The emergence of new high-throughput genotyping technologies, such as Next Generation Sequencing (NGS), allows the study of the human genome at an unprecedented depth and scale. The discovery of germline rare variants (RVs) through NGS is a very challenging issue in the field of human genetics. Since RVs have extremely low frequencies, traditional strategies that analyze one variant at a time are underpowered for detecting associations with RVs. Gene-level or region-based statistics can provide a first step in the analysis of RVs that can lead to further experimental validation. Bayesian analysis is not well developed for RV analysis. Our goal in this thesis is to develop such approaches and show their interests for germline RV analyses in the context of case-control studies. Chapter 1 gives a general overview about NGS data analysis and methods for association tests with RV data. In Chapter 2, we propose a novel region-based statistical approach based on the Bayes Factor (BF) to assess evidence of association between a set of RVs located on the same genomic region and a disease outcome in the context of case-control design. We derive the theoretical null distribution of the BF under our prior setting. Informative priors are introduced using prior evidence of association from a Kolmogorov-Smirnov test statistic. In Chapter 3, we introduce a Bayesian procedure to control the False Discovery Rate (BFDR) in the context of genome-wide inference. We develop a simulation program, sim1000G, to generate RV data similar to the 1,000 genomes sequencing project and assess our BFDR procedure. Our simulation studies show that the new BF statistic outperforms standard methods (SKAT, SKAT-O, Burden test) in case-control studies with moderate sample sizes and is equivalent to them under large sample size scenarios. Chapter 4 concludes this thesis with an extension of the BF approach that integrates individual-level and variant-level covariates by using a Bayesian regression approach and inference based on the Integrated Nested Laplace Approximation (INLA). Finally, the interests of our methodological developments are illustrated throughout the thesis by real data applications to a lung cancer case-control study seeking RV association with known and novel cancer genes.Ph.D
