Abstract
PurposeOne of the greatest challenges currently facing those studying Mendelian disease is identifying the pathogenic variant from the long list produced by a next-generation sequencing test. We investigate the predictive ability of homozygosity mapping for identifying the regions likely to contain the causative variant.MethodsWe use 179 homozygous pathogenic variants from three independent cohorts to investigate the predictive power of homozygosity mapping.ResultsWe demonstrate that homozygous pathogenic variants in our cohorts are disproportionately likely to be found within one of the largest regions of homozygosity: 80% of pathogenic variants are found in a homozygous region that is in the ten largest regions in a sample. The maximal predictive power is achieved in patients with <8% homozygosity and variants >3 Mb from a telomere; this gives an area under the curve (AUC) of 0.735 and results in 92% of the causative variants being in one of the ten largest homozygous regions.ConclusionThis predictive power can be used to prioritize the list of candidate variants in gene discovery studies. When classifying a homozygous variant the size and rank of the region of homozygosity in which the candidate variant is located can also be considered as supporting evidence for pathogenicity.
Highlights
The advent of high-throughput next-generation sequencing has been a boon to the study of Mendelian disease
This predictive power can be used to prioritize the list of candidate variants in gene discovery studies
It is important to differentiate the two cases as disease-causing variants are likely to be in regions of recent homozygosity; variants in ancestral regions of homozygosity have been exposed to selection in a homozygous state for sufficient time for selection to act on them
Summary
We demonstrate that homozygous pathogenic variants in our cohorts are disproportionately likely to be found within one of the largest regions of homozygosity: 80% of pathogenic variants are found in a homozygous region that is in the ten largest regions in a sample.
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