Statistical genetics develop mathematical models and statistical inference methodologies that relate genetic variations to human diseases or phenotypes usually from data collected on large samples of families or individuals. Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and results in the identification of novel drug targets. However, most disease-associated loci GWAS identified lie in non-coding regions of the genome, and it is unclear which gene they regulate and in which cell types or physiological contexts this regulation occurs. Unlike GWAS focusing on analysis of common genetic variants, genetic association studies testing rare variants have emerged as powerful tools having potential directly implicating causal genes for therapeutic target.

In this talk, I will first discuss how we developed a new family data based rare variants association method to identify a novel gene associated with obstructive sleep apnea, the most common sleep-related breathing disorder. Then, I will present the application of our innovative cross-phenotype association analysis of GWAS of brain magnetic resonance imaging (MRI) results to successfully identify a novel gene associated with Alzheimer’s Disease, the most common dementia in elderly. At last, I will discuss my recent study using human genetic variants encoding drug target proteins to provide evidence for the likelihood of side effects in the initial phases of compound development to drive early preclinical toxicology plans. In sum, my talk will show how analytical workflows leveraging large-scale population-based genomic data linked to clinical information can be used to identify drug targets and estimate potential drug side effects in clinical medicine.