NOVEL CANDIDATE LOCI FOR LATE-ONSET ALZHEIMER’S DISEASE FROM BAYESIAN MIXED MODELING OF WHOLE-GENOME AND WHOLE-EXOME SEQUENCING

Recent initiatives such as the Alzheimer's Disease Sequencing Project (ADSP) have created sequencing-based study cohorts for late-onset Alzheimer's disease. While this resource provides nucleotide-level resolution of genetic variants, it suffers from relatively small sample size and population substructure that limits association power. We addressed this limitation by devising a method of genome-wide association that implements a generalized linear mixed model in a Bayesian context. Our approach, Bayes-GLMM, has four main features: (1) support of categorical, binary and quantitative variables; (2) cohesive integration of previous GWAS results for related traits; (3) correction for sample relatedness by mixed modeling; and (4) model estimation by both Markov chain Monte Carlo (MCMC) sampling and maximal likelihood estimation. We applied Bayes-GLMM to the ADSP whole-genome sequencing cohort, which includes categorical disease diagnoses of 570 individuals drawn from 111 families. We also analyzed ADSP whole-exome data from 9134 individuals to identify rare and common coding variants. From the whole-genome sequencing cohort, we identified four variants in three loci significantly associated with Alzheimer's disease. The loci were not identified using traditional methods. The four variants (rs10490263, rs74944275, rs149372995, rs140233081) are located in intergenic regions with the closest genes not previously associated with AD. The two linked variants lie between the genes PRKAR1B and PDGFA. These proteins are localized to the glial-vascular unit, further implicating vascular function in modifying susceptibility to AD. Analysis of whole-exome sequences validated association of many known loci (e.g. TREM2, ABCA7) and identified potentially novel variants, including both rare and common alleles. This work provides new candidates loci for Alzheimer's disease, obtained from the first implementation of a flexible, generalized mixed model approach in a Bayesian framework for sequence-based association studies.