Cecelia A. Laurie

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

The paper develops a method to quantify somatic mutation burden in whole-genome sequencing data without paired-tissue samples and identifies genetic and phenotypic correlates of clonal hematopoiesis without known driver mutations.

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

The paper uses bidirectional Mendelian randomization to show LTL lengthening increases CHIP risk, while CHIP accelerates LTL shortening, with modest mediation between CHIP and CAD via LTL.

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 describes a system for harmonizing phenotype data across multiple studies in the TOPMed program, enabling reproducible and shared phenotype data for over 80 studies.

This study identifies 59 sentinel variants and two novel genes associated with telomere length using whole genome sequences from 109,122 trans-ethnic individuals.

The paper introduces a framework for region-based association testing in family-based studies, showing improved performance over existing methods in simulations and a real dataset.

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 germline genetic variants associated with clonal hematopoiesis of indeterminate potential (CHIP) using whole genome sequences from the TOPMed program.

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.