Germline Mutations in the Kallikrein 6 Region and Predisposition for Aggressive Prostate Cancer.

Background: There is a need for markers that can specifically identify individuals at increased risk of harboring aggressive forms of prostate cancer (PCa). Methods: We surveyed the Kallikrein ( KLK ) region ( KLK 1-15) for single-nucleotide polymorphisms (SNPs) associated with aggressive PCa (Gleason Score ≥ 8) in 1858 PCa patients. Discovery cohorts (Swiss arm of the European Randomized Study of Screening for PCa, n = 379; Toronto, Canada, n = 540) and a validation cohort (Prostate, Lung, Colorectal and Ovarian [PLCO] screening trial, n = 939) were analyzed. Fine-mapping within the KLK region was carried out by genotyping and imputation in the discovery cohort, whereas PLCO data were provided through database of Genotypes and Phenotypes ( dbGaP ). The influence of SNPs of interest on biochemical-free survival was evaluated in a cohort of localized PCa from the International Cancer Genome Consortium (ICGC; n = 130) analyzed with next-generation sequencing. Single- and multi-SNP association studies, as well as haplotype analyses, were performed. All statistical tests were two-sided. Results: Several SNPs in very strong linkage disequilibrium in the KLK 6 region and located within the same haplotype (rs113640578, rs79324425, rs11666929, rs28384475, rs3810287), identified individuals at increased risk of aggressive PCa in both discovery (odds ratio [OR] = 3.51-3.64, 95% confidence interval [CI] = 2.01 to 6.36, P = 1.0x10 -5 -8.4x10 -6 ) and validation (OR = 1.89-1.96, 95% CI = 0.99 to 3.71, P = .04-.05) cohorts. The overall test of haplotype association was highly statistically significant in each cohort ( P = 3.5x10 -4 and .006, respectively) and in the three data sets combined ( P = 2.3x10 -5 ). These germline SNPs independently predicted relapse in the ICGC cohort (hazard ratio = 3.15, 95% CI = 1.57 to 6.34, P = .001). Conclusions: Our fine-mapping study has identified novel loci in the KLK 6 region strongly associated with aggressive PCa.

A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS).

The analysis of GWAS data has long been restricted to simple models that cannot fully capture the genetic architecture of complex human diseases. As a shift from standard approaches, we propose here a general statistical framework for multi-SNP analysis of GWAS data based on a Bayesian graphical model. Our goal is to develop a general approach applicable to a wide range of genetic association problems, including GWAS and fine-mapping studies, and, more specifically, be able to: (1) Assess the joint effect of multiple SNPs that can be linked or unlinked and interact or not; (2) Explore the multi-SNP model space efficiently using the Mode Oriented Stochastic Search (MOSS) algorithm and determine the best models. We illustrate our new methodology with an application to the CGEM breast cancer GWAS data. Our algorithm selected several SNPs embedded in multi-locus models with high posterior probabilities. Most of the SNPs selected have a biological relevance. Interestingly, several of them have never been detected in standard single-SNP analyses. Finally, our approach has been implemented in the open source R package genMOSS.