We present CIT-Lasso, a framework that uses only summary statistics to identify, genome-wide, sets of variants carrying non-redundant information on a phenotype, distinguishing likely causal variants from correlated variants that are merely associated. The open-source implementation completes genome-wide analysis in under 15 min on one CPU. In simulations, it outperforms existing methods in false discovery rate control, power, and fine-mapping resolution. Applied to an Alzheimer's disease meta-analysis, it identified 82 loci, 37 beyond conventional GWAS; prior MPRA and CRISPR-Cas9 studies corroborate prioritized variants. Results on other 67 large-scale GWAS reveal the method's generalizability to make discoveries beyond conventional GWAS pipeline.
Zihuai He, Benjamin B. Chu, James Yang et al.· Genome Biology· 0 citations
Genome‐wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies show that knockoff‐based methods can identify variants with unique, potentially causal effects on phenotypes. However, their statistical validity and effectiveness in studies with related individuals, such as the UK Biobank, remain unexplored. In this paper, we extensively evaluate a simple and effective analytical strategy that integrates GhostKnockoffs and state‐of‐the‐art marginal association tests. We show that this approach is robust to arbitrary relatedness structure as long as the input Z‐scores are derived from valid generalized linear mixed models. This robustness also extends GhostKnockoffs to other GWASs settings, including meta‐analysis of studies with sample overlap when the input score test Z‐scores are properly calibrated, and association test statistics beyond score tests in independent sample settings. We demonstrate the method's validity and practical utility using simulation studies and a meta‐analysis of nine European ancestral genome‐wide association studies and whole exome/genome sequencing studies for the Alzheimer's disease.
Xinran Qi, M. Belloy, Jiaqi Gu et al.· Genetic Epidemiology· 0 citations
Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood (n = 6454) and lung (n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank-derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.
Peter Orchard, T. Blackwell, L. Kachuri et al.· Science· 0 citations
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