Effector gene prioritization is the central interpretation step that bridges variant-level statistical fine-mapping and gene-level biological hypothesis at GWAS loci. The agent takes a GWAS lead variant (or credible set), interrogates a portfolio of variant-to-gene (V2G) methods (Open Targets L2G, MAGMA, FUMA, cS2G, PoPS, FUSION/S-PrediXcan TWAS, ABC, ENCODE-rE2G), and integrates orthogonal evidence streams (fine-mapping PIP, colocalization PP.H4, distance, chromatin enhancer-gene linking, polygenic similarity priors) into a concordance-based confidence tier.
The dominant failure mode is the nearest-gene assumption: roughly 30-50% of fine-mapped GWAS variants regulate a gene that is NOT the closest TSS. The L2G + PoPS combination plus enhancer-gene evidence (ABC / ENCODE-rE2G) plus eQTL/pQTL coloc is the modern publication-grade triangulation standard. No single tool is sufficient; concordance across >= 3 of 6 orthogonal evidence streams is the operational rule for a "high-confidence causal effector" call.
Install:
# MAGMA ships as a pre-compiled zip, not a git repo. Download it manually from the official CNCR page
# (https://cncr.nl/research/magma/) -- the old ctg.cncr.nl and vu.data.surfsara.nl direct links now
# redirect to an HTML landing page, so a hard-coded wget would save that page AS the zip and unzip
# would fail. Once the real zip is in the working directory, unzip it (filename tracks the version):
unzip magma_v*.zip
# PoPS
git clone https://github.com/FinucaneLab/pops
pip install -r pops/requirements.txt
# Open Targets Genetics access
pip install gentropy # official Open Targets
# OR: pip install otargenpy # community GraphQL wrapper
# OR query GraphQL directly via requests
Pre-computed scores:
# cS2G (Gazal 2022; hosted on Zenodo record 7754032)
wget https://zenodo.org/records/7754032/files/cS2G_UKBB.zip
# PoPS pre-built features
# See FinucaneLab/pops releases for PoPS_features_full.txt
# Open Targets L2G: queried via API; no local download
The panel choice dominates coloc and TWAS sensitivity. Match to the causal tissue identified via LDSC-SEG or stratified LDSC before locking in.
| Panel | N donors | Tissues / cells | Use case |
|---|---|---|---|
| GTEx v8 | 838 | 49 tissues | PredictDB standard for 2026 TWAS pipelines |
| GTEx v10 (2024) | ~1,000 | 49 tissues | Limited PredictDB coverage; transitional |
| eQTLGen | 31,684 | Whole blood | Maximum statistical power for blood-relevant traits |
| eQTLGen Phase 2 (sc) | ~30,000 | sc immune | Cell-type-resolved blood eQTLs |
| OneK1K | 982 | PBMC, 14 cell types | sc-eQTL discovery for immune traits |
| INTERVAL pQTL | 3,301 | Plasma proteome | Drug-target cross-reference via pQTL-MR |
Tell the AI agent what to do: - "Prioritise effector genes at my GWAS lead locus using Open Targets L2G + PoPS + coloc concordance" - "Run MAGMA gene-based and gene-set enrichment on my GWAS sumstats, then layer PoPS for polygenic priority" - "I have a fine-mapped credible set; which gene is the most likely causal effector?" - "Triangulate L2G, PoPS, coloc PP.H4, ABC, and distance to nominate a candidate causal gene at the SORT1 locus" - "Run FUMA SNP2GENE on my GWAS and compare with Open Targets L2G calls for the top 20 hits" - "Use ABC enhancer-gene predictions to assign a distal regulatory variant to its likely target gene"
"My GWAS is on type 2 diabetes (UKB-2020). Query Open Targets Genetics for the L2G top-ranked genes at all genome-wide-significant loci. Cross-check each with V2G sub-scores and flag genes where yProbaDistance dominates yProbaMolecularQTL (distance-only candidates). Report the top 50 with L2G score >= 0.5."
"I have GWAS summary statistics for a custom rare-disease phenotype not in Open Targets. Run MAGMA gene-based with a 35kb upstream + 10kb downstream window using the 1000G EUR LD reference, then run PoPS on the MAGMA Z output with the pre-built feature matrix. Cross-reference with SuSiE fine-mapping credible sets and coloc PP.H4 against eQTLGen whole blood. Report concordance per gene."
"For my LDL-cholesterol GWAS, the causal tissue is liver. Run S-PrediXcan with GTEx liver MASHR weights and coloc.susie with GTEx liver eQTL panel. Layer ABC enhancer-gene predictions from HepG2 (ENCODE) for distal regulation. Integrate with Open Targets L2G and report effector-gene candidates with >= 3 concordant evidence streams."
"I have a schizophrenia GWAS but the causal cell type within brain is unclear. Run LDSC-SEG to prioritise brain tissues, then S-MultiXcan across all GTEx brain sub-regions, then PoPS for polygenic priority. Report per-gene concordance across L2G, PoPS, and tissue-prioritised coloc."
"At my chr8:9p21 GWAS lead, the nearest gene is CDKN2A but I suspect long-range regulation. Run ABC and ENCODE-rE2G in matched cell type (CMK or vascular smooth muscle), cross-check with Open Targets L2G, and report whether the fine-mapped credible variant maps to CDKN2A or a distal gene (e.g. MTAP, ANRIL)."
"I am writing up an effector-gene nomination for ANGPTL4 at a triglycerides GWAS lead. Triangulate (a) fine-mapping (SuSiE PIP), (b) coloc PP.H4 with GTEx subcutaneous adipose eQTL, (c) Open Targets L2G score, (d) PoPS score, (e) ABC enhancer-gene linking in adipocytes (Engreitz portal), (f) distance to TSS. Report concordance per locus and flag whether ANGPTL4 meets the >= 3 of 6 high-confidence threshold."
"At my chr11p15.5 lipid GWAS locus, three genes (CLU, NCAM1, MTHFD1L) all show modest L2G scores. Run conditional analysis (FUSION.post_process.R conditional/joint analysis, run by default, or GCTA-COJO) to test independence, then per-gene coloc.susie at each independent signal, and report whether the locus is multi-effector or LD-tied."
credibleSet.l2GPredictions; record per-gene score and per-feature shapValue.