Bio Causal Genomics Effector Gene Prioritization

Version Compatibility

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.

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 gentropy; introspect endpoints at api.platform.opentargets.org/api/v4/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.

Effector Gene Prioritization

"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.

• 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
• Web (integrative): FUMA SNP2GENE at fuma.ctglab.nl (positional + eQTL + Hi-C + chromatin in one workflow)
• 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)
• 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
• Lookup (combined SNP-to-gene): cS2G pre-computed gene scores at zenodo.org/records/7754032
• CLI (enhancer-gene): ABC pipeline or ENCODE-rE2G (cross-reference atac-seq/enhancer-gene-linking)

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.

Algorithmic Taxonomy

Methodology evolves; verify against the current Open Targets release (platform-docs.opentargets.org), the latest PoPS feature matrix at FinucaneLab/pops, and ABC / ENCODE-rE2G releases before locking on a single prioritiser. The L2G + PoPS combination is the current de facto two-method baseline; cS2G is the heritability-calibrated lookup; FUMA is the no-install community standard.

Decision Tree by Scenario

Per-Method Failure Modes

Nearest-gene assumption fails (most common pitfall)

Trigger: Assigning the GWAS lead variant to the closest gene without checking long-range regulation.

Mechanism: Approximately 30-50% of well-fine-mapped GWAS variants regulate a gene that is NOT the nearest TSS (Mountjoy 2021; Fulco 2019 Nat Genet 51:1664). Distal enhancer-promoter contacts span 50 kb to > 1 Mb; LD around the lead variant often spans only kilobases, so the credible-set centroid may sit closer to a passenger gene than to the true target.

Symptom: Distance-based prioritisation names the nearest gene; subsequent eQTL coloc, ABC, and ENCODE-rE2G converge on a different gene at the same locus. Functional validation (CRISPRi at the variant) confirms the distal gene.

Fix: Use L2G (which includes distance but does not let it dominate), PoPS (which is distance-orthogonal by construction), and ABC / ENCODE-rE2G when matched epigenome data are available. Report all candidate genes at the locus with their evidence-stream contributions; do not collapse to the nearest by default.

eQTL tissue mis-specification

Trigger: Using a single-tissue eQTL panel (e.g. whole blood) when the causal tissue is different (e.g. liver for lipid traits, hypothalamus for energy balance).

Mechanism: Cis-eQTL effect sizes are tissue-specific; eQTLs in the wrong tissue still tag the GWAS signal via LD and produce spurious colocalisations or TWAS hits. The right gene at the wrong tissue is statistically detectable but biologically uninterpretable.

Symptom: Strong colocalisation in a tissue biologically irrelevant to the trait; null in the expected tissue. LDSC-SEG / CELLEX / EWCE prioritisation on the GWAS sumstats independently disagrees with the eQTL tissue.

Fix: Run multi-tissue eQTL coloc (e.g. all GTEx tissues via S-MultiXcan + per-tissue coloc) and prioritise the tissue identified by LDSC-SEG (Finucane 2018 Nat Genet 50:621) or CELLEX. For cell-type-specific traits, move to sc-eQTL panels (OneK1K, Yazar 2022 Science 376:eabf3041). S-MultiXcan (Barbeira 2019 PLoS Genet 15:e1007889) jointly tests per-tissue z-scores via PC-decomposition of LD-induced covariance and is preferred for standard GTEx v8 panels (pre-computed weights available); UTMOST (Hu 2019 Nat Genet 51:568) imputes cross-tissue expression weights before testing and is preferred when retraining cross-tissue weights for a custom panel.

MAGMA gene-window choice

Trigger: Default --gene-loc with 0kb upstream / 0kb downstream window assigns all SNPs only within annotated gene bodies.

