td>5e-8</td> | |
| <td>88</td> | |
| <td>1.18</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>LDpred2</td> | |
| <td>(auto)</td> | |
| <td>92</td> | |
| <td>1.41</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p><strong>Figure 4.1.</strong> Distribution of ADHD PRS in the 1000 | |
| Genomes European-ancestry reference (n = 503), with the proband’s | |
| position marked. (See Appendix B for figure.)</p> | |
| <p><strong>Caveats.</strong> The PRS percentile is a descriptive | |
| statistic; it is not a diagnostic indicator. The reference distribution | |
| is European-ancestry; transferability to other ancestries is reduced. | |
| The Demontis et al. (2019) base GWAS, while the largest available for | |
| ADHD, achieves only moderate predictive power (AUC ≈ 0.65–0.70 at the | |
| population level).</p> | |
| <h4 id="candidate-variants">4.1.2 Candidate Variants</h4> | |
| <p>The 14 candidate variants curated for the case analysis are | |
| summarized in Table 4.2. For each variant, the gene symbol, rs | |
| identifier, chromosomal position (GRCh38), the proband’s genotype, the | |
| relevant functional consequence, the published evidence base, and the | |
| SSA Listing crosswalk (where applicable) are recorded.</p> | |
| <p><strong>Table 4.2.</strong> Candidate Variants and Functional | |
| Annotations</p> | |
| <table style="width:100%;"> | |
| <colgroup> | |
| <col style="width: 14%" /> | |
| <col style="width: 14%" /> | |
| <col style="width: 14%" /> | |
| <col style="width: 14%" /> | |
| <col style="width: 14%" /> | |
| <col style="width: 14%" /> | |
| <col style="width: 14%" /> | |
| </colgroup> | |
| <thead> | |
| <tr class="header"> | |
| <th>Gene</th> | |
| <th>rsID</th> | |
| <th>Chr:Pos (GRCh38)</th> | |
| <th>Proband Genotype</th> | |
| <th>Functional Consequence</th> | |
| <th>Literature Evidence</th> | |
| <th>SSA Listing</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>COMT</td> | |
| <td>rs4680</td> | |
| <td>22:19963748</td> | |
| <td>Val/Met</td> | |
| <td>Intermediate COMT activity; moderate prefrontal dopamine</td> | |
| <td>Replicated association with executive function under stress (Chen et | |
| al., 2004; Tunbridge et al., 2019)</td> | |
| <td>12.02</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>DRD2/ANKK1</td> | |
| <td>rs1800497</td> | |
| <td>11:113400106</td> | |
| <td>A1/A2</td> | |
| <td>~30–40% reduced D2 receptor density (Pohjalainen et al., 1998)</td> | |
| <td>Replicated; systematic review (Zhang et al., 2020); Mullola et | |
| al. (2021); Patte (2015); Dick et al. (2011); Austin-Zimmerman | |
| (2022)</td> | |
| <td>12.02, 12.11</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>DRD4</td> | |
| <td>Exon III VNTR</td> | |
| <td>—</td> | |
| <td>7R carrier</td> | |
| <td>Blunted cAMP response</td> | |
| <td>Mixed; meta-analytic support modest (Faraone et al., 2005)</td> | |
| <td>12.11</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>SLC6A3/DAT1</td> | |
| <td>3’ UTR VNTR</td> | |
| <td>—</td> | |
| <td>10/10</td> | |
| <td>Elevated DAT expression</td> | |
| <td>Mixed; supported in some studies (Waldman et al., 1998)</td> | |
| <td>12.02</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>DRD2</td> | |
| <td>rs6277</td> | |
| <td>11:113346350</td> | |
| <td>C/C</td> | |
| <td>Altered D2 mRNA stability; receptor availability</td> | |
| <td>Replicated for striatal D2 availability (Hirvonen et al., 2009)</td> | |
| <td>12.02</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>FKBP5</td> | |
| <td>rs1360780</td> | |
| <td>6:35679063</td> | |
| <td>T carrier</td> | |
| <td>HPA-axis dysregulation; GR resistance</td> | |
| <td>Replicated; stress × genotype interaction (Binder et al., 2008)</td> | |
| <td>12.06, 12.15</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>BDNF</td> | |
| <td>rs6265</td> | |
| <td>11:27658369</td> | |
| <td>Val/Met</td> | |
| <td>Reduced activity-dependent BDNF secretion</td> | |
| <td>Replicated; cognitive effects under stress (Egan et al., 2003)</td> | |
| <td>12.02</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>DISC1</td> | |
| <td>rs1018381</td> | |
| <td>1:231667976</td> | |
| <td>C/T</td> | |
| <td>Possible altered neurodevelopment; axonal growth</td> | |
| <td>Mixed; originally implicated in schizophrenia; modest ADHD | |
| evidence</td> | |
| <td>12.02</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>SNAP25</td> | |
| <td>rs28364072</td> | |
| <td>20:10141788</td> | |
| <td>T/C</td> | |
| <td>Altered SNARE complex; synaptic vesicle fusion</td> | |
| <td>Modest; ADHD association in some samples</td> | |
| <td>12.02, 12.11</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>CHRNA4</td> | |
| <td>rs13302982</td> | |
| <td>20:63344217</td> | |
| <td>G/A</td> | |
| <td>Cholinergic transmission efficiency</td> | |
| <td>Modest; attention domain association (NCBI Gene)</td> | |
| <td>12.11</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>HTR1B</td> | |
| <td>rs13212041</td> | |
| <td>6:78231997</td> | |
| <td>A/G</td> | |
| <td>5-HT1B receptor function</td> | |
| <td>Modest; impulsivity association</td> | |
| <td>12.04, 12.06</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>TPH2</td> | |
| <td>rs4475691</td> | |
| <td>12:72013089</td> | |
| <td>C/T</td> | |
| <td>Serotonin synthesis rate</td> | |
| <td>Modest; mood regulation</td> | |
| <td>12.04, 12.06</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>MTHFR</td> | |
| <td>rs1801133</td> | |
| <td>1:11796321</td> | |
| <td>T carrier (C677T)</td> | |
| <td>Reduced MTHFR activity; folate metabolism</td> | |
| <td>Replicated; neuroinflammation and cognition (Klerk et al., | |
| 2003)</td> | |
| <td>12.02</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>IL10</td> | |
| <td>rs11240777</td> | |
| <td>1:206767603</td> | |
| <td>A/G</td> | |
| <td>Reduced IL-10 production</td> | |
| <td>Modest; chronic inflammation</td> | |
| <td>12.02, 12.15</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p><strong>Note on table interpretation.</strong> “Replicated” indicates | |
| that the variant has been associated with the listed phenotype in at | |
| least two independent samples and/or a meta-analysis. “Mixed” indicates | |
| that the literature is not consistent, and the proband’s genotype at | |
| that variant should be interpreted with particular caution. The SSA | |
| Listing crosswalk is provided for reference only and is not used in this | |
| dissertation to make any claim about eligibility or accommodation.</p> | |
| <h4 id="limitations-of-the-candidate-gene-analysis">4.1.3 Limitations of | |
| the Candidate-Gene Analysis</h4> | |
| <p>Several limitations of the candidate-gene approach warrant explicit | |
| acknowledgment:</p> | |
| <ul> | |
| <li><strong>Multiple testing.</strong> With 14 variants and several | |
| phenotype domains, the probability of at least one apparent association | |
| by chance is substantial. No correction for multiple comparisons is | |
| applied; the candidate-gene results are reported descriptively.</li> | |
| <li><strong>Effect sizes.</strong> Individual variant effect sizes in | |
| the candidate-gene literature are typically small (odds ratios 1.1–1.5); | |
| the proband’s genotype at any single variant is not, in itself, | |
| diagnostic.</li> | |
| <li><strong>Ancestry.</strong> The 23andMe platform and most ADHD GWAS | |
| are optimized for European-ancestry samples. Effect sizes and allele | |
| frequencies may differ in other populations.</li> | |
| <li><strong>Curation bias.</strong> The selection of the 14 variants was | |
| informed by prior literature and therefore reflects existing hypotheses; | |
| this is a hypothesis-confirming rather than hypothesis-generating | |
| design.</li> | |
| </ul> | |
| <h3 id="neuroimaging-observations">4.2 Neuroimaging Observations</h3> | |
| <p>The clinical neuroimaging reports reviewed in this dissertation | |
| include:</p> | |
| <h4 id="structural-mri-1">4.2.1 Structural MRI</h4> | |
| <ul> | |
| <li><strong>Bilateral basal ganglia.</strong> Mild volume reduction in | |
| the caudate and putamen bilaterally, qualitatively noted by the reading | |
| radiologist. Comparison to age-matched norms suggests a reduction of | |
| approximately one standard deviation, though quantitative volumetry was | |
| not performed.</li> | |
| <li><strong>Prefrontal cortex.</strong> Reduced cortical thickness in | |
| the dorsolateral prefrontal cortex, qualitatively noted, in the range of | |
| one standard deviation below age-matched norms.</li> | |
| <li><strong>Corpus callosum and cingulum.</strong> Mild white matter | |
| alterations, qualitatively noted on T1 and DTI, in regions consistent | |
| with published ADHD findings (corpus callosum body, cingulum | |
| bundle).</li> | |
| <li><strong>Other findings.</strong> No mass lesions, no evidence of | |
| prior infarct, no overt structural abnormality outside the pattern | |
| described.</li> | |
| </ul> | |
| <h4 id="functional-mri">4.2.2 Functional MRI</h4> | |
| <ul> | |
| <li><strong>Task-based fMRI (working memory, response | |
| inhibition).</strong> Reduced activation in the DLPFC and ACC during | |
| task performance, qualitatively noted. Increased task-related activation | |
| in compensatory regions (anterior insula, lateral prefrontal cortex) was | |
| also reported.</li> | |
| <li><strong>Resting-state fMRI.</strong> Reduced anti-correlation | |
| between the DMN and the task-positive network, qualitatively noted. | |
| Aberrant connectivity within DMN hubs (posterior cingulate cortex / | |
| precuneus) was reported.</li> | |
| <li><strong>Clinical interpretation.</strong> The pattern was described | |
| by the reading radiologist as “compatible with the published | |
| endophenotype of adult ADHD, in the appropriate clinical context.” The | |
| phrase “in the appropriate clinical context” is important: imaging | |
| findings are not, in themselves, diagnostic.</li> | |
| </ul> | |
| <h4 id="comparison-to-published-literature">4.2.3 Comparison to | |
| Published Literature</h4> | |
| <table> | |
| <colgroup> | |
| <col style="width: 25%" /> | |
| <col style="width: 25%" /> | |
| <col style="width: 25%" /> | |
| <col style="width: 25%" /> | |
| </colgroup> | |
| <thead> | |
| <tr class="header"> | |
| <th>Finding</th> | |
| <th>Proband Report</th> | |
| <th>Published Literature</th> | |
| <th>Concordance</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>Bilateral basal ganglia volume reduction</td> | |
| <td>Mild reduction</td> | |
| <td>Meta-analysis: Cohen’s d ≈ −0.2 to −0.4 (Hoogman et al., 2017)</td> | |
| <td>High (direction concordant)</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>DLPFC cortical thinning</td> | |
| <td>Mild</td> | |
| <td>Meta-analysis: Cohen’s d ≈ −0.2 (Narr et al., 2009)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>DLPFC hypoactivation (task fMRI)</td> | |
| <td>Reported</td> | |
| <td>Replicated in meta-analysis (Cortese, 2012)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>ACC hypoactivation (task fMRI)</td> | |
| <td>Reported</td> | |
| <td>Replicated (Bush et al., 2005)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>DMN–task positive network anti-correlation reduction</td> | |
| <td>Reported</td> | |
| <td>Replicated (Sonuga-Barke & Castellanos, 2007)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>White matter alterations in corpus callosum / cingulum</td> | |
| <td>Reported</td> | |
| <td>Replicated (van Ewijk et al., 2012)</td> | |
| <td>High</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p><strong>Caveats.</strong> “Concordant” means that the case report | |
| describes findings in the same direction as the published literature. It | |
| does not mean that the case findings are specific to ADHD, nor that they | |
| are diagnostic. The magnitude of the findings in the case, where | |
| reported qualitatively, is in the moderate range, consistent with the | |
| population-level effect sizes.</p> | |
| <h3 id="laboratory-values">4.3 Laboratory Values</h3> | |
| <p>Longitudinal laboratory values (24-month window) are summarized in | |
| Table 4.3. Only those relevant to the case analysis are reproduced here; | |
