Datasets:

bolinas-dna
/

evals_complex_traits

	---
	license: apache-2.0
	tags:
	- biology
	- genomics
	- dna
	- variant-effect-prediction
	- complex-traits
	- gwas
	- fine-mapping
	size_categories:
	- 10K<n<100K
	---

	# evals_complex_traits

	Variant-effect-prediction benchmark of UKBB fine-mapped complex-trait SNVs vs
	low-PIP SNVs, 1:9 matched within consequence categories on `(chrom,
	consequence_final)` plus subset-targeted distance bins, with MAF entering as
	a continuous matching feature.

	## Description

	\| \| \|
	\|---\|---\|
	\| Positives \| UKBB SuSiE+FINEMAP fine-mapped variants with max(PIP) > 0.9 across 119 traits \|
	\| Negatives \| max(PIP) < 0.01 across 119 traits, 1:9 matched per positive \|
	\| Genome build \| GRCh38 (lifted from hg19) \|
	\| Variant type \| SNVs only \|
	\| Coordinates \| 1-based (`pos` is 1-based; `ref`/`alt` are single bases) \|
	\| Matching ratio \| 1:9 \|

	## Splits

	\| Split \| Variants (pos + 9·neg) \| Positives \| Chromosomes \|
	\|---\|---:\|---:\|---\|
	\| `train` \| 11,630 \| 1,163 \| odd: 1, 3, …, X \|
	\| `test` \| 10,000 \| 1,000 \| even: 2, 4, …, Y \|
	\| total \| 21,630 \| 2,163 \| \|

	## Columns

	\| Column \| Type \| Description \|
	\|---\|---\|---\|
	\| `chrom`, `pos`, `ref`, `alt` \| str / int / str / str \| Variant coordinates (1-based, GRCh38) \|
	\| `label` \| bool \| `True` for high-PIP positive, `False` for low-PIP matched negative \|
	\| `subset` \| str \| Consequence-group label for stratified eval \|
	\| `match_group` \| int \| Integer ID grouping each positive with its 9 matched negatives \|
	\| `rsid` \| str \| dbSNP rsID (when available) \|
	\| `pip` \| float \| Maximum PIP across the 119 traits \|
	\| `traits` \| str \| Comma-separated list of traits with PIP > 0.9 (positives only) \|
	\| `MAF` \| float \| UKBB EUR minor allele frequency \|
	\| `ld_score` \| float \| UKBB EUR LD score (passthrough, not a matching feature) \|
	\| `consequence`, `consequence_cre`, `consequence_final`, `consequence_group` \| str \| Ensembl VEP consequence + grouping \|
	\| `distance_tss_pc`, `distance_tss_nc`, `distance_tss` \| int \| Distances to nearest protein-coding / non-protein-coding TSS (and min, used for `consequence_group` recategorization) \|
	\| `tss_closest_pc_gene_id`, `tss_closest_nc_gene_id`, `tss_closest_gene_id` \| str \| Ensembl gene IDs (passthrough — gene-id was not used in matching) \|
	\| `distance_exon_pc`, `distance_exon_nc`, `distance_exon` \| int \| Same shape, for nearest exon \|
	\| `exon_closest_pc_gene_id`, `exon_closest_nc_gene_id`, `exon_closest_gene_id` \| str \| Same shape \|
	\| `distance_tss_pc_bin`, `distance_exon_pc_bin` \| str \| Subset-prefixed bin labels used as exact-match keys; `BIN_NA` outside the binned subsets \|

	## Per-subset retention

	\| Subset \| n_pos in `dataset_all` \| matched (kept) \| retention \|
	\|---\|---:\|---:\|---:\|
	\| `distal` \| 1,193 \| 1,193 \| 100.0% \|
	\| `missense_variant` \| 454 \| 454 \| 100.0% \|
	\| `tss_proximal` \| 244 \| 244 \| 100.0% \|
	\| `3_prime_UTR_variant` \| 78 \| 78 \| 100.0% \|
	\| `non_coding_transcript_exon_variant` \| 75 \| 75 \| 100.0% \|
	\| `5_prime_UTR_variant` \| 56 \| 56 \| 100.0% \|
	\| `synonymous_variant` \| 33 \| 33 \| 100.0% \|
	\| `splicing` \| 30 \| 30 \| 100.0% \|
	\| `mature_miRNA_variant` \| 2 \| 0 \| 0.0% \|
	\| total \| 2,165 \| 2,163 \| 99.9% \|

	## Matching design

	Matching is exact on every categorical key, then Euclidean-nearest on the
	(RobustScaler-scaled) continuous features as a within-group tie-breaker.
	Without replacement, k=9.

