Paper Info
Title
A hidden two-locus disease association pattern in genome-wide association studies.
Abstract
Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.The software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip.
Year
DOI
Venue
2011
10.1186/1471-2105-12-156
BMC Bioinformatics
Keywords
Field
DocType
single nucleotide polymorphism,genome wide association study,bioinformatics,computational biology,microarrays,genetic variance,algorithms,experience design
Genome,Biology,Multifactor dimensionality reduction,Genome-wide association study,Genetic association,Correlation,Single-nucleotide polymorphism,Bioinformatics,Genetics,Locus (genetics),Replicate
Journal
Volume
Issue
ISSN
12
1
1471-2105
Citations 
PageRank 
References 
9
0.42
6
Authors
6
Name
Order
Citations
PageRank
Can Yang172643.12
Xiang Wan241433.22
Qiang Yang317039875.69
Hong Xue437321.40
Nelson L S Tang523315.16
Weichuan Yu694357.38