Approved research

Comparison of different genetic modeling methods for complex genetic conditions

Lay summary

There is a currently a large focus in patient-facing genomics to accurately identify individuals who are at risk for heritable or partially heritable diseases. To-date, there have been advances in accounting for this heritable risk, but gaps in performance remain as there remains a portion of unexplained inheritance. Our goal is to produce genetic risk prediction models that can accurately assess whether an individual is at an increased risk for disease. We believe that complex phenotypes have differing underlying genetic architecture. Therefore, it may be that different modeling methods will work better for different conditions. For example, for a condition with several high-effect variants, such as age-related macular degeneration, a simple genetic model may perform well with few genetic variants. However, for a condition like Crohn's disease where hundreds of genetic loci have been identified, more complex models with more genetic variants are likely to be more powerful. We will create and test our genetic predisposition models for their validity to predict individuals at-risk of genetically correlated diseases using polygenic risk scores, machine learning, and hand-curated genetic models. These models will then be compared to see which kind of modeling best reflects the genetic architecture of each condition, then see if there are any patterns amongst different conditions.