Browsing by Author "Woo, Hyung Jun"
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- ItemCollective interaction effects associated with mammalian behavioral traits reveal genetic factors connecting fear and hemostasis.(0000-00-00) Woo, Hyung Jun; Reifman, JaquesBACKGROUND Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes These traits however are often highly polygenic with individual loci contributing only small effects to the overall association The polygenicity makes it challenging to explain for example the widely observed comorbidity between stress and cardiac disease METHODS We present an algorithm for inferring the collective association of a large number of interacting gene variants with a quantitative trait Using simulated data we demonstrate that by taking into account the non uniform distribution of genotypes within a cohort we can achieve greater power than regression based methods for high dimensional inference RESULTS We analyzed genome wide data sets of outbred mice and pet dogs and found neurobiological pathways whose associations with behavioral traits arose primarily from interaction effects carboxylated coagulation factors and downstream neuronal signaling were highly associated with conditioned fear consistent with our previous finding in human post traumatic stress disorder PTSD data Prepulse inhibition in mice was associated with serotonin transporter and platelet homeostasis and noise induced fear in dogs with hemostasis CONCLUSIONS Our findings suggest a novel explanation for the observed comorbidity between PTSD anxiety and cardiovascular diseases key coagulation factors modulating hemostasis also regulate synaptic plasticity affecting the learning and extinction of fear
- ItemGenetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks.(0000-00-00) Woo, Hyung Jun; Reifman, JaquesBACKGROUND Common metabolic diseases including type 2 diabetes coronary artery disease and hypertension arise from disruptions of the body s metabolic homeostasis with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity Although genome wide association studies have revealed many genomic loci robustly associated with these diseases biological interpretation of such association is challenging because of the difficulty in mapping single nucleotide polymorphisms SNPs onto the underlying causal genes and pathways Furthermore common diseases are typically highly polygenic and conventional single variant based association testing does not adequately capture potentially important large scale interaction effects between multiple genetic factors METHODS We analyzed moderately sized case control data sets for type 2 diabetes coronary artery disease and hypertension to characterize the genetic risk factors arising from non additive collective interaction effects using a recently developed algorithm discrete discriminant analysis We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group RESULTS In contrast to non interacting SNP mapping which produced few genome wide significant loci our analysis revealed extensive arrays of pathways many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies They comprised cell stress and apoptotic pathways for insulin producing cells in type 2 diabetes processes covering different atherosclerotic stages in coronary artery disease and elements of both type 2 diabetes and coronary artery disease risk factors cell cycle apoptosis and hemostasis associated with hypertension CONCLUSIONS Our results support the view that non additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs
- ItemQuantitative modeling of virus evolutionary dynamics and adaptation in serial passages using empirically inferred fitness landscapes.(2013-12-31) Woo, Hyung Jun; Reifman, JaquesWe describe a stochastic virus evolution model representing genomic diversification and within host selection during experimental serial passages under cell culture or live host conditions The model incorporates realistic descriptions of the virus genotypes in nucleotide and amino acid sequence spaces as well as their diversification from error prone replications It quantitatively considers factors such as target cell number bottleneck size passage period infection and cell death rates and the replication rate of different genotypes allowing for systematic examinations of how their changes affect the evolutionary dynamics of viruses during passages The relative probability for a viral population to achieve adaptation under a new host environment quantified by the rate with which a target sequence frequency rises above 50 was found to be most sensitive to factors related to sequence structure distance from the wild type to the target and selection strength host cell number and bottleneck size For parameter values representative of RNA viruses the likelihood of observing adaptations during passages became negligible as the required number of mutations rose above two amino acid sites We modeled the specific adaptation process of influenza A H5N1 viruses in mammalian hosts by simulating the evolutionary dynamics of H5 strains under the fitness landscape inferred from multiple sequence alignments of H3 proteins In light of comparisons with experimental findings we observed that the evolutionary dynamics of adaptation is strongly affected not only by the tendency toward increasing fitness values but also by the accessibility of pathways between genotypes constrained by the genetic code