Investigating penetrance of rare genetic variants using population cohorts

Abstract

The same genetic variant found in different individuals can cause a spectrum of phenotypes, with some individuals showing no signs of any clinical illness, and some displaying severe illness. Variants that cause this can be said to show incomplete penetrance, where the related genotype either causes clinical disease or not, or they can be said to display variable expressivity, in which the clinical symptoms can vary across a spectrum. Incomplete penetrance and variable expressivity are both thought to be influenced by a large number of factors, including genetic modifiers, epigenetics, and environmental factors. Many thousands of genetic variants have been identified as causal of monogenic disorders, mostly determined through small clinical studies, and thus the penetrance and expressivity of these variants may be overestimated when compared to their effect in the general population. With the wealth of population cohort data currently available, the penetrance and expressivity of such genetic variants can be investigated across a much wider contingent, potentially helping to reclassify variants that were previously thought to be completely penetrant. This thesis aims to investigate the penetrance and expressivity of rare genetic variants in large population cohorts, and to potentially identify any genetic modifiers that could also affect the phenotypic effect of these variants, including the presence of other rare variants, and the aggregation of small effect common variants. We show that putatively damaging variants in a large number of genes are present at a higher rate than previously expected in healthy population cohorts. Furthermore, we show that as an aggregate, individuals who carry one of these variants have sub-clinical phenotypes related to the traits seen in clinical disease cases with variants in similar genes. We also show that the penetrance and expressivity of these rare variants can be modified by the presence of other rare variants in similar genes, and through common genetic variant, aggregated as polygenic scores. We then investigate methods of identifying rare non-coding variants that could be potential genetic modifiers.