Approved Research

Comprehensive evaluation of direct contributions of rare and common variants to genetic risk prediction

Lay summary

Complex traits and diseases, like Type II diabetes, coronary artery disease, and schizophrenia, come from a mix of environmental and genetic factors. Until recently, it has been difficult to understand these factors well enough to predict these diseases using genetic information. We want to improve genetic risk predictions for these diseases and others by using advanced techniques on new genetic data, including whole-exome and whole-genome data. We will also consider how environmental factors interact with genetics and how they might be related.

To do this, we'll compare the accuracy of genetic risk scores based on family and population data. If the scores from family data are less accurate, it means environmental factors related to genetics might be affecting the results. These factors can come from population differences or how parents' genes create different environments for their children. By using family data, we can avoid these issues.

On the other hand, gene-environment interactions suggest that the real effects of genes might change depending on other factors, like socioeconomic status. This is because socioeconomic status can affect how genes contribute to diseases or traits. To improve risk predictions, we need to consider these factors and adjust the importance of genetic risks accordingly.

However, our understanding of these interactions is still new, and we don't know if our findings will apply to rare genetic variants or different populations. To address these questions, we will use a variety of approaches, including family-level analysis, on the latest available data. This will help us answer broader questions about how genes and environments are connected.