Project 2 - Deciphering the Molecular Drivers of Common Forms of Human Infertility Using Integrative Genomic, Cellular, and Phenomic Approaches

PROJECT ABSTRACT Infertility is a common condition that affects over one-tenth of couples attempting to conceive, and the costs of managing infertility exceed 18 billion US dollars worldwide. There are significant gaps in our knowledge of the underlying mechanisms and risk factors for infertility, and these gaps impede the development of targeted treatments that address the root causes of infertility. Project 2 will fill these gaps by conducting human genetic studies of unprecedented scale to study infertility, and related reproductive traits. Through collaborations with large biobanks worldwide, we will access data from over 18 million individuals to identify genetic variants associated with risk for female and male infertility as well as those associated with a number of fertility-related traits, including sex-hormone concentrations, pubertal onset, and pituitary gland and olfactory bulb size (Aim 1). We will then use state-of-the-art computational approaches to pinpoint the specific variants, genes, pathways, and cell types that influence these conditions and traits (Aim 2). These approaches include fine-mapping, pathway analyses, and intersection of genetic association data across studies, and they will be fueled by data from Project 2, Aim 1, as well as data on genetic variants associated with rare infertility conditions identified in Project 1, data on gene expression generated by the Core, as well as extensive publicly available data on genetic linkage patterns, gene expression, protein-protein interactions, and other experimental findings. We will then integrate all these findings to understand how infertility and the reproductive traits studied in Aim 1 relate to each other, with the rare infertility conditions in Project 1, and with general reproductive and non-reproductive health (Aim 3). Phenome-wide association studies will analyze a large number of phenotypes to reveal novel phenotypic associations for the genetic variants studied, investigations of pleiotropy will determine the extent to which the genetic causes of these conditions and traits overlap, and the technique of Mendelian randomization will be used to infer causal relationships. By leveraging the power of large-scale human genetics, this project will lead to a comprehensive understanding of the biology of fertility and infertility, identify targets for fertility treatments, and reveal the pathways by which reproductive health influences overall health.