Simulation, modelling, and estimation of direct and indirect genetic architectures and intergenerational dynamics - Project Summary Genetic association studies have become the de facto standard study design for identifying genetic variants associated with complex traits. Thousands of such studies, enrolling tens of millions of people worldwide, have been conducted to date. Their results have been used for myriad purposes ranging from basic science to clinical translation and including studies of trait genetic architectures. However, recent work from our groups and others have shown that a widely held belief about genetic association study results is frequently incorrect. Specifically, the assumption that underlying the loci associated with the trait of interest are causal variants directly impacting the trait. We and others have established that these associations also contain substantial contributions from confounding factors: gene-environment correlation due to indirect genetic effects (effects of relatives’ genotypes mediated through the environment) and population stratification, as well as correlations with other genetic variants due to assortative mating and population structure. Here we aim to develop methods to quantify and resolve this issue, dissecting these often-overlooked components of intergenerational dynamics when studying trait genetic architectures. In Specific Aim 1, we are set to develop a comprehensive simulation framework. This framework will efficiently model realistic intergenerational dynamics on a large scale, crucial for understanding the nuances of genetic transmission across generations. It will serve to quantify and correct the biases inherent in current methodologies used for studying genetic architecture, providing a more accurate lens through which to view the genetic underpinnings of traits. In Specific Aim 2, we develop a theoretical framework for understanding the joint impact of indirect genetic effects and assortative mating. This then enables us to develop methods that separate these statistically confounded factors that are key to explaining intergenerational transmission of traits. By leveraging the unique properties of family data, we will be able to build an accurate picture of the different factors that contribute to both genetic associations and to intergenerational health and social inequalities. Central to the success of this project is our team of diverse collaborators. Comprising experts from various geographical locations and spanning junior to senior investigators, our team brings together a wealth of experience in this niche area. The multidisciplinary expertise of our faculty members, encompassing genetics, computational biology, psychology, social sciences, and other relevant fields, ensures a comprehensive approach to tackling these complex genetic issues. By addressing the intricate intergenerational dynamics and refining the analysis of genetic architectures, we are not only enhancing the scientific understanding of genetics but also paving the way for more precise and meaningful applications in health and medicine.
