Paper Info
Title
An Ant-Colony Based Approach For Identifying A Minimal Set Of Rare Variants Underlying Complex Traits
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.
Year
DOI
Venue
2017
10.1007/978-3-319-63312-1_30
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II
Keywords
Field
DocType
Genetic association approach, Rare variants, Ant-colony optimization, Minimal candidate set problem
Ant colony optimization algorithms,Small number,Computer science,Genomics,Artificial intelligence,Ant colony,Statistical power,Machine learning
Conference
Volume
ISSN
Citations 
10362
0302-9743
0
PageRank 
References 
Authors
0.34
5
9
Name
Order
Citations
PageRank
Xuanping Zhang1296.63
Zhongmeng Zhao2539.51
Yan Chang3192.98
Aiyuan Yang430.94
Yixuan Wang501.35
ruoyu liu6155.40
Maomao720.79
Xiao Xiao842.26
Jiayin Wang9104.75