| Title: | Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index. |
| Journal: | Biocomputing |
| Published: | 1 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36540998/ |
Publication 8007
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.19 Authors
- Daniel Hui
- Brenda Xiao
- Ozan Dikilitas
- Robert R. Freimuth
- Marguerite R. Irvin
- Gail P. Jarvik
- Leah Kottyan
- Iftikhar Kullo
- Nita A. Limdi
- Cong Liu
- Yuan Luo
- Bahram Namjou
- Megan J. Puckelwartz
- Daniel Schaid
- Hemant Tiwari
- Wei-Qi Wei
- Shefali Verma
- Dokyoon Kim
- Marylyn D. Ritchie
1 Application
| Application ID | Title |
|---|---|
| 32133 | Integration of multi-organ imaging phenotypes, clinical phenotypes, and genomic data |
Enabling scientific discoveries that improve human health