Developing pedigree-based strategies to analyse whole genome sequencing data for complex disorders: learning from schizophrenia

Abstract

Complex genetic disorders are impacted by a range of DNA variation. Next generation sequencing (NGS) allows for the direct examination of this variation, but large sample numbers are required to identify rare variants in unrelated cohorts. Pedigree-based cohorts can partly resolve this, as densely affected pedigrees are likely to be influenced by the same collection of rare variants. This thesis examined approaches for disease-gene prioritisation from pedigree-based NGS data for complex disorders. As a model phenotype, schizophrenia was considered. A spectrum of rare and common variants is known to increase individual risk for schizophrenia, although identifying such variants remains challenging. This work has wider implications, making substantial contributions in aiding researchers to elucidate the genetic architecture of pedigree-based NGS data for complex genetic disorders in psychiatry and beyond.