Mechanism: A wide window (e.g. 35kb upstream + 10kb downstream) captures more regulatory SNPs per gene but assigns each tag-SNP to multiple genes simultaneously, diluting per-gene signal and inflating false positives at gene-dense regions. A narrow window misses regulatory SNPs outside the gene body and loses true positives at intergenic enhancers. The three window conventions in circulation are not interchangeable: MAGMA-native default is 0+0 (no expansion); the MAGMA paper recommended a 50+50 sensitivity check; FUMA SNP2GENE uses 35+10 (35 kb upstream + 10 kb downstream) which is FUMA's convention, NOT MAGMA's default. For brain traits, 50+50 captures distal cis-eQTL signal; for cardiometabolic traits a tighter 10+10 is more conservative. State explicitly which window was used in methods reporting.

Symptom: Many genes per locus flagged at p < 0.05/22k with the wide window; few genes at all flagged with the narrow window; top genes change substantially across window choices.

Fix: Use a sensible default (35kb upstream + 10kb downstream is the FUMA recommendation; 0+0 is MAGMA-native; 50+50 is the MAGMA-paper sensitivity window). Always pair MAGMA with eQTL-based mapping (S-PrediXcan, coloc) for distal-regulatory signal; MAGMA alone is the lightweight baseline, not the full answer.

Wide-window 1Mb warning: Going to 100+100 kb or 1 Mb assigns one SNP to 8-12 genes simultaneously at gene-dense loci (e.g. MHC, chr19q13, chr17q21), diluting power and creating interpretation ambiguity. Avoid 1Mb windows; if distal regulation is suspected supplement with ABC / ENCODE-rE2G enhancer-gene linkage (cross-reference atac-seq/enhancer-gene-linking) rather than widening the MAGMA window.

Coloc fails when the locus has multiple causal variants

Trigger: PP.H4 < threshold despite biological evidence that the gene is causal.

Mechanism: coloc.abf's single-causal-variant assumption forces posterior mass to PP.H3 (distinct causal variants) when 2+ independent signals in moderate LD drive both traits. The result is a false-negative coloc call at a true effector-gene locus.

Symptom: Visual LocusZoom overlap is convincing but PP.H4 stays in 0.3-0.6; coloc.susie or eCAVIAR reveals multiple credible sets and a per-credible-set PP.H4 > 0.7.

Fix: Run coloc.susie (not coloc.abf) at gene-dense / signal-rich loci. Cross-reference causal-genomics/colocalization-analysis; do not rely on coloc.abf as the sole coloc evidence stream when allelic heterogeneity is plausible.

PoPS vs L2G discordance

Trigger: PoPS top-ranked gene at locus disagrees with L2G top-ranked gene.

Mechanism: PoPS uses similarity-based features (pathway membership, co-expression, PPI), L2G uses per-locus features (distance, fine-mapping, coloc, chromatin). They are orthogonal by construction; disagreement is informative, not a failure.

Symptom: Same locus, different top gene under each method.

Fix: Use BOTH and treat concordance (top gene matches across L2G and PoPS) as the strongest single-locus signal short of CRISPR validation. Concordance between PoPS and locus-based methods markedly increases positive predictive value over either method alone (Weeks 2023 Nat Genet 55:1267). Report both ranks; flag concordance.

Pleiotropic locus / multiple causal genes per locus

Trigger: Two or more genes at a single GWAS locus are each independently causal (different SNPs or different mechanisms).

Mechanism: Standard V2G frameworks assume one causal gene per locus. Real biology violates this: an estimated 5-10% of GWAS loci have multiple causal genes (a working convention; CRISPRi-FlowFISH catalogs document multi-gene loci).

Symptom: Two genes at the locus both pass conditional independence checks (FUSION.post_process.R conditional/joint analysis, GCTA-COJO); both show strong eQTL coloc; both have CRISPRi support.

