A Bayesian multilocus association method: allowing for higher-order interaction in association studies

TY - JOUR

T1 - A Bayesian multilocus association method: allowing for higher-order interaction in association studies

AU - Albrechtsen, Anders

AU - Castella, Sofie

AU - Andersen, Gitte

AU - Hansen, Torben

AU - Pedersen, Oluf

AU - Nielsen, Rasmus

N1 - Keywords: Bayes Theorem; Chromosome Mapping; Computer Simulation; Environment; Genetic Variation; Genotype; Markov Chains; Models, Genetic; Monte Carlo Method; Polymorphism, Single Nucleotide

PY - 2007

Y1 - 2007

N2 - For most common diseases with heritable components, not a single or a few single-nucleotide polymorphisms (SNPs) explain most of the variance for these disorders. Instead, much of the variance may be caused by interactions (epistasis) among multiple SNPs or interactions with environmental conditions. We present a new powerful statistical model for analyzing and interpreting genomic data that influence multifactorial phenotypic traits with a complex and likely polygenic inheritance. The new method is based on Markov chain Monte Carlo (MCMC) and allows for identification of sets of SNPs and environmental factors that when combined increase disease risk or change the distribution of a quantitative trait. Using simulations, we show that the MCMC method can detect disease association when multiple, interacting SNPs are present in the data. When applying the method on real large-scale data from a Danish population-based cohort, multiple interactions are identified that severely affect serum triglyceride levels in the study individuals. The method is designed for quantitative traits but can also be applied on qualitative traits. It is computationally feasible even for a large number of possible interactions and differs fundamentally from most previous approaches by entertaining nonlinear interactions and by directly addressing the multiple-testing problem.

AB - For most common diseases with heritable components, not a single or a few single-nucleotide polymorphisms (SNPs) explain most of the variance for these disorders. Instead, much of the variance may be caused by interactions (epistasis) among multiple SNPs or interactions with environmental conditions. We present a new powerful statistical model for analyzing and interpreting genomic data that influence multifactorial phenotypic traits with a complex and likely polygenic inheritance. The new method is based on Markov chain Monte Carlo (MCMC) and allows for identification of sets of SNPs and environmental factors that when combined increase disease risk or change the distribution of a quantitative trait. Using simulations, we show that the MCMC method can detect disease association when multiple, interacting SNPs are present in the data. When applying the method on real large-scale data from a Danish population-based cohort, multiple interactions are identified that severely affect serum triglyceride levels in the study individuals. The method is designed for quantitative traits but can also be applied on qualitative traits. It is computationally feasible even for a large number of possible interactions and differs fundamentally from most previous approaches by entertaining nonlinear interactions and by directly addressing the multiple-testing problem.

U2 - 10.1534/genetics.107.071696

DO - 10.1534/genetics.107.071696

M3 - Journal article

C2 - 17435250

SN - 1943-2631

VL - 176

SP - 1197

EP - 1208

JO - Genetics

JF - Genetics

IS - 2

ER -