Author

Zihuai He

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#software testing Open access Sep 2026

It’s a wrap: deriving distinct discoveries with FDR control after a GWAS analysis

The standard analysis pipeline for genome-wide association studies (GWAS) is based on marginal tests of association. These are computationally convenient and portable, but the discoveries are not immediately interpretable, and require post-processing such as “clumping” and “fine mapping.” An interesting alternative i...

Benjamin B. Chu, Zi-Huai He, C. Sabatti · Bioinformatics Advances · 0 citations

Open access Jul 2026

CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics

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

Open access Aug 2026

Robust Inference With Ghostknockoffs in Genome‐Wide Association Studies With Sample Relatedness

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