Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations

Abstract

Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Genetically Adjusted PSA Levels for Prostate Cancer Screening

“…We considered two approaches for constructing a polygenic score for PSA: 1) clumping and thresholding and 2) the genome-wide Bayesian PRS-CSx algorithm 42,43 (see Methods) . The first score was comprised of the 128 independent index variants with P <5.0 10 −8 from the multi-ancestry meta-analysis (PGS 128 ).…”

“…The first score was comprised of the 128 independent index variants with P <5.0 10 −8 from the multi-ancestry meta-analysis (PGS 128 ). The second score was generated from ancestry-specific GWAS summary statistics, where the posterior effect sizes for each variant were inferred under coupled continuous shrinkage priors across populations (PGS CSx ) 42,43 . Each PGS was validated in the Prostate Cancer Prevention Trial (PCPT) and Selenium and Vitamin E Cancer Prevention Trial (SELECT), both of which were excluded from the discovery GWAS (see Supplementary Figure 3 for ancestry composition and Methods for details on ancestry assignment).…”

“…Each variant in PGS 128 was weighted by the meta-analysis effect size estimated using METAL. As an alternative strategy to clumping and thresholding, we fit a genome-wide score using the PRS-CSx algorithm 42,43 , which takes GWAS summary statistics from each ancestry group as inputs and estimates posterior SNP effect sizes under coupled continuous shrinkage priors across populations (PGS CSx ). Analyses were conducted using pre-computed population-specific LD reference panels from the UKB, which included 1,287,078 HapMap3 variants that are available in both the UKB and 1000 Genomes Phase 3.…”

“…This approach is more robust to differences in genetic ancestry assignments across studies and does not require separate testing and validation datasets for parameter tuning each ancestry group 42 . To facilitate this type of analysis, PRS-CSx provides a --meta option that integrates population-specific posterior SNP effects using an inverse-variance-weighted meta-analysis in the Gibbs sampler 42,43 . The global shrinkage parameter was set to φ=0.0001.…”

Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

“…The publicly available Genome-wide Association Studies (GWAS) for alcohol use phenotypes, derived from studies including both individuals of European ancestry (EA) and African ancestry (AA), that were used in PRS calculations in this study are listed in Table 4. We created PRS using PRS-CSx [63][64][65][66][67], which is a recent, validated method for crossancestry polygenic prediction [68]. The PRS-CSx computation method is detailed elsewhere (https://github.com/getian107/PRScsx) and also briefly described in the Section 1.7 of the Supplementary Material.…”