Project description:Cognitive impairment (CI) is a prevalent neurological condition characterized deficient attention, causal reasoning, learning and/or memory. Many genetic and environmental factors increase risk for CI, and the gut microbiome is increasingly implicated. However, the identity of gut microbes associated with CI risk, their effects on CI, and their mechanisms of action remain unclear. Here we examine the gut microbiome in response to restricted diet and intermittent hypoxia, known environmental risk factors for CI. Modeling the environmental factors together in mice potentiates CI and alters the gut microbiota. Depleting the microbiome by antibiotic treatment or germ-free rearing prevents the adverse effects of environmental risk on CI, whereas transplantation of the risk-associated microbiome into naïve mice confers CI. Parallel sequencing and gnotobiotic approaches identify the pathobiont Bilophila wadsworthia as enriched by the environmental risk factors for CI and as sufficient to induce CI. Consistent with CI-related behavioral abnormalities, B. wadsworthia and the risk-associated microbiome disrupt hippocampal activity, neurogenesis and gene expression. The CI induced by B. wadsworthia and by environmental risk factors is associated with microbiome-dependent increases in intestinal IFNy-producing Th1 cells. Inhibiting Th1 cells abrogates the adverse effects of both B. wadsworthia and environmental risk factors on CI. Together, these findings identify select gut bacteria that contribute to environmental risk for CI in mice by promoting inflammation and hippocampal dysfunction.
Project description:Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a novel framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal muscle, fat, and liver relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWAS from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.
Project description:RATIONALE: Evaluating the knowledge and attitudes of healthy participants toward a new diet and gene test for colorectal cancer risk may help doctors improve acceptance of colorectal cancer screening.
PURPOSE: This clinical trial is studying the knowledge and attitudes of healthy participants toward genetic and environmental risk assessment for colorectal cancer.
Project description:Accumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10-9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10-7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.