A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Functional Analysis of a Breast Cancer-Associated FGFR2 Single Nucleotide Polymorphism Using Zinc Finger Mediated Genome Editing

The authors thank Barts Charity for funding and Breast Cancer Campaign for funding the Barts Breast Tissue Bank

Genetic variants in FGFR2 and FGFR4 genes and skin cancer risk in the Nurses' Health Study

<p>Abstract</p> <p>Background</p> <p>The human fibroblast growth factor (FGF) and its receptor (FGFR) play an important role in tumorigenesis. Deregulation of the <it>FGFR2 </it>gene has been identified in a number of cancer sites. Overexpression of the <it>FGFR4 </it>protein has been linked to cutaneous melanoma progression. Previous studies reported associations between genetic variants in the <it>FGFR2 </it>and <it>FGFR4 </it>genes and development of various cancers.</p> <p>Methods</p> <p>We evaluated the associations of four genetic variants in the <it>FGFR2 </it>gene highly related to breast cancer risk and the three common tag-SNPs in the <it>FGFR4 </it>gene with skin cancer risk in a nested case-control study of Caucasians within the Nurses' Health Study (NHS) among 218 melanoma cases, 285 squamous cell carcinoma (SCC) cases, 300 basal cell carcinoma (BCC) cases, and 870 controls.</p> <p>Results</p> <p>We found no evidence for associations between these seven genetic variants and the risks of melanoma and nonmelanocytic skin cancer.</p> <p>Conclusion</p> <p>Given the power of this study, we did not detect any contribution of genetic variants in the <it>FGFR2 </it>or <it>FGFR4 </it>genes to inherited predisposition to skin cancer among Caucasian women.</p