Datasets:

HuggingFaceBio
/

traitgym

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 (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 `brca_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:

reproduction-evo2-evals/brca/brca_eval.py — gene-agnostic centered+full-LL eval
Sharded variant: reproduction-evo2-evals/traitgym/ — array-job sharding for Evo2 7B+/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 on HF Spaces.