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A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

Many joint-SNVs (single-nucleotide variants) analysis methods were proposed to tackle the ‘missing heritability’ problem, which emphasizes that the joint genetic variants can explain more heritability of traits and diseases. However, there is still lack of a systematic comparison and investigation on the relative strengths and weaknesses of these methods. In this paper, we evaluated their performance on extensive simulated data generated by varying sample size, linkage disequilibrium (LD), odds ratios (OR), and minor allele frequency (MAF), which aims to cover almost all scenarios encountered in practical applications. Results indicated that a method called Statistics-space Boundary Based Test (S-space BBT) showed stronger detection power than other methods. Results on a real dataset of gastric cancer for Korean population also validate the effectiveness of the S-space BBT method.

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Acknowledgements

This work was supported by the Zhi-Yuan chair professorship start-up grant (WF220103010) from Shanghai Jiao Tong University.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jinxiong Lv, Shikui Tu & Lei Xu

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

    Lei Xu

Authors
  1. Jinxiong Lv
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  2. Shikui Tu
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  3. Lei Xu
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Corresponding author

Correspondence to Lei Xu .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

Appendix

Appendix

As the Table 3 showed, 6 genes belong to the group C. The somatic mutation of DCTN1 was discovered both in the primary cancer and the metastatic cancer [17]. The PSCA had been detected via the GWAS for Japanese and Korean populations [18] and conferred susceptibility to urinary bladder cancer in US and European populations [31]. While the GAL3ST1 was identified via the whole exome sequencing for people from same family [20]. The FRS2/FRS3 is related to the autophosphorylated FGFRs in the FGF signaling pathway, which is associated with the later stage for gastric cancer [21]. Hasegawa et al. found the altered expression of PPP2R1B in the lymph node metastasis for intestinal-type gastric cancer [22]. The expression of B4GALNT1 plays an crucial role in the molecular mechanisms underlying the regulation of cancer-associated GM2 expression in stomach and colon [23]. The group B consists of 7 genes. The FBXO11 induces the BCL6 degradation to suppress the tumorigenicity for the diffuse large B-cell lymphomas [28]. The MYLK2 is associated with the colorectal cancer [30]. These genes in group B might be common pathogenic genes for both gastric cancer and other kinds of cancers. As for the group A, to our limited knowledge, no literature clarified whether they are associated with cancers or not, which might play a potential role in the occurrence of gastric cancer on the genetic level.

Table 3. Literature survey for the top 20 genes
Full size table

In summary, there are 65% genes associated with the gastric cancer and other kinds of cancers. It indicates that the S-space BBT is reliable in the joint-SNVs analysis.

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Lv, J., Tu, S., Xu, L. (2017). A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_56

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