Statistical Methods for detection of genome-wide GxE interactions in longitudinal

描述（由申请人提供）：一些关于各种复杂疾病的全基因组关联研究（GWAS）已经发表，其中收集了大量单核苷酸多态性（snp）的基因型数据，以研究这些snp与疾病之间的关联。虽然在这些GWAS中发现了与不同疾病相关的新位点，但它们通常对这些疾病的遗传风险解释得很少。大部分剩余的性状变异可能是由于基因、环境因素及其相互作用的综合影响。然而，大多数进行全基因组关联研究的研究者在寻找新基因时并未考虑基因-环境（GxE）或基因-基因（GxG）的相互作用。此外，这些研究大多是横断面的。复杂疾病往往是动态的，随着时间的推移随着环境和生理因素的变化或积累而变化。基因对这些疾病的影响也可能随着年龄、发育阶段或其他时代性环境因素等因素的相互作用而随时间变化。在生命的不同阶段，基因变异的影响会显著改变性状的轨迹。因此，不考虑遗传关联纵向变异可能性的研究可能会导致过于简单化的模型

DESCRIPTION (provided by applicant): Several genome-wide association studies (GWAS) have been published on various complex diseases, where genotype data on a large number of single nucleotide polymorphisms (SNPs) are collected to study the association between these SNPs and a disease. Although new loci are found to be associated with different diseases in these GWAS, they generally explain very little of the genetic risk for these diseases. Much of the remaining trait variation is likely to be due to the combined effect of genes, environmental factors, and their interactions. How- ever, most investigators conducting genome-wide association studies do not consider gene-environment (GxE) or gene-gene (GxG) interactions in their search for new genes. Moreover, most of these studies are cross-sectional. Complex diseases are frequently dynamic, varying over time with changing or accumulating environmental and physiological factors. The influence of genes on these diseases may also vary over time through interaction with factors such as age, developmental stage or other time-dependent environmental factors. Variation in the effects of genetic variants at different stages of life could significantly alter the trajectories of traits. Hence, studies that do not consider te possibility of longitudinal variation in genetic associations may lead to over-simplistic models of variant effects and hence lack power to detect them. This is in part due to a current lack of efficient statistical methods and corresponding software to detect the interplay of high-volume genetic data and time-dependent environmental factors. The purpose of this proposal responds to this urgent need by developing advanced statistical methods and efficient computing algorithms to analyze high-throughput data from gene-environment longitudinal studies with data on unrelated individuals as well as families. We propose to develop two efficient methods to detect GxE interactions in longitudinal studies. They are as follows: (1) to develop techniques for robust and efficient estimation of GxE interactions in longitudinal study designs using a likelihood-based dimension reduction approach; (2) to develop a powerful random-effect model for high-dimensional data to detect joint-effects of multiple SNPs and time-dependent environmental factors. The proposed methods are motivated by and to be applied to the Minnesota Center for Twin and Family Research (MCTFR) data, a longitudinal genome-wide study on genes and environments and their interactions with different behavioral traits. We intend to study the etiological underpinnings of substance use disorders (SUDs) derived from various interacting biological and psychosocial factors that work together dynamically over the course of development. Open access user-friendly statistical software will be developed and distributed.