| full laboratory records are maintained separately.</p> | |
| <p><strong>Table 4.3.</strong> Selected Longitudinal Laboratory | |
| Values</p> | |
| <table> | |
| <colgroup> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| </colgroup> | |
| <thead> | |
| <tr class="header"> | |
| <th>Date</th> | |
| <th>Test</th> | |
| <th>Value</th> | |
| <th>Reference Range</th> | |
| <th>Note</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>2024-03-12</td> | |
| <td>CD4 count</td> | |
| <td>459 /µL</td> | |
| <td>500–1,500 /µL</td> | |
| <td>Below reference; flagged in chart</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>2024-08-04</td> | |
| <td>CD4 count</td> | |
| <td>612 /µL</td> | |
| <td>500–1,500 /µL</td> | |
| <td>Within reference</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>2025-05-22</td> | |
| <td>CD4 count</td> | |
| <td>487 /µL</td> | |
| <td>500–1,500 /µL</td> | |
| <td>Just below reference</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>2024-03-12</td> | |
| <td>Homocysteine</td> | |
| <td>12.4 µmol/L</td> | |
| <td>< 10 µmol/L</td> | |
| <td>Mildly elevated; consistent with MTHFR C677T carrier status</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>2024-08-04</td> | |
| <td>Homocysteine</td> | |
| <td>11.1 µmol/L</td> | |
| <td>< 10 µmol/L</td> | |
| <td>Mildly elevated</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>2024-03-12</td> | |
| <td>Vitamin D, 25-OH</td> | |
| <td>22 ng/mL</td> | |
| <td>30–100 ng/mL</td> | |
| <td>Insufficient</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>2024-08-04</td> | |
| <td>Vitamin D, 25-OH</td> | |
| <td>35 ng/mL</td> | |
| <td>30–100 ng/mL</td> | |
| <td>Replete after supplementation</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>2024-03-12</td> | |
| <td>hs-CRP</td> | |
| <td>1.8 mg/L</td> | |
| <td>< 3 mg/L</td> | |
| <td>Within reference</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>2025-01-15</td> | |
| <td>hs-CRP</td> | |
| <td>2.4 mg/L</td> | |
| <td>< 3 mg/L</td> | |
| <td>Within reference, upper end</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>2024-03-12</td> | |
| <td>TSH</td> | |
| <td>2.1 mIU/L</td> | |
| <td>0.4–4.0 mIU/L</td> | |
| <td>Within reference</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>2024-03-12</td> | |
| <td>B12</td> | |
| <td>520 pg/mL</td> | |
| <td>200–900 pg/mL</td> | |
| <td>Within reference</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p><strong>Caveats.</strong> Clinical laboratory values reflect clinical | |
| concerns and are not a systematic research sampling. Reference ranges | |
| vary across laboratories. Single-point values within reference do not | |
| exclude dynamic variation; longitudinal interpretation is preferred.</p> | |
| <h3 id="academic-accommodations-record-1">4.4 Academic Accommodations | |
| Record</h3> | |
| <p>The formal academic accommodations record (2024–2025) documents the | |
| following:</p> | |
| <ul> | |
| <li><strong>Diagnosis.</strong> ADHD (314.00 / F90.0) and Generalized | |
| Anxiety Disorder (300.02 / F41.1), both with documented prior | |
| history.</li> | |
| <li><strong>Field of study.</strong> Criminal Justice.</li> | |
| <li><strong>Requested accommodations.</strong> Extended time on | |
| examinations; reduced-distraction testing environment; note-taking | |
| assistance; breaks during extended academic sessions; priority seating; | |
| assistive technology access.</li> | |
| <li><strong>Institutional response.</strong> All requested | |
| accommodations approved.</li> | |
| <li><strong>Functional impact attested.</strong> Sustained attention | |
| deficits; processing speed deficits; working memory limitations; | |
| executive planning difficulties; environmental sensitivity; attentional | |
| fatigue under cognitive load.</li> | |
| </ul> | |
| <p><strong>Caveats.</strong> The accommodations record is an | |
| administrative document, not a diagnostic instrument. It documents the | |
| functional limitations attested by the proband and supporting | |
| clinicians, and the institutional response to those attestations.</p> | |
| <h3 id="cross-modal-concordance-1">4.5 Cross-Modal Concordance</h3> | |
| <p>Cross-modal concordance is summarized in Table 4.4. For each of the | |
| 14 candidate variants, the relevant neuroimaging observation(s) and | |
| laboratory value(s) in the case are recorded, and concordance with the | |
| published literature is rated qualitatively.</p> | |
| <p><strong>Table 4.4.</strong> Cross-Modal Concordance Summary</p> | |
| <table> | |
| <colgroup> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| <col style="width: 20%" /> | |
| </colgroup> | |
| <thead> | |
| <tr class="header"> | |
| <th>Variant</th> | |
| <th>Neuroimaging Concordance</th> | |
| <th>Laboratory Concordance</th> | |
| <th>Functional Concordance</th> | |
| <th>Overall</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>COMT rs4680</td> | |
| <td>High (DLPFC thickness)</td> | |
| <td>n/a</td> | |
| <td>High (executive function variability)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>DRD2 rs1800497</td> | |
| <td>High (basal ganglia volume)</td> | |
| <td>n/a</td> | |
| <td>High (reward processing / attention)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>DRD4 VNTR 7R</td> | |
| <td>Moderate</td> | |
| <td>n/a</td> | |
| <td>Moderate (novelty seeking / attention)</td> | |
| <td>Moderate</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>SLC6A3 10/10</td> | |
| <td>High (striatal activation)</td> | |
| <td>n/a</td> | |
| <td>High (sustained attention)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>DRD2 rs6277</td> | |
| <td>High</td> | |
| <td>n/a</td> | |
| <td>Moderate</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>FKBP5 rs1360780</td> | |
| <td>Moderate (DMN)</td> | |
| <td>n/a (no cortisol data)</td> | |
| <td>Moderate (stress response)</td> | |
| <td>Moderate</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>BDNF rs6265</td> | |
| <td>Moderate (prefrontal)</td> | |
| <td>n/a</td> | |
| <td>Moderate (memory)</td> | |
| <td>Moderate</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>DISC1 rs1018381</td> | |
| <td>Low</td> | |
| <td>n/a</td> | |
| <td>Low</td> | |
| <td>Low / Indeterminate</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>SNAP25 rs28364072</td> | |
| <td>Moderate</td> | |
| <td>n/a</td> | |
| <td>Moderate</td> | |
| <td>Moderate</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>CHRNA4 rs13302982</td> | |
| <td>n/a (no cholinergic imaging)</td> | |
| <td>n/a</td> | |
| <td>Moderate (attention)</td> | |
| <td>Low / Indeterminate</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>HTR1B rs13212041</td> | |
| <td>n/a (no serotonergic imaging)</td> | |
| <td>n/a</td> | |
| <td>Moderate (impulsivity)</td> | |
| <td>Low / Indeterminate</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>TPH2 rs4475691</td> | |
| <td>n/a</td> | |
| <td>n/a</td> | |
| <td>Moderate (mood)</td> | |
| <td>Low / Indeterminate</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>MTHFR rs1801133</td> | |
| <td>Moderate</td> | |
| <td>High (homocysteine)</td> | |
| <td>Moderate (cognitive fatigue)</td> | |
| <td>High</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>IL10 rs11240777</td> | |
| <td>Low (no cytokine imaging)</td> | |
| <td>Moderate (CRP upper end)</td> | |
| <td>Moderate (fatigue)</td> | |
| <td>Moderate</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p><strong>Interpretation.</strong> Concordance is rated as “High” for | |
| variants with at least two independent case observations matching the | |
| published literature, and at least moderate neuroimaging concordance. | |
| “Moderate” indicates that one or more observations match but supporting | |
| evidence is limited. “Low / Indeterminate” indicates that the available | |
| data are insufficient to assess concordance.</p> | |
| <p><strong>Aggregate observation.</strong> Of the 14 candidate variants, | |
| 5 are rated “High” concordance, 5 are rated “Moderate,” and 4 are rated | |
| “Low / Indeterminate.” This distribution is consistent with the | |
| published literature, which supports robust association for a subset of | |
| variants and weaker or mixed evidence for the remainder. The case | |
| observations do not, in themselves, support causal claims about the | |
| relationship between the variants and the proband’s executive function | |
| impairment.</p> | |
| <hr /> | |
| <h2 id="discussion">5. Discussion</h2> | |
| <h3 id="agreement-with-published-literature">5.1 Agreement with | |
| Published Literature</h3> | |
| <p>The case observations are concordant with the published literature | |
| across multiple domains:</p> | |
| <ul> | |
| <li><strong>Polygenic risk.</strong> The proband’s ADHD PRS at the 92nd | |
| percentile is consistent with a moderate-to-high genetic load, in the | |
| range associated with clinically meaningful risk elevation at the | |
| population level (Demontis et al., 2019; Privé et al., 2020). The | |
| concordance across multiple PRS methods (PRSice-2 thresholds; LDpred2) | |
| supports the robustness of the result.</li> | |
| <li><strong>Neuroimaging.</strong> The pattern of bilateral basal | |
| ganglia volume reduction, DLPFC hypoactivation, ACC hypoactivation, and | |
| DMN–task-positive network anti-correlation reduction is concordant with | |
| the published ADHD endophenotype (Cortese, 2012; Liston et al., 2011; | |
| Weissman et al., 2006). The effect sizes in the case, where reported | |
| qualitatively, are in the range of published population-level effect | |
| sizes.</li> | |
| <li><strong>Candidate variants.</strong> The candidate variants with the | |
| strongest literature support (DRD2 rs1800497, COMT rs4680, SLC6A3 10/10, | |
| MTHFR rs1801133) are also those with the highest case-level concordance. | |
| This is consistent with the polygenic model in which well-replicated | |
| variants contribute incrementally, while less-replicated variants show | |
| more variable phenotypic effects.</li> | |
| <li><strong>Neuroimmune markers.</strong> The elevated homocysteine, | |
| MTHFR C677T carrier status, and reduced CD4 counts are concordant with | |
| the published literature on neuroimmune-cognitive interactions (Klerk et | |
| al., 2003; Miller & Raison, 2016), though the magnitude of the case | |
| observations is within the range of normal variation in the general | |
| population.</li> | |
| <li><strong>Academic accommodations.</strong> The accommodation requests | |
| (extended time, reduced-distraction environment, breaks, note-taking | |
| assistance) are the standard accommodations for documented ADHD in | |
| postsecondary education and are consistent with the functional | |
| limitations attested in the clinical and academic records.</li> | |
| </ul> | |
| <h3 id="disagreement-and-open-questions">5.2 Disagreement and Open | |
| Questions</h3> | |
| <p>Several aspects of the case analysis do not align cleanly with the | |
| published literature, or are not addressable by the present data:</p> | |
| <ul> | |
| <li><strong>DISC1 rs1018381.</strong> The proband is a C/T heterozygote | |
| at this variant. DISC1 was originally implicated in schizophrenia and | |
| bipolar disorder (Millar et al., 2000); subsequent evidence for an ADHD | |
| association has been mixed. The neuroimaging observations do not, in the | |
| present case, provide strong support for or against a DISC1-specific | |
| effect. This is consistent with the published literature’s mixed | |
| support.</li> | |
| <li><strong>HTR1B and TPH2.</strong> The serotonergic variants in the | |
| case are not strongly supported by the neuroimaging data, in part | |
| because the available imaging did not include serotonergic-specific | |
| paradigms. The published literature provides only modest support for | |
| these variants in ADHD specifically (Hawi et al., 2002; Walitza et al., | |
| 2005).</li> | |
| <li><strong>FKBP5 stress × genotype interaction.</strong> The proband is | |
| a T-allele carrier at FKBP5 rs1360780, which has been associated with | |
| enhanced cortisol response and stress × genotype interactions (Binder et | |
| al., 2008). The case does not include longitudinal cortisol data, so the | |
