Articles

Building input objects

Constructing the individual-level and summary-statistic S4 containers from manifests or by hand.

Building QtlDataset and MultiStudyQtlDataset objects

Building QtlSumStats and GwasSumStats objects

LD and quality control

Loading, computing and conditioning LD references, and the allele alignment and LD-mismatch QC that matches summary statistics to them.

Working with LD references

Quality Control for GWAS and QTL Summary Statistics

Fine-mapping

SuSiE-family fine-mapping for individual-level QTL data, QTL summary statistics, and GWAS summary statistics.

Fine-mapping with pecotmr

Post-processing fine-mapping results

TWAS

Training TWAS weights-methods from QTL data (individual-level and summary-statistics, single and multi-context), computing per-gene TWAS Z-scores from GWAS summary statistics, and fine-mapping genes jointly with SNPs so a gene’s PIP is conditioned on the signal beside it.

Learning TWAS weights with pecotmr

Causal inference with pecotmr: TWAS Z-scores and MR

Causal TWAS with ctwasPipeline

Cross-context modeling

Multivariate adaptive shrinkage across contexts: learning the covariance structure of effect sharing and shrinking per-context estimates toward it.

Multivariate adaptive shrinkage across contexts with mash

Colocalization

Pairwise QTL/GWAS colocalization (coloc.bf_bf) and multi-trait colocalization with ColocBoost.

Pairwise colocalization with colocPipeline

Multi-trait colocalization with ColocBoost

Heritability and enrichment

SNP heritability from GWAS summary statistics by four estimators, functional enrichment across annotations, postprocessing of external polyfun sLDSC runs, and per-variant molecular-QTL enrichment.

SNP heritability and functional enrichment

Molecular QTL Enrichment Analysis for Complex Traits

Resources

Publicly available QTL and GWAS summary statistics resources