| Title: | Transcriptome-wide Association Study of Cardiometabolic Traits in the UK Biobank Cohort |
| Lead Institution: | University of North Carolina at Chapel Hill |
| Principal investigator: | Dr Kari North |
Application 25953
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About
The proposed research will leverage novel transcriptome-wide association study (TWAS) approaches to impute predicted gene expression in the UK Biobank cohort. We will then assess association of predicted gene expression with a number of quantitative cardiometabolic traits relevant to diseases like diabetes, heart disease, and stroke. This analysis will allow us to compare the results from novel and published TWAS methods in a large, well-phenotyped cohort. Along with this methods development goal, we will hopefully validate known genes related to cardiometabolic traits and may discover new genes which have been missed by single variant genetic association analyses. This research will help test a new genetic analysis method, which can be applied to understanding the genetic risk factors for a diverse range of disease-related traits. We will also apply these methods to attempt to discover novel genetic risk factors for important cardiometabolic risk factors relevant to common, complex diseases. Genetic risk factor discovery can lead both to improved risk prediction and novel understanding of disease mechanisms, potentially guiding development of new therapies. There are a number of publically available datasets (for example, the Genotype-Tissue Expression (GTEx) project) which relate genotypes at millions of sites across the genome to measured gene expression in a number of different tissues, such as adipose, brain, or muscle. These datasets can be used to estimate gene expression in large numbers of individuals where gene expression has not been assessed. We will test both a novel and existing method for estimating gene expression. We will then test to see if this predicted gene expression is associated with changes in a number of cardiometabolic traits. We plan to include all UK biobank participants with available genome-wide association study data and data for any of the requested cardiometabolic phenotypes.5 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 9663 | Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts | Mohammad Yaser Anwar (+20) | 2023 | Human Genetics |
| 8240 | Branched chain amino acids harbor distinct and often opposing effects on health and disease | Christy L. Avery (+12) | 2023 | Communications Medicine |
| 9653 | Genetic Variants Associated With Hidradenitis Suppurativa | Quan Sun (+18) | 2023 | JAMA Dermatology |
| 8793 | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI | Quan Sun (+17) | 2024 | Nature Communications |
| 10540 | MagicalRsq: Machine-learning-based genotype imputation quality calibration | Quan Sun (+18) | 2022 | American Journal of Human Genetics |
Enabling scientific discoveries that improve human health