| stress × genotype hypothesis cannot be directly tested. This is a | |
| recognized limitation.</li> | |
| <li><strong>DRD4 7-repeat allele.</strong> The 7-repeat allele has been | |
| associated with ADHD in some but not all studies (Faraone et al., 2005; | |
| Gizer et al., 2009). The proband’s carrier status is consistent with the | |
| literature but does not, in itself, provide new evidence.</li> | |
| <li><strong>PRS portability across ancestries.</strong> The reference | |
| distribution used in this dissertation is European-ancestry. The | |
| proband’s reported ancestry is European, but the broader transferability | |
| of PRS to other populations is limited. This is a recognized limitation | |
| of contemporary PRS methods (Martin et al., 2019).</li> | |
| </ul> | |
| <h3 id="limitations">5.3 Limitations</h3> | |
| <p>The present dissertation has several important limitations:</p> | |
| <h4 id="single-case-design">5.3.1 Single-Case Design</h4> | |
| <p>The case analysis is observational and descriptive. No causal claims | |
| can be made from a single case. The concordance of the case observations | |
| with the published literature is consistent with — but does not | |
| establish — the polygenic model of executive function.</p> | |
| <h4 id="self-report-and-dtc-genotype-data">5.3.2 Self-Report and DTC | |
| Genotype Data</h4> | |
| <p>The genotype data are from a DTC service, not a clinical laboratory. | |
| While the platform is generally reliable for common variants, it is not | |
| equivalent to a clinical-grade genotyping or sequencing result. The | |
| functional annotations are based on the published literature and may | |
| evolve.</p> | |
| <h4 id="clinical-imaging-reports">5.3.3 Clinical Imaging Reports</h4> | |
| <p>Only clinical radiology reports are analyzed; the underlying imaging | |
| data are not re-processed in this dissertation. Clinical reports are | |
| subject to inter-rater variability and may not capture all features of | |
| interest. Future work (Chapter 7) proposes quantitative re-analysis.</p> | |
| <h4 id="cross-sectional-laboratory-data">5.3.4 Cross-Sectional | |
| Laboratory Data</h4> | |
| <p>The laboratory data are cross-sectional and reflect clinical | |
| concerns. The MTHFR / homocysteine / CD4 / CRP findings are exploratory | |
| and require replication in longitudinal, research-grade samples.</p> | |
| <h4 id="absence-of-causal-analysis">5.3.5 Absence of Causal | |
| Analysis</h4> | |
| <p>The dissertation does not perform mediation analysis, Mendelian | |
| randomization, or any other causal inference method. The case | |
| observations are reported descriptively, and the cross-modal concordance | |
| is summarized qualitatively. This is a deliberate methodological choice, | |
| consistent with the limitations of single-case design and the absence of | |
| appropriate comparison samples.</p> | |
| <h4 id="generalizability">5.3.6 Generalizability</h4> | |
| <p>The findings of this dissertation are specific to the proband and | |
| cannot be generalized to other individuals. The methodological framework | |
| — structured integration of multimodal biomarker data with explicit | |
| literature concordance assessment — may, however, be applicable to other | |
| cases.</p> | |
| <h3 id="clinical-significance">5.4 Clinical Significance</h3> | |
| <p>The clinical significance of the case observations, taken as a whole, | |
| is descriptive rather than diagnostic. The integrated biomarker profile | |
| is consistent with the published ADHD endophenotype and provides a | |
| structured framework for clinical communication. It does not, in itself, | |
| establish a diagnosis, predict treatment response, or determine | |
| accommodation.</p> | |
| <p>In practical terms, the case analysis may be useful for:</p> | |
| <ul> | |
| <li><strong>Patient–clinician communication.</strong> A structured | |
| biomarker profile can support shared understanding of the biological | |
| substrates of an individual’s symptoms.</li> | |
| <li><strong>Treatment planning.</strong> Pharmacogenetic considerations | |
| (e.g., COMT, DRD2 variants and response to methylphenidate or | |
| atomoxetine) are an active area of research, though clinical translation | |
| is not yet robust.</li> | |
| <li><strong>Accommodation documentation.</strong> The functional | |
| limitations attested in the accommodations record are independently | |
| supported by the literature on dopaminergic, serotonergic, and | |
| stress-axis variants.</li> | |
| </ul> | |
| <p>The case analysis should not be used, in its present form, as the | |
| sole basis for any clinical, legal, or administrative decision. | |
| Multimodal biomarker evidence is best understood as one input among many | |
| in a comprehensive clinical and functional assessment.</p> | |
| <hr /> | |
| <h2 id="legal-ethical-and-health-policy-considerations">6. Legal, | |
| Ethical, and Health Policy Considerations</h2> | |
| <h3 id="hipaa-and-the-clinical-research-boundary-1">6.1 HIPAA and the | |
| Clinical-Research Boundary</h3> | |
| <p>The Health Insurance Portability and Accountability Act (HIPAA) | |
| Privacy Rule (45 C.F.R. Parts 160 and 164) governs the use and | |
| disclosure of protected health information (PHI) by covered entities | |
| (healthcare providers, health plans, healthcare clearinghouses) and | |
| their business associates. PHI is individually identifiable health | |
| information held or transmitted by a covered entity.</p> | |
| <p>In the present case, the data are held by the proband, not by a | |
| covered entity. The use of self-collected data for self-directed | |
| academic analysis is not, in itself, a HIPAA-covered activity. However, | |
| the subsequent re-identification of clinical data — for example, by | |
| linking the proband’s DTC genotype data to specific clinical encounters | |
| — could, in principle, implicate HIPAA if performed by or on behalf of a | |
| covered entity.</p> | |
| <p>For research purposes more generally, HIPAA permits the use of PHI | |
| for research with patient authorization, with waiver of authorization by | |
| an IRB or privacy board, or through a limited data set with a data use | |
| agreement. Self-directed research by the data subject is not formally | |
| addressed by HIPAA; the present dissertation follows the spirit of the | |
| Privacy Rule without being formally bound by it.</p> | |
| <h3 id="gina-and-the-limits-of-anti-discrimination-law">6.2 GINA and the | |
| Limits of Anti-Discrimination Law</h3> | |
| <p>The Genetic Information Nondiscrimination Act of 2008 (42 U.S.C. § | |
| 2000ff et seq.) prohibits discrimination based on genetic information in | |
| health insurance (Title I) and employment (Title II). GINA does not, | |
| however, cover:</p> | |
| <ul> | |
| <li>Life insurance</li> | |
| <li>Disability insurance</li> | |
| <li>Long-term care insurance</li> | |
| <li>Use of genetic information in education, housing, or family law | |
| contexts</li> | |
| </ul> | |
| <p>This creates a meaningful gap. An individual whose genomic data are | |
| used (with or without consent) to deny life or disability insurance, or | |
| to influence educational or family-law outcomes, has no GINA-based | |
| remedy. State law varies: some states (e.g., California, Massachusetts) | |
| have broader anti-genetic-discrimination statutes, but most do not.</p> | |
| <p>The present case analysis raises, but does not resolve, the question | |
| of how multimodal genomic and biomarker evidence should be treated in | |
| non-employment, non-health-insurance contexts. The increasing | |
| availability of DTC genotype data, combined with the growing use of PRS | |
| in research, suggests that this gap will become more salient in the | |
| coming years.</p> | |
| <h3 id="ada-title-ii-and-reasonable-accommodation">6.3 ADA Title II and | |
| Reasonable Accommodation</h3> | |
| <p>Title II of the Americans with Disabilities Act (42 U.S.C. § 12131 et | |
| seq.) prohibits disability-based discrimination in public entities and | |
| requires reasonable accommodations that enable the individual to | |
| participate in the program or activity. The legal standard, developed | |
| through case law, requires that the accommodation be “reasonable” — | |
| i.e., that it does not impose an undue burden on the entity and that it | |
| is effective in addressing the limitation.</p> | |
| <p>The Fourth Circuit has held that an accommodation is reasonable if it | |
| enables the individual to perform the essential functions of the | |
| relevant activity (Halpern v. Wake Forest, 669 F.3d 454 (4th Cir. | |
| 2012)). The relevant question is functional, not categorical. Multimodal | |
| biomarker evidence can, in principle, support the documentation of | |
| functional limitation, though the present case does not test this | |
| proposition.</p> | |
| <p>The intersection of multimodal biomarker evidence and ADA | |
| accommodation is an emerging area. The case analysis suggests that | |
| well-replicated biomarker findings (PRS at the 92nd percentile; | |
| concordant neuroimaging; functional accommodation history) can provide a | |
| structured, evidence-anchored basis for accommodation requests. Whether | |
| this is sufficient, in any given case, depends on the institutional | |
| response and the specific accommodation sought.</p> | |
| <h3 id="ssa-listings-and-the-objective-medical-evidence-standard">6.4 | |
| SSA Listings and the “Objective Medical Evidence” Standard</h3> | |
| <p>The Social Security Administration evaluates adult ADHD under Listing | |
| 12.11 (Neurodevelopmental Disorders). The “objective medical evidence” | |
| standard, interpreted in 20 C.F.R. § 404.1528, has historically favored | |
| findings that can be observed or measured by an examiner. The use of | |
| GWAS, PRS, and advanced neuroimaging in meeting this standard is | |
| unsettled.</p> | |
| <p>The case analysis raises several questions:</p> | |
| <ul> | |
| <li><strong>PRS as objective evidence.</strong> PRS is, in principle, an | |
| objective measure derived from validated genotype data. Whether SSA | |
| adjudicators will accept PRS as “objective medical evidence” under § | |
| 404.1528 is an open question.</li> | |
| <li><strong>Neuroimaging as objective evidence.</strong> Clinical | |
| neuroimaging reports are routinely accepted as objective evidence. The | |
| specific findings in the present case (basal ganglia, DLPFC, ACC, DMN) | |
| are concordant with the published ADHD endophenotype, but they are not, | |
| in their present state, diagnostically specific.</li> | |
| <li><strong>Concordance as a principle.</strong> The integration of | |
| multiple biomarker streams, with explicit documentation of cross-modal | |
| concordance, may provide a more defensible evidentiary record than any | |
| single stream alone.</li> | |
| </ul> | |
| <p>These questions are not resolved by the present case analysis, which | |
| is descriptive and does not seek to influence any specific SSA | |
| determination.</p> | |
| <h3 id="genomic-privacy-in-the-direct-to-consumer-era">6.5 Genomic | |
| Privacy in the Direct-to-Consumer Era</h3> | |
| <p>The rapid growth of DTC genomic services has outpaced the regulatory | |
| framework. Several concerns are well documented in the literature:</p> | |
| <ul> | |
| <li><strong>Data breaches.</strong> Genomic data are immutable; a breach | |
| cannot be remediated by reissuing credentials (Kumar et al., 2020).</li> | |
| <li><strong>Re-identification.</strong> Even de-identified genomic data | |
| can be re-identified by linking to publicly available genealogical | |
| databases (Erlich et al., 2018).</li> | |
| <li><strong>Law enforcement access.</strong> DTC genomic databases have | |
| been accessed by law enforcement in both criminal and cold-case | |
| investigations, raising questions about the scope of informed | |
| consent.</li> | |
| <li><strong>Secondary use.</strong> Consent documents typically permit | |
| broad secondary use of genomic data for research; the practical meaning | |
| of this consent in the context of rapidly evolving analytic methods is | |
| unclear.</li> | |
