Kenneth M. Rice

This paper develops a semi-empirical Bayesian framework to calibrate weak instrumental bias in sex-specific Mendelian randomization studies, revealing significant sex differences in causal effects of sleep phenotypes on cardiovascular diseases.

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

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

The paper shows that variant effect sizes on quantitative endophenotypes support pathogenicity in monogenic disease genes.

This study investigates how rare and common genetic variations contribute to QT interval duration, finding that both monogenic and polygenic factors influence QTc prolongation, with polygenic risk modulating the effect of rare variants.

The paper introduces BinomiRare, a test for rare variant association with binary outcomes in mixed models, showing it controls type 1 error in simulations and applies it to study genetic variants in disease traits.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

This study identifies new genetic loci associated with myocardial infarction and coronary heart disease using genome-wide association analyses across multiple cohorts.

The paper identifies eQTLs for ACE2 and TMPRSS2 in children's airway transcriptomes and links their expression to T2 inflammation and interferon responses, influencing SARS-CoV-2 infectivity.

The paper addresses variance stratification in pooled whole genome sequencing data, showing how study-specific phenotypic variances affect analysis and introducing a method to improve statistical power and reduce false positives.

The paper identifies genetic variants and transcripts associated with glycemic traits by analyzing exome-array data from 144,600 individuals, highlighting tissue-specific roles in glucose regulation.

This study conducts a large GWAS on heart failure, identifying 12 genetic loci with associations to CAD, atrial fibrillation, and reduced left ventricular function, and provides causal evidence for several risk factors.

The paper introduces a fully-adjusted two-stage method for rank normalization in genetic association studies to reduce Type I errors and improve power by adjusting for covariates in both stages.

The paper introduces SMMAT, a variant-set mixed model association test for continuous and binary traits in large-scale WGS studies, showing it controls type I error rates and applies to population structured and related samples.

FastSKAT improves SKAT's efficiency by approximating tail probabilities with fewer eigenvalues, enabling faster analysis of large genetic datasets.

Genetic correlations across 25 brain disorders (215,683 cases) reveal broad psychiatric common-variant sharing, largely distinct neurological disorders, and migraine as the only significant cross-category link.