Fix: Allow multi-gene reporting. Each candidate gene needs its own credible variant set (SuSiE / coloc.susie). Report the locus as multi-effector; consider CRISPRi-FlowFISH or MPRA for ground-truth resolution. Do not force a single-gene assignment. Existing CRISPRi enhancer-gene perturbation catalogs for cross-checking computational predictions: Fulco 2019 Nat Genet 51:1664 (>3,500 CRISPRi-FlowFISH enhancer-gene connections for 30 genes in K562); Gasperini 2019 Cell 176:377 (~75,000 pairs at-scale); Schraivogel 2020 Nat Methods 17:629 (TAP-seq / targeted Perturb-seq enhancer-gene screen in K562). Cite the specific catalog when reporting "validated against CRISPRi" rather than the generic term.

Per-Credible-Set Gene-Assignment Hierarchy

When fine-mapping returns credible sets (cross-reference causal-genomics/fine-mapping), each credible variant should be assigned to a gene using a fixed lexicographic ladder rather than a single feature. The ladder collapses ambiguity by preferring direct mechanistic evidence first and falling back to weaker signals only when stronger ones are absent:

1. Coding consequence at credible variant (missense, splice-donor / splice-acceptor, stop-gained, start-lost via VEP / Ensembl consequence) -> assign variant to that gene.
2. eQTL / pQTL colocalization PP.H4 >= 0.7 with the gene's expression QTL (matched tissue) -> assign to that gene (cross-reference causal-genomics/colocalization-analysis).
3. ABC or ENCODE-rE2G enhancer-gene linkage when matched ATAC + H3K27ac (+ optional Hi-C) is available in the candidate tissue -> assign to the linked gene (cross-reference atac-seq/enhancer-gene-linking).
4. Nearest TSS -> assign as last-resort fallback; flag as low-confidence (nearest-gene is correct only 50-70% of the time at well-fine-mapped loci).

If a single credible variant ties across two or more genes at the same rung (e.g. coding consequence in gene A AND a competing eQTL coloc to gene B), report multi-gene candidacy explicitly rather than forcing a single assignment; the locus may be multi-effector or the credible set may straddle a regulatory boundary.

Multi-Evidence Integration Framework

A strong candidate causal gene at a GWAS locus requires concordance across multiple orthogonal evidence streams. The six canonical streams:

Operational rule: Report a gene as a "high-confidence causal effector" only when >= 3 of the 6 core evidence streams are concordant (deep-learning variant effect and cross-trait coincidence are optional supplementary streams). >= 4 concordant is "strong-confidence"; >= 5 is "near-certain pending experimental validation". Single-stream evidence is associational only; two-stream concordance is suggestive. This is the standard used by Open Targets Genetics (Mountjoy 2021), GTEx-derived target nomination pipelines (Open Targets Platform), and pharma drug-discovery workflows.

Quantitative Thresholds

MAGMA Gene-Based and Gene-Set Pipeline

Goal: Compute gene-level p-values from GWAS summary statistics and test gene sets (e.g. MSigDB pathways) for enrichment.

Approach: Pre-format the SNP-to-gene annotation (per-gene SNP membership using a configurable window); run gene-based analysis with --gene-results; downstream, test gene sets via --set-annot. MAGMA's lambda-correction handles LD via the reference panel.

# Step 1: SNP-to-gene annotation using a 35kb upstream + 10kb downstream window (FUMA default)
magma --annotate window=35,10 \
    --snp-loc gwas.snploc \
    --gene-loc NCBI37.3.gene.loc \
    --out annot_35_10

# Step 2: Gene-based association (raw GWAS sumstats; multi-model approach)
magma --bfile g1000_eur \
    --pval gwas.pval ncol=N \
    --gene-annot annot_35_10.genes.annot \
    --out gene_step

# Step 3: Gene-set enrichment via competitive testing (recommended over self-contained)
magma --gene-results gene_step.genes.raw \
    --set-annot msigdb_v7.5_C2.gmt \
    --out gene_set_step

# Inspect: gene_step.genes.out (per-gene Z, p); gene_set_step.gsa.out (per-set p)

The --gene-annot window choice is the dominant methodological lever; 35kb upstream + 10kb downstream is the FUMA recommendation but is not universally accepted. Sensitivity over 0+0, 35+10, and 50+50 is good practice for high-stakes reports.

