bio-causal-genomics-transcriptome-wide-association

What this skill does

## Version Compatibility

Reference examples tested with: FUSION (head of `gusevlab/fusion_twas`, scripts dated 2023+), MetaXcan / S-PrediXcan / S-MultiXcan 0.7.5+ (`hakyimlab/MetaXcan`), PrediXcan model files from PredictDB (GTEx v8 elastic-net + MASHR), UTMOST (head of `Joker-Jerome/UTMOST`), pyfocus 0.8+ (`bogdanlab/focus`), MA-FOCUS (head of `mancusolab/ma-focus`), TIGAR-V2 (head of `yanglab-emory/TIGAR`), PLINK 1.9 + PLINK 2.0, R 4.3+, Python 3.9-3.11.

Before using code patterns, verify installed versions match. If versions differ:
- R: `Rscript --version`; for FUSION scripts inspect `--help` flags directly in the source
- Python: `pip show metaxcan pyfocus` then `SPrediXcan.py --help`, `SMulTiXcan.py --help`, `focus finemap --help`
- CLI: `plink2 --version`; FUSION ships as R scripts not a binary

If a script throws an error about an argument that has moved (e.g. `--gwas_file` vs `--gwas-file`) or a model database schema change, introspect the installed script with `--help` and adapt rather than retrying. PredictDB model file paths change with GTEx version; pin the version explicitly in scripts.

# Transcriptome-Wide Association

**"Find genes whose predicted tissue expression is associated with my GWAS trait"** -> Train SNP -> expression prediction models on a reference eQTL panel, apply the per-gene SNP weights to GWAS summary statistics or genotypes, and produce a gene-level Z-score equivalent to a weighted sum of SNP Z-scores. The output is a gene-by-tissue association, but TWAS is NOT direct evidence of causal mediation: an LD-tagged eQTL signal produces the same statistical association as a truly causal one, and the dominant failure modes are LD-induced false positives at gene-dense loci, tissue mis-specification, and ancestry mismatch between GWAS and prediction weights.

- CLI (sumstat TWAS, R): `FUSION.assoc_test.R --sumstats g.sumstats --weights weights.pos --weights_dir wgt/ --ref_ld_chr 1KG/EUR. --chr 22 --out chr22.dat`
- CLI (S-PrediXcan, Python): `SPrediXcan.py --model_db_path gtex_v8.db --covariance gtex_v8.cov --gwas_file g.txt --output_file out.csv`
- CLI (S-MultiXcan joint): `SMulTiXcan.py --models_folder mashr_models/ --gwas_folder gwas/ --metaxcan_folder spredixcan_per_tissue/ --output joint.csv`
- CLI (UTMOST cross-tissue): joint test across tissues via UTMOST's per-tissue GBJ / GBJ2 step
- CLI (FOCUS fine-mapping): `focus finemap chr22.dat 1KG_EUR focus.db --p-threshold 5e-8 --out chr22.focus`
- CLI (MA-FOCUS multi-ancestry): `focus finemap` with colon-separated per-ancestry sumstats / LD / weights and ancestry codes in `--locations`

TWAS, cis-eQTL MR, and coloc operate on overlapping evidence: TWAS asks "is the gene's predicted expression associated with the trait?"; cis-eQTL MR asks "does the eQTL effect on expression mediate the trait effect under IV assumptions?"; coloc asks "do the GWAS and eQTL share a causal variant?". Strong causal claims require triangulation, not single-method significance.

