This module builds on the advanced quantitative genetics in Module 14: Advanced Quantitative Genetics, but now focusing on the analysis of genetic data for qualitative phenotypes, such as disease status from case-control or cohort studies, and interpretation of the ensuing results particularly with respect to risk prediction.
The module considers, in detail, the statistical genetics of binary disease with emphasis on the equivalences and relationships between different models. It contrasts and synthesizes the traditional viewpoints of quantitative geneticists and epidemiologists. The module demonstrates the caution needed in interpreting “precision medicine” risk predictors for common complex diseases.
Topics will include: risk models on different scales including the observed (or disease) scale and the liability threshold scale; estimation of heritability from familial risk ratios; estimation of the contribution of individual and multiple risk loci to disease; estimation of variance attributable to genome-wide SNPs individually and together; approaches for the analysis of rare genetic variants; polygenic modeling; risk profile scoring; power; GxE and pleiotropy.
Participants should have basic R programming skills, matrix algebra, statistical methods and analysis of GWAS data.