Abstract

Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer.

Highlights

  • The polygenic model of inheritance predicts that the genetic basis of complex phenotypes consists of small effects from thousands of genetic variants

  • We propose applying the Truncated Lasso penalty and the elastic net penalty to calculate polygenic risk scores using summary statistic data and linkage disequilibrium information

  • We demonstrate via simulation that the TlpSum produces sparser models when the underlying genetic architecture is sparse, and does a good job recovering truly nonzero effect sizes while limiting false positives

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Summary

Introduction

The polygenic model of inheritance predicts that the genetic basis of complex phenotypes consists of small effects from thousands of genetic variants. Extensions on this method include thresholding [8], in which SNPs with marginal p-values below a certain cutoff point are excluded, and pruning and thresholding, which combines thresholding with the exclusion of highly correlated SNPs via pruning [9] These methods use only marginal effect size estimates, and do not attempt to construct a joint model that estimates effect sizes under linkage disequilibrium. We propose a method for constructing polygenic risk scores that integrates marginal effect size estimates with publicly available reference panel data, which is used to estimate linkage disequilibrium. By estimating effect sizes under linkage disequilibrium, we more closely model the true structure of the genetic effects This allows us to capture more of the genetic heritability, as shown via simulation and application to real data

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