Methods for leveraging family-based designs and summary data to elucidate complex trait genetics - To better understand genetic basis of complex human traits, two fundamentally different and complementary designs employed in genome-wide association studies (GWAS) are population-based and family-based designs. With the advent of biobanks and large-scale biomedical databases, recent years have seen an explosion in genetic studies of adult traits/diseases, and consequently, a rapid advancement in methodology for population-based designs that these biobanks depend on. In contrast, methods for family-based designs have received little to no attention although they play an important role in the investigation of genetic basis of low-prevalence/rare disorders and of child health outcomes. Analysis methods based on family-based designs can protect against population stratification and admixture, and can be more powerful than a population-based study of similar sample size. Another consequence of large-scale biobanks is the public availability of aggregate-level genotype-trait association results (or GWAS summary statistics) for a wide spectrum of complex human traits, including molecular traits that are intermediate between genotype and a disease-related trait. Methods that can leverage GWAS summary statistics to understand biology underlying diseases are in high demand since they are nearly as efficient and avoid logistical/ethical concerns related to sharing individual-level data. In this application, I propose a research program of developing novel statistical methods and open-access tools for genetic epidemiology studies, with a particular focus on family based designs. Some of these methods/tools will leverage only association summary statistics to innovatively integrate omics with disease data, thereby helping improve understanding of regulatory mechanisms underlying human health. We seek to address some of the open problems of human trait genetics, including methodological challenges in identifying non-additive genetic effects (e.g. gene-gene interaction, gene-environment interaction, parent-of-origin effect), effects of rare variants, and in prioritizing causal variants through integrative omics. We will bring obscure mathematical functions from statistical literature to real public health applications while illustrating them on existing databases. This research program will make three distinct contributions: support methodological advancement of family-based designs that overcome challenges related to sampling; efficient methods/tools that allow genomic researchers to conduct genetic epidemiology studies using publicly available summary data even in resource-poor environments; and help train graduate students recruited annually by the Johns Hopkins School of Public Health through research engagement and skill-building opportunities. In the last 5 years, I have built a research profile in family-based genetic studies alongside population-based ones, have developed cutting-edge methods based on summary-level data, have enabled data-driven policy-making via reproducible data science methods/tools, and have acquired mentoring skills. My multidisciplinary training and my prior experience put me in a unique position to successfully complete this program.
