Multivariate Modelling and Inference of Dependent and High-Dimensional Data in Recent Genetic Studies

点击翻译按钮获取中文摘要

Statistical genetics is undergoing the same transition to big data that several branches of applied statistics are experiencing, and this transition is accelerating with the advent of high-throughput genomic experiments. My research activities have evolved accordingly throughout the years to address the new challenges brought on by the emerging genomic technologies. This research program consists of three innovative research themes dealing with such challenges, with a focus on the development of multivariate statistical tools for dependent genomics data. ******Next-generation sequencing (NGS) technology is now providing an exhaustive catalog of DNA sequence variation and the challenge becomes understanding the phenotypic consequences of these variants. The first objective deals with the optimal use of multiple phenotypes in NGS association studies. Multiple correlated phenotypes often measure the same underlying trait and can bear a more direct relationship with the disease diagnosis. By providing flexible modeling of the phenotypes dependence structure via copula models, and exploiting Kernel trick (i.e. machine learning methods for exploring phenotype-genotype relationship), this research axis lays out a broad modeling of the underlying relationship between correlated phenotypes and genetic variants. The framework will increase power in identifying novel genetic variants responsible for human complex diseases, which may help to better understand disease etiology.******Data-regularization is appealing for high-throughput genomics data to detect/select a smaller subset of relevant predictors for an outcome. Such data display also heterogeneity which is of interest to many researchers but it tends to be overlooked by existing predictive models. The second objective implements pillar algorithms of modern computational statistics within penalized robust regression models to capture within-subject dependence and select/detect relevant heterogeneous predictors for dependent genomics data. Such prediction models will be a useful tool to build genetic risk scores that can be very useful for risk stratification and clinical decision-making. *** ***The third objective is a long-term goal which couples statistical tools from the first and second research axes to build a unified copula-based association framework capable to identify heterogeneous genetic variants while providing flexible modeling of the phenotypes dependence. It will gain more insight on how genetic variation is explaining the phenotypic variation; this is known as “missing heritability” problem and is encountered by most existing genetic studies.******The lack of efficient statistical methods to analyze modern genomics data is a major bottleneck faced by the genomics research community to better understand the related biology. I strongly believe that the strategies I propose in this proposal will be very useful for analyzing and integrating such complex data, and will help with maximizing their utility.**