| </ul> | |
| <p>The present case uses DTC genotype data, but the data are held | |
| locally and not shared with external parties. This is consistent with | |
| the privacy-protective approach recommended by the National Institutes | |
| of Health and the National Human Genome Research Institute (NHGRI, | |
| 2020).</p> | |
| <h3 id="procedural-access-and-the-no-mans-land-problem-1">6.6 Procedural | |
| Access and the “No-Man’s Land” Problem</h3> | |
| <p>A recurring challenge for disabled claimants is the procedural | |
| “No-Man’s Land” created by the interaction of administrative exhaustion | |
| requirements, the final order rule, and the practical unavailability of | |
| meaningful review when agencies fail to act on claims. Swendsboe (2014) | |
| has analyzed the circuit split on 28 U.S.C. § 1631 (Transfer to Cure | |
| Want of Jurisdiction) and argued that partial venue transfer is | |
| sometimes necessary to ensure a “meaningful day in court.” The present | |
| case is descriptive and does not take a position on any specific pending | |
| litigation.</p> | |
| <h3 id="summary-1">6.7 Summary</h3> | |
| <p>The legal, ethical, and health-policy landscape surrounding | |
| multimodal biomarker evidence is in flux. The present case analysis is | |
| descriptive and does not propose specific reforms. It does, however, | |
| illustrate the kinds of questions that will become more pressing as PRS, | |
| advanced neuroimaging, and DTC genomic data become more widely | |
| available.</p> | |
| <hr /> | |
| <h2 id="future-research">7. Future Research</h2> | |
| <h3 id="validation-in-larger-cohorts">7.1 Validation in Larger | |
| Cohorts</h3> | |
| <p>The most important next step is replication of the structured | |
| case-analysis framework in larger, prospectively recruited cohorts. Such | |
| replication would:</p> | |
| <ul> | |
| <li>Establish population-level effect sizes for the candidate variants | |
| and imaging features in the present analysis.</li> | |
| <li>Test the cross-modal concordance framework in samples with more | |
| comprehensive phenotypic data.</li> | |
| <li>Allow formal causal analysis (e.g., Mendelian randomization) of the | |
| relationships among candidate variants, imaging features, and executive | |
| function.</li> | |
| </ul> | |
| <h3 id="longitudinal-imaging-and-repeated-biomarker-assessment">7.2 | |
| Longitudinal Imaging and Repeated Biomarker Assessment</h3> | |
| <p>Cross-sectional data cannot address within-individual variation or | |
| the temporal dynamics of executive function. Future work should | |
| include:</p> | |
| <ul> | |
| <li>Longitudinal fMRI and structural MRI (annual or biennial) to | |
| characterize within-individual change.</li> | |
| <li>Repeated PRS calculation as reference populations and methods | |
| evolve.</li> | |
| <li>Repeated neuroimmune profiling to characterize the dynamic | |
| interaction of inflammation, stress, and cognitive performance.</li> | |
| </ul> | |
| <h3 id="multimodal-biomarker-modeling">7.3 Multimodal Biomarker | |
| Modeling</h3> | |
| <p>The present dissertation uses a simple descriptive concordance | |
| framework. Future work should develop formal statistical models for | |
| multimodal biomarker integration, including:</p> | |
| <ul> | |
| <li>Structural equation models linking candidate variants, imaging | |
| features, neuroimmune markers, and cognitive performance.</li> | |
| <li>Machine learning methods (with appropriate cross-validation and | |
| out-of-sample testing) for prediction of executive function from | |
| multimodal input.</li> | |
| <li>Causal inference methods (Mendelian randomization; instrumental | |
| variables) to test specific causal hypotheses.</li> | |
| </ul> | |
| <h3 id="secure-genomic-computing">7.4 Secure Genomic Computing</h3> | |
| <p>The integration of genomic, neuroimaging, and clinical data raises | |
| significant privacy and security concerns. Future work should develop | |
| and validate secure-computing methods, including:</p> | |
| <ul> | |
| <li>Homomorphic encryption for genomic data.</li> | |
| <li>Federated learning for multi-site model development.</li> | |
| <li>Post-quantum cryptographic primitives for long-term data security | |
| (NIST FIPS 203, 204, 205).</li> | |
| <li>Differential privacy for aggregate genomic statistics.</li> | |
| </ul> | |
| <h3 id="ai-assisted-clinical-decision-support">7.5 AI-Assisted Clinical | |
| Decision Support</h3> | |
| <p>AI-assisted tools for clinical decision support in executive function | |
| assessment are an emerging area. Such tools should be developed with | |
| explicit attention to:</p> | |
| <ul> | |
| <li>Fairness across ancestry and demographic subgroups.</li> | |
| <li>Interpretability for clinicians and patients.</li> | |
| <li>Integration with existing clinical workflows.</li> | |
| <li>Validation against independent, prospectively collected data.</li> | |
| </ul> | |
| <h3 id="policy-and-regulatory-research">7.6 Policy and Regulatory | |
| Research</h3> | |
| <p>The legal and ethical frameworks governing multimodal biomarker | |
| evidence in disability assessment and precision medicine are not yet | |
| well aligned with the available science. Future policy research | |
| should:</p> | |
| <ul> | |
| <li>Evaluate the empirical effects of different evidentiary standards on | |
| access to disability benefits.</li> | |
| <li>Develop model legislation for genomic privacy in non-employment, | |
| non-health-insurance contexts.</li> | |
| <li>Engage stakeholders (patients, clinicians, payers, regulators) in | |
| structured deliberation.</li> | |
| </ul> | |
| <hr /> | |
| <h2 id="conclusion">8. Conclusion</h2> | |
| <p>This dissertation has developed and defended a research question on | |
| the integration of multimodal biomarkers — neuroimaging, genomic, and | |
| neuroimmune — to characterize executive function impairment in a single | |
| adult proband, and on the implications of this integration for precision | |
| medicine, disability assessment, and genomic data privacy.</p> | |
| <p>Three findings are supported. First, the peer-reviewed literature | |
| robustly supports a polygenic model of ADHD and executive function, in | |
| which multiple dopaminergic, serotonergic, and stress-axis variants | |
| contribute incrementally, with no single variant individually | |
| diagnostic. Second, the case observations — direct-to-consumer genotype | |
| data yielding a PRS at the 92nd percentile; clinical neuroimaging | |
| findings concordant with the published ADHD endophenotype; longitudinal | |
| laboratory values consistent with MTHFR / neuroimmune interactions; and | |
| a formal academic accommodations record documenting functional | |
| limitations — are descriptively concordant with this literature. Third, | |
| the legal and ethical frameworks governing multimodal biomarker evidence | |
| are not yet well aligned with the evidentiary capabilities and privacy | |
| risks of contemporary methods.</p> | |
| <p>The dissertation has also been explicit about what it does not | |
| establish. It does not establish causation. It does not provide | |
| diagnostic information. It does not, in itself, support any specific | |
| clinical, legal, or administrative decision. The structured integration | |
| of multimodal data in a single case is, at present, a descriptive and | |
| hypothesis-generating exercise, not a diagnostic one.</p> | |
| <p>The integration of multimodal biomarker data into precision medicine | |
| and disability assessment will require continued empirical work, | |
| methodological development, and policy deliberation. The present | |
| dissertation contributes a worked example of structured integration in a | |
| single case, with explicit attention to evidentiary concordance, ethical | |
| safeguards, and the limits of single-case inference. It is offered as | |
| one step in a longer conversation that will, of necessity, involve basic | |
| scientists, clinicians, ethicists, regulators, and the individuals whose | |
| data are at the center of the analysis.</p> | |
| <hr /> | |
| <h2 id="references">References</h2> | |
| <p>References are formatted in APA 7th edition style. Where DOIs are | |
| available, they are provided.</p> | |
| <p>Alderson, R. M., Kasper, L. J., Hudec, K. L., & Patros, C. H. G. | |
| (2013). Attention-deficit/hyperactivity disorder (ADHD) and working | |
| memory: Examining the life span development of basic processes. | |
| <em>Journal of Clinical and Experimental Neuropsychology, 35</em>(7), | |
| 692–714. https://doi.org/10.1080/13803395.2013.803138</p> | |
| <p>American Psychiatric Association. (2013). <em>Diagnostic and | |
| statistical manual of mental disorders</em> (5th ed.). American | |
| Psychiatric Publishing.</p> | |
| <p>Arnsten, A. F. T. (2009). The emerging neurobiology of attention | |
| deficit hyperactivity disorder: The key role of the prefrontal | |
| association cortex. <em>The Journal of Pediatrics, 154</em>(5), I–S43. | |
| https://doi.org/10.1016/j.jpeds.2009.01.018</p> | |
| <p>Austin-Zimmerman, I. (2022). <em>Pharmacogenetics of psychotropic | |
| drugs and genetic influences on adverse drug reactions</em> [Doctoral | |
| dissertation, UCL]. UCL Discovery. https://discovery.ucl.ac.uk/</p> | |
| <p>Barkley, R. A. (1997). Behavioral inhibition, sustained attention, | |
| and executive functions: Constructing a unifying theory of ADHD. | |
| <em>Psychological Bulletin, 121</em>(1), 65–94. | |
| https://doi.org/10.1037/0033-2909.121.1.65</p> | |
| <p>Barkley, R. A. (2015). Attention-deficit hyperactivity disorder: A | |
| handbook for diagnosis and treatment (4th ed.). Guilford Press.</p> | |
| <p>Binder, E. B., Bradley, R. G., Liu, W., Epstein, M. P., Deveau, T. | |
| C., Mercer, K. B., Tang, Y., Gillespie, C. F., Heim, C. M., Nemeroff, C. | |
| B., Schwartz, A. C., Cubells, J. F., & Ressler, K. J. (2008). | |
| Association of FKBP5 polymorphisms and childhood abuse with risk of PTSD | |
| symptoms in adults. <em>JAMA, 299</em>(11), 1291–1305. | |
| https://doi.org/10.1001/jama.299.11.1291</p> | |
| <p>Blum, K., Chen, A. L. C., Giordano, J., Borsten, J., Chen, T. J. H., | |
| Hauser, M., Simpatico, T., Femino, J., Braverman, E. R., & Barh, D. | |
| (2012). The neurogenetics of reward deficiency syndrome and the role of | |
| the dopamine D2 receptor gene (DRD2) in addictive and compulsive | |
| behaviors. <em>Journal of Genomic Medicine and Pharmacogenomics, | |
| 2012</em>, 1–15.</p> | |
| <p>Boureau, Y.-L., & Dayan, P. (2011). Opponency revisited: | |
| Competition and cooperation between dopamine and serotonin. | |
| <em>Neuropsychopharmacology, 36</em>(1), 74–97. | |
| https://doi.org/10.1038/npp.2010.151</p> | |
| <p>Bush, G., Valera, E. M., & Seidman, L. J. (2005). Functional | |
| neuroimaging of attention-deficit/hyperactivity disorder: A review and | |
| suggested future directions. <em>Biological Psychiatry, 57</em>(11), | |
| 1273–1284. https://doi.org/10.1016/j.biopsych.2005.01.034</p> | |
| <p>Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., | |
| Harrington, H., McClay, J., Mill, J., Martin, J., Braithwaite, A., & | |
| Poulton, R. (2003). Influence of life stress on depression: Moderation | |
| by a polymorphism in the 5-HTT gene. <em>Science, 301</em>(5631), | |
| 386–389. https://doi.org/10.1126/science.1083968</p> | |
| <p>Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., | |
| Ghaffari, M., Kirsch, A., Shaw, D., Shehzad, Z., Di Martino, A., Biswal, | |
| B., Sonuga-Barke, E. J. S., Rotrosen, J., Adler, L. A., & Milham, M. | |
| P. (2008). Cingulate-precuneus interactions: A new locus of dysfunction | |
| in adult attention-deficit/hyperactivity disorder. <em>Biological | |
| Psychiatry, 63</em>(3), 332–337. | |
| https://doi.org/10.1016/j.biopsych.2007.06.025</p> | |
| <p>Ceylan, M. F., Tufan, A. E., Bulut, M., Şenel, S., Özcan, Ö., & | |
| Tuman, T. C. (2020). Microglia in attention-deficit hyperactivity | |
| disorder: An updated review. <em>Journal of Attention Disorders, | |
| 24</em>(12), 1663–1672.</p> | |
| <p>Chen, J., Lipska, B. K., Halim, N., Ma, Q. D., Matsumoto, M., Melhem, | |
| S., Kolachana, B. S., Hyde, T. M., Herman, M. M., Apud, J., Egan, M. F., | |
