Analyses of Next-Generation Sequencing Data to Identify Genes Associated with Complex Neuropsychiatric Disorders

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

Perch exposure and predation risk: a comparative study in passerines

Singing birds optimize signal transmission by perching in exposed locations. However, conspicuous singing may be risky, and previous research has found that individuals trade off singing performance with song perch exposure. We studied the relationship between predation risk (degree of concealment, height in tree or shrub, and distance to the forest edge) and time allocated to singing and vigilance in a group of 13 passerine species living in an East African savanna. Concealed birds sang more and were less vigilant. Vigilance increased as distance to the forested edge increased, but distance had no effect on time allocated to singing. Body size was significantly correlated with vigilance but not singing; larger passerines were more sensitive to both relative concealment and the distance to the forest edge, while song was influenced by neither of these factors. Perch height had no effect on either behavior. Our results suggest that birds modify vigilance and, to some extent, singing behavior to minimize their exposure to predators