Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models

• Published in: PLoS genetics Vol. 16; no. 5; p. e1008766
• Main Authors: Bhatnagar, Sahir R., Yang, Yi, Lu, Tianyuan, Schurr, Erwin, Loredo-Osti, JC, Forest, Marie, Oualkacha, Karim, Greenwood, Celia M. T.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.05.2020; Public Library of Science (PLoS)
• Subjects: Adjustment; Algorithms; Animals; Biology and Life Sciences; Computer Simulation; Crosses, Genetic; Fines & penalties; Genetics, Population - methods; Genome-Wide Association Study - methods; Genome-Wide Association Study - statistics & numerical data; Genomes; Humans; Leishmania tropica - genetics; Leishmaniasis, Cutaneous - genetics; Linear Models; Methods; Mice; Mice, Inbred Strains; Mixed-effects models; Models, Genetic; Multifactorial Inheritance - genetics; Mycobacterium bovis; Physical Sciences; Polymorphism, Single Nucleotide; Population; Population Dynamics; Population genetics; Population structure; Prediction models; Research and Analysis Methods; Sample Size; Simulation; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Software; Tuberculosis - genetics; Tuberculosis - pathology; Variables; Canada
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1008766

Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix).