Widespread signatures of natural selection across human complex traits and functional genomic categories

Journal article

Purpose: To evaluate the trans-ancestry portability of current myopia polygenic risk scores (PRS) to predict high myopia (HM) and myopic macular degeneration (MMD) in an Asian population. Design: Population-based study. Subjects: A total of 5,894 (2,141 Chinese, 1,913 Indians, and 1,840 Malays) adults from the Singapore Epidemiology of Eye Diseases (SEED) study were included in the analysis. The mean age was 57.0 (standard deviation, SD = 9.31) years. A total of 361 adults had HM (spherical equivalent, SE -0.5D). Methods: The PRS, derived from 687,289 HapMap3 SNPs from the largest genome-wide association study of myopia in Europeans to date (n = 260,974), was assessed on its ability to predict HM and MMD versus controls. Main outcome measures: The primary outcomes were the area under the receiver operating characteristic curve (AUROC) to predict HM and MMD. Results: The PRS had an AUROC of 0.73 (95% CI: 0.70, 0.75) for HM and 0.66 (95% CI: 0.63, 0.70) for MMD versus no myopia controls. The inclusion of the PRS with other predictors (age, sex, educational attainment (EA), and ancestry; age-by-ancestry; sex-by-ancestry and EA-by-ancestry interactions; and 20 genotypic principal components) increased the AUROC to 0.84 (95% CI: 0.82, 0.86) for HM and 0.79 (95% CI: 0.76, 0.82) for MMD. Individuals with a PRS in the top 5% had 4.66 (95% CI: 3.34, 6.42) times higher risk for HM and 3.43 (95% CI: 2.27, 5.05) times higher risk for MMD compared to the remaining 95% of individuals. Conclusion: The PRS is a good predictor for HM and will facilitate the identification of high-risk children to prevent myopia progression to HM. In addition, the PRS also predicts MMD and will help to identify high-risk myopic adults who require closer monitoring for myopia-related complications.info:eu-repo/semantics/publishedVersio

Authors: Zeng, J; Xue, A; Jiang, L; Lloyd-Jones, LR; Wu, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Communications
• Volume: 12
• Issue: 1
• Pages: 1164-1164
• Article number: 1164
• Publication date: 2021-02-19
• DOI: 10.1038/s41467-021-21446-3
• EISSN: 2041-1723
• ISSN: 2041-1723
• Language: English
• Keywords: Genome-wide association study; Computational biology; Machine learning; Computer science; Single-nucleotide polymorphism; Quantitative trait locus; Genetic architecture; Evolutionary biology; Natural selection; Gene; Genetics; Selection (genetic algorithm); Biology