Genetic association studies have been successful in identifying >1,000 genetic loci associated with complex disease traits in human populations. However, it remains a central challenge to interpret the vast amounts of data generated by GWAS studies towards an improved understanding of disease markers and, thus, mechanisms, which are critical for translating GWAS findings into genomic medicine applications enabling improvements in diagnostics, therapies, and outcomes. Recent efforts to incorporate prior biological information into GWAS analysis has greatly enhanced the interpretation of GWAS findings by providing biological frameworks for prioritizing associations, and for interpreting multiple associated loci within the contexts of biological networks and pathways. We recently demonstrated that position-specific evolutionary priors could be incorporated into analysis of GWAS results to prioritize variants that were more reproducible across studies. We propose to develop, investigate, and apply evolutionary informed integrative methods that embrace and leverage the genetic complexity of common disease. We hypothesize that position-specific evolutionary features can be incorporated into multiscale biological pathway and network analysis, and that evolutionary informed pathway and network analysis can be applied to existing GWAS and clinical data sets to identify mechanisms giving rise to complex disease phenotypes in populations and individuals. We propose to develop and evaluate these hypotheses through pursuit of the following specific aims: (1) Develop novel evolutionary-informed pathway and network analysis method for interpreting GWAS findings. (2) Apply novel methods to established GWAS and clinical data for T2D to elucidate disease mechanisms underlying the genetic architecture across populations. (3) Develop a public database and software tool to enable evolutionary informed network analysis of GWAS findings for the broader research community.
Type 2 diabetes and other common diseases are characterized as having complex genetic architectures involving up to many hundreds or thousands of genetic factors. Genetic association studies are being performed to uncover these factors, but it remains challenging to use the result of these studies to learn more about the genetic basis of these diseases. We propose to develop and apply advanced evolutionary and integrative genomic methods to explore the existing genetic association data for type 2 diabetes and further elucidate the underlying genetic causes of disease.
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