Multivariate Modelling and Inference in Genetic Studies

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Whole genome sequencing is becoming a standard means of genome scanning for human complex diseases. Thus, almost all genetic variability in each person will be measured. However, statistical power to detect genes of interest is a challenge due to the number and frequency of much of the genetic variation. Many human complex diseases manifest themselves through correlated quantitative phenotypes (traits). For instance, obesity is associated with weight, waist circumference and waist to hip ratio. However, direct measured trait along (e. g. weight, height) may not be informative enough to detect susceptibility variants. For instance, body mass index (BMI), defined as weight divided by squared height, is a typical example of a very informative composite phenotype that has shown to be linked with genetic variants not associated with weight nor height. Consequently, given the comprehensive data on DNA variation that will be provided by whole genome sequencing, we need to optimize other aspects of the study design, such as the choice of phenotypes to be studied. This ''phenotype optimization'' problem is believed to be part of the way that genotypes map to the phenotypes for complex diseases. Statistical genetic methods that take advantage of optimizing phenotypes may improve power to localize genes of interest by exploiting correlational structures to identify genes jointly influencing suites of related phenotypes or by using new phenotypes more proximal to gene action, which are designed to highlight independent measures underlying the correlations among a set of related traits.