Dataset Information

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.

ABSTRACT: Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

SUBMITTER: Wang Y

PROVIDER: S-EPMC9903818 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.

Wang Ying Y Namba Shinichi S Lopera Esteban E Kerminen Sini S Tsuo Kristin K Läll Kristi K Kanai Masahiro M Zhou Wei W Wu Kuan-Han KH Favé Marie-Julie MJ Bhatta Laxmi L Awadalla Philip P Brumpton Ben B Deelen Patrick P Hveem Kristian K Lo Faro Valeria V Mägi Reedik R Murakami Yoshinori Y Sanna Serena S Smoller Jordan W JW Uzunovic Jasmina J Wolford Brooke N BN Willer Cristen C Gamazon Eric R ER Cox Nancy J NJ Surakka Ida I Okada Yukinori Y Martin Alicia R AR Hirbo Jibril J

Cell genomics 20230104 1

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both ...[more]

PMID: 36777179

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Project description:Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Dataset Information

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.

Publications

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.

Similar Datasets

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