RT Journal
A1 Weissbrod, Omer
A1 Geiger, Dan
A1 Rosset, Saharon
T1 Multikernel linear mixed models for complex phenotype prediction
JF Genome Research 
JO Genome Research 
YR 2016 
FD July 01 
VO 26 
IS 7 
SP 969 
OP 979 
DO 10.1101/gr.201996.115 
UL http://genome.cshlp.org/content/26/7/969.abstract 
AB Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.