An Expanded Association Approach for Rare Germline Variants with Copy-Number Alternation

被引:1
|
作者
Geng, Yu [1 ,4 ,6 ]
Zhao, Zhongmeng [1 ,4 ]
Cui, Daibin [1 ,4 ]
Zheng, Tian [2 ,3 ,4 ]
Zhang, Xuanping [1 ,4 ]
Xiao, Xiao [4 ,5 ]
Wang, Jiayin [2 ,3 ,4 ]
机构
[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Shaanxi, Peoples R China
[2] Xi An Jiao Tong Univ, Sch Management, Xian, Shaanxi, Peoples R China
[3] Shaanxi Engn Res Ctr Med & Hlth Big Data, Xian, Shaanxi, Peoples R China
[4] Xi An Jiao Tong Univ, Inst Data Sci & Informat Qual, 28 West Xianning Rd, Xian, Shaanxi, Peoples R China
[5] Xijing Hosp Digest Dis, State Key Lab Canc Biol, Xian, Shaanxi, Peoples R China
[6] Jinzhou Med Univ, Jinzhou, Peoples R China
基金
美国国家科学基金会;
关键词
Cancer genomics; Association approach; Germline variant; Copy-number alternation; Hidden markov random field model;
D O I
10.1007/978-3-319-56154-7_9
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Tumorigenesis is considered as a complex process that is often driven by close interactions between germline variants and accumulated somatic mutational events. Recent studies report that some somatic copy-number alternations show such interactions by harboring germline susceptibility variants under potential selection in clonal expansions. Incorporating these interactions into genetic association approach could be valuable in not only discovering novel susceptibility variants, but providing insight into tumor heterogeneity and clinical implications. To address this need, in this article, we propose RareProbG, an expanded version of a computational method, which is designed for identifying rare germline susceptibility variants located in the somatic allelic amplification or loss of heterozygosity regions. RareProb-G is based on a hidden Markov random field model. The interactions among germline variants and somatic events are modeled by a neighborhood system, which is bounded by a t-test on variant allelic frequencies. Each variant is assigned four hidden states, which represent the regional status and causal/ neutral status, respectively. A hidden Markov model is also introduced to estimate the initial values of the hidden states and unknown model parameters. To verify this approach, we conduct a series of simulation experiments under different configurations, and RareProb-G outperforms than RareProb on both sensitivity and specificity.
引用
收藏
页码:81 / 94
页数:14
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