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[An improved association analysis pipeline for tumor susceptibility variant in haplotype amplification area]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2020; 40:1493-1499. [PMID: 33118521 PMCID: PMC7606235 DOI: 10.12122/j.issn.1673-4254.2020.10.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Haplotype amplification on germline variants is suggested to imply potential selective advantages and clonal expansion susceptibility and has become an important signature for seeking cancer susceptibility gene.Here we propose an improved association method that fully considers the haplotype amplification status. METHODS The haplotype amplification status was estimated by the variant allelic frequencies.We adopted a permutation test on variant allelic frequencies to divide the candidate variants into multiple groups.A likelihood clustering method was then applied to establish the neighborhood system of the hidden Markov random field framework.A filtering pipeline was introduced into the proposed method to further refine the candidate variants, including a Wilson's interval filter and a false discovery rate controller.The final candidate set along with the haplotype amplification status was collapsed into the weighted virtual sites for association tests. RESULTS Through simulated tests on a series of datasets, we compared the type Ⅰ error rates of different minor allele frequencies, which stably fell within 2%, suggesting good robustness of the algorithm.In addition, we compared another 5 published association approaches for Type-Ⅰ and Type-Ⅱ error rates with the proposed method, which resulted in the error rates all within 2%, demonstrating significant advantages and a good statistical ability of the proposed method. CONCLUSIONS The proposed method can accurately identify tumor susceptibility variants in haplotype amplification area with good robustness and stability.
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Xu M, Zhao Z, Zhang X, Gao A, Wu S, Wang J. Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures. Molecules 2018; 23:molecules23082055. [PMID: 30115851 PMCID: PMC6222865 DOI: 10.3390/molecules23082055] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 08/02/2018] [Accepted: 08/07/2018] [Indexed: 12/22/2022] Open
Abstract
Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.
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Affiliation(s)
- Mingzhe Xu
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
- Department of Automation, College of Intelligent Manufacturing and Automation, Henan University of Animal Husbandry and Economy, Zhengzhou 450011, China.
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Zhongmeng Zhao
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Xuanping Zhang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Aiqing Gao
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Shuyan Wu
- Department of Network Technology, College of Intelligent Manufacturing and Automation, Henan University of Animal Husbandry and Economy, Zhengzhou 450011, China.
| | - Jiayin Wang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
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Geng Y, Zhao Z, Zhang X, Wang W, Cui X, Ye K, Xiao X, Wang J. An improved burden-test pipeline for identifying associations from rare germline and somatic variants. BMC Genomics 2017. [PMID: 29513197 PMCID: PMC5657102 DOI: 10.1186/s12864-017-4133-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying rare germline and somatic variants associated with cancer progression is an important research topic in cancer genomics. Although many approaches are proposed for rare variant association study, they are not fit for cancer sequencing data due to multiple issues, such as overly relying on pre-selection, losing sight of interacting hotspots, etc. RESULTS In this article, we propose an improved pipeline to identify germline variant and somatic mutation interactions influencing cancer susceptibility from pair-wise cancer sequencing data. The proposed pipeline, RareProb-C performs an algorithmic selection on the given variants by incorporating the variant allelic frequencies. The interactions among the variants are considered within the regions which are limited by a four-gamete test. Then it filters singular cases according to the posterior probability at each site. Finally, it outputs the selected candidates that pass a collapse test. CONCLUSIONS We apply RareProb-C on a series of carefully constructed simulation cases and it outperforms six existing genetic model-free approaches. We also test RareProb-C on 429 TCGA ovarian cancer cases, and RareProb-C successfully identifies the known highlighted variants which are considered increasing disease susceptibilities.
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Affiliation(s)
- Yu Geng
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Jinzhou Medical University, Jinzhou, Liaoning, 121001, China
| | - Zhongmeng Zhao
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China. .,Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
| | - Xuanping Zhang
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Wenke Wang
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Xingjian Cui
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Kai Ye
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Xiao Xiao
- Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,State Key Laboratory of Cancer Biology, Xijing Hospital of Digestive Diseases, Xi'an, 710032, Shaanxi, China
| | - Jiayin Wang
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China. .,Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
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Freytag S, Bickeböller H. Comparison of three summary statistics for ranking genes in genome-wide association studies. Stat Med 2013; 33:1828-41. [PMID: 24323702 DOI: 10.1002/sim.6063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Revised: 09/11/2013] [Accepted: 11/18/2013] [Indexed: 01/30/2023]
Abstract
Problems associated with insufficient power have haunted the analysis of genome-wide association studies and are likely to be the main challenge for the analysis of next-generation sequencing data. Ranking genes according to their strength of association with the investigated phenotype is one solution. To obtain rankings for genes, researchers can draw from a wide range of statistics summarizing the relationships between variants mapped to a gene and the phenotype. Hence, it is of interest to explore the performance of these statistics in the context of rankings. To this end, we conducted a simulation study (limited to genes of equal sizes) of three different summary statistics examining the ability to rank genes in a meaningful order. The weighted sum of squared marginal score test (Pan, 2009), RareCover algorithm (Bahtia et al., 2010) and the elastic net regularization (Zou and Hastie, 2005) were chosen, because they can handle common as well as rare variants. The test based on the score statistic outperformed both other methods in almost all investigated scenarios. It was the only measure to consistently detect genes with interacting causal variants. However, the RareCover algorithm proved better at identifying genes including causal variants with small effect sizes and low minor allele frequency than the weighted sum of squared marginal score test. The performance of the elastic net regularization was unimpressive for all but the simplest scenarios.
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Affiliation(s)
- Saskia Freytag
- Institute of Genetic Epidemiology, University of Göttingen, Humboltallee 32, Medical School, 37073 Göttingen, Germany
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