Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics

• Published in: American journal of human genetics Vol. 107; no. 1; pp. 46 - 59
• Main Authors: Chun, Sung, Imakaev, Maxim, Hui, Daniel, Patsopoulos, Nikolaos A., Neale, Benjamin M., Kathiresan, Sekar, Stitziel, Nathan O., Sunyaev, Shamil R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 02.07.2020; Elsevier
• Subjects: Aged; Cohort Studies; Diabetes Mellitus, Type 2 - genetics; Female; genome-wide association study; Genome-Wide Association Study - methods; Genotype; Humans; linkage disequilibrium; Linkage Disequilibrium - genetics; Male; Middle Aged; Models, Genetic; Multifactorial Inheritance - genetics; non-parametric prediction; Phenotype; phenotype prediction; polygenic score; Polymorphism, Single Nucleotide - genetics; prognosis; Quantitative Trait Loci - genetics; summary statistics; phenotype prediction; summary statistics; genome-wide association study; prognosis; polygenic score; non-parametric prediction; linkage disequilibrium
• ISSN: 0002-9297; 1537-6605; 1537-6605
• DOI: 10.1016/j.ajhg.2020.05.004

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait’s heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.