Systematic analysis of paralogous regions in 41,755 exomes uncovers clinically relevant variation

Wouter Steyaert, Lonneke Haer-Wigman, Rolph Pfundt, Debby Hellebrekers, Marloes Steehouwer, Juliet Hampstead, Elke Boer, Alexander Stegmann, Helger Yntema, Erik-Jan Kamsteeg, Han Brunner, Alexander Hoischen and Christian Gilissen ()
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Wouter Steyaert: Radboud University Medical Center
Lonneke Haer-Wigman: Radboud University Medical Center
Rolph Pfundt: Radboud University Medical Center
Debby Hellebrekers: Maastricht University Medical Center + , Department of Clinical Genetics
Marloes Steehouwer: Radboud University Medical Center
Juliet Hampstead: Radboud University Medical Center
Elke Boer: Radboud University Medical Center
Alexander Stegmann: Maastricht University Medical Center + , Department of Clinical Genetics
Helger Yntema: Radboud University Medical Center
Erik-Jan Kamsteeg: Radboud University Medical Center
Han Brunner: Radboud University Medical Center
Alexander Hoischen: Radboud University Medical Center
Christian Gilissen: Radboud University Medical Center

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract The short lengths of short-read sequencing reads challenge the analysis of paralogous genomic regions in exome and genome sequencing data. Most genetic variants within these homologous regions therefore remain unidentified in standard analyses. Here, we present a method (Chameleolyser) that accurately identifies single nucleotide variants and small insertions/deletions (SNVs/Indels), copy number variants and ectopic gene conversion events in duplicated genomic regions using whole-exome sequencing data. Application to a cohort of 41,755 exome samples yields 20,432 rare homozygous deletions and 2,529,791 rare SNVs/Indels, of which we show that 338,084 are due to gene conversion events. None of the SNVs/Indels are detectable using regular analysis techniques. Validation by high-fidelity long-read sequencing in 20 samples confirms >88% of called variants. Focusing on variation in known disease genes leads to a direct molecular diagnosis in 25 previously undiagnosed patients. Our method can readily be applied to existing exome data.

Date: 2023
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DOI: 10.1038/s41467-023-42531-9

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