Colocalization of eQTLs With Type 2 Diabetes and Glycemic Traits Using Whole-Genome Sequences in Diverse Populations From the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program

Authors

• Ningyuan Wang, School of Public Health
• Daniel A. Dicorpo, School of Public Health
• Yixin Zhang, School of Public Health
• Erica Kleinbrink, McGill Faculty of Medicine and Health Sciences
• Donna K. Arnett, University of South Carolina
• John Barnard, Cleveland Clinic Foundation
• John Blangero, The University of Texas Rio Grande ValleyFollow
• Joanne E. Curran, The University of Texas Rio Grande ValleyFollow
• Ravindranath Duggirala, Texas A&M University-San Antonio

Document Type

Article

Publication Date

8-1-2026

Abstract

Large-scale multiancestry genome-wide association studies have identified hundreds of loci associated with type 2 diabetes (T2D) and glycemic traits, yet imputed genotyping arrays limit the detection of low-frequency and rare variants. Whole-genome sequencing (WGS) offers a more complete view of genetic variation, especially across diverse populations. We analyzed high-coverage (38×) WGS data from 21,913 T2D case subjects, 61,036 control subjects, and up to 50,011 individuals with no diabetes with fasting glucose, fasting insulin, and HbA1c from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine Program. We performed single-variant association testing, conditional analysis, fine-mapping, and Bayesian colocalization to identify genetic signals and assess regulatory relevance in diabetes-related tissues. We identified 76 distinct association signals across 34 loci, including novel variants at DUSP9 for T2D, and ROBO1, NDN, and MYT1 for HbA1c. Fine-mapping narrowed credible sets and improved causal variant resolution. Colocalization highlighted 80 expression signals in diabetes-related tissues, linking genetic associations to functional regulatory mechanisms. Our findings demonstrate the utility of WGS to uncover novel variants in diverse populations, enhance locus resolution, and link regulatory variation to disease-relevant tissues. This work refines the genetic architecture of T2D and glycemic traits and supports precision medicine efforts target-ing diverse populations.

Comments

© 2026 by the American Diabetes Association. Readers may use this work for educational, noncommercial purposes if properly cited and unaltered.

Recommended Citation

Wang, N., DiCorpo, D. A., Zhang, Y., Kleinbrink, E., Arnett, D. K., Barnard, J., ... & Manning, A. K. (2026). Colocalization of eQTLs With Type 2 Diabetes and Glycemic Traits Using Whole-Genome Sequences in Diverse Populations From the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program. Diabetes, 75(8), 1477-1491. https://doi.org/10.2337/db25-0557

First Page

1477

Last Page

1491

Publication Title

Diabetes

DOI

10.2337/db25-0557