	- Continuous features: `distance_tss_pc`, `distance_tss_nc`, `distance_exon_pc`, `distance_exon_nc`, `MAF`.
	- Categorical features:
	- `chrom`, `consequence_final`
	- `distance_tss_pc_bin` — `tss_proximal`: edges `[0, 100, 1000, ∞]`; `BIN_NA` elsewhere
	- `distance_exon_pc_bin` —
	- `tss_proximal`: edges `[0, 100, 1000, ∞]`
	- `splicing`: edges `[0, 5, 30, ∞]`
	- `BIN_NA` elsewhere

	Gene-ID columns are kept as passthrough metadata but not used as match
	keys.

	## Matched-feature AUPRC diagnostic

	Each continuous matching feature `f` is scored as a single-feature predictor
	within each subset: `{f}_auprc = max(AP(label, +f), AP(label, −f))`.
	Baseline = 0.1 for 1:9 matching.

	<details>
	<summary>Per-(subset, feature) AUPRC table</summary>

	\| subset \| n \| distance_tss_pc \| distance_tss_nc \| distance_exon_pc \| distance_exon_nc \| MAF \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| `distal` \| 1,193 \| 0.101 \| 0.102 \| 0.109 \| 0.105 \| 0.101 \|
	\| `missense_variant` \| 454 \| 0.108 \| 0.106 \| 0.102 \| 0.104 \| 0.108 \|
	\| `tss_proximal` \| 244 \| 0.114 \| 0.114 \| 0.110 \| 0.108 \| 0.101 \|
	\| `3_prime_UTR_variant` \| 78 \| 0.119 \| 0.116 \| 0.108 \| 0.108 \| 0.114 \|
	\| `non_coding_transcript_exon_variant` \| 75 \| 0.110 \| 0.112 \| 0.124 \| 0.100 \| 0.106 \|
	\| `5_prime_UTR_variant` \| 56 \| 0.123 \| 0.109 \| 0.102 \| 0.110 \| 0.109 \|
	\| `synonymous_variant` \| 33 \| 0.120 \| 0.106 \| 0.107 \| 0.111 \| 0.107 \|
	\| `splicing` \| 30 \| 0.124 \| 0.131 \| 0.108 \| 0.131 \| 0.122 \|

	</details>

	## Provenance

	Built by the [`bolinas-dna`](https://github.com/Open-Athena/bolinas-dna) eval pipeline at commit
	[`main`](https://github.com/Open-Athena/bolinas-dna/tree/main/snakemake/evals).

	- Curation pipeline: [`snakemake/evals`](https://github.com/Open-Athena/bolinas-dna/tree/main/snakemake/evals)
	- Matching algorithm: [`src/bolinas/pipelines/evals/matching.py`](https://github.com/Open-Athena/bolinas-dna/blob/main/src/bolinas/pipelines/evals/matching.py)
	- Diagnostic helper: [`src/bolinas/pipelines/evals/matching_qc.py`](https://github.com/Open-Athena/bolinas-dna/blob/main/src/bolinas/pipelines/evals/matching_qc.py)

	The curation is a from-scratch reimplementation of the [TraitGym](https://github.com/songlab-cal/TraitGym) complex-traits pipeline.

	## License

	Released under the same terms as its sources. UKBB summary-level data and
	the [Finucane lab fine-mapping release](https://huggingface.co/datasets/gonzalobenegas/finucane-ukbb-finemapping)
	are intended for non-commercial research; check upstream license if you plan
	to use commercially.

