Discovery of phenotypic networks from genotypic association studies with application to obesity.

Genome-wide Association Studies (GWAS) have resulted in many discovered risk variants for several obesity-related traits. However, before clinical relevance of these discoveries can be achieved, molecular or physiological mechanisms of these risk variants needs to be discovered. One strategy is to perform data mining of phenotypically-rich data sources such as those present in dbGAP (database of Genotypes and Phenotypes) for hypothesis generation. Here we propose a technique that combines the power of existing Bayesian Network (BN) learning algorithms with the statistical rigour of Structural Equation Modelling (SEM) to produce an overall phenotypic network discovery system with optimal properties. We illustrate our method using the analysis of a candidate SNP data set from the AMERICO sample, a multi-ethnic cross-sectional cohort of roughly 300 children with detailed obesity-related phenotypes. We demonstrate our approach by showing genetic mechanisms for three obesity-related SNPs.

Expression signature of IFN/STAT1 signaling genes predicts poor survival outcome in glioblastoma multiforme in a subtype-specific manner.

Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over-expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype-dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi-gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype-specific molecular mechanisms for GBM tumor progression and treatment response.

Using protein abundance to indicate underlying mRNA expression levels in E.coli: an SEM modelling approach.

Do steady-state protein levels accurately predict mRNA levels? Based on the central dogma (DNA RNA protein) current protein levels are representative of mRNA present at an earlier time. However, most cellular mRNA protein comparative studies try to relate steady-state protein levels to current mRNA levels in cells. Protein steady-states are more correctly related to protein production, protein degradation and other complex cellular conditions. Using Structural Equation Modelling (SEM) we relate linear protein measurements to latent mRNA in E.coli. This method can be used to find the optimal protein measurements that explain underlying mRNA expression, and better understand the proteomic and transcriptomic relationship in E.coli gene expression.

Associations of obesity genes with obesity-related outcomes in multiethnic children.

BACKGROUND AND AIMS: Genome-wide association studies (GWAS) have identified several loci that are associated with body mass index (BMI = kg/m(2)). However, little is known regarding whether the genetic basis of BMI differs among children of diverse racial/ethnic backgrounds, how the cumulative effect of these genes influences weight, or the contribution of these variants to body composition. This study examined the association between 17 GWAS-identified loci located in 16 genes and body-composition phenotypes in a multiethnic pediatric sample and evaluated the association of a composite genetic risk score with these phenotypes. METHODS: Anthropometric measures of BMI, waist circumference and waist-to-hip ratio were obtained in a sample of 298 children. Lean and fat mass were obtained from dual-energy X-ray absorptiometry (DXA). Genotypes of 17 single nucleotide polymorphisms (SNPs) were tested for association with the phenotypic measures, adjusted by standard covariates and estimates of genetic admixture. RESULTS: Both SNPs rs8050136 and rs9939609 in FTO were associated with BMI and waist circumference in a direction opposite to that observed among adults, and an inverse association was detected between the risk variant in MC4R and total lean body mass. Lean body mass mediated the association between TMEM18 and BMI. The association between the genetic risk score and body composition differed according to ethnic/racial classification. CONCLUSIONS: Our findings suggest that genetic associations with BMI among children are different from those in adults, that some loci may operate through lean body mass, and that genetic risk scores will not have universal applicability across ethnic/racial groups.

The TRIM5{alpha} genotype of rhesus macaques affects acquisition of simian immunodeficiency virus SIVsmE660 infection after repeated limiting-dose intrarectal challenge.

It has recently been shown that polymorphism at the rhesus macaque TRIM5 locus can affect simian immunodeficiency virus (SIV) replication. Here we show that TRIM5 alleles can also affect acquisition of SIVsmE660. Animals coexpressing the TRIM5(TFP) and TRIM5(CypA) alleles took significantly longer to become infected with SIVsmE660, but not SIVmac239, after repeated limiting-dose intrarectal challenge than did animals expressing other TRIM5 allele combinations. Our results indicate that the TRIM5 alleles can be a barrier to productive infection and that this should be taken into account when designing acquisition studies using SIVsmE660 or related viruses.

A Hybrid Bayesian Network/Structural Equation (BN/SEM) Modeling Approach for Detecting Physiological Networks for Obesity-related Genetic Variants.

GWAS studies have been successful in finding genetic determinants of obesity. To translate discovered genetic variants into new therapies or prevention strategies, molecular or physiological mechanisms need to be discovered. One strategy is to perform data mining of data sets with detailed phenotypic data, such as those present in dbGAP (database of Genotypes and Phenotypes) for hypothesis generation. We propose a novel technique that combines the power and computational efficiency of existing Bayesian Network (BN) learning algorithms with the statistical rigor of Structural Equation Modeling (SEM) to produce an overall system that searches the space of potential networks and evaluates promising candidates using standard SEM model selection criteria. We demonstrate our method using the analysis of a candidate SNP data set from the AMERICO sample, a multi-ethnic cross-sectional cohort of roughly three hundred children with detailed obesity-related phenotypes. We demonstrate our approach by showing genetic mechanisms for three obesity-related SNPs.