Identifying and characterizing de novo tandem repeat mutations and their contribution to autism spectrum disorders

Genetic factors are known to make a large contribution to the risk of Autism Spectrum Disorders (ASD). The heritability of ASD is estimated to be over 50%, and it is estimated that de novo rare variants contribute in about 30% of simplex autism-affected cases. To date, population sequencing studies have been limited to analyzing single nucleotide variants (SNVs), small insertions and deletions (indels), or copy number variants (CNVs). This dissertation expands genetic research to further identify potential genomic regions and pathogenic mutations associated with ASD. Tandem repeats (TRs) are a class of repetitive structural variants composed of 1-20 base pair repeating units. TRs exhibit mutation rates that are orders of magnitude higher than SNPs, indels, or CNVs (6), and thus represent one of the largest sources of human genomic variability (4,5). TRs are often associated with diseases characterized by neurological and developmental symptoms (7–9). for example, Fragile X Syndrome, the most prevalent genetic cause of ASD. To date, direct studies of de novo TR mutations have been limited in population genetic studies. In this dissertation, I present a framework for population-scale characterization of genome-wide de novo TR mutations and their contribution to the genetic etiology of ASD. In my first chapter, I present my bioinformatics pipeline using MonSTR to analyze whole genome sequencing data to identify high- confidence, germline de novo TRs within parent-offspring trios. MonSTR, a novel statistical method, takes genotype likelihood values reported by a TR variant caller as input and estimates the posterior probability of a mutation resulting in a repeat copy number change at each TR loci in each child. In the following chapters, I present the results from identifying de novo TR mutations in autism- affected and unaffected children. I characterize patterns of TR mutational mechanism in the general population, in which I found an average of 54 de novo TRs per individual. I show that ASD affected individuals have a higher number of de novo TR mutations, specifically in regulatory regions and brain-related genes, as well as larger sized mutations, compared to matched unaffected siblings. Lastly, I applied a novel natural selection-based method (SISTR) to identify deleterious de novo TR mutations, and show that autism probands are enriched for rare and pathogenic TR mutations. Overall, this dissertation presents and applies a novel framework for identifying and prioritizing de novo TR mutations in order to better understand TR mutational mechanisms and the genetic etiology of ASD