L'utilisation d'approches statistiques multidimensionnelles pour maximiser l'utilité de plusieurs phénotypes corrélés dans les études génétiques


Karim Oualkacha

Université du Québec à Montréal


Domaine : génétique humaine

Programme chercheurs-boursiers - Junior 1

Concours 2015-2016

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.

In this research program, I propose new statistical approaches to better capture genetic information that will be provided in the whole genome sequencing era. In fact, 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. Consequently, I suggest statistical genetic methods that take advantage of optimizing phenotypes in order to improve power to localize genes of interest. Such methods tend to exploit correlational structures to identify genes jointly influencing suites of related phenotypes or search for new phenotypes more proximal to gene action, which are designed to highlight independent measures underlying the correlations among a set of related traits.

Finally, the new methodologies will be applied to real data sets on complex genetic disorders, such as, osteoporosis, schizophrenia and bipolar disorder.