Abstract
Identifying the associations between genetic variants and observed traits is one of the basic problems in genomics. Existing association approaches mainly adopt the collapsing strategy for rare variants. However, these approaches largely rely on the quality of variant selection, and lose statistical power if neutral variants are collapsed together. To overcome the weaknesses, in this article, we propose a novel association approach that aims to obtain a minimal set of candidate variants. This approach incorporates an ant-colony optimization into a collapsing model. Several classes of ants are designed, and each class is assigned to one particular interval in the solution space. An ant prefers to build optimal solution on the region assigned, while it communicates with others and votes for a small number of locally optimal solutions. This framework improves the performance on searching globally optimal solutions. We conduct multiple groups of experiments on semi-simulated datasets with different configurations. The results outperform three popular approaches on both increasing the statistical powers and decreasing the type-I and II errors.
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Acknowledgement
This work is supported by the National Science Foundation of China (Grant No: 81400632), Shaanxi Science Plan Project (Grant No: 2014JM8350) and the Fundamental Research Funds for the Central Universities (XJTU).
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Zhang, X. et al. (2017). An Ant-Colony Based Approach for Identifying a Minimal Set of Rare Variants Underlying Complex Traits. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_30
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DOI: https://doi.org/10.1007/978-3-319-63312-1_30
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