Date of Award

Spring 5-15-2021

Author's School

Graduate School of Arts and Sciences

Author's Department

Biology & Biomedical Sciences (Computational & Systems Biology)

Degree Name

Doctor of Philosophy (PhD)

Degree Type



Cardiovascular disease (CVD) is a complex disease responsible for more deaths worldwide than any other cause according to the World Health Organization. Genetic association studies for CVD and related risk factors have successfully identified hundreds of loci associated with these complex diseases and traits, although much of their heritability remains unexplained. Structural variants (SVs) - including insertions, deletions, duplications, and inversions - are an understudied class of genomic variation that have the potential to explain much of the missing heritability of CVD and other complex traits. Here, we discuss advances emerging from the study of SVs in the context of CVD genetics using Finnish genomes.Variant interpretation is an important step both in clinical sequencing pipelines and rare variant association studies of the genetics of complex traits such as CVD. However, due to the difficulty in detection and genotyping of SVs as well as the broad diversity of SV types, there has been a scarcity of methods for interpreting these variants relative to those available for point mutations. Here, we describe SVScore, a novel method for SV impact prediction by aggregating existing genome-wide scores while incorporating SV type and transcript annotations. Using allele frequency in Finns as a proxy for pathogenicity, we show SVScore’s efficacy and uncover interesting signatures of selection among SVs. Furthermore, a genome-wide association study of SVs by another member of our group led to the observation of a strong association between mitochondrial DNA copy number (MT-CN) and several cardiometabolic risk factors for CVD. We identify several nuclear genomic loci associated with MT-CN and use a modified Mendelian randomization framework to provide evidence for a causal role for MT-CN in determining serum insulin levels. We further leverage UK Biobank data to replicate the association between MT-CN and cardiometabolic traits in an independent data set and show that adjusting for blood cell counts largely eliminates this signal. In summary, our work suggests that MT-CN is in large part a proxy for blood cell counts, and thus inflammatory status, in its association with metabolic traits.


English (en)

Chair and Committee

Ira M. Hall Nathan Stitziel

Committee Members

Carlos Cruchaga, Heather Lawson, Adam Locke, Nancy Saccone,