## Algorithmic Taxonomy

| Tool | Model | Input | Output | Strength | Fails when |
|------|-------|-------|--------|----------|------------|
| FUSION (Gusev 2016 Nat Genet 48:245) | Weighted sum of SNP Z-scores; per-gene multi-model (BLUP, lasso, elnet, top1, bslmm) selected by cross-validation R^2 | GWAS sumstats + pre-computed weights (.pos + per-gene RData) | TWAS Z, p; conditional joint analysis | Mature, ENCODE-style, pre-trained weights for many tissues (GTEx, CMC, METSIM, YFS, NTR) | LD-induced false positives at gene-dense loci; needs ancestry-matched LD reference; weights are heritability-thresholded so low-h2 genes drop |
| PrediXcan (Gamazon 2015 Nat Genet 47:1091) | Elastic-net SNP -> expression prediction; individual-level genotypes | PLINK genotypes + GWAS phenotype + GTEx prediction DB | Per-gene association test with full regression machinery | Most flexible (allows covariates, interactions, binary outcomes); same TWAS interpretation | Requires individual-level data; biobank-scale compute |
| S-PrediXcan (Barbeira 2018 Nat Commun 9:1825) | Summary-statistic equivalent of PrediXcan; Z-score weighted sum analogous to FUSION | GWAS sumstats + PredictDB model + covariance | Per-gene Z, p | Public PredictDB models (GTEx v8 elastic-net + MASHR-EUR); minimal compute | Pre-trained weights ancestry-specific (EUR primarily); covariance file must match model DB |
| S-MultiXcan (Barbeira 2019 PLoS Genet 15:e1007889) | Joint multi-tissue test combining per-tissue S-PrediXcan via PCA-regularised regression | Folder of per-tissue S-PrediXcan outputs + model folder | Single joint p per gene + per-tissue significance | Boosts power when causal tissue is unknown; standard for transcriptome-wide screens | Joint test cannot pinpoint causal tissue; correlated tissues produce ill-conditioned regression |
| UTMOST (Hu 2019 Nat Genet 51:568) | Cross-tissue elastic-net (group lasso) for joint tissue prediction + GBJ test | Per-tissue eQTL data + GWAS sumstats | Cross-tissue joint statistic | Often better-powered than S-MultiXcan at cross-tissue genes | Computationally heavier than MetaXcan; tissue weights are less interpretable |
| MOSTWAS (Bhattacharya 2021 PLoS Genet 17:e1009398) | Mediator-aware TWAS adding distal trans-mediating SNPs to cis-only models | GWAS sumstats + MOSTWAS weights | Per-gene Z, p (TWAS + distal mediator extension) | Recovers signal at genes with non-cis genetic regulation | Trans-mediation models need large reference panel; weights less broadly available |
| kTWAS (Cao 2021 Brief Bioinform 22:bbaa270) | Kernel-based TWAS using SKAT-style aggregation | GWAS sumstats or genotypes + per-gene SNP set | Per-gene p | Robust to non-linear and rare-variant contributions | Loses the eQTL-weighting interpretability; less standardised |
| EpiXcan (Zhang 2019 Nat Commun 10:3300) | Adds epigenome-derived per-SNP prior to weight training | eQTL + epigenome + GWAS sumstats | Per-gene Z, p (epigenome-informed) | Higher prediction R^2 in epigenome-rich tissues | Requires matched epigenome data for the prediction tissue |
| TIGAR-V2 (Tang 2021 NAR 49:e64) | Dirichlet process regression (non-parametric Bayes) for SNP -> expression | Reference eQTL + GWAS sumstats | Per-gene Z, p | Captures non-elastic-net effect structures; more flexible weight learning | Slower training; benefits depend on locus genetics |
| FOCUS (Mancuso 2019 Nat Genet 51:675) | Probabilistic gene-level fine-mapping over TWAS Z-scores using gene-by-gene predicted-expression correlation as analog of LD | FUSION/S-PrediXcan TWAS Z + ancestry-matched LD reference | Per-gene PIP + credible gene set | Resolves co-regulated gene clusters into a probabilistic causal gene; standard add-on after TWAS | Requires the same prediction-weight panel that produced TWAS Z; PIPs depend on prior |
| MA-FOCUS (Lu 2022 AJHG 109:1388-1404) | Multi-ancestry FOCUS; joint gene fine-mapping across ancestries with shared causal-gene assumption | Per-ancestry TWAS sumstats + per-ancestry weights + per-ancestry LD | Cross-ancestry PIP | Smaller credible gene sets when AFR/EAS contribute non-EUR LD information | Trans-ethnic gene-effect heterogeneity violated; weights must be ancestry-matched |
| JEPEG / JEPEG-Mix (Lee 2015 / 2016) | Gene-based test combining eQTL and functional weights | GWAS sumstats + JEPEG annotation database | Per-gene p | Lightweight gene-burden alternative to TWAS | Less granular than full PrediXcan/FUSION machinery; minimally updated |

Methodology evolves; the FUSION-vs-PrediXcan landscape has been stable but probabilistic fine-mapping (FOCUS, MA-FOCUS) and integrative methods (MOSTWAS, EpiXcan, OmicsXcan) continue to advance. Verify against the current PredictDB release notes (predictdb.org) and the latest FUSION weight panels (gusevlab.org/projects/fusion) before locking on a tissue or model.

S-PrediXcan and FUSION are mathematically near-identical: both compute a weighted sum of GWAS SNP Z-scores using per-gene SNP-expression weights and an LD-aware va