Open AccessEfficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models
Author(s) -
Joel A. Mefford,
Danny S. Park,
Zhili Zheng,
Arthur Ko,
Mika AlaKorpela,
Markku Laakso,
Päivi Pajukanta,
Jian Yang,
John S. Witte,
Noah Zaitlen
Publication year - 2020
Publication title -
journal of computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2019.0325
Subject(s) - overfitting , computer science , pleiotropy , imputation (statistics) , inference , genome wide association study , machine learning , artificial intelligence , data mining , biology , missing data , single nucleotide polymorphism , genetics , phenotype , artificial neural network , genotype , gene
Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.