Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.

TitleNon-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.
Publication TypeJournal Article
Year of Publication2020
AuthorsChun, S, Imakaev, M, Hui, D, Patsopoulos, NA, Neale, BM, Kathiresan, S, Stitziel, NO, Sunyaev, SR
JournalAm J Hum Genet
Date Published2020 07 02
KeywordsAged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci

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.

Alternate JournalAm J Hum Genet
PubMed ID32470373
PubMed Central IDPMC7332650
Grant ListR01 HL107816 / HL / NHLBI NIH HHS / United States
K08 HL114642 / HL / NHLBI NIH HHS / United States
UM1 HG008853 / HG / NHGRI NIH HHS / United States
R35 GM127131 / GM / NIGMS NIH HHS / United States
U01 HG006500 / HG / NHGRI NIH HHS / United States
R01 HL131961 / HL / NHLBI NIH HHS / United States
R01 MH101244 / MH / NIMH NIH HHS / United States