Cell Biology & Molecular Genetics

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Author: search.filters.author.Cao, Chen

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    GWAS and drug targets
    (Springer Nature, 2014-05-20) Cao, Chen; Moult, John
    Genome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets. We find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities. Although GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development.
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    COMPUTATIONAL METHODS IN PROTEIN STRUCTURE, EVOLUTION AND NETWORKS.
    (2013) Cao, Chen; Moult, John; Molecular and Cell Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The advent of new sequencing technology has resulted in the accumulation of a large amount of information on human DNA variation. In order to make sense of these data in the context of biology and medicine, new methods are needed both for analysis and for integration with other resources. In this work: 1) I studied the distribution pattern of human DNA variants across populations using data from the 1000 genomes project and investigated several evolutionary biology questions from the perspective of population genomics. I found population level support for trends previously observed between species, including selection against deleterious variants, and lower frequency of variants in highly expressed genes and highly connected genes. I was also able to show that the correlation between synonymous and non-synonymous variant levels is a consequence of both mutation prevalence variation across the genome and shared selection pressure. 2) I performed a systematic evaluation of the effectiveness of GWAS (Genome Wide Association Studies) for finding potential drug targets and discovered the method is very ineffective for this purpose. I proposed two reasons to explain this finding, selection against variants in drug targets and the relatively short length of drug target genes. I discovered that GWAS genes and drug targets are closely associated in the biological network, and on that basis, developed a machine learning algorithm to leverage the GWAS results for the identification of potential drug targets, making use of biological network information. As a result, I identified some potential drug repurposing opportunities. 3) I developed a method to increase the number of protein structure models available for interpreting the impact of human non-synonymous variants, important for not only the understanding the mechanisms of genetic disease but also in the study of human protein evolution. The method enables the impact of approximately 40% more missense variants to be reliably modeled. In summary, these three projects demonstrate that value of computational methods in addressing a wide range of problems in protein structure, evolution, and networks.