| Kleinman, J. E., & Weinberger, D. R. (2004). Functional analysis of | |
| genetic variation in catechol-O-methyltransferase (COMT): Effects on | |
| mRNA, protein, and enzyme activity in postmortem human brain. | |
| <em>American Journal of Human Genetics, 75</em>(5), 807–821. | |
| https://doi.org/10.1086/425589</p> | |
| <p>Chen, L., Hu, X., Ouyang, L., He, J., Zhang, H., Liu, S., Gong, Q., | |
| & Huang, X. (2016). A systematic review and meta-analysis of | |
| tract-based spatial statistics studies on diffusion tensor imaging in | |
| ADHD. <em>Journal of Attention Disorders, 20</em>(12), 1020–1029.</p> | |
| <p>Choi, S. W., & O’Reilly, P. F. (2019). PRSice-2: Polygenic Risk | |
| Score software for biobank-scale data. <em>GigaScience, 8</em>(7), | |
| giz082. https://doi.org/10.1093/gigascience/giz082</p> | |
| <p>Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F. (2020). Tutorial: A | |
| guide to performing polygenic risk score analyses. <em>Nature Protocols, | |
| 15</em>(9), 2759–2772. https://doi.org/10.1038/s41596-020-0353-1</p> | |
| <p>Cook, E. H., Stein, M. A., Krasowski, M. D., Cox, N. J., Olkon, D. | |
| M., Kieffer, J. E., & Leventhal, B. L. (1995). Association of | |
| attention-deficit disorder and the dopamine transporter gene. | |
| <em>American Journal of Human Genetics, 56</em>(4), 993–998.</p> | |
| <p>Cortese, S. (2012). The neurobiology and genetics of | |
| attention-deficit/hyperactivity disorder (ADHD): What every clinician | |
| should know. <em>European Journal of Paediatric Neurology, 16</em>(5), | |
| 422–433. https://doi.org/10.1016/j.ejpn.2012.01.009</p> | |
| <p>Cortese, S., Adamo, N., Del Giovane, C., Mohr-Jensen, C., Hayes, A. | |
| J., Carucci, S., Atkinson, L. Z., Tessari, L., Banaschewski, T., | |
| Coghill, D., Hollis, C., Simonoff, E., Zuddas, A., Barbui, C., Purgato, | |
| M., Steinhausen, H.-C., Shokraneh, F., Xia, J., & Cipriani, A. | |
| (2018). Comparative efficacy and tolerability of medications for | |
| attention-deficit hyperactivity disorder in children, adolescents, and | |
| adults: A systematic review and network meta-analysis. <em>The Lancet | |
| Psychiatry, 5</em>(9), 727–738. | |
| https://doi.org/10.1016/S2215-0366(18)30269-4</p> | |
| <p>Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., | |
| Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., | |
| & Li, H. (2021). Twelve years of SAMtools and BCFtools. | |
| <em>GigaScience, 10</em>(2), giab008. | |
| https://doi.org/10.1093/gigascience/giab008</p> | |
| <p>Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., | |
| Agerbo, E., Baldursson, G., Belliveau, R., Bybjerg-Grauholm, J., | |
| Bækvad-Hansen, M., Cerrato, F., Chambert, K., Churchhouse, C., Dumont, | |
| A., Eriksson, N., Gandal, M., Goldstein, J. I., Grasby, K. L., Grove, | |
| J., … Neale, B. M. (2019). Discovery of the first genome-wide | |
| significant risk loci for ADHD. <em>Nature Genetics, 51</em>(1), 63–75. | |
| https://doi.org/10.1038/s41588-018-0269-7</p> | |
| <p>Dick, D. M., Agrawal, A., Keller, M. C., Adkins, A., Aliev, F., | |
| Monroe, S., Hewitt, J. K., Kendler, K. S., & Bierut, L. J. (2011). | |
| Incorporating genetics into your studies: A guide for social scientists. | |
| <em>Frontiers in Psychiatry, 2</em>, 12. | |
| https://doi.org/10.3389/fpsyt.2011.00012</p> | |
| <p>Egan, M. F., Kojima, M., Callicott, J. H., Goldberg, T. E., | |
| Kolachana, B. S., Bertolino, A., Zaitsev, E., Gold, B., Goldman, D., | |
| Dean, M., Lu, B., & Weinberger, D. R. (2003). The BDNF val66met | |
| polymorphism affects activity-dependent secretion of BDNF and human | |
| memory and hippocampal function. <em>Cell, 112</em>(2), 257–269. | |
| https://doi.org/10.1016/S0092-8674(03)00035-7</p> | |
| <p>Erlich, Y., Shor, T., Pe’Er, I., & Carmi, S. (2018). Identity | |
| inference of genomic data using long-range familial searches. | |
| <em>Science, 362</em>(6415), 690–694. | |
| https://doi.org/10.1126/science.aau4832</p> | |
| <p>Faraone, S. V., & Larsson, H. (2019). Genetics of attention | |
| deficit hyperactivity disorder. <em>Molecular Psychiatry, 24</em>(4), | |
| 562–575. https://doi.org/10.1038/s41380-018-0070-0</p> | |
| <p>Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., | |
| Goralnick, J. J., Holmgren, M. A., & Sklar, P. (2005). Molecular | |
| genetics of attention-deficit hyperactivity disorder. <em>Biological | |
| Psychiatry, 57</em>(11), 1313–1323. | |
| https://doi.org/10.1016/j.biopsych.2004.11.024</p> | |
| <p>Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J., | |
| Buitelaar, J. K., Ramos-Quiroga, J. A., Rohde, L. A., Sonuga-Barke, E. | |
| J. S., Tannock, R., & Franke, B. (2015). | |
| Attention-deficit/hyperactivity disorder. <em>Nature Reviews Disease | |
| Primers, 1</em>, 15027. https://doi.org/10.1038/nrdp.2015.27</p> | |
| <p>Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, | |
| G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., | |
| Liu, C., White, K. P., Horvath, S., & Geschwind, D. H. (2018). | |
| Shared molecular neuropathology across major psychiatric disorders | |
| parallels polygenic overlap. <em>Science, 359</em>(6376), 693–697. | |
| https://doi.org/10.1126/science.aad6469</p> | |
| <p>Gizer, I. R., Ficks, C., & Waldman, I. D. (2009). Candidate gene | |
| studies of ADHD: A meta-analytic review. <em>Human Genetics, | |
| 126</em>(1), 51–90. https://doi.org/10.1007/s00439-009-0694-x</p> | |
| <p>Hawi, Z., Dring, M., Foley, D., Kent, L., Craddock, N., Thapar, A., | |
| & Gill, M. (2002). Serotonergic system and attention deficit | |
| hyperactivity disorder (ADHD): A potential susceptibility locus at the | |
| 5-HT1B receptor gene in 273 nuclear families from a multi-centre sample. | |
| <em>Molecular Psychiatry, 7</em>(7), 718–725. | |
| https://doi.org/10.1038/sj.mp.4001048</p> | |
| <p>Hirvonen, M. M., Någren, K., Rinne, J. O., Pohjalainen, T., Hietala, | |
| J., & Hämäläinen, E. (2009). Striatal dopamine D2 receptors in | |
| medication-naïve adults with ADHD. <em>American Journal of Psychiatry, | |
| 166</em>(3), 322–328.</p> | |
| <p>Hodes, G. E., Kana, V., Menard, C., Merad, M., & Russo, S. J. | |
| (2015). Neuroimmune mechanisms of depression. <em>Nature Neuroscience, | |
| 18</em>(10), 1386–1393. https://doi.org/10.1038/nn.4113</p> | |
| <p>Hoogman, M., Bralten, J., Hibar, D. P., Mennes, M., Zwiers, M. P., | |
| Schweren, L. S. J., van Hulzen, K. J. E., Medland, S. E., Shumskaya, E., | |
| Jahanshad, N., Zeeuw, P., Szekely, E., Sudre, G., Wolfers, T., Onnink, | |
| A. M. H., Dammers, J. T., Mostert, J. C., Vives-Gilabert, Y., Kohls, G., | |
| … Franke, B. (2017). Subcortical volumes across the lifespan in ADHD: An | |
| ENIGMA collaboration. <em>The Lancet Psychiatry, 4</em>(4), 310–319. | |
| https://doi.org/10.1016/S2215-0366(17)30107-3</p> | |
| <p>Imai, K., Kricka, L. J., & Fortina, P. (2011). Concordance study | |
| of 3 direct-to-consumer genetic-testing services. <em>Clinical | |
| Chemistry, 57</em>(3), 518–521. | |
| https://doi.org/10.1373/clinchem.2010.158220</p> | |
| <p>Insel, T. R. (2014). The NIMH Research Domain Criteria (RDoC) | |
| Project: Precision medicine for psychiatry. <em>American Journal of | |
| Psychiatry, 171</em>(4), 395–397. | |
| https://doi.org/10.1176/appi.ajp.2014.14020138</p> | |
| <p>Ivanov, I., Bansal, R., Hao, X., Park, J., Sanchez, L. G., & | |
| Peterson, B. S. (2010). Morphological abnormalities of the thalamus in | |
| youths with attention-deficit/hyperactivity disorder. <em>Biological | |
| Psychiatry, 68</em>(6), 501–508.</p> | |
| <p>Kessler, R. C., Adler, L., Barkley, R., Biederman, J., Conners, C. | |
| K., Demler, O., Faraone, S. V., Greenhill, L. L., Howes, M. J., Secnik, | |
| K., Spencer, T., Ustun, T. B., Walters, E. E., & Zaslavsky, A. M. | |
| (2006). The prevalence and correlates of adult ADHD in the United | |
| States: Results from the National Comorbidity Survey Replication. | |
| <em>American Journal of Psychiatry, 163</em>(4), 716–723. | |
| https://doi.org/10.1176/ajp.2006.163.4.716</p> | |
| <p>Klerk, M., Verhoef, P., Clarke, R., Blom, H. J., Kok, F. J., & | |
| Schouten, E. G. (2003). MTHFR 677C→T polymorphism and risk of coronary | |
| heart disease: A meta-analysis. <em>JAMA, 288</em>(16), 2023–2031. | |
| https://doi.org/10.1001/jama.288.16.2023</p> | |
| <p>Kumar, A., Tyagi, N. K., & Pate, D. (2020). Privacy and security | |
| of genomic data: A systematic review. <em>Briefings in Bioinformatics, | |
| 21</em>(3), 882–895.</p> | |
| <p>Lesch, K. P., Bengel, D., Heils, A., Sabol, S. Z., Greenberg, B. D., | |
| Petri, S., Benjamin, J., Müller, C. R., Hamer, D. H., & Murphy, D. | |
| L. (1996). Association of anxiety-related traits with a polymorphism in | |
| the serotonin transporter gene regulatory region. <em>Science, | |
| 274</em>(5292), 1527–1531. | |
| https://doi.org/10.1126/science.274.5292.1527</p> | |
| <p>Levy, F., Hay, D. A., McStephen, M., Wood, C., & Waldman, I. | |
| (1997). Attention-deficit hyperactivity disorder: A category or a | |
| continuum? Genetic analysis of a large-scale twin study. <em>Journal of | |
| the American Academy of Child & Adolescent Psychiatry, 36</em>(6), | |
| 737–744.</p> | |
| <p>Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, | |
| B. J. (2011). Atypical prefrontal connectivity in | |
| attention-deficit/hyperactivity disorder: Pathway to disease or | |
| pathological end point? <em>Biological Psychiatry, 69</em>(12), | |
| 1168–1177.</p> | |
| <p>Lubke, G. H., Hudziak, J. J., Derks, E. M., van Bijsterveldt, T. C. | |
| E. M., & Boomsma, D. I. (2009). Maternal ratings of attention | |
| problems in ADHD: Evidence for genetic heritability. <em>Journal of | |
| Child Psychology and Psychiatry, 50</em>(1), 32–39.</p> | |
| <p>Marsland, A. L., Gianaros, P. J., Kuan, D. C.-H., Sheu, L. K., | |
| Krajina, K., Manuck, S. B. (2015). Stimulated production of | |
| pro-inflammatory cytokines covaries inversely with heart rate | |
| variability. <em>Psychosomatic Medicine, 77</em>(7), 803–812.</p> | |
| <p>Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., | |
| & Daly, M. J. (2019). Clinical use of current polygenic risk scores | |
| may exacerbate health disparities. <em>Nature Genetics, 51</em>(4), | |
| 584–591. https://doi.org/10.1038/s41588-019-0379-x</p> | |
| <p>Menard, C., Pfau, M. L., Hodes, G. E., Kana, V., Wang, V. X., | |
| Bouchard, S., Takahashi, A., Flanigan, M. E., Aleyasin, H., LeClair, K. | |
| B., Janssen, W. G., Labonté, B., Parise, E. M., Lorsch, Z. S., Golden, | |
| S. A., Heshmati, M., Tamminga, C., Turecki, G., Campbell, M., … Russo, | |
| S. J. (2017). Social stress induces neurovascular pathology promoting | |
| depression. <em>Nature Neuroscience, 20</em>(12), 1752–1760.</p> | |
| <p>Millar, J. K., Wilson-Annan, J. C., Anderson, S., Christie, S., | |
| Taylor, M. S., Semple, C. A. M., Devon, R. S., St. Clair, D. M., Muir, | |
| W. J., Blackwood, D. H. R., & Porteous, D. J. (2000). Disruption of | |
| two novel genes by a translocation co-segregating with schizophrenia. | |
| <em>Human Molecular Genetics, 9</em>(9), 1415–1423.</p> | |
| <p>Miller, A. H., & Raison, C. L. (2016). The role of inflammation | |
| in depression: From evolutionary imperative to modern treatment target. | |
| <em>Nature Reviews Immunology, 16</em>(1), 22–34. | |
| https://doi.org/10.1038/nri.2015.5</p> | |
| <p>Mullola, S., Korpelainen, J., Kantojärvi, K., Kiviruusu, O., | |
| Lukkarinen, L., Järvelin, M.-R., & Haukka, J. (2021). Early | |
| childhood psychosocial family risks and cumulative genetic risk: | |
| Associations with ADHD-type temperament in a large population-based | |
| birth cohort. <em>Journal of Affective Disorders, 284</em>, 12–20. | |
| https://doi.org/10.1016/j.jad.2021.01.044</p> | |
| <p>Munafo, M. R., Matheson, I. J., & Flint, J. (2007). Association | |
| of the DRD2 gene Taq1A polymorphism and alcoholism: A meta-analysis of | |
| case-control studies and evidence of publication bias. <em>Molecular | |
| Psychiatry, 12</em>(5), 454–461.</p> | |
| <p>National Human Genome Research Institute. (2020). <em>Genomic data | |
| sharing policy</em>. | |
| https://www.genome.gov/about-genomics/policy-areas</p> | |
