A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Mucaki, Eliseos; Caminsky, Natasha; Perri, Ami; Lu, Ruipeng; Laederach, Alain; Halvorsen, Matthew; Knoll, Joan; Rogan, Peter
• Is Part Of: BMC medical genomics. Volume 9:Issue 1(2016)
• Page Start: 1
• Page End: 25
• Publication Date: 2016-12
• Subjects: Information theory -- Hereditary breast and ovarian cancer -- Transcription factor binding -- RNA-binding protein -- Prioritization -- Variants of uncertain significance -- Splicing -- Non-coding -- Next-generation sequencing; Medical genetics -- Periodicals; Genomics -- Periodicals; 616.042
• DOI: 10.1186/s12920-016-0178-5
• Languages: English
• ISSNs: 1755-8794

Abstract

Abstract Background Sequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions. Methods We captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream ofATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, andTP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk, anonymized patients without identified mutations inBRCA1/2 . Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented byin silico and laboratory analysis of UTR structure. Results 15, 311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. An intragenic 32.1 kb interval inBRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels). Conclusions We have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.