Benjamin B. Chu

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

This paper improves Perturb-seq by analyzing guide multiplets, reducing costs and maintaining signal recovery.

This paper introduces solveblock, a software tool that enables FDR-controlled conditional independence analysis using GWAS summary statistics, leading to additional discoveries in British individuals.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

This paper introduces a method to identify causal variants from genome-wide summary statistics, outperforming existing methods and discovering additional loci for complex traits.

This paper introduces a penalized regression algorithm for multivariate GWAS using iterative hard thresholding, showing improved efficiency and reduced false positives compared to existing methods.

This paper introduces an unsupervised framework for selecting ancestry informative markers and estimating admixture proportions in large biobank datasets, demonstrating scalability and effectiveness.

MendelImpute.jl provides a fast, data-driven method for genotype imputation, phasing, and ancestry inference using linear algebra instead of hidden Markov models.

This paper introduces an improved iterative hard thresholding method for GWAS that enhances true positive rates and reduces false positives compared to traditional approaches.