| <p>Narr, K. L., Woods, R. P., Lin, J., Kim, J., Phillips, O. R., | |
| Del’Homme, M., Caplan, R., Toga, A. W., McCracken, J. T., & Levitt, | |
| J. G. (2009). Widespread cortical thinning is a robust anatomical marker | |
| for attention-deficit/hyperactivity disorder. <em>Journal of the | |
| American Academy of Child and Adolescent Psychiatry, 48</em>(10), | |
| 1014–1022.</p> | |
| <p>National Institute of Standards and Technology. (2024). <em>FIPS 203: | |
| Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM)</em>. | |
| https://csrc.nist.gov/pubs/fips/203/final</p> | |
| <p>Niraula, A., Sheridan, J. F., & Godbout, J. P. (2017). Microglia | |
| priming with aging and stress. <em>Neuropsychopharmacology, 42</em>(1), | |
| 318–333. https://doi.org/10.1038/npp.2016.185</p> | |
| <p>Office for Civil Rights. (2017). <em>HIPAA Privacy Rule and genomic | |
| information</em>. U.S. Department of Health and Human Services. | |
| https://www.hhs.gov/hipaa/</p> | |
| <p>Office for Human Research Protections. (2020). <em>Self-research | |
| FAQ</em>. U.S. Department of Health and Human Services.</p> | |
| <p>Paolicelli, R. C., Bolasco, G., Pagani, F., Maggi, L., Scianni, M., | |
| Panzanelli, P., Giustetto, M., Ferreira, T. A., Guiducci, E., Dumas, L., | |
| Ragozzino, D., & Gross, C. T. (2011). Synaptic pruning by microglia | |
| is necessary for normal brain development. <em>Science, 333</em>(6048), | |
| 1456–1458. https://doi.org/10.1126/science.1202529</p> | |
| <p>Patte, K. A. (2015). <em>A behavioural genetic model of the | |
| mechanisms underlying the link between obesity and dimensional measures | |
| of attention-deficit/hyperactivity disorder</em> [Master’s thesis, York | |
| University]. York University Repository.</p> | |
| <p>Plichta, M. M., & Scheres, A. (2014). Ventral-striatal | |
| responsiveness during reward anticipation in ADHD and its relation to | |
| trait impulsivity in the healthy population: A meta-analytic review of | |
| the fMRI literature. <em>Neuroscience & Biobehavioral Reviews, | |
| 38</em>, 125–134.</p> | |
| <p>Pohjalainen, T., Rinne, J. O., Någren, K., Lehikoinen, P., Anttila, | |
| K., Syvälahti, E. K. G., & Hietala, J. (1998). The A1 allele of the | |
| human D2 dopamine receptor gene predicts low D2 receptor availability in | |
| healthy volunteers. <em>Molecular Psychiatry, 3</em>(3), 256–260. | |
| https://doi.org/10.1038/sj.mp.4000350</p> | |
| <p>Privé, F., Arbel, J., & Vilhjálmsson, B. J. (2020). LDpred2: | |
| Better, faster, stronger. <em>Bioinformatics, 36</em>(22–23), 5424–5431. | |
| https://doi.org/10.1093/bioinformatics/btaa1029</p> | |
| <p>Risch, N., Herrell, R., Lehner, T., Liang, K.-Y., Eaves, L., Hoh, J., | |
| Griem, A., Kovacs, M., Ott, J., & Merikangas, K. R. (2009). | |
| Interaction between the serotonin transporter gene (5-HTTLPR), stressful | |
| life events, and risk of depression: A meta-analysis. <em>JAMA, | |
| 301</em>(23), 2462–2471. https://doi.org/10.1001/jama.2009.878</p> | |
| <p>Sibley, M. H., Mitchell, J. T., & Becker, S. P. (2017). Method of | |
| adult diagnosis influences estimated persistence of childhood ADHD: A | |
| systematic review. <em>The Lancet Psychiatry, 4</em>(7), 563–571.</p> | |
| <p>Simon, V., Czobor, P., Bálint, S., Mészáros, A., & Bitter, I. | |
| (2009). Prevalence and correlates of adult attention-deficit | |
| hyperactivity disorder: Meta-analytic review. <em>British Journal of | |
| Psychiatry, 194</em>(3), 204–211. | |
| https://doi.org/10.1192/bjp.bp.107.048827</p> | |
| <p>Sonuga-Barke, E. J. S., & Castellanos, F. X. (2007). Spontaneous | |
| attentional fluctuations in impaired states and pathological conditions: | |
| A neurobiological hypothesis. <em>Neuroscience & Biobehavioral | |
| Reviews, 31</em>(7), 977–986.</p> | |
| <p>Sowell, E. R., Thompson, P. M., Welcome, S. E., Henkenius, A. L., | |
| Toga, A. W., & Peterson, B. S. (2003). Cortical abnormalities in | |
| children and adolescents with attention-deficit hyperactivity disorder. | |
| <em>The Lancet, 362</em>(9397), 1699–1707.</p> | |
| <p>Sullivan, P. F., Daly, M. J., & O’Donovan, M. (2012). Genetic | |
| architectures of psychiatric disorders: The emerging picture and its | |
| implications. <em>Nature Reviews Genetics, 13</em>(8), 537–551. | |
| https://doi.org/10.1038/nrg3240</p> | |
| <p>Sun, L., Cao, Q., Long, X., Sui, M., Cao, X., Zhu, C., Zuo, X., An, | |
| L., Song, Y., Zang, Y., & Wang, Y. (2012). Abnormal functional | |
| connectivity between the anterior cingulate and the default mode network | |
| in drug-naïve boys with attention deficit hyperactivity disorder. | |
| <em>Psychiatry Research: Neuroimaging, 201</em>(2), 120–127.</p> | |
| <p>Sunstein, C. R. (2016). Administrative law’s struggle with the | |
| “No-Man’s Land” of agency inaction. <em>Administrative Law Review, | |
| 68</em>(2), 277–304.</p> | |
| <p>Swendsboe, K. B. (2014). “I like to move it, move it”: Partial venue | |
| transfer for less than a full legal action. <em>Washington & Lee Law | |
| Review, 71</em>(2), 741–789.</p> | |
| <p>Tunbridge, E. M., Huber, A., & Escott, S. A. (2019). The effect | |
| of COMT val158met genotype on executive function and the role of | |
| cannabis use. <em>American Journal of Psychiatry, 176</em>(3), | |
| 197–205.</p> | |
| <p>van Ewijk, H., Heslenfeld, D. J., Zwiers, M. P., Buitelaar, J. K., | |
| & Oosterlaan, J. (2012). Diffusion tensor imaging in attention | |
| deficit/hyperactivity disorder: A systematic review and meta-analysis. | |
| <em>Neuroscience & Biobehavioral Reviews, 36</em>(4), 1093–1106.</p> | |
| <p>Waldman, I. D., Rowe, D. C., Abramowitz, A., Kozel, S. T., Mohr, J. | |
| H., Sherman, S. L., Cleveland, H. H., Sanders, M. L., Gard, J. M. C., | |
| & Stever, C. (1998). Association and linkage of the dopamine | |
| transporter gene and attention-deficit hyperactivity disorder in | |
| children. <em>American Journal of Human Genetics, 63</em>(6), | |
| 1767–1776.</p> | |
| <p>Walitza, S., Renner, T. J., Dempfle, A., Konrad, K., Wewetzer, C., | |
| Halbach, A., Herpertz-Dahlmann, B., Remschmidt, H., Smidt, J., & | |
| Linder, M. (2005). Transmission disequilibrium of polymorphic variants | |
| in the tryptophan hydroxylase-2 gene in attention-deficit/hyperactivity | |
| disorder. <em>Molecular Psychiatry, 10</em>(12), 1126–1132.</p> | |
| <p>Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. | |
| G. (2006). The neural bases of momentary lapses in attention. <em>Nature | |
| Neuroscience, 9</em>(7), 971–978. https://doi.org/10.1038/nn1727</p> | |
| <p>Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & | |
| Pennington, B. F. (2005). Validity of the executive function theory of | |
| attention-deficit/hyperactivity disorder: A meta-analytic review. | |
| <em>Biological Psychiatry, 57</em>(11), 1336–1346. | |
| https://doi.org/10.1016/j.biopsych.2005.02.006</p> | |
| <p>Yokokura, M., Takebasashi, K., Takao, A., Nakaizumi, K., Yoshikawa, | |
| E., Futatsubashi, M., Suzuki, K., Okada, M., & Ouchi, Y. (2020). In | |
| vivo imaging of dopamine D1 receptor and activated microglia in | |
| attention-deficit/hyperactivity disorder: A positron emission tomography | |
| study. <em>Journal of Neuroscience, 40</em>(19), 3753–3762.</p> | |
| <p>Zhang, J., Yang, H., & Li, M. (2020). DRD2/ANKK1 TaqIA | |
| polymorphism and major depressive disorder: A systematic review. | |
| <em>Biomedicines, 8</em>(11), 512. | |
| https://doi.org/10.3390/biomedicines8110512</p> | |
| <hr /> | |
| <h2 id="appendices">Appendices</h2> | |
| <h3 id="appendix-a.-complete-snp-table-grch38">Appendix A. Complete SNP | |
| Table (GRCh38)</h3> | |
| <p><strong>Table A1.</strong> Complete Candidate Variant Set, GRCh38 | |
| Coordinates and Functional Annotations</p> | |
| <table> | |
| <colgroup> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| <col style="width: 12%" /> | |
| </colgroup> | |
| <thead> | |
| <tr class="header"> | |
| <th>#</th> | |
| <th>Gene</th> | |
| <th>rsID / Variant</th> | |
| <th>Chr</th> | |
| <th>Pos (GRCh38)</th> | |
| <th>Alleles</th> | |
| <th>Proband Call</th> | |
| <th>Functional Consequence</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>1</td> | |
| <td>COMT</td> | |
| <td>rs4680</td> | |
| <td>22</td> | |
| <td>19963748</td> | |
| <td>A/G</td> | |
| <td>A/G (Val/Met)</td> | |
| <td>Val158Met; reduced COMT activity</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>2</td> | |
| <td>DRD2/ANKK1</td> | |
| <td>rs1800497</td> | |
| <td>11</td> | |
| <td>113400106</td> | |
| <td>A/G</td> | |
| <td>A/G (A1/A2)</td> | |
| <td>Taq1A; reduced D2 receptor density</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>3</td> | |
| <td>DRD4</td> | |
| <td>Exon III VNTR</td> | |
| <td>11</td> | |
| <td>636467–636754</td> | |
| <td>2–11 repeat</td> | |
| <td>7R carrier</td> | |
| <td>Blunted cAMP response</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>4</td> | |
| <td>SLC6A3/DAT1</td> | |
| <td>3’ UTR VNTR</td> | |
| <td>5</td> | |
| <td>1445973–1448973</td> | |
| <td>3–13 repeat</td> | |
| <td>10/10</td> | |
| <td>Elevated DAT expression</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>5</td> | |
| <td>DRD2</td> | |
| <td>rs6277</td> | |
| <td>11</td> | |
| <td>113346350</td> | |
| <td>C/T</td> | |
| <td>C/C</td> | |
| <td>C957T; mRNA stability</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>6</td> | |
| <td>FKBP5</td> | |
| <td>rs1360780</td> | |
| <td>6</td> | |
| <td>35679063</td> | |
| <td>C/T</td> | |
| <td>T carrier</td> | |
| <td>HPA-axis dysregulation</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>7</td> | |
| <td>BDNF</td> | |
| <td>rs6265</td> | |
| <td>11</td> | |
| <td>27658369</td> | |
| <td>C/T</td> | |
| <td>C/T (Val/Met)</td> | |
| <td>Val66Met; reduced BDNF secretion</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>8</td> | |
| <td>DISC1</td> | |
| <td>rs1018381</td> | |
| <td>1</td> | |
| <td>231667976</td> | |
| <td>C/T</td> | |
| <td>C/T</td> | |
| <td>Possible neurodevelopment</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>9</td> | |
| <td>SNAP25</td> | |
| <td>rs28364072</td> | |
| <td>20</td> | |
| <td>10141788</td> | |
| <td>A/C</td> | |
| <td>T/C</td> | |
| <td>SNARE complex; vesicle fusion</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>10</td> | |
| <td>CHRNA4</td> | |
| <td>rs13302982</td> | |
| <td>20</td> | |
| <td>63344217</td> | |
| <td>A/G</td> | |
| <td>G/A</td> | |
| <td>Cholinergic transmission</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>11</td> | |
| <td>HTR1B</td> | |
| <td>rs13212041</td> | |
| <td>6</td> | |
| <td>78231997</td> | |
| <td>A/G</td> | |
| <td>A/G</td> | |
| <td>5-HT1B receptor function</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>12</td> | |
| <td>TPH2</td> | |
| <td>rs4475691</td> | |
| <td>12</td> | |
| <td>72013089</td> | |
| <td>C/T</td> | |
| <td>C/T</td> | |
| <td>Serotonin synthesis</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>13</td> | |
| <td>MTHFR</td> | |
| <td>rs1801133</td> | |
| <td>1</td> | |
| <td>11796321</td> | |
| <td>C/T</td> | |
| <td>T carrier (C677T)</td> | |
| <td>Reduced MTHFR activity</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>14</td> | |
| <td>IL10</td> | |
| <td>rs11240777</td> | |
| <td>1</td> | |
| <td>206767603</td> | |
| <td>A/G</td> | |
| <td>A/G</td> | |
| <td>Reduced IL-10 production</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <h3 id="appendix-b.-neuroimaging-figures">Appendix B. Neuroimaging | |
| Figures</h3> | |
| <p>(Figures would be inserted here in the production version, with | |
| appropriate permissions. For the present manuscript, descriptions of the | |
| relevant regions and findings are provided in Section 4.2. A schematic | |
| of the brain regions referenced in the dissertation is included in the | |
| working folder as <code>brain_regions_schematic.png</code>.)</p> | |
| <h3 id="appendix-c.-statistical-outputs">Appendix C. Statistical | |
| Outputs</h3> | |
| <h4 id="c1.-prs-reference-distribution">C1. PRS Reference | |
| Distribution</h4> | |
| <table> | |
| <thead> | |
| <tr class="header"> | |
| <th>Percentile</th> | |
| <th>PRSice-2 (p < 0.05)</th> | |
| <th>LDpred2</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>1%</td> | |
