Alisa Knodle Manning

This paper develops a method to estimate stratified and marginal summary statistics from a joint gene-environment interaction model, demonstrating its accuracy through simulations and real data applications.

The paper analyzes gene-environment interaction effects across multiple ancestries and traits, identifying population-specific genetic mechanisms and methodological biases in GxE studies.

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.

The paper identifies genetic variants affecting HbA1c levels in multi-ancestry cohorts, showing ancestry-specific impacts on diabetes diagnosis via HbA1c.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.

This study identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.