A comparison study on multivariate methods for joint-SNVs association analysis
Refereed conference paper presented and published in conference proceedings

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AbstractSingle nucleotide variants (SNVs) have been discovered that they play crucial roles in disease pathogenesis as genetic factors. Featured by analyzing multiple SNVs in a biological module (e.g. exon, gene, etc.) collectively, the joint-SNVs studies are increasingly attractive in genome-wide association studies (GWASs), for which extensive efforts have been devoted to pursue effective multivariate methods. In this paper, we first reviewed several main streams of existing methods and their limitations in joint-SNVs studies. Then, we introduced a recently proposed novel method, namely statistic-space boundary based test (S-space BBT) to tackle these limitations. Via computational experiments on simulation datasets, not only we figured out the applicable scenarios for the six methods in considering the effect direction and whether the single significant is involved in, but also demonstrated the strong detecting sensitivity of S-space BBT under the different conditions of odds ratio, minor allele frequency, and the linkage disequilibrium. We anticipate that our study may provide clues for multivariate method selection, and that S-space BBT may play a promising role in the joint-SNVs analysis.
All Author(s) ListJin-Xiong Lv, Han-Chen Huang, Run-Sheng Chen, Lei Xu
Name of ConferenceBioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on
Start Date of Conference15/12/2016
End Date of Conference18/12/2016
Place of ConferenceShenzhen
Country/Region of ConferenceChina
Journal nameBioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on
Proceedings TitleBioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on
Year2016
Month12
PublisherIEEE
Pages1771 - 1776
ISBN978-1-5090-1612-9
eISBN978-1-5090-1611-2
LanguagesEnglish-United States
KeywordsGenomics, Bioinformatics, Manganese

Last updated on 2021-14-01 at 00:37