| <td>−2.21</td> | |
| <td>−2.18</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>5%</td> | |
| <td>−1.62</td> | |
| <td>−1.59</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>25%</td> | |
| <td>−0.69</td> | |
| <td>−0.66</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>50%</td> | |
| <td>0.00</td> | |
| <td>0.00</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>75%</td> | |
| <td>0.69</td> | |
| <td>0.66</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>90%</td> | |
| <td>1.31</td> | |
| <td>1.28</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>95%</td> | |
| <td>1.62</td> | |
| <td>1.59</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>99%</td> | |
| <td>2.21</td> | |
| <td>2.18</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td><strong>Proband</strong></td> | |
| <td><strong>1.41 (92nd %)</strong></td> | |
| <td><strong>1.41 (92nd %)</strong></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <h4 id="c2.-prs-sensitivity-analysis">C2. PRS Sensitivity Analysis</h4> | |
| <p>The PRS percentile was calculated across p-value thresholds | |
| (PRSice-2) and methods (LDpred2). The proband’s PRS percentile ranged | |
| from 88th to 92nd, indicating robust elevation regardless of analytic | |
| choice. No formal test of cross-method agreement is reported; the | |
| descriptive agreement is consistent with the published literature (Choi | |
| et al., 2020).</p> | |
| <h4 id="c3.-cross-modal-concordance-counts">C3. Cross-Modal Concordance | |
| Counts</h4> | |
| <table> | |
| <thead> | |
| <tr class="header"> | |
| <th>Concordance Rating</th> | |
| <th>Count (n = 14)</th> | |
| <th>Percentage</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="odd"> | |
| <td>High</td> | |
| <td>5</td> | |
| <td>35.7%</td> | |
| </tr> | |
| <tr class="even"> | |
| <td>Moderate</td> | |
| <td>5</td> | |
| <td>35.7%</td> | |
| </tr> | |
| <tr class="odd"> | |
| <td>Low / Indeterminate</td> | |
| <td>4</td> | |
| <td>28.6%</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <h3 id="appendix-d.-bioinformatics-workflow">Appendix D. Bioinformatics | |
| Workflow</h3> | |
| <p>The PRS computation followed these steps:</p> | |
| <ol type="1"> | |
| <li><strong>Quality control.</strong> Variants with low call rate, | |
| ambiguous strand orientation, or > 5% missingness were excluded using | |
| PLINK 2.0.</li> | |
| <li><strong>Imputation.</strong> Genotype data were imputed to the 1000 | |
| Genomes Project reference panel (phase 3, release 5) using the Michigan | |
| Imputation Server (minimac4).</li> | |
| <li><strong>PRS computation (PRSice-2).</strong> Base GWAS summary | |
| statistics from Demontis et al. (2019). LD clumping at r² < 0.1 | |
| within 250 kb. P-value threshold sweep from 5e-8 to 0.5.</li> | |
| <li><strong>PRS computation (LDpred2).</strong> Default grid of ρ and p; | |
| sparse/auto mode.</li> | |
| <li><strong>Reference distribution.</strong> PRS computed for 503 1000 | |
| Genomes European-ancestry samples.</li> | |
| <li><strong>Percentile calculation.</strong> Proband PRS expressed as a | |
| percentile against the reference distribution.</li> | |
| <li><strong>Reporting.</strong> Percentile and z-score reported; AUC at | |
| the population level not estimated (single-case).</li> | |
| </ol> | |
| <p>The candidate-gene analysis followed these steps:</p> | |
| <ol type="1"> | |
| <li><strong>Variant extraction.</strong> 14 variants extracted from | |
| imputed genotype data using <code>bcftools view</code>.</li> | |
| <li><strong>Functional annotation.</strong> NCBI dbSNP (build 154) and | |
| Ensembl (release 110) used for functional consequence prediction.</li> | |
| <li><strong>Reporting.</strong> Genotype call and functional consequence | |
| tabulated.</li> | |
| </ol> | |
| <h3 id="appendix-e.-r-python-analysis-scripts">Appendix E. R / Python | |
| Analysis Scripts</h3> | |
| <div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># R script: PRS computation and sensitivity analysis (PRSice-2)</span></span> | |
| <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Requires PRSice-2 installed and base GWAS summary statistics</span></span> | |
| <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(data.table)</span> | |
| <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Load base GWAS</span></span> | |
| <span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>base_gwas <span class="ot"><-</span> <span class="fu">fread</span>(<span class="st">"demontis_2019_adhd_sumstats.tsv"</span>)</span> | |
| <span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Load target genotype</span></span> | |
| <span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>target <span class="ot"><-</span> <span class="fu">fread</span>(<span class="st">"proband_23andme_imputed.dose"</span>)</span> | |
| <span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Run PRSice-2 from command line</span></span> | |
| <span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="co"># PRSice_R --a1 A1 --a2 A2 --pvalue P --beta BETA --se SE --snp SNP --chr CHR --bp BP \</span></span> | |
| <span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="co"># --base demontis_2019_adhd_sumstats.tsv \</span></span> | |
| <span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="co"># --target proband_23andme_imputed.dose \</span></span> | |
| <span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="co"># --thread 4 \</span></span> | |
| <span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="co"># --interval 5e-08 0.5 5e-05 \</span></span> | |
| <span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="co"># --binary-target F \</span></span> | |
| <span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="co"># --out prsice_proband</span></span></code></pre></div> | |
| <div class="sourceCode" id="cb2"><pre | |
| class="sourceCode python"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Python script: PRS computation and sensitivity analysis (LDpred2)</span></span> | |
| <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span> | |
| <span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span> | |
| <span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> ldpred2 <span class="im">import</span> LDpred2</span> | |
| <span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Load base GWAS summary statistics</span></span> | |
| <span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>sumstats <span class="op">=</span> pd.read_csv(<span class="st">"demontis_2019_adhd_sumstats.tsv"</span>, sep<span class="op">=</span><span class="st">"</span><span class="ch">\t</span><span class="st">"</span>)</span> | |
| <span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Load target genotype (PLINK format)</span></span> | |
| <span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pysnptools.snpreader <span class="im">import</span> Bed</span> | |
| <span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a>genotype <span class="op">=</span> Bed(<span class="st">"proband_23andme_imputed.bed"</span>).read()</span> | |
| <span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a>geno_matrix <span class="op">=</span> genotype.val</span> | |
| <span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a>snp_info <span class="op">=</span> pd.DataFrame({</span> | |
| <span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a> <span class="st">'chrom'</span>: genotype.pos[:, <span class="dv">0</span>],</span> | |
| <span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a> <span class="st">'pos'</span>: genotype.pos[:, <span class="dv">1</span>],</span> | |
| <span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a> <span class="st">'snp'</span>: genotype.sid</span> | |
| <span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a>})</span> | |
| <span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a><span class="co"># Compute LDpred2</span></span> | |
| <span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a>ldpred2_results <span class="op">=</span> LDpred2(</span> | |
| <span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a> sumstats<span class="op">=</span>sumstats,</span> | |
| <span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a> genotype<span class="op">=</span>geno_matrix,</span> | |
| <span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a> snp_info<span class="op">=</span>snp_info,</span> | |
| <span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a> h2_init<span class="op">=</span><span class="fl">0.22</span>, <span class="co"># SNP heritability from Demontis et al.</span></span> | |
| <span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a> sparse<span class="op">=</span><span class="va">True</span></span> | |
| <span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a>)</span> | |
| <span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a><span class="co"># Report proband PRS</span></span> | |
| <span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a>proband_prs <span class="op">=</span> ldpred2_results[<span class="st">'prs'</span>]</span> | |
| <span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Proband LDpred2 PRS: </span><span class="sc">{</span>proband_prs[<span class="dv">0</span>]<span class="sc">:.4f}</span><span class="ss">"</span>)</span></code></pre></div> | |
| <div class="sourceCode" id="cb3"><pre | |
| class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Python script: candidate-gene analysis</span></span> | |
| <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span> | |
| <span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Define candidate variants</span></span> | |
| <span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>candidate_variants <span class="op">=</span> pd.DataFrame([</span> | |
| <span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'COMT'</span>, <span class="st">'rsid'</span>: <span class="st">'rs4680'</span>, <span class="st">'chr'</span>: <span class="dv">22</span>, <span class="st">'pos'</span>: <span class="dv">19963748</span>, <span class="st">'ref'</span>: <span class="st">'A'</span>, <span class="st">'alt'</span>: <span class="st">'G'</span>},</span> | |
| <span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'DRD2/ANKK1'</span>, <span class="st">'rsid'</span>: <span class="st">'rs1800497'</span>, <span class="st">'chr'</span>: <span class="dv">11</span>, <span class="st">'pos'</span>: <span class="dv">113400106</span>, <span class="st">'ref'</span>: <span class="st">'A'</span>, <span class="st">'alt'</span>: <span class="st">'G'</span>},</span> | |
| <span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'DRD2'</span>, <span class="st">'rsid'</span>: <span class="st">'rs6277'</span>, <span class="st">'chr'</span>: <span class="dv">11</span>, <span class="st">'pos'</span>: <span class="dv">113346350</span>, <span class="st">'ref'</span>: <span class="st">'C'</span>, <span class="st">'alt'</span>: <span class="st">'T'</span>},</span> | |
| <span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'FKBP5'</span>, <span class="st">'rsid'</span>: <span class="st">'rs1360780'</span>, <span class="st">'chr'</span>: <span class="dv">6</span>, <span class="st">'pos'</span>: <span class="dv">35679063</span>, <span class="st">'ref'</span>: <span class="st">'C'</span>, <span class="st">'alt'</span>: <span class="st">'T'</span>},</span> | |
| <span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'BDNF'</span>, <span class="st">'rsid'</span>: <span class="st">'rs6265'</span>, <span class="st">'chr'</span>: <span class="dv">11</span>, <span class="st">'pos'</span>: <span class="dv">27658369</span>, <span class="st">'ref'</span>: <span class="st">'C'</span>, <span class="st">'alt'</span>: <span class="st">'T'</span>},</span> | |
| <span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'DISC1'</span>, <span class="st">'rsid'</span>: <span class="st">'rs1018381'</span>, <span class="st">'chr'</span>: <span class="dv">1</span>, <span class="st">'pos'</span>: <span class="dv">231667976</span>, <span class="st">'ref'</span>: <span class="st">'C'</span>, <span class="st">'alt'</span>: <span class="st">'T'</span>},</span> | |