	## Citation

	- TraitGym — Benegas et al. 2025, [bioRxiv 2025.02.11.637758](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v2)
	- UKBB fine-mapping — Wang et al. (Nat Commun 2021) and the [Finucane lab release](https://huggingface.co/datasets/gonzalobenegas/finucane-ukbb-finemapping)
	- LD scores — Bulik-Sullivan et al. (Nat Genet 2015)

	---
	license: apache-2.0
	tags:
	- biology
	- genomics
	- dna
	- variant-effect-prediction
	- complex-traits
	- gwas
	- fine-mapping
	size_categories:
	- 10K<n<100K
	---

	# evals_complex_traits

	Variant-effect-prediction benchmark of UKBB fine-mapped complex-trait SNVs vs
	low-PIP SNVs, 1:9 matched within consequence categories on `(chrom,
	consequence_final)` plus subset-targeted distance bins, with MAF entering as
	a continuous matching feature.

	## Description

	\| \| \|
	\|---\|---\|
	\| Positives \| UKBB SuSiE+FINEMAP fine-mapped variants with max(PIP) > 0.9 across 119 traits \|
	\| Negatives \| max(PIP) < 0.01 across 119 traits, 1:9 matched per positive \|
	\| Genome build \| GRCh38 (lifted from hg19) \|
	\| Variant type \| SNVs only \|
	\| Coordinates \| 1-based (`pos` is 1-based; `ref`/`alt` are single bases) \|
	\| Matching ratio \| 1:9 \|

	## Splits

	\| Split \| Variants (pos + 9·neg) \| Positives \| Chromosomes \|
	\|---\|---:\|---:\|---\|
	\| `train` \| 11,630 \| 1,163 \| odd: 1, 3, …, X \|
	\| `test` \| 10,000 \| 1,000 \| even: 2, 4, …, Y \|
	\| total \| 21,630 \| 2,163 \| \|

	## Columns

	\| Column \| Type \| Description \|
	\|---\|---\|---\|
	\| `chrom`, `pos`, `ref`, `alt` \| str / int / str / str \| Variant coordinates (1-based, GRCh38) \|
	\| `label` \| bool \| `True` for high-PIP positive, `False` for low-PIP matched negative \|
	\| `subset` \| str \| Consequence-group label for stratified eval \|
	\| `match_group` \| int \| Integer ID grouping each positive with its 9 matched negatives \|
	\| `rsid` \| str \| dbSNP rsID (when available) \|
	\| `pip` \| float \| Maximum PIP across the 119 traits \|
	\| `traits` \| str \| Comma-separated list of traits with PIP > 0.9 (positives only) \|
	\| `MAF` \| float \| UKBB EUR minor allele frequency \|
	\| `ld_score` \| float \| UKBB EUR LD score (passthrough, not a matching feature) \|
	\| `consequence`, `consequence_cre`, `consequence_final`, `consequence_group` \| str \| Ensembl VEP consequence + grouping \|
	\| `distance_tss_pc`, `distance_tss_nc`, `distance_tss` \| int \| Distances to nearest protein-coding / non-protein-coding TSS (and min, used for `consequence_group` recategorization) \|
	\| `tss_closest_pc_gene_id`, `tss_closest_nc_gene_id`, `tss_closest_gene_id` \| str \| Ensembl gene IDs (passthrough — gene-id was not used in matching) \|
	\| `distance_exon_pc`, `distance_exon_nc`, `distance_exon` \| int \| Same shape, for nearest exon \|
	\| `exon_closest_pc_gene_id`, `exon_closest_nc_gene_id`, `exon_closest_gene_id` \| str \| Same shape \|
	\| `distance_tss_pc_bin`, `distance_exon_pc_bin` \| str \| Subset-prefixed bin labels used as exact-match keys; `BIN_NA` outside the binned subsets \|

	## Per-subset retention

	\| Subset \| n_pos in `dataset_all` \| matched (kept) \| retention \|
	\|---\|---:\|---:\|---:\|
	\| `distal` \| 1,193 \| 1,193 \| 100.0% \|
	\| `missense_variant` \| 454 \| 454 \| 100.0% \|
	\| `tss_proximal` \| 244 \| 244 \| 100.0% \|
	\| `3_prime_UTR_variant` \| 78 \| 78 \| 100.0% \|
	\| `non_coding_transcript_exon_variant` \| 75 \| 75 \| 100.0% \|
	\| `5_prime_UTR_variant` \| 56 \| 56 \| 100.0% \|
	\| `synonymous_variant` \| 33 \| 33 \| 100.0% \|
	\| `splicing` \| 30 \| 30 \| 100.0% \|
	\| `mature_miRNA_variant` \| 2 \| 0 \| 0.0% \|
	\| total \| 2,165 \| 2,163 \| 99.9% \|

	## Matching design

	Matching is exact on every categorical key, then Euclidean-nearest on the
	(RobustScaler-scaled) continuous features as a within-group tie-breaker.
	Without replacement, k=9.

