Principles and methods for transferring polygenic risk scores across global populations

Kachuri, Linda; Chatterjee, Nilanjan; Hirbo, Jibril; Schaid, Daniel J.; Martin, Iman; Kullo, Iftikhar J.; Kenny, Eimear E.; Pasaniuc, Bogdan; ,; Auer, Paul L.; Conomos, Matthew P.; Conti, David V.; Ding, Yi; Wang, Ying; Zhang, Haoyu; Zhang, Yuji; Witte, John S.; Ge, Tian

doi:10.1038/s41576-023-00637-2

Cited by 67 publications

(36 citation statements)

References 164 publications

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“…To evaluate this hypothesis we leveraged polygenic risk scores (PRS), a weighted sum of common variant effects with weights determined by results from GWASs. 46 PRSs for hundreds of traits have been widely studied, many are strongly correlated with complex traits, and there is ongoing evaluation for clinical translational potential. 47 Here we computed PRS for each trait of interest ( Table 2 ), restricting to the unrelated white British population to reduce confounding from population structure 48 (see Methods).…”

Section: Resultsmentioning

confidence: 99%

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Wei,

Border,

et al. 2023

Preprint

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Over three percent of people carry a dominant pathogenic mutation, yet only a fraction of carriers develop disease (incomplete penetrance), and phenotypes from mutations in the same gene range from mild to severe (variable expressivity). Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMe Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable penetrance and expressivity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.

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Section: Resultsmentioning

confidence: 99%

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Wei,

Border,

et al. 2023

Preprint

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“…The efficacy of PRS is known to hinge on the similarity of the target population to the training population (Ding et al, 2023). With the PRIMED consortium working on methods to improve the performance of PRS in diverse populations, simulations will be pivotal for method evaluation (Kachuri et al, 2023). In this context, we expect that admix-kit will be an essential part of this effort.…”

Section: Discussionmentioning

confidence: 99%

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Hou,

Gogarten,

Kim

et al. 2023

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SummaryAdmixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.Availability and implementationAdmix-kit package is open-source and available athttps://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available athttps://github.com/UW-GAC/admix-kit_workflow.

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“…Many methods that utilize summary data from multiple populations have already been proposed and demonstrate improved prediction in under-represented populations 25,29,30,[70][71][72][73][74] . As genetics research moves towards greater diversity, ALL-Sum serves as a valuable foundation for extension to incorporating data from multiple ancestries, as well as admixed individuals [75][76][77] .…”

Section: Discussionmentioning

confidence: 99%

Ensembled best subset selection using summary statistics for polygenic risk prediction

Chen,

Zhang,

Mazumder

et al. 2023

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Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L0L2penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

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Principles and methods for transferring polygenic risk scores across global populations

Cited by 67 publications

References 164 publications

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Ensembled best subset selection using summary statistics for polygenic risk prediction

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