Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort

Privé, Florian; Aschard, Hugues; Carmi, Shai; Folkersen, Lasse; Hoggart, Clive; O’Reilly, Paul; Vilhjálmsson, Bjarni J.

doi:10.1016/j.ajhg.2021.11.008

Cited by 206 publications

(195 citation statements)

References 69 publications

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“…Genetic differentiation likely contributes to the reduction in the prediction accuracy of polygenic scores in unrepresented populations, as groups with increasing genetic distance from GWAS samples experience a greater loss in prediction accuracy (Scutari et al, 2016;Bitarello and Mathieson, 2020;Cavazos and Witte, 2021;Privé et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Population differentiation of polygenic score predictions under stabilizing selection

Yair¹,

Coop²

2021

Preprint

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Given the many loci uncovered by genome-wide association studies (GWAS), polygenic scores have become central to the drive for genomic medicine and have spread into various areas including evolutionary studies of adaptation. While promising, these scores are fraught with issues of portability across populations, due to the misestimation of effect sizes and missing causal loci across populations not represented in large-scale GWAS. The poor portability of polygenic scores at first seems at odds with the view that much of common genetic variation is shared among populations (Lewontin 1972). Here we investigate one potential cause of this discrepancy: phenotypic stabilizing selection drives the turnover of genetic variation shared between populations at causal loci. Somewhat counter-intuitively, while stabilizing selection to the same optimum phenotype leads to lower phenotypic differentiation among populations, it increases genetic differentiation at GWAS loci and reduces the portability of polygenic scores constructed for unrepresented populations. We also find that stabilizing selection can lead to potentially misleading signals of the differentiation of average polygenic scores among populations. We extend our baseline model to investigate the impact of pleiotropy, gene-by-environment interactions, and directional selection on polygenic score predictions. Our work emphasizes stabilizing selection as a null evolutionary model to understand patterns of allele frequency differentiation and its impact on polygenic score portability and differentiation.

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Section: Introductionmentioning

confidence: 99%

Population differentiation of polygenic score predictions under stabilizing selection

Yair¹,

Coop²

2021

Preprint

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“…This formula is used in LDpred2 Privé et al . (2020b, 2022). Note that, for a binary trait for which logistic regression is used, we have instead Where .…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, using the notations from Privé et al . (2020b), , where and is the GWAS effect of variant j and n is the GWAS sample size (Mak et al ., 2017; Privé et al ., 2022). Then computing is the most time-consuming part of each iteration.…”

Section: Methodsmentioning

confidence: 99%

“…, where r j = β j = γ j n j se( γ j ) 2 + γ 2 j and γ j is the GWAS effect of variant j and n is the GWAS sample size (Mak et al, 2017;Privé et al, 2022). Then computing R T .,j β (t−1) is the most time-consuming part of each iteration.…”

Section: Data For Real Analysesmentioning

confidence: 99%

“…where γj is the marginal GWAS effect size of variant j, n j is the GWAS sample size associated with variant j, y is the vector of phenotypes and G j is the vector of genotypes for variant j. This formula is used in LDpred2 Privé et al (2020bPrivé et al ( , 2022. Note that, for a binary trait for which logistic regression is used, we have instead…”

Section: Gwas Sample Size Imputationmentioning

confidence: 99%

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Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores

Privé

Tsh

2021

Preprint

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We present lassosum2, a new version of the polygenic score method lassosum, which we re-implement in R package bigsnpr. This new version uses the exact same input data as LDpred2 and is also very fast, which means that it can be run with almost no extra coding nor computational time when already running LDpred2. It can also be more robust than LDpred2, e.g. in the case of a large GWAS sample size misspecification. Therefore, lassosum2 is complementary to LDpred2.

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Multi‐Omics Studies in Historically Excluded Populations: The Road to Equity

Yang

Mishra

Perera

2023

Clin Pharma and Therapeutics

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Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non‐coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi‐omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non‐coding region, other machine learning and omics approaches have been developed to identify the potential causative single‐nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome‐wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome‐wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi‐omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

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Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort

Cited by 206 publications

References 69 publications

Population differentiation of polygenic score predictions under stabilizing selection

Population differentiation of polygenic score predictions under stabilizing selection

Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores

Multi‐Omics Studies in Historically Excluded Populations: The Road to Equity

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