Structural genomic variation in patients with congenital dease: Evolution in variant detection and interpretation

Genetic variation: How to separate the chaff from the wheat

Alterations in human DNA can result in congenital disease. Approximately 40-80/1000 live births have a genetic disorder. The field of genome diagnostics is aimed at finding those genomic alterations associated with disease. In the early years of genome diagnostics, the genomes of patients suffering from congenital disease were inspected using a technique known as karyotyping, in which banded chromosomes were analyzed optically under a light-microscope. Karyotyping has a relatively low resolution, which means that karyotypically visible variations have to involve large stretches of DNA composed of at least millions of base pairs. Because of the large size of karyotypically visible chromosomal variations, almost all of these are disease-associated. Such large chromosomal aberrations, mostly affecting numerous genes, are highly unlikely to be present in healthy individuals. Due to the low resolution of karyotyping, the percentage of patients receiving a diagnosis through karyotyping is relatively low; in patients with intellectual disability and/or multiple congenital abnormalities (ID/MCA) 5-8% shows an abnormal karyotype. The introduction of array-based techniques enabled the detection of Copy Number Variations (CNVs) with a resolution of a few thousand base pairs. CNV detection using Array-based techniques in these ID/MCA referrals doubled the amount of diagnosis and was therefore introduced in genomic diagnostics as the first tier test. However, as opposed to karyotyping, not all detectable CNVs cause disease; the genomes of healthy individuals carry numerous CNVs, many of which contain protein coding genes. Recently, next generation sequencing techniques like whole exome sequencing and whole genome sequencing (WES, WGS) were introduced into diagnostics. These techniques can explore the human genome at the highest resolution. Especially WGS, which is able to detect basically all types of genomic variants, holds great potential to replace all other techniques. Next to a significant increase in diagnosis, this will also result in a significant increase in variants that have to be interpreted. One of the challenges in the field of clinical genetics is distinguishing between neutral and pathogenic variations. Within the pile of variations detected, the chaff has to be separated from the wheat. But what is needed to do this successfully? If we know the precise structure of a variation, and the mechanism by which it arose, can we make a better prediction of whether it is pathogenic? What does the revolution in genetic techniques to detect variants mean for the field of clinical genetics? These questions will be addressed in this thesis.