Integrating Diverse Technologies for Genomic Variant Discovery

Abstract

Accurate detection of variation in the human genome is important for understanding diversity in the human species and for identifying the cause of genetic diseases. The technology for interrogating the genome has vastly improved since the sequencing of the first human genome, improving our ability to accurately detect and characterize more complex variation. However, there are still biases and limitations for all currently available technologies that we must work within. An integrative approach using multiple genotyping or sequencing platforms is a practical strategy that can work within these limitations while improving variation detection beyond what can be achieved with a single technology. In this thesis, I apply an integrative approach to variant detection for different but related scenarios. First, I use Illumina short read sequencing and SNP microarrays to validate variant calls from BGI nanoball short read sequencing to provide a resource of variants present in individuals of Ukrainian decent, a previously underrepresented group in publicly available genome sequencing databases. Second, I study the ability to detect tandem repeat variation genome wide using both short and long read sequencing datasets through the comparison of multiple tandem repeat characterization methods. Lastly, I combine whole genome short read sequencing datasets to understand the relationship between SNP haplotypes and tandem repeat lengths to estimate tandem repeat lengths in individuals with ALS genotyped using SNP microarrays. Taken altogether, these examples represent case studies that demonstrate the utility of an integrative approach to genomic variant detection, analysis, and characterization.