Power and Predictive Accuracy of Polygenic Risk Scores

By Frank Dudbridge. PLoS Genetics, 2013

1,493 citations recorded by the source corpus.

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/c2b1eab16b833b6451ce2bc12477c40c2274e802

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Citations in the corpus

An Examination of Polygenic Score Risk Prediction in Individuals With First-Episode Psychosis. (2017, background): “One such approach is the generation of polygenic risk scores (PRSs), which represents a promising technique for predicting risk (6,7).”
Polygenic scores via penalized regression on summary statistics (2016, background): “The vast majority of such genetic contribution is thus spread across the huge landscape of the genome, with many loci each contributing a small, almost undetectable eﬀect on the phenotypes (Dudbridge, 2013, 2016).”
Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease (2017, methodology): “The predictive capacity of PSs will likely continue to improve as larger discovery GWAS studies more precisely define the effect sizes for common polymorphisms across the genome (9-11).”
A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis (2018, neutral): “If more samples are available, additional subjects should be included in the discovery sample to maximize the accuracy of the estimation of the effect sizes (Dudbridge, 2013).”
ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social-communication difficulties (2017, neutral): “Polygenic risk scores (PGS)43,44 were analysed to estimate the explained phenotypic variance in social-communication difficulties due to risk-increasing alleles for clinical disorder.”
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores (2015, background): “In addition, many of thesemethods become computationally intractable at the very large sample sizes (>100,000 individuals) that would be required for achieving clinically relevant predictions for most common diseases.(15,18,19) In this study, we propose LDpred, a Bayesian PRS that estimates posterio”
Polygenic Influence on Educational Attainment: New evidence from The National Longitudinal Study of Adolescent to Adult Health (2015, background): “Polygenic scores can include all SNPs measured in a GWAS or some subset, typically defined by a p value threshold for the GWAS results (for a detailed discussion of polygenic scoring methods, see Dudbridge, 2013; Purcell et al., 2009; Wray, Goddard, & Visscher, 2007).”
Using Genetic Distance to Infer the Accuracy of Genomic Prediction (2015, background): “The accuracy of genomic predictions will depend on a number of factors: relatedness among genotyped individuals [7, 8]; the density of the markers [7, 9, 10]; and the genetic architecture of the trait, in particular the allele frequencies of causal variants [11, 12] and the distribution of their eff”