J. Comput. Biol. - Markov logic networks in the analysis of genetic data.

Tópicos

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Resumo

Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics.

Resumo Limpo

complex nonaddit genet interact common can critic determin phenotyp genomewid associ studi gwas similar statist studi linkag data howev assum addit model gene interact look genotypephenotyp associ statist method view compound effect multipl gene phenotyp sum influenc gene often miss substanti part herit effect method use biolog knowledg under mechan model approach artifici intellig ai field incorpor determinist knowledg model perform statist analysi can appli includ prior knowledg genet analysi chose use general approach markov logic network mlns combin determinist knowledg statist analysi use simpl logist regressiontyp mlns can replic result tradit statist method also show abl go beyond find independ marker link phenotyp use joint infer without independ assumpt method appli genet data yeast sporul complex phenotyp gene interact addit detect previous identifi loci associ sporul method identifi four loci smaller effect sinc effect sporul small four loci detect method account depend marker due gene interact show gene interact can detect use complex model can use general framework incorpor system biolog genet

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