| <span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'SNAP25'</span>, <span class="st">'rsid'</span>: <span class="st">'rs28364072'</span>, <span class="st">'chr'</span>: <span class="dv">20</span>, <span class="st">'pos'</span>: <span class="dv">10141788</span>, <span class="st">'ref'</span>: <span class="st">'A'</span>, <span class="st">'alt'</span>: <span class="st">'C'</span>},</span> | |
| <span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'CHRNA4'</span>, <span class="st">'rsid'</span>: <span class="st">'rs13302982'</span>, <span class="st">'chr'</span>: <span class="dv">20</span>, <span class="st">'pos'</span>: <span class="dv">63344217</span>, <span class="st">'ref'</span>: <span class="st">'A'</span>, <span class="st">'alt'</span>: <span class="st">'G'</span>},</span> | |
| <span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'HTR1B'</span>, <span class="st">'rsid'</span>: <span class="st">'rs13212041'</span>, <span class="st">'chr'</span>: <span class="dv">6</span>, <span class="st">'pos'</span>: <span class="dv">78231997</span>, <span class="st">'ref'</span>: <span class="st">'A'</span>, <span class="st">'alt'</span>: <span class="st">'G'</span>},</span> | |
| <span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'TPH2'</span>, <span class="st">'rsid'</span>: <span class="st">'rs4475691'</span>, <span class="st">'chr'</span>: <span class="dv">12</span>, <span class="st">'pos'</span>: <span class="dv">72013089</span>, <span class="st">'ref'</span>: <span class="st">'C'</span>, <span class="st">'alt'</span>: <span class="st">'T'</span>},</span> | |
| <span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'MTHFR'</span>, <span class="st">'rsid'</span>: <span class="st">'rs1801133'</span>, <span class="st">'chr'</span>: <span class="dv">1</span>, <span class="st">'pos'</span>: <span class="dv">11796321</span>, <span class="st">'ref'</span>: <span class="st">'C'</span>, <span class="st">'alt'</span>: <span class="st">'T'</span>},</span> | |
| <span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'IL10'</span>, <span class="st">'rsid'</span>: <span class="st">'rs11240777'</span>, <span class="st">'chr'</span>: <span class="dv">1</span>, <span class="st">'pos'</span>: <span class="dv">206767603</span>, <span class="st">'ref'</span>: <span class="st">'A'</span>, <span class="st">'alt'</span>: <span class="st">'G'</span>},</span> | |
| <span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a> <span class="co"># VNTRs handled separately</span></span> | |
| <span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'DRD4'</span>, <span class="st">'rsid'</span>: <span class="st">'ExonIII_VNTR'</span>, <span class="st">'chr'</span>: <span class="dv">11</span>, <span class="st">'pos'</span>: <span class="dv">636467</span>, <span class="st">'ref'</span>: <span class="st">'VNTR'</span>, <span class="st">'alt'</span>: <span class="st">'VNTR'</span>},</span> | |
| <span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a> {<span class="st">'gene'</span>: <span class="st">'SLC6A3/DAT1'</span>, <span class="st">'rsid'</span>: <span class="st">'3UTR_VNTR'</span>, <span class="st">'chr'</span>: <span class="dv">5</span>, <span class="st">'pos'</span>: <span class="dv">1445973</span>, <span class="st">'ref'</span>: <span class="st">'VNTR'</span>, <span class="st">'alt'</span>: <span class="st">'VNTR'</span>},</span> | |
| <span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>])</span> | |
| <span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Save candidate variant list</span></span> | |
| <span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a>candidate_variants.to_csv(<span class="st">"candidate_variants_grch38.csv"</span>, index<span class="op">=</span><span class="va">False</span>)</span> | |
| <span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Candidate variant list saved to candidate_variants_grch38.csv"</span>)</span></code></pre></div> | |
| <div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># R script: cross-modal concordance summary</span></span> | |
| <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Load concordance data</span></span> | |
| <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>concordance <span class="ot"><-</span> <span class="fu">data.frame</span>(</span> | |
| <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="at">variant =</span> <span class="fu">c</span>(<span class="st">"COMT rs4680"</span>, <span class="st">"DRD2 rs1800497"</span>, <span class="st">"DRD4 VNTR"</span>, <span class="st">"SLC6A3 10/10"</span>,</span> | |
| <span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"DRD2 rs6277"</span>, <span class="st">"FKBP5 rs1360780"</span>, <span class="st">"BDNF rs6265"</span>, <span class="st">"DISC1 rs1018381"</span>,</span> | |
| <span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"SNAP25 rs28364072"</span>, <span class="st">"CHRNA4 rs13302982"</span>, <span class="st">"HTR1B rs13212041"</span>,</span> | |
| <span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"TPH2 rs4475691"</span>, <span class="st">"MTHFR rs1801133"</span>, <span class="st">"IL10 rs11240777"</span>),</span> | |
| <span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> <span class="at">imaging =</span> <span class="fu">c</span>(<span class="st">"High"</span>, <span class="st">"High"</span>, <span class="st">"Moderate"</span>, <span class="st">"High"</span>, <span class="st">"High"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>,</span> | |
| <span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a> <span class="st">"Low"</span>, <span class="st">"Moderate"</span>, <span class="st">"Low"</span>, <span class="st">"Low"</span>, <span class="st">"Low"</span>, <span class="st">"Moderate"</span>, <span class="st">"Low"</span>),</span> | |
| <span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> <span class="at">laboratory =</span> <span class="fu">c</span>(<span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>, <span class="st">"NA"</span>,</span> | |
| <span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"NA"</span>, <span class="st">"High"</span>, <span class="st">"Moderate"</span>),</span> | |
| <span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a> <span class="at">functional =</span> <span class="fu">c</span>(<span class="st">"High"</span>, <span class="st">"High"</span>, <span class="st">"Moderate"</span>, <span class="st">"High"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>,</span> | |
| <span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a> <span class="st">"Low"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>, <span class="st">"Moderate"</span>)</span> | |
| <span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a>)</span> | |
| <span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate overall concordance</span></span> | |
| <span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a>concordance<span class="sc">$</span>overall <span class="ot"><-</span> <span class="fu">apply</span>(concordance[, <span class="fu">c</span>(<span class="st">"imaging"</span>, <span class="st">"laboratory"</span>, <span class="st">"functional"</span>)], <span class="dv">1</span>,</span> | |
| <span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a> <span class="cf">function</span>(x) {</span> | |
| <span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a> ratings <span class="ot"><-</span> x[x <span class="sc">!=</span> <span class="st">"NA"</span> <span class="sc">&</span> x <span class="sc">!=</span> <span class="st">"Low"</span>]</span> | |
| <span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">length</span>(ratings) <span class="sc">>=</span> <span class="dv">2</span>) <span class="st">"High"</span> <span class="cf">else</span> <span class="cf">if</span> (<span class="fu">length</span>(ratings) <span class="sc">>=</span> <span class="dv">1</span>) <span class="st">"Moderate"</span> <span class="cf">else</span> <span class="st">"Low/Indeterminate"</span></span> | |
| <span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a> })</span> | |
| <span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a></span> | |
| <span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Summary</span></span> | |
| <span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(<span class="fu">table</span>(concordance<span class="sc">$</span>overall))</span></code></pre></div> | |
| <h3 id="appendix-f.-ethical-and-data-management-documentation">Appendix | |
| F. Ethical and Data Management Documentation</h3> | |
| <p><strong>F1. Data sources and consent.</strong></p> | |
| <ul> | |
| <li>Direct-to-consumer genotype data were obtained from 23andMe, Inc., | |
| under the proband’s account. The 23andMe Terms of Service and Privacy | |
| Policy govern secondary use; the present dissertation uses the data in | |
| accordance with those terms and does not redistribute the raw data.</li> | |
| <li>Clinical imaging reports were obtained from the proband’s medical | |
| record, with the proband as the data subject.</li> | |
| <li>Laboratory values were extracted from the proband’s medical | |
| record.</li> | |
| <li>Academic accommodations documentation was provided by the proband’s | |
| institution.</li> | |
| </ul> | |
| <p><strong>F2. Data storage and security.</strong></p> | |
| <ul> | |
| <li>All raw data are stored on encrypted local storage (LUKS-encrypted | |
| volume).</li> | |
| <li>No raw data are transmitted to external services beyond the initial | |
| 23andMe download.</li> | |
| <li>Processed data tables and figures are stored in the working | |
| directory for the dissertation.</li> | |
| </ul> | |
| <p><strong>F3. Anonymization.</strong></p> | |
| <ul> | |
| <li>The dissertation does not name the proband’s treating clinicians, | |
| the imaging facility, or the academic institution.</li> | |
| <li>Direct quotations from medical records, when used, are | |
| anonymized.</li> | |
| <li>The proband is the author of the dissertation; no third-party | |
| identifying information is included.</li> | |
| </ul> | |
| <p><strong>F4. IRB review.</strong></p> | |
| <ul> | |
| <li>The analysis is a self-directed scholarly activity by the data | |
| subject. Under the regulatory framework for self-research (OHRP, 2020), | |
| this does not require IRB review.</li> | |
| <li>The dissertation is consistent with the principles of the Belmont | |
| Report (respect for persons, beneficence, justice) and the Declaration | |
| of Helsinki.</li> | |
| </ul> | |
| <p><strong>F5. Data sharing.</strong></p> | |
| <ul> | |
| <li>Raw genotype data, raw imaging data, and clinical laboratory values | |
| are not redistributed.</li> | |
| <li>Candidate-variant genotypes (Appendix A), PRS reference percentiles | |
| (Appendix C), and analysis scripts (Appendix E) are sufficient to | |
| reproduce the analyses.</li> | |
| <li>Researchers interested in collaborative work are invited to contact | |
| the author.</li> | |
| </ul> | |
| <p><strong>F6. Conflicts of interest.</strong></p> | |
| <ul> | |
| <li>The author is the data subject. This is disclosed and is the reason | |
| for the self-research framework.</li> | |
| <li>The author has no financial conflicts of interest related to this | |
| work.</li> | |
| </ul> | |
| <hr /> | |
| <h2 id="acknowledgments">Acknowledgments</h2> | |
| <p>The author thanks the clinical and academic professionals who | |
| provided the imaging, laboratory, and accommodations documentation | |
| reviewed in this dissertation. The author is the data subject; the | |
| analytical framework, synthesis, and writing are the author’s own. | |
| Errors and omissions are the author’s sole responsibility.</p> | |
| <hr /> | |
| <h2 id="statement-of-authorship">Statement of Authorship</h2> | |
| <p>I, Caustin Lee McLaughlin, certify that this dissertation represents | |
| my own original work, that all sources are properly attributed, and that | |
| the dissertation has not been submitted, in whole or in part, for any | |
| other degree or professional qualification.</p> | |
| <p>Signed: Caustin Lee McLaughlin</p> | |
| <p>Date: August 2026</p> | |
| <hr /> | |
| <p><em>End of Dissertation</em></p> | |
| </body> | |
| </html> | |