Open Targets L2G via GraphQL

Goal: Query pre-computed L2G scores for a study and locus without re-running the integrative pipeline.

Approach: Query the Open Targets GraphQL endpoint with a study ID and lead variant; parse per-gene scores.

Open Targets Platform vs Genetics Portal (2024 Consolidation)

In 2024 Open Targets Genetics was merged into the Open Targets Platform GraphQL API at api.platform.opentargets.org/api/v4/graphql. The legacy Genetics endpoint (api.genetics.opentargets.org/graphql) still responds but is deprecated; new pipelines should target the Platform API. The schema also changed: the Platform exposes credibleSet(studyLocusId) with l2GPredictions (target, score, SHAP per-feature explainability), whereas the legacy schema exposed studyLocus2GeneTable with yProbaModel and per-component sub-scores.

Legacy (Genetics, deprecated):

query L2G_legacy($studyId: String!, $variantId: String!) {
  studyLocus2GeneTable(studyId: $studyId, variantId: $variantId) {
    rows {
      gene { symbol }
      yProbaModel
      yProbaDistance
      yProbaMolecularQTL
      hasColoc
    }
  }
}

Modern (Platform, recommended):

query L2G_modern($studyId: String!) {
  credibleSet(studyLocusId: $studyId) {
    l2GPredictions {
      rows {
        target { approvedSymbol }
        score
        features { name value shapValue }
        shapBaseValue
      }
    }
  }
}

import requests
import pandas as pd

resp = requests.post('https://api.platform.opentargets.org/api/v4/graphql',
                     json={'query': '...modern L2G query...',
                           'variables': {'studyId': 'GCST006464_locus_42'}})
preds = resp.json()['data']['credibleSet']['l2GPredictions']['rows']
l2g_df = pd.json_normalize(preds).sort_values('score', ascending=False)

The headline score (Platform) corresponds to yProbaModel (legacy). Platform features[].shapValue values replace the legacy yProba* sub-scores and explain what drove the prediction. Genes whose SHAP is dominated by the distance feature but minimal on QTL or chromatin features are distance-only candidates; trust the integrated score as primary.

PoPS Polygenic Priority Score

Goal: Add a distance-orthogonal similarity-based prior to ranked gene candidates.

Approach: Run MAGMA genome-wide to produce gene Z; feed gene Z plus a curated gene-feature matrix (pathway membership + co-expression + PPI) to PoPS ridge (L2-penalized) regression. Per-gene priority scores are produced; per-locus relative ranking is informative.

# PoPS requires the gene-feature matrix and MAGMA gene Z output
# Download features and gene_annot from FinucaneLab/pops releases

python pops.py \
    --gene_annot_path gene_annot.txt \
    --feature_mat_prefix PoPS_features_full \
    --control_features_path control.features \
    --num_feature_chunks 10 \
    --magma_prefix gene_step \
    --out_prefix pops_out

# Output pops_out.preds: per-gene priority score
# Output pops_out.coefs: per-feature ridge coefficients (interpretation)

PoPS is biology-agnostic; the feature matrix encodes biology. Bias in the features (e.g. cancer-pathway-heavy gene sets for a non-cancer trait) propagates to the output; verify feature coverage matches the trait.

Multi-Evidence Integration: Concordance Scoring

Goal: Combine fine-mapping, coloc, ABC, L2G, PoPS, and distance into a per-locus per-gene concordance score; flag high-confidence candidates.

