| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - dna |
| - variant-effect-prediction |
| - biology |
| - genomics |
| - non-coding |
| - regulatory-variants |
| configs: |
| - config_name: mendelian_traits |
| data_files: |
| - split: test |
| path: mendelian_traits_vep.parquet |
| - config_name: complex_traits |
| data_files: |
| - split: test |
| path: complex_traits_vep.parquet |
| --- |
| |
| # TraitGym + 8,192 bp pre-extracted windows |
|
|
| This dataset is a **repackaging** of [`songlab/TraitGym`](https://huggingface.co/datasets/songlab/TraitGym) (Benegas, Eraslan & Song, bioRxiv 2025.02.11.637758), with one extra step: for every variant we pre-extract the 8,192 bp window centered on the variant from the hg38 reference, plus the same window with the alt allele substituted. |
|
|
| The variants, labels and matched controls are **identical** to the original `songlab/TraitGym` `_matched_9` configs. |
|
|
| ## Configs |
|
|
| - **`mendelian_traits`** (n = 3,380): 338 putative-causal non-coding variants from 113 monogenic Mendelian diseases (curated from OMIM), matched 9:1 against gnomAD common-variant controls on chromosome × consequence × TSS-distance. |
| - **`complex_traits`** (n = 11,400): 1,140 putative-causal non-coding variants from 83 polygenic UK BioBank traits (PIP > 0.9 from statistical fine-mapping), matched 9:1 against PIP < 0.01 controls on chromosome × consequence × TSS-distance × MAF × LD score. |
|
|
| ## Schema |
|
|
| | column | description | |
| |--------|-------------| |
| | `chrom` | chromosome (`1`–`22`, `X`, `Y`) | |
| | `pos` | 1-indexed hg38 position | |
| | `ref`, `alt` | single bases (SNVs only) | |
| | `label` | int 0/1 (1 = causal/positive, 0 = matched control) | |
| | `class` | `"LOF"` (label=1) or `"FUNC/INT"` (label=0) — added so the standard `vep_eval.py` AUROC code path works without changes | |
| | `consequence` | molecular consequence from the source dataset | |
| | `tss_dist` | distance to nearest TSS | |
| | `match_group` | matched-controls group ID (preserve from source) | |
| | `score` | continuous PIP for `complex_traits`; `None` for `mendelian_traits` | |
| | `ref_seq`, `var_seq` | **8,192 bp window centered on the variant** (variant at index 4096), forward strand from chr-fasta-hg38 (UCSC). `var_seq` is `ref_seq` with the alt base substituted at index 4096. Reverse-complement is computed at eval time when `--rev_comp_avg` is requested (matches TraitGym's `run_vep_evo2.py` strand-symmetric scoring). | |
|
|
| ## Eval methodology |
|
|
| Same recipe as our other VEP evals (BRCA1, BRCA2, ClinVar): `delta = LL(var_seq) − LL(ref_seq)` from a centered 8,192 bp window. Score per-variant; AUROC / AUPRC / `AUPRC_by_chrom_weighted_average` (the TraitGym leaderboard convention) against the binary label. Optional `--rev_comp_avg` averages the LLR computed on the forward window and on its reverse-complement, exactly as TraitGym does. |
|
|
| Eval scripts: |
| - [`carbon/evaluation/vep_eval.py`](https://github.com/huggingface/carbon/blob/main/carbon/evaluation/vep_eval.py) — gene-agnostic centered+full-LL eval |
| - Slurm launchers: [`carbon/evaluation/slurm/`](https://github.com/huggingface/carbon/tree/main/carbon/evaluation/slurm) — one `vep_traitgym.sbatch` per model, including Evo2 7B/20B/40B |
|
|
| ## Citation |
|
|
| If you use TraitGym, cite the original paper: |
|
|
| ``` |
| @article{benegas2025traitgym, |
| title = {Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics}, |
| author = {Benegas, Gonzalo and Eraslan, Gokcen and Song, Yun S.}, |
| journal = {bioRxiv}, |
| year = {2025}, |
| doi = {10.1101/2025.02.11.637758} |
| } |
| ``` |
|
|
| Leaderboard: [`songlab/TraitGym-leaderboard`](https://huggingface.co/spaces/songlab/TraitGym-leaderboard) on HF Spaces. |
|
|