	- Continuous features: `distance_tss_pc`, `distance_tss_nc`, `distance_exon_pc`, `distance_exon_nc`, `MAF`.
	- Categorical features:
	- `chrom`, `consequence_final`
	- `distance_tss_pc_bin` — `tss_proximal`: edges `[0, 100, 1000, ∞]`; `BIN_NA` elsewhere
	- `distance_exon_pc_bin` —
	- `tss_proximal`: edges `[0, 100, 1000, ∞]`
	- `splicing`: edges `[0, 5, 30, ∞]`
	- `BIN_NA` elsewhere

	Gene-ID columns are kept as passthrough metadata but not used as match
	keys.

	## Matched-feature AUPRC diagnostic

	Each continuous matching feature `f` is scored as a single-feature predictor
	within each subset: `{f}_auprc = max(AP(label, +f), AP(label, −f))`.
	Baseline = 0.1 for 1:9 matching.

	<details>
	<summary>Per-(subset, feature) AUPRC table</summary>

	\| subset \| n \| distance_tss_pc \| distance_tss_nc \| distance_exon_pc \| distance_exon_nc \| MAF \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| `distal` \| 1,193 \| 0.101 \| 0.102 \| 0.109 \| 0.105 \| 0.101 \|
	\| `missense_variant` \| 454 \| 0.108 \| 0.106 \| 0.102 \| 0.104 \| 0.108 \|
	\| `tss_proximal` \| 244 \| 0.114 \| 0.114 \| 0.110 \| 0.108 \| 0.101 \|
	\| `3_prime_UTR_variant` \| 78 \| 0.119 \| 0.116 \| 0.108 \| 0.108 \| 0.114 \|
	\| `non_coding_transcript_exon_variant` \| 75 \| 0.110 \| 0.112 \| 0.124 \| 0.100 \| 0.106 \|
	\| `5_prime_UTR_variant` \| 56 \| 0.123 \| 0.109 \| 0.102 \| 0.110 \| 0.109 \|
	\| `synonymous_variant` \| 33 \| 0.120 \| 0.106 \| 0.107 \| 0.111 \| 0.107 \|
	\| `splicing` \| 30 \| 0.124 \| 0.131 \| 0.108 \| 0.131 \| 0.122 \|

	</details>

	## Provenance

	Built by the [`bolinas-dna`](https://github.com/Open-Athena/bolinas-dna) eval pipeline at commit
	[`main`](https://github.com/Open-Athena/bolinas-dna/tree/main/snakemake/evals).

	- Curation pipeline: [`snakemake/evals`](https://github.com/Open-Athena/bolinas-dna/tree/main/snakemake/evals)
	- Matching algorithm: [`src/bolinas/pipelines/evals/matching.py`](https://github.com/Open-Athena/bolinas-dna/blob/main/src/bolinas/pipelines/evals/matching.py)
	- Diagnostic helper: [`src/bolinas/pipelines/evals/matching_qc.py`](https://github.com/Open-Athena/bolinas-dna/blob/main/src/bolinas/pipelines/evals/matching_qc.py)

	The curation is a from-scratch reimplementation of the [TraitGym](https://github.com/songlab-cal/TraitGym) complex-traits pipeline.

	## License

	Released under the same terms as its sources. UKBB summary-level data and
	the [Finucane lab fine-mapping release](https://huggingface.co/datasets/gonzalobenegas/finucane-ukbb-finemapping)
	are intended for non-commercial research; check upstream license if you plan
	to use commercially.

	## Citation

	- TraitGym — Benegas et al. 2025, [bioRxiv 2025.02.11.637758](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v2)
	- UKBB fine-mapping — Wang et al. (Nat Commun 2021) and the [Finucane lab release](https://huggingface.co/datasets/gonzalobenegas/finucane-ukbb-finemapping)
	- LD scores — Bulik-Sullivan et al. (Nat Genet 2015)