Over the past decade, decreases in the cost of DNA sequencing has allowed for a surge in the amount of data being generated. This has led to the discovery of genes causal for hundreds of Mendelian disorders and genes associated with many complex disorders. Given the opportunity to use sequencing data to tackle neuropsychiatric diseases with a complex genetic architecture, I take a data-first approach to study two diseases, bipolar disorder and autism spectrum disorder (ASD). Combining information gleaned from next-gen sequencing (NGS) data with the latest analytic methods shines new light on the biology of these diseases.
In the first part of this dissertation, I present a whole-exome analysis of nine affected individuals from four families in which bipolar disorder was transmitted over several generations, and six unrelated, affected individuals. Our results demonstrate the genetic heterogeneity of bipolar disorder and provide support for rare-variant oligogenic disease model. In the second part, I present an approach to identify rare variants associated with autism spectrum disorder (ASD) in a whole-genome sequencing study of 71 individuals diagnosed with ASD and their family members. I demonstrate that by incorporating knowledge of population-wide variant frequencies to analyses of NGS data and taking an approach sensitive to complex family structures, as opposed to utilizing only case-control or trio data, one can identify patterns that would otherwise have been missed and thus gain novel insights into disease etiology.
Finally, I present a mutational burden dataset called SORVA (Significance Of Rare VAriants), which is useful in vetting candidate variants and genes from NGS studies. In effect, my studies of complex disorders using next-gen sequencing show the field is constantly evolving with new computational approaches allowing for many advances being made in the areas of psychiatric disorders and ASD, in particular.