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Sequencing individual genomes with recurrent deletions reveals allelic architecture and disease loci for autosomal recessive traits

By Bo Yuan, Katharina Schulze, Nurit Assia Batzir, Jefferson Sinson, Hongzheng Dai, Wenmiao Zhu, Francia Bocanegra, Chin-To Fong, Jimmy Holder, Joanne Nguyen, Christian Schaaf, Yaping Yang, Weimin Bi, Christine Eng, Chad Shaw, James R. Lupski, Pengfei Liu

Posted 22 Feb 2021
medRxiv DOI: 10.1101/2021.02.16.21251842

In medical genetics, discovery and characterization of new "disease genes" and alleles depend on patient ascertainment strategies to enrich previously uncharacterized alleles. Here, we present a novel strategy of new allele and gene discovery for recessive/biallelic disease traits. In this approach, patients with large recurrent genomic deletions mediated by nonallelic homologous recombination (NAHR) are sequenced, and new discoveries are revealed in the hemizygous chromosomal regions in trans to the large deletion, essentially enabling haploid genomic segment genetics. We demonstrate through computational analyses that a collection of 30 large recurrent genomic deletions scattered in the human genome contribute to more than 10% of individual disease load for 2.13% of all known "recessive disease genes". We performed meta-analyses for all literature reported patients affected with the 13 genes whose carrier burden are predicted to be almost exclusively from large recurrent genomic deletions. The results suggest that current sequencing efforts for personal genomes with large recurrent deletions is under-appreciated. By retrospective analyses of previously undiagnostic exome sequencing (ES) data on 69 subjects harboring 26 types of recurrent deletions, probable diagnostic variants were uncovered in genes including COX10, ERCC6, PRRT2 and OTUD7A, demonstrating new disease allele/gene/mechanism characterization. Findings from this study support the contention that more whole genome sequencing (WGS) may further resolve molecular diagnoses and provide evidence for multi-locus pathogenic variation (MPV). Such analyses benefit all stakeholders in both research development and patient clinical care.

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