Rare, Evolutionarily Unlikely Missense Substitutions in ATM
详细信息 查看全文
- 作者：Sean V. Tavtigian ; Peter J. Oefner ; Davit Babikyan ; Anne Hartmann ; Sue Healey ; Florence Le Calvez-Kelm ; Fabienne Lesueur ; Graham B. Byrnes ; Shu-Chun Chuang ; Nathalie Forey ; Corinna Feuchtinger ; Lydie Gioia ; Janet Hall ; Mia Hashibe ; Barbara Herte ; S ; rine McKay-Chopin ; Alun Thomas ; Maxime P. Vallé
- 刊名：The American Journal of Human Genetics
- 出版年：2009
- 出版时间：9 October 2009
- 年：2009
- 卷：85
- 期：4
- 页码：427-446
- 全文大小：907 K
文摘
The susceptibility gene for ataxia telangiectasia, ATM, is also an intermediate-risk breast-cancer-susceptibility gene. However, the spectrum and frequency distribution of ATM mutations that confer increased risk of breast cancer have been controversial. To assess the contribution of rare variants in this gene to risk of breast cancer, we pooled data from seven published ATM case-control mutation-screening studies, including a total of 1544 breast cancer cases and 1224 controls, with data from our own mutation screening of an additional 987 breast cancer cases and 1021 controls. Using an in silico missense-substitution analysis that provides a ranking of missense substitutions from evolutionarily most likely to least likely, we carried out analyses of protein-truncating variants, splice-junction variants, and rare missense variants. We found marginal evidence that the combination of ATM protein-truncating and splice-junction variants contribute to breast cancer risk. There was stronger evidence that a subset of rare, evolutionarily unlikely missense substitutions confer increased risk. On the basis of subset analyses, we hypothesize that rare missense substitutions falling in and around the FAT, kinase, and FATC domains of the protein may be disproportionately responsible for that risk and that a subset of these may confer higher risk than do protein-truncating variants. We conclude that a comparison between the graded distributions of missense substitutions in cases versus controls can complement analyses of truncating variants and help identify susceptibility genes and that this approach will aid interpretation of the data emerging from new sequencing technologies.