Sequence based identification of genetic variation associated with intellectual disability
Sammanfattning: Intellectual disability (ID) is a common neurodevelopmental condition, often caused by genetic defects. De novo variation (DNV) is an important cause of ID, especially in severe or syndromic forms of the disorder. Next generation sequencing has been a successful application for finding pathogenic variation in ID patients. The main focus of this thesis is to use whole exome sequencing (WES) and whole genome sequencing (WGS) to identify pathogenic variants in undiagnosed ID patients. In Paper I, WES was used in family trios to identify pathogenic DNVs in patients diagnosed with ID in combination with epilepsy. This work led to the identification of several DNVs in both new and known disease genes, including the first report of variation in the HECW2 gene in association with neurodevelopmental disorder and epilepsy. Paper II is the first independent validation of PIGG as a disease-causing gene in patients with developmental disorder. We used WES to identify the homozygous variation in PIGG, and transcriptome analysis as well as flow-cytometry studies were used to validate the pathogenicity of the PIGG variation. We discovered that PIGG variation give different effects in different cell types, contributing new insights into the disease mechanism. Paper III is also an application of WES in trio families with patients diagnosed with ID in order to identify causal variants, a strategy similar to that of Paper I. Several pathogenic variants were identified in this study; in particular, the gene NAA15 is highlighted as a new disease gene, and was recently confirmed in independent studies. This study also adds evidence to support that variation in the PUF60 gene is causing the symptoms in patients with Verheij syndrome. In Paper IV, WGS was used to analyze families with consanguineous marriages. All families in this study had been previously analyzed with WES without finding a disease cause. A number of new disease-causing variants were identified in the study, including a first validation of FRMD4A as a disease-associated gene. This study also shows that WGS performs better than WES in finding variants, even for variants in coding parts of the genome.
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