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, outperforms existing methods in false discovery rate control, power, and fine-...
Zi-Huai He, Benjamin B. Chu, James Yang et al.· Genome Biology· 0 citations
This paper extensively evaluate a simple and effective analytical strategy that integrates GhostKnockoffs and state‐of‐the‐art marginal association tests that is robust to arbitrary relatedness structure and extends GhostKnockoffs to other GWASs settings.
Xinran Qi, M. Belloy, Jiaqi Gu et al.· Genetic Epidemiology· 0 citations
This work 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.
Peter Orchard, T. Blackwell, L. Kachuri et al.· Science· 0 citations
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