title: A robust model-free approach for rare variants association studies incorporating gene-gene and gene-environmental interactions.
abstract: Recently more and more evidence suggest that rare variants with much lower minor allele frequencies play significant roles in disease etiology. Advances in next-generation sequencing technologies will lead to many more rare variants association studies. Several statistical methods have been proposed to assess the effect of rare variants by aggregating information from multiple loci across a genetic region and testing the association between the phenotype and aggregated genotype. One limitation of existing methods is that they only look into the marginal effects of rare variants but do not systematically take into account effects due to interactions among rare variants and between rare variants and environmental factors. In this article, we propose the summation of partition approach (SPA), a robust model-free method that is designed specifically for detecting both marginal effects and effects due to gene-gene (G×G) and gene-environmental (G×E) interactions for rare variants association studies. SPA has three advantages. First, it accounts for the interaction information and gains considerable power in the presence of unknown and complicated G×G or G×E interactions. Secondly, it does not sacrifice the marginal detection power; in the situation when rare variants only have marginal effects it is comparable with the most competitive method in current literature. Thirdly, it is easy to extend and can incorporate more complex interactions; other practitioners and scientists can tailor the procedure to fit their own study friendly. Our simulation studies show that SPA is considerably more powerful than many existing methods in the presence of G×G and G×E interactions.
authors.affiliation: Department of Statistics, Columbia University, New York, New York, United States of America.
authors.forename: Ruixue
authors.initials: R
authors.lastname: Fan
authors.affiliation: Department of Statistics, Columbia University, New York, New York, United States of America.
authors.forename: Shaw-Hwa
authors.initials: SH
authors.lastname: Lo
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004843:Epistasis, Genetic; D005787:Gene Frequency; D059647:Gene-Environment Interaction; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D017422:Sequence Analysis, DNA