bio-causal-genomics-effector-gene-prioritization
Maps 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 (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene,
npx skills add BioTender-max/awesome-bio-agent-skills --skill effector-gene-prioritization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Maps GWAS-implicated loci to candidate effector (causal) genes by integrating multiple variant-to-gene features and gene-based scores, to help choose between proximity, eQTL-based, and similarity-based prioritizers; aims for per-locus per-gene confidence scores with concordance across several evidence streams.
How it works
- CLI example: 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
- Web reference: FUMA SNP2GENE (positional + eQTL + Hi-C + chromatin) for annotated loci and prioritized genes
- API reference: Open Targets Genetics GraphQL studyLocus2GeneTable (live endpoint at api.platform.opentargets.org/api/v4/graphql)
- Python-based similarity prior: python pops.py --gene_annot gene_annot.txt --features features --magma_prefix magma_out --out out
- Combined SNP-to-gene: cS2G pre-computed scores at alkesgroup.broadinstitute.org/cS2G/
- Enhancer-gene: ABC pipeline or ENCODE-rE2G for enhancer-gene linkage
- The approach combines Open Targets L2G, PoPS, MAGMA, FUMA, cS2G, and enhancer-gene predictors to form a per-(locus, gene) prioritization framework; emphasize concordance across orthogonal signals rather than a single method.
When to use it
Use when narrowing a GWAS lead locus to a candidate causal gene, reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade nomination.
What it can touch
Refer to mixed toolset: Open Targets L2G, MAGMA, FUMA SNP2GENE, cS2G, PoPS, ABC/ENCODE-rE2G, and Python/R tools as described; endpoints and pipelines explicitly named above.
Caveats
- Requires verification of tool versions (MAGMA, Open Targets Genetics API, PoPS, ABC-Enhancer-Gene) before use.
- Notes that L2G + PoPS is the current baseline; cS2G provides a heritability-calibrated lookup; FUMA is a no-install reference workflow.
- Trait- and tissue-specific considerations apply; mis-specification of tissue or trait can mislead prioritization.
- Excludes or cautions in HLA region due to complex LD structure and potential method breakdowns.
- Ensure to report the specific MAGMA gene-loc window used (0+0, 35+10, 50+50, etc.) and pair MAGMA results with distal-eQTL evidence.
## Version Compatibility Reference examples tested with: MAGMA 1.10+ (ctglab.nl/software/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 (alkesgroup.broadinstitute.org/cS2G/, 2022), ABC-Enhancer-Gene-Prediction 0.2.2+, ENCODE-rE2G v1.0+ (2024), DEPICT v1 rel194, INQUISIT (Fachal 2020 supplementary), Python 3.9-3.11, R 4.3+, PLINK 1.9 + PLINK 2.0. Before using code patterns, verify installed versions match. If versions differ: - CLI: `magma --help` to confirm gene-window, gene-annot, and gene-set flag names - Python: `pip show ot-graphql opentargets-genetics`; introspect endpoints at `api.genetics.opentargets.org/graphql` - R: `packageVersion('coloc')` etc. for upstream evidence integration 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. # Effect
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Per-Method Failure Modes
- Nearest-gene assumption fails (most common pitfall)
- eQTL tissue mis-specification
- MAGMA gene-window choice
- Coloc fails when the locus has multiple causal variants
- PoPS vs L2G discordance
- Pleiotropic locus / multiple causal genes per locus
- Per-Credible-Set Gene-Assignment Hierarchy
- Multi-Evidence Integration Framework
- Quantitative Thresholds
- MAGMA Gene-Based and Gene-Set Pipeline
Step 1: SNP-to-gene annotation using a 35kb upstream + 10kb downstream window (FUMA default) magma --annotate window=35,10 \ Step 2: Gene-based association (raw GWAS sumstats; multi-model approach) magma --bfile g1000_eur \ Step 3: Gene-set enrichment via competitive testing (recommended over self-contained) magma --gene-results gene_step.genes.raw \ PoPS requires the gene-feature matrix and MAGMA gene Z output Download features and gene_annot from FinucaneLab/pops releases python pops.py \ Output pops_out.preds: per-gene priority score
What does the bio-causal-genomics-effector-gene-prioritization skill do?
Maps 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 (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene,
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill effector-gene-prioritization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.