UC San Diego

Over the last decade, a substantial amount of work in genetics has been done with the goal of understanding how genetic variation affects human traits and diseases, primarily via genome-wide association studies (GWAS). Until recently, these studies have focused on associations with single nucleotide variants (SNVs), largely because they have traditionally been easier to genotype. However, the genome contains diverse classes of non-SNV variation such as short tandem repeats (STRs) and structural variants (SVs) that have been shown in some cases to affect human traits. The increasing availability of deep whole genome sequencing (WGS) data, has now enabled algorithms to robustly detect high resolution structural variants and STRs, and the potential for deeper understanding of these variants. Here I present two studies that focus on characterizing the extent and functional impact of SVs and STRs in the human genome. First, I present a study in which I built a comprehensive high quality map of SVs and STRs using over 700 deeply sequenced genomes. I also describe a novel method of filtering variants using reproducibility of genotypes within genetically duplicate sample pairs, and use this information to make insights into the quality of diverse classes of variants called using different methods. I then utilize this high quality map of genetic variation to assess the impact of different classes of variation on gene expression, and show that the functional properties of unique classes of genetic variation is associated with their likelihood to affect genes and linkage to complex traits in humans.