Project description:Complex traits such as susceptibility to diseases are determined in part by variants at multiple genetic loci. Genome-wide association studies can identify these loci, but most phenotype-associated variants lie distal to protein-coding regions and are likely involved in regulating gene expression. Understanding how these genetic variants affect complex traits depends on the ability to predict and test the function of the genomic elements harboring them. Community efforts such as the ENCODE Project provide a wealth of data about epigenetic features associated with gene regulation. These data enable the prediction of testable functions for many phenotype-associated variants.
Project description:Knowledge of the inherited risk for cancer is an important component of preventive oncology. In addition to well-established syndromes of cancer predisposition, much remains to be discovered about the genetic variation underlying susceptibility to common malignancies. Increased knowledge about the human genome and advances in genotyping technology have made possible genome-wide association studies (GWAS) of human diseases. These studies have identified many important regions of genetic variation associated with an increased risk for human traits and diseases including cancer. Understanding the principles, major findings, and limitations of GWAS is becoming increasingly important for oncologists as dissemination of genomic risk tests directly to consumers is already occurring through commercial companies. GWAS have contributed to our understanding of the genetic basis of cancer and will shed light on biologic pathways and possible new strategies for targeted prevention. To date, however, the clinical utility of GWAS-derived risk markers remains limited.
Project description:Completion of the human genome a decade ago laid the foundation for: using genetic information in assessing risk to identify individuals and populations that are likely to develop cancer, and designing treatments based on a person's genetic profiling (precision medicine). Genome-wide association studies (GWAS) completed during the past few years have identified risk-associated single nucleotide polymorphisms that can be used as screening tools in epidemiologic studies of a variety of tumor types. This led to the conduct of epigenome-wide association studies (EWAS). This article discusses the current status, challenges and research opportunities in GWAS and EWAS. Information gained from GWAS and EWAS has potential applications in cancer control and treatment.
Project description:Genome-wide association studies (GWAS) provide a powerful new approach to identify common, low-penetrance susceptibility loci without prior knowledge of biologic function. Results from three GWAS conducted in populations of European ancestry are available for colorectal cancer (CRC). These studies have identified 11 disease loci that, for the majority, were not previously suspected to be related to CRC. The proportions of the familial and population risks explained by these loci are small and they currently are not useful for risk prediction. However, the power of these studies was low, indicating that a number of other loci may be identified in new ongoing GWAS, and in pooled analyses. Thus, the risk prediction ability of susceptibility markers identified in GWAS for CRC may improve as more variants are discovered. This may, in turn, have important implications for targeting high-risk individuals for colonoscopy screening.
Project description:Pathway analysis of genome-wide association studies (GWAS) offer a unique opportunity to collectively evaluate genetic variants with effects that are too small to be detected individually. We applied a pathway analysis to a bladder cancer GWAS containing data from 3,532 cases and 5,120 controls of European background (n?=?5 studies). Thirteen hundred and ninety-nine pathways were drawn from five publicly available resources (Biocarta, Kegg, NCI-PID, HumanCyc, and Reactome), and we constructed 22 additional candidate pathways previously hypothesized to be related to bladder cancer. In total, 1421 pathways, 5647 genes and ?90,000 SNPs were included in our study. Logistic regression model adjusting for age, sex, study, DNA source, and smoking status was used to assess the marginal trend effect of SNPs on bladder cancer risk. Two complementary pathway-based methods (gene-set enrichment analysis [GSEA], and adapted rank-truncated product [ARTP]) were used to assess the enrichment of association signals within each pathway. Eighteen pathways were detected by either GSEA or ARTP at P?0.01. To minimize false positives, we used the I(2) statistic to identify SNPs displaying heterogeneous effects across the five studies. After removing these SNPs, seven pathways ('Aromatic amine metabolism' [P(GSEA)?=?0.0100, P(ARTP)?=?0.0020], 'NAD biosynthesis' [P(GSEA)?=?0.0018, P(ARTP)?=?0.0086], 'NAD salvage' [P(ARTP)?=?0.0068], 'Clathrin derived vesicle budding' [P(ARTP)?=?0.0018], 'Lysosome vesicle biogenesis' [P(GSEA)?=?0.0023, P(ARTP)<0.00012], 'Retrograde neurotrophin signaling' [P(GSEA)?=?0.00840], and 'Mitotic metaphase/anaphase transition' [P(GSEA)?=?0.0040]) remained. These pathways seem to belong to three fundamental cellular processes (metabolic detoxification, mitosis, and clathrin-mediated vesicles). Identification of the aromatic amine metabolism pathway provides support for the ability of this approach to identify pathways with established relevance to bladder carcinogenesis.
Project description:This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process.