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<title>Integrating Neuroimaging, Genomic Data, and Neuroimmune Biomarkers to Characterize Executive Function Impairment</title>
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<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 />
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<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>
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