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README.md

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+---
+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 **convenience repackaging** of [`songlab/TraitGym`](https://huggingface.co/datasets/songlab/TraitGym) (Benegas, Eraslan & Song, bioRxiv 2025.02.11.637758), with one extra step done offline: 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. This avoids loading and querying the human genome at eval time when running zero-shot DNA-LM scoring through `brca_eval.py`-style pipelines.
+
+The variants, labels and matched controls are **identical** to the original `songlab/TraitGym` `_matched_9` configs. We add only the `ref_seq` and `var_seq` columns. Cite the original paper for any use of this benchmark.
+
+## 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.
+
+Source datasets:
+- [`songlab/TraitGym/mendelian_traits_matched_9`](https://huggingface.co/datasets/songlab/TraitGym)
+- [`songlab/TraitGym/complex_traits_matched_9`](https://huggingface.co/datasets/songlab/TraitGym)
+
+## 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`](https://github.com/huggingface/carbon/tree/evo2-evals/evaluation/reproduction-evo2-evals/brca/brca_eval.py) — gene-agnostic centered+full-LL eval
+- Sharded variant: [`reproduction-evo2-evals/traitgym/`](https://github.com/huggingface/carbon/tree/evo2-evals/evaluation/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`](https://huggingface.co/spaces/songlab/TraitGym-leaderboard) on HF Spaces.