Author

Benjamin B. Chu

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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 Jul 2026

Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq

Overall, this work recommends a design that intentionally includes single-guide cells, guide doublets, and guide triplets to improve cost efficiency while preserving signal recovery, and develops PerturbMatch, a scalable statistical framework to analyze guide multiplets.

Jake Yeung, Jenille Tan, Liang Wang et al. · bioRxiv · 0 citations