Recent advances in genomic technologies have provided unparalleled opportunities for identifying the relationship of genetic variation to health and disease. Most complex human diseases are influenced by interacting networks of multiple genes (QTL) and environmental factors. Interactions (gene-gene and gene- environment) and genetic mechanisms (e.g., genomic imprinting, X-linked effects, pleiotropy) play an important role in the genetic control of complex diseases. The ideal analysis of complex diseases is to simultaneously consider multiple genomic loci, environmental factors, and possible interactions rather than one (or a few) locus at a time. Despite recent methodological developments, genome-wide analysis of interacting QTL remains a challenge. The objectives of the proposed research are to develop new Bayesian methods and software for simultaneously identifying multiple genes, environmental factors, and their interactions, and exploring important genetic mechanisms (e.g., genomic imprinting, X-linked effects, pleiotropy). The proposed approach incorporates all advantages of generalized linear models and hierarchical modeling into genome-wide analysis of interacting genes, allowing us to deal with various types of phenotypes, to simultaneously analyze many correlated variables, and to develop stable and flexible algorithms and software.
The specific aims of our proposal are to 1) develop new Bayesian generalized linear models and algorithms for mapping interacting QTL in experimental crosses and population association studies;2) develop new Bayesian generalized linear models and algorithms for simultaneously detecting a) interacting QTL and genomic imprinting, b) interacting QTL on autosomes and X chromosome, and c) interacting QTL for multiple correlated traits;3) evaluate the proposed methods by extensive simulation studies, apply the proposed methods to multiple real data sets, and propose Bayesian methods of model checking and comparison for multiple interacting QTL analysis;and 4) incorporate the proposed new methods into our R/qtlbim software (www.qtlbim.org) and release the extended R/qtlbim for public use. In this proposal, we focus on inbred animal models of human diseases because they continue to be a powerful approach to understanding the pathological mechanisms of human diseases. However, the proposed methods can also be extended to association studies in humans. The project is expected to make an important impact on the field of genetics/genomics of complex diseases.
Most complex human diseases are influenced by interacting networks of multiple genes and environmental factors. Interactions (gene-gene and gene-environment) and genetic mechanisms (e.g., genomic imprinting, X-linked effects, pleiotropy) play an important role in the genetic control of complex diseases. The goal of this research proposal is to develop new statistical methods and computer software to unravel the complexity of these interacting risk factors. The proposed methods can simultaneously identify multiple genes, relevant environmental factors and their interactions, and explore important genetic mechanisms for various types of phenotypes. The project is expected to make an important impact on the field of genetics/genomics of complex diseases.
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