Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases
et al. (2011) Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for
Common Diseases. PLoS Genet 7(3): e1001338. doi:10.1371/journal.pgen.1001338
Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases
Yang Liu 0
Haiming Xu 0
Suchao Chen 0
Xianfeng Chen 0
Zhenguo Zhang 0
Zhihong Zhu 0
Xueying Qin 0
Landian Hu 0
Jun Zhu 0
Guo-Ping Zhao 0
Xiangyin Kong 0
David B. Allison, University of Alabama at Birmingham, United States of America
0 1 The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Ruijin Hospital, Shanghai Jiaotong University School of Medicine , Shanghai , People's Republic of China, 2 Institute of Bioinformatics, Zhejiang University , Hangzhou , People's Republic of China, 3 State Key Lab of CAD&CG, Zhejiang University , Hangzhou , People's Republic of China, 4 National Human Genome Center , Shanghai , People's Republic of China
Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named ''pair-wise interaction-based association mapping'' (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P,0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P,0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.0961027). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P,0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.
-
Funding: This work is supported by the National High Technology Research and Development Program of China (2006AA02Z330, 2006AA02A301), the National
Basic Research Program of China (No. 2007CB512202, 2011CBA00400, 2011CB510100), the National Natural Science Foundation of China (No. 30530450,
30871356), and the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KSCX1-YW-R-74). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Recent genome-wide association studies (GWAS) have identified
many common genetic variants associated with common diseases.
This has rapidly expanded our knowledge of the genetic
architecture of these diseases. For example, the Wellcome Trust
Case Control Consortium (WTCCC) study [1] and other
largescale GWASs (including meta-analyses) have discovered many
susceptibility loci for common diseases, including coronary artery
disease (CAD) [2], Crohns disease (CD) [3,4], type 1 diabetes (T1D)
[5], and type 2 diabetes (T2D) [6]. However, compared with the
successes of single-locus approaches, the achievements of
interaction-based approaches, which seek susceptibilities that derive from
gene-gene interactions, have lagged behind [7,8]. Thus, gene-gene
interactions that are largely undetected may explain