Penny Gordon-Larsen
University of North Carolina at Chapel Hill, NC·Public Health & Epidemiology
Severe obesity means a body mass index (BMI) of 40 or more, roughly 100 pounds overweight. It raises the risk of heart and metabolic disease, sleep disorders and cancer, and its impact differs across populations.
- Fields
Public Health & Epidemiology
Genetics
Medicine & Clinical Research
- Topics
severe obesity
cardiometabolic disease
transcriptomics
- Grants
- 1 known grant, through May 2029
What they work on
- Identify gene-expression signals underlying severe obesity using whole-blood RNA sequencing from more than 15,000 people, and build ancestry-informed eQTL maps for underrepresented populations.
- Identify blood metabolite signals underlying severe obesity in more than 46,000 people, and derive ancestry-informed metaboQTL maps linking genetic variants to metabolite levels.
- Infer causal pathways for severe obesity by combining transcript and metabolite data with GWAS data from about 240,000 people, using colocalization, pathway and causal inference modeling.
- Test the clinical value of GWAS-, transcript- and metabolite-informed polygenic risk scores for severe obesity in large biobanks linked to electronic health records.
More about the research
Yet little is known about its biological causes. People with severe obesity are often excluded from clinical studies, and many ancestral groups are underrepresented in genetic research. This project combines several types of large-scale biological data to uncover the mechanisms behind severe obesity across diverse populations.
Why it matters
Severe obesity drives cardiometabolic disease, sleep disorders and cancer, yet it remains understudied, especially in underrepresented ancestral groups. Identifying the genes, gene activity and metabolites behind it could reveal targets for early prevention, diagnosis and drug treatment. Multi-omics risk scores tested in health-record-linked biobanks could help flag people at high risk.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
William Bush
University of Miami, FL·Genetics
Identify genetic risk factors for Alzheimer disease that are distinct to African American, African, and Hispanic/Latino populations through multiple statistical analyses of genetic data generated in these cohorts.
Marguerite Irvin
University of Alabama at Birmingham, AL·Public Health & Epidemiology
Identify genetic variants that predict blood pressure response to chlorthalidone (a diuretic) or lisinopril (an ACE inhibitor) in more than 6,500 African Americans from the randomized GenHAT trial, with validation through the ICAPS consortium.
Hongzhe Lee
University of Pennsylvania, PA·Mathematics & Statistics
Develop transfer learning methods that borrow information across ancestry groups and tissues to improve polygenic risk score prediction and increase power to detect trait-associated variants in minority populations.
The school
University of North Carolina at Chapel Hill: 72 labs on PI Pages, $1.6B a year on research, 523 research doctorates a year.
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