Identification of pathogenic mutations in neurodegenerative disorders: bioinformatic analysis of next generation sequencing data

Abstract

Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and ataxias belong to a large group of disorders, characterized by progressive neuronal cell death in specific parts of the brain. They exhibit Mendelian and also complex inheritance patterns, thus a deeper understanding of genetic factors underlying disease pathogenesis is crucial to improve new and more targeted therapy methods. Next generation sequencing is a highly efficient and powerful method for the identification of disease-causing variants in complex disorders. In the framework of this study, one dominant and 12 recessive pedigrees were subjected to exome sequencing. Selection of families was performed by considering factors, such as consanguinity and the presence of more than one affected member in the family. Homozygosity mapping was performed for determination of homozygous stretches. Candidate variants were validated in family members and segregation of mutations in the families was shown. The data and bioinformatic analyses methods described above were for the first time adapted, developed and applied to several families in our laboratory within this thesis work. The analysis of next generation sequencing data is expected to unravel the genetic factors that cause neurodegeneration in these complex syndromes.