Ruhollah Shemirani

460 haplotypes from 230 genomes, selected to maximize common-variant coverage, capture 99.43% of All of Us common small variants and halve structurally unreliable regions and base errors versus HPRC1.

The paper analyzes genetic and health record data from a diverse population to uncover gene-phenotype associations and assess polygenic score performance across ancestries.

SPC improves population structure adjustment in genomic analysis by capturing fine-scale patterns better than principal components, reducing p-value inflation and improving rare variant associations.

This study compares phenome-wide admixture mapping and genome-wide association in a diverse biobank, finding 77 significant AM signals, 48 missed by GWAS.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

The paper identifies a novel risk locus for PAD on 2q35 in Dominicans using admixture mapping and health systems data.

The paper evaluates clustering algorithms for IBD mapping, finding that Markov Clustering outperforms others in statistical power and efficiency.

The paper introduces iLASH, an algorithm for fast and accurate detection of identity-by-descent tracts in large genomic datasets, achieving significant speed improvements over existing methods.

This study links genomic data with EHRs to identify health outcomes enriched in sub-populations, revealing both known and novel associations.

This study compares supervised learning methods to differential expression analysis for gene selection in RNA-Seq case-control studies, showing improved survival-associated gene identification in cancer datasets.