Polygenic scores for UK Biobank scale data

Tsh, Mak; Porsch, Robert M.; Choi, Shing Wan; Sham, Pak-Chung

doi:10.1101/252270

Cited by 12 publications

(10 citation statements)

References 42 publications

(52 reference statements)

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“…In the absence of an independent data set, the target sample can be subdivided into training and validation data sets, and this process can be repeated with different partitions of the sample, e.g. performing 10-fold cross-validation [56,66,67], to obtain more robust model estimates.…”

Section: Overfitting In Prs Association Testingmentioning

confidence: 99%

Tutorial: a guide to performing polygenic risk score analyses

2020

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The application of polygenic risk scores (PRS) has become routine across genetic research. Among a range of applications, PRS are exploited to assess shared aetiology between phenotypes, to evaluate the predictive power of genetic data for use in clinical settings, and as part of experimental studies in which, for example, experiments are performed on individuals, or their biological samples (eg. tissues, cells), at the tails of the PRS distribution and contrasted. As GWAS sample sizes increase and PRS become more powerful, they are set to play a key role in personalised medicine. However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here we provide detailed guidelines for performing polygenic risk score analyses relevant to different methods for their calculation, outlining standard quality control steps and offering recommendations for bestpractice. We also discuss different methods for the calculation of PRS, common misconceptions regarding the interpretation of results and future challenges.Genome-wide association studies (GWAS) have identified a large number of genetic variants, typically single nucleotide polymorphisms (SNP), associated with a wide range of complex traits [1-3]. However, the majority of these variants have a small effect and typically correspond to a small fraction of truly associated variants, meaning that they have limited predictive power [4][5][6]. Using a linear mixed model in the Genome-wide Complex Trait Analysis software (GCTA) [7], Yang et al (2010) demonstrated that much of the heritability of height can be explained by evaluating the effects of all SNPs simultaneously [6]. Subsequently, statistical techniques such as LD score regression (LDSC) [8,9] and the polygenic risk score (PRS) method [4,10] have also aggregated the effects of variants across the genome to estimate heritability, to infer genetic overlap between traits and to predict phenotypes based on genetic profile or that of other phenotypes [4,5,[8][9][10].While GCTA, LDSC and PRS can all be exploited to infer heritability and shared aetiology among complex traits, PRS is the only approach that provides an estimate of genetic propensity to a trait at the individual-level. In the standard approach [4,[11][12][13], polygenic risk scores are calculated by computing the sum of risk alleles corresponding to a phenotype of .

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Section: Overfitting In Prs Association Testingmentioning

confidence: 99%

Tutorial: a guide to performing polygenic risk score analyses

2020

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“…It has also been proposed to use external or internal validation to choose tuning parameters and avoid permutations. However, external validation data sets are often not available, especially for rarely studied phenotypes (Mak, Porsch, Choi, & Sham, 2018), and in smaller samples, splitting the data into training and validation sets can decrease power. As an alternative to the optimization approach, one could a priori choose a single tuning parameter setting (e.g., fixing the p ‐value threshold and LD pruning level) to construct a single PRS.…”

Section: Introductionmentioning

confidence: 99%

A principal component approach to improve association testing with polygenic risk scores

Coombes

Ploner

Bergen

et al. 2020

Genetic Epidemiology

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Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS–PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS–PCA approach with a permutation test and an a priori selected p‐value threshold. The PRS–PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

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“…Our study is not without limitations. We could potentially have overfitting in our model ( Mak et al, 2018 ) due to any overlap between the discovery data and the shrinkage applied to the GWAS effect size based on HCHS/SOL data, but it is unlikely given the different data sources. The study design involves complex sampling, and therefore, we utilized complex survey methodology in our analysis.…”

Section: Discussionmentioning

confidence: 99%

Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions

et al. 2021

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Introduction: Hispanic/Latinos experience a disproportionate burden of obesity. Acculturation to US obesogenic diet and practices may lead to an exacerbation of innate genetic susceptibility. We examined the role of gene–environment interactions to better characterize the sociocultural environmental determinants and their genome-scale interactions, which may contribute to missing heritability of obesity. We utilized polygenic risk scores (PRSs) for body mass index (BMI) to perform analyses of PRS-by-acculturation and other environmental interactors among self-identified Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).Methods: PRSs were derived using genome-wide association study (GWAS) weights from a publicly available, large meta-analysis of European ancestry samples. Generalized linear models were run using a set of a priori acculturation-related and environmental factors measured at visit 1 (2008–2011) and visit 2 (2014–2016) in an analytic subsample of 8,109 unrelated individuals with genotypic, phenotypic, and complete case data at both visits. We evaluated continuous measures of BMI and waist-to-hip ratio. All models were weighted for complex sampling design, combined, and sex-stratified.Results: Overall, we observed a consistent increase of BMI with greater PRS across both visits. We found the best-fitting model adjusted for top five principal components of ancestry, sex, age, study site, Hispanic/Latino background genetic ancestry group, sociocultural factors and PRS interactions with age at immigration, years since first arrival to the United States (p < 0.0104), and healthy diet (p < 0.0036) and explained 16% of the variation in BMI. For every 1-SD increase in PRS, there was a corresponding 1.10 kg/m2 increase in BMI (p < 0.001). When these results were stratified by sex, we observed that this 1-SD effect of PRS on BMI was greater for women than men (1.45 vs. 0.79 kg/m2, p < 0.001).Discussion: We observe that age at immigration and the adoption of certain dietary patterns may play a significant role in modifying the effect of genetic risk on obesity. Careful consideration of sociocultural and immigration-related factors should be evaluated. The role of nongenetic factors, including the social environment, should not be overlooked when describing the performance of PRS or for promoting population health in understudied populations in genomics.

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Polygenic scores for UK Biobank scale data

Cited by 12 publications

References 42 publications

Tutorial: a guide to performing polygenic risk score analyses

Tutorial: a guide to performing polygenic risk score analyses

A principal component approach to improve association testing with polygenic risk scores

Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions

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