Grant: Project Grant

The promise of human genetics is that we will be able to predict an individual's risk for many diseases. The challenge to achieve this goal is to map the genotype, the genetic makeup an individual inherits from their parents, to phenotype, the set of traits an individual possesses, including information about diseases they have or are at risk of getting. Understanding the heritability of a trait involves exploring how much of that trait is passed down from parents to offspring through genes. The predictability of a trait based on genetic information hinges on the specific genomic regions, like genes, and their interactions with one another. Environmental factors also influence the expression of genes, modifying the heritability of certain traits. Recently, databases of genetic information about millions of individual humans (biobanks) have been created, which provides a new opportunity to explore and understand human genetics like never before. However, working with these enormous databases requires novel computational and statistical methods that are faster and more sensitive. To address this issue, we developed a statistical framework for genetic analysis, SLMM, that is orders of magnitude faster and much more accurate than the current state-of-the-art. To advance our understanding of human genetics and improve human health, we now propose to implement SLMM to analyze large biobanks to map biological pathways and find modifiers of disease, which will help explain which individuals are at risk for a disease based on their genome.