Grant: Discovery Grants Program - Individual

Research on the genetics of complex traits overwhelmingly focuses on the additive effects of genes. Yet, animal studies have shown that non-additive effects, in particular homozygosity effects and interaction with the environment, can strongly shape complex traits. Most human populations display restricted ranges of homozygosity levels, yielding low power to measure whether and how homozygosity relates to complex traits. Founder populations have helped identify many genes for Mendelian diseases and can also be advantageous in the study of complex traits. Advantages of founder populations include greater genetic homogeneity, longer genomic segments shared identical-by-descent (IBD) and a wider range of homozygosity levels. In the French-Canadian founder population, it is also possible to reconstruct genealogies going back over four centuries using the BALSAC database. However, novel analytical and computational methods are still lacking to efficiently utilize information from extensive genealogical data in combination with genomic data. Thus, the potential of genealogical data to improve study power and accuracy has been underutilized. The overall goal of this proposal is to develop approaches to best capture non-additive effects of genetic factors, leveraging the strengths of founder populations. My specific objectives for the next 5 years are to: 1) develop methods to combine genomic and genealogical data in the estimation of homozygosity-by-descent (HBD); 2) implement the methods created in 1) into the GENLIB R package designed to analyze genealogical data and maintained by my research team; and 3) develop a procedure to assess gene-environment interactions using data from the historical geographic information system (GIS) currently being incorporated into the i-BALSAC platform. Objective 1 will include two approaches: a) using genealogical characteristics to improve the detection of true runs of homozygosity (ROHs) in dense genomic data, and b) incorporating genealogical parameters into a hidden Markov model (HMM) of IBD. Additive genetic effects, captured by polygenic risk scores, have advanced our ability to predict complex health-related traits. However, additive effects cannot capture the complete effect of genes on human health. The results of this proposal will provide novel tools to increase efficiency and power to study non-additive genetic effects in founder populations, ultimately improving personalized medicine by incorporating non-additive genetic effects into risk prediction. The French-Canadian founder population represents an especially powerful platform for this purpose because of key demographic events in its history and its current size. This study contributes to the optimal integration of the i-BALSAC platform in genetic research and will also benefit genetic research in other Canadian and international founder populations.