Including diverse populations enhances the discovery of type 2 diabetes loci
S Fatumo, Nat Rev Genetics, November 22, 2023
From the paper: "A recent multi-ancestry GWAS meta-analysis greatly advances our understanding of the genetic basis of T2D by encompassing a broad range of populations. The insights gained from this research provide a foundation for future functional investigations, therapeutic development and the translation of GWAS findings to improve global health outcomes for all, regardless of genetic background. "
Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations.
Betzaida L Maldonado et al. J Community Genet 2023 11
From the abstract: "Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. "
Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations.
Aniruddh P Patel et al. Curr Protoc 2023 11 (11) e911
From the abstract: " We present a pragmatic approach to optimize a PRS for a population of interest that leverages publicly available data and methods and consists of seven steps that are easily implemented without the requirement of expertise in complex genetics: step 1, selecting source genome-wide association studies (GWAS) and imputation; step 2, selecting methods to compute polygenic score; step 3, adjusting scores using principal components of genetic ancestry; step 4, selecting the best performing score; step 5, defining percentiles of a population distribution; step 6, validating performance of the optimized polygenic score; and step 7, implementing the optimized polygenic score in clinical practice."
A linear weighted combination of polygenic scores for a broad range of traits improves prediction of coronary heart disease.
Kristjan Norland et al. Eur J Hum Genet 2023 9
From the abstract: "Polygenic scores (PGS) for coronary heart disease (CHD) are constructed using GWAS summary statistics for CHD. However, pleiotropy is pervasive in biology and disease-associated variants often share etiologic pathways with multiple traits. Therefore, incorporating GWAS summary statistics of additional traits could improve the performance of PGS for CHD. "