A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer's Disease.

Xulong Wang, The Jackson Laboratory
Vivek M. Philip, The Jackson Laboratory
Guruprasad Ananda
Charles C White
Ankit Malhotra
Paul J Michalski
Krishna R Murthy Karuturi
Sumana R Chintalapudi
Casey Acklin, The Jackson Laboratory
Michael Sasner, The Jackson Laboratory
David A Bennett
Philip L De Jager
Gareth R Howell, The Jackson Laboratory
Gregory W. Carter, The Jackson Laboratory


Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized linear mixed model (GLMM) in a Bayesian framework, called