Splicing and multifactorial analysis of intronic BRCA1 and BRCA2 sequence variants identifies clinically significant splicing aberrations up to 12 nucleotides from the intron/exon boundary
✍ Scribed by Phillip J. Whiley; Lucia Guidugli; Logan C. Walker; Sue Healey; Bryony A. Thompson; Sunil R. Lakhani; Leonard M. Da Silva; kConFab Investigators; Sean V. Tavtigian; David E. Goldgar; Melissa A. Brown; Fergus J. Couch; Amanda B. Spurdle
- Publisher
- John Wiley and Sons
- Year
- 2011
- Tongue
- English
- Weight
- 293 KB
- Volume
- 32
- Category
- Article
- ISSN
- 1059-7794
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✦ Synopsis
Clinical management of breast cancer families is complicated by identification of BRCA1 and BRCA2 sequence alterations of unknown significance. Molecular assays evaluating the effect of intronic variants on native splicing can help determine their clinical relevance. Twentysix intronic BRCA1/2 variants ranging from the consensus dinucleotides in the splice acceptor or donor to 53 nucleotides into the intron were identified in multiple-case families. The effect of the variants on splicing was assessed using HSF matrices, MaxEntScan and NNsplice, followed by analysis of mRNA from lymphoblastoid cell lines. A total of 12 variants were associated with splicing aberrations predicted to result in production of truncated proteins, including a variant located 12 nucleotides into the intron. The posterior probability of pathogenicity was estimated using a multifactorial likelihood approach, and provided a pathogenic or likely pathogenic classification for seven of the 12 spliceogenic variants. The apparent disparity between experimental evidence and the multifactorial predictions is likely due to several factors, including a paucity of likelihood information and a nonspecific prior probability applied for intronic variants outside the consensus dinucleotides. Development of prior probabilities of pathogenicity incorporating bioinformatic prediction of splicing aberrations should improve identification of functionally relevant variants and enhance multifactorial likelihood analysis of intronic variants.