Approach: Per-locus, gather evidence per candidate gene from each method; score each evidence stream as pass / fail at the canonical threshold; sum the passing streams; report >= 3 passing as high-confidence.

library(dplyr)

# Per-locus candidate gene table; one row per (locus, gene)
candidates <- read.table('locus_candidates.tsv', header = TRUE, sep = '\t')

# Score each evidence stream against canonical thresholds
candidates <- candidates %>%
  mutate(
    pass_finemap = pip_top_variant > 0.5 & credible_set_purity > 0.5,
    pass_coloc = coloc_pph4 >= 0.7,
    pass_distance = distance_to_tss <= 100000,
    pass_pops = pops_decile_rank == 1,
    pass_l2g = l2g_score >= 0.5,
    pass_abc = abc_score >= 0.02 | encode_re2g_score >= 0.5,
    concordance = pass_finemap + pass_coloc + pass_distance +
                  pass_pops + pass_l2g + pass_abc,
    confidence_tier = case_when(
      concordance >= 5 ~ 'near_certain',
      concordance >= 4 ~ 'strong',
      concordance >= 3 ~ 'high',
      concordance >= 2 ~ 'suggestive',
      TRUE ~ 'associational_only'))

# Report
candidates %>%
  filter(concordance >= 3) %>%
  arrange(desc(concordance), desc(l2g_score)) %>%
  select(locus, gene, concordance, confidence_tier, l2g_score, pops_decile_rank, coloc_pph4)

Concordance scoring is conservative; some real causal genes score 2-of-6 because not all evidence streams are available at all loci. Report the per-stream availability alongside the concordance score so readers know whether failure reflects negative evidence or absent evidence.

Reconciliation: When Methods Disagree

Operational rule: Concordance across L2G + PoPS + (coloc OR ABC) is the publication-grade triangulation standard. Single-method calls are weak; require >= 3 of 6 orthogonal evidence streams. Disagreement between L2G and PoPS at a locus often points to (a) a multi-effector locus, (b) a method-feature artefact, or (c) genuine biological complexity. Report both, with their per-locus ranks.

Common Errors

Tool Install Notes

• MAGMA: Pre-compiled binary from cncr.nl/research/magma. Linux / Mac / Windows. Ships as magma CLI; needs PLINK bfile reference (e.g. 1000G g1000_eur).
• FUMA: Web platform at fuma.ctglab.nl/snp2gene. No local install. Requires user account; SNP2GENE jobs run server-side with current reference annotations.
• Open Targets Platform (current): GraphQL at api.platform.opentargets.org/api/v4/graphql. Query L2G via credibleSet(studyLocusId) -> l2GPredictions { rows { target, score, features } }. Recommended for new pipelines.
• Open Targets Genetics (legacy, deprecated): GraphQL at api.genetics.opentargets.org/graphql. Python pip install gentropy (official Open Targets) or pip install otargenpy (community GraphQL wrapper) OR direct GraphQL queries via requests. Genetics Portal was consolidated into the integrated Open Targets Platform in 2024; legacy endpoint still responds but new work should target the Platform.
• PoPS: git clone https://github.com/FinucaneLab/pops. Python; ships with feature matrix download instructions. Pre-built feature matrix at the releases page.
• cS2G: Pre-computed gene scores downloadable from zenodo.org/records/7754032 (cS2G_UKBB.zip / cS2G_1000GEUR.zip). No install; lookup table.
• DEPICT: git clone https://github.com/perslab/depict. Java + Python; legacy method, see Pers 2015.
• FLAMES: Recent (Schipper M et al 2025 Nat Genet 57:323); check the publication's GitHub for the current install path.
• INQUISIT: Originally for breast cancer (Fachal 2020 Nat Genet 52:56); see the paper's supplementary methods for adaptation to other traits.
• ABC: git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction. Python; see atac-seq/enhancer-gene-linking for the full pipeline.
• ENCODE-rE2G: git clone https://github.com/EngreitzLab/ENCODE_rE2G. Snakemake; see atac-seq/enhancer-gene-linking.

Anticipated Reviewer Pushback

References

• Mountjoy E, Schmidt EM, Carmona M, Schwartzentruber J, Peat G et al 2021 Nat Genet 53:1527 (Open Targets L2G; locus-to-gene integrative scorer)
• Ghoussaini M, Mountjoy E, Carmona M, Peat G, Schmidt EM et al 2021 Nucleic Acids Res 49:D1311 (Open Targets V2G/Genetics)
• de Leeuw CA, Mooij JM, Heskes T, Posthuma D 2015 PLoS Comput Biol 11:e1004219 (MAGMA gene-based association)
• Watanabe K, Taskesen E, van Bochoven A, Posthuma D 2017 Nat Commun 8:1826 (FUMA SNP2GENE integrative annotation)
• Gazal S, Weissbrod O, Hormozdiari F, Dey KK, Nasser J et al 2022 Nat Genet 54:827 (cS2G combined SNP-to-gene)
• Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, Fine RS et al 2023 Nat Genet 55:1267 (PoPS Polygenic Priority Score)
• Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT et al 2019 Nat Genet 51:1664 (ABC enhancer-gene linking; CRISPRi-FlowFISH validation)
• Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR et al 2021 Nature 593:238 (ABC genome-wide application)
• Gschwind AR, Mualim KS, Karbalayghareh A, Sheth MU, Dey KK et al 2023 bioRxiv 2023.11.09.563812 (ENCODE-rE2G; preprint)
• Pers TH, Karjalainen JM, Chan Y, Westra HJ, Wood AR et al 2015 Nat Commun 6:5890 (DEPICT)
• Fachal L, Aschard H, Beesley J, Barnes DR, Allen J et al 2020 Nat Genet 52:56 (INQUISIT breast-cancer V2G)
• Finucane HK, Reshef YA, Anttila V, Slowikowski K, Gusev A et al 2018 Nat Genet 50:621 (LDSC-SEG tissue prioritisation)
• Yazar S, Alquicira-Hernandez J, Wing K, Senabouth A, Gordon MG et al 2022 Science 376:eabf3041 (OneK1K sc-eQTL)
• Stacey D, Fauman EB, Ziemek D, Sun BB, Harshfield EL et al 2019 Nucleic Acids Res 47:e3 (ProGeM)
• Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR et al 2018 Nature 558:73 (pQTL panel)
• Vosa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P et al 2021 Nat Genet 53:1300 (eQTLGen reference)
• Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G et al 2019 Nat Genet 51:675 (FOCUS for gene-level fine-mapping; cross-reference TWAS skill)
• Schaid DJ, Chen W, Larson NB 2018 Nat Rev Genet 19:491 (fine-mapping review)
• Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK 2019 PLoS Genet 15:e1007889 (S-MultiXcan; joint per-tissue TWAS via PC decomposition)
• Hu Y, Li M, Lu Q, Weng H, Wang J et al 2019 Nat Genet 51:568 (UTMOST; cross-tissue weight imputation TWAS)
• Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S et al 2019 Cell 176:377 (at-scale CRISPRi-FlowFISH enhancer-gene screen, ~75,000 pairs)
• Schraivogel D, Gschwind AR, Milbank JH, Leonce DR, Jakob P et al 2020 Nat Methods 17:629 (TAP-seq / targeted Perturb-seq enhancer-gene screen in K562)
• Amemiya HM, Kundaje A, Boyle AP 2019 Sci Rep 9:9354 (ENCODE blacklist v2; HLA-region exclusion reference)

Related Skills

• causal-genomics/fine-mapping - Variant-level credible sets feeding L2G and concordance scoring
• causal-genomics/colocalization-analysis - coloc PP.H4 as one of the six evidence streams
• causal-genomics/transcriptome-wide-association - TWAS gene-level association and FOCUS fine-mapping
• causal-genomics/mendelian-randomization - cis-eQTL MR as orthogonal causal evidence
• causal-genomics/mediation-analysis - Downstream gene-mediated trait effects
• causal-genomics/proteome-mr-drug-target - pQTL-based effector-gene nomination for drug targets
• atac-seq/enhancer-gene-linking - ABC and ENCODE-rE2G enhancer-gene predictions feeding distal-regulation evidence
• atac-seq/atac-peak-calling - Generating enhancer candidates from matched-tissu

…(truncated)