effector-gene-prioritization
starsforks
How to install effector-gene-prioritization?
gptomics/bioskills/effector-gene-prioritization
$npx -y skills add GPTomics/bioSkills --skill effector-gene-prioritizationMaps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | Reference examples tested with: MAGMA 1.10+ (cncr.nl/research/magma), FUMA web platform v1.6+ (fuma.ctglab.nl), Open Targets Genetics API (REST + GraphQL, June 2024 release), PoPS (head of `FinucaneLab/pops`, 2024), cS2G pre-computed scores (Zenodo record 7754032, Gazal 2022), ABC-Enhancer-Gene-Prediction 0.2.2+, ENCODE-rE2G v1.0+ (Gschwind 2023 preprint), DEPICT v1 rel194, INQUISIT (Fachal 2020 supplementary), Python 3.9-3.11, R 4.3+, PLINK 1.9 + PLINK 2.0. |
| 4 | |
| 5 | Before using code patterns, verify installed versions match. If versions differ: |
| 6 | - CLI: `magma --help` to confirm gene-window, gene-annot, and gene-set flag names |
| 7 | - Python: `pip show gentropy`; introspect endpoints at `api.platform.opentargets.org/api/v4/graphql` |
| 8 | - R: `packageVersion('coloc')` etc. for upstream evidence integration |
| 9 | |
| 10 | If a script throws an error about an argument that has moved (e.g. an Open Targets endpoint renamed during a release) or a model file schema change, introspect the installed tool and adapt rather than retrying. Open Targets Genetics deprecated the standalone Genetics Portal in 2024 in favour of the integrated platform; verify endpoint URLs at the time of use. |
| 11 | |
| 12 | # Effector Gene Prioritization |
| 13 | |
| 14 | **"Which gene at this GWAS locus is actually the causal mediator?"** -> Integrate fine-mapping, colocalization, chromatin-based enhancer-gene predictions, distance, and gene-similarity priors into a per-locus per-gene confidence score, then require concordance across multiple orthogonal evidence streams before nominating a causal effector. Effector gene prioritization is the bridge between statistical fine-mapping (variant level) and biological hypothesis (gene level); it is the most failure-prone step in GWAS-to-target pipelines because the nearest-gene assumption is wrong roughly 30-50% of the time at well-studied loci. |
| 15 | |
| 16 | - CLI (gene-level association): `magma --bfile ref --gene-loc geneloc.txt --pval gwas.tsv ncol=N --out out` -> `magma --gene-results out.genes.raw --set-annot annot.txt --out out` |
| 17 | - Web (integrative): FUMA SNP2GENE at fuma.ctglab.nl (positional + eQTL + Hi-C + chromatin in one workflow) |
| 18 | - API (pre-computed L2G): Open Targets Genetics GraphQL `studyLocus2GeneTable` query (note: Open Targets Genetics was consolidated into the Open Targets Platform in 2024; verify the live endpoint at `api.platform.opentargets.org/api/v4/graphql`) |
| 19 | - Python (similarity prior): `python pops.py --gene_annot_path gene_annot.txt --feature_mat_prefix features --control_features_path control.features --magma_prefix magma_out --out_prefix out` |
| 20 | - Lookup (combined SNP-to-gene): cS2G pre-computed gene scores at zenodo.org/records/7754032 |
| 21 | - CLI (enhancer-gene): ABC pipeline or ENCODE-rE2G (cross-reference atac-seq/enhancer-gene-linking) |
| 22 | |
| 23 | V2G is not one method but a portfolio. Open Targets L2G aggregates per-locus per-gene features (distance + coloc + chromatin + V2G) trained on curated gold-standard genes; PoPS adds an orthogonal genome-wide polygenic prior from gene-pathway co-membership; MAGMA provides the lightweight gene-level p-value baseline. Strong effector calls emerge from concordance across these orthogonal signal types, not from any single tool. |
| 24 | |
| 25 | ## Algorithmic Taxonomy |
| 26 | |
| 27 | | Tool | Model | Inputs | Output | Strength | Fails when | |
| 28 | |------|-------|--------|--------|----------|------------| |
| 29 | | Open Targets L2G (Mountjoy 2021 Nat Genet 53:1527) | Gradient-boosting classifier on per-(locus, gene) features (distance, fine-mapping, coloc, chromatin, V2G) trained on curated gold standards | Pre-computed per study; queried via API | Per-(study, locus, gene) L2G score 0-1 | Most validated integrative scorer; built into Open Targets Platform; updated quarterly | Trait must be in OT release; custom traits need re-training; coverage limited to OT-curated GWAS catalog | |
| 30 | | V2G (Ghoussaini 2021 Nucleic Acids Res 49:D1311) | Open Targets V2G feature aggregator: per-variant eQTL/sQTL/pQTL + chromatin + distance | OT pre-computed | Per-(variant, gene) score | Variant-resolution; complements locus-resolution L2G |
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