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Long-read DNA and cDNA sequencing identify cancer-predisposing deep intronic variation in tumor-suppressor genes

生物 遗传学 基因 互补DNA DNA测序 抑制器 癌症 计算生物学
作者
Süleyman Gülsüner,Amal Abu Rayyan,Jessica B. Mandell,M. Lee,Greta V Bernier,Barbara M. Norquist,Sarah B. Pierce,Mary‐Claire King,Tom Walsh
出处
期刊:Genome Research [Cold Spring Harbor Laboratory]
标识
DOI:10.1101/gr.279158.124
摘要

The vast majority of deeply intronic genomic variants are benign, but some extremely rare or private deep intronic variants lead to exonification of intronic sequence with abnormal transcriptional consequences. Damaging variants of this class are likely underreported as causes of disease for several reasons: Most clinical DNA and RNA testing does not include full intronic sequences; many of these variants lie in complex repetitive regions that cannot be aligned from short-read whole-genome sequence; and, until recently, consequences of deep intronic variants were not accurately predicted by in silico tools. We evaluated the frequency and consequences of rare deep intronic variants for families severely affected with breast, ovarian, pancreatic, and/or metastatic prostate cancer, but with no causal variant identified by any previous genomic or cDNA-based approach. For 10 tumor-suppressor genes, we used multiplexed adaptive sampling long-read DNA sequencing and cDNA sequencing, based on patient-derived DNA and RNA, to systematically evaluate deep intronic variation. We identified all variants across the full genomic loci of targeted genes, applied the in silico tools SpliceAI and Pangolin to predict variants of functional consequence, and then carried out long-read cDNA sequencing to identify aberrant transcripts. For eight of the 120 (6%) previously unsolved families, rare deep intronic variants in BRCA1 , PALB2 , and ATM create intronic pseudoexons that are spliced into transcripts, leading to premature truncations. These results suggest that long-read DNA and cDNA sequencing can be integrated into variant discovery, with strategies for accurately characterizing pathogenic variants.
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