Yang Hai1, Yalu Wen1. 1. Department of Statistics, University of Auckland, Auckland, 1010, New Zealand.
Abstract
MOTIVATION: Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. RESULTS: We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. AVAILABILITY: The R-package is available at https://github.com/yhai943/BLMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. RESULTS: We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. AVAILABILITY: The R-package is available at https://github.com/yhai943/BLMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Jian Yang; Beben Benyamin; Brian P McEvoy; Scott Gordon; Anjali K Henders; Dale R Nyholt; Pamela A Madden; Andrew C Heath; Nicholas G Martin; Grant W Montgomery; Michael E Goddard; Peter M Visscher Journal: Nat Genet Date: 2010-06-20 Impact factor: 38.330
Authors: R C Petersen; P S Aisen; L A Beckett; M C Donohue; A C Gamst; D J Harvey; C R Jack; W J Jagust; L M Shaw; A W Toga; J Q Trojanowski; M W Weiner Journal: Neurology Date: 2009-12-30 Impact factor: 9.910
Authors: Stefan Taudien; Ludwig Lausser; Evangelos J Giamarellos-Bourboulis; Christoph Sponholz; Franziska Schöneweck; Marius Felder; Lyn-Rouven Schirra; Florian Schmid; Charalambos Gogos; Susann Groth; Britt-Sabina Petersen; Andre Franke; Wolfgang Lieb; Klaus Huse; Peter F Zipfel; Oliver Kurzai; Barbara Moepps; Peter Gierschik; Michael Bauer; André Scherag; Hans A Kestler; Matthias Platzer Journal: EBioMedicine Date: 2016-09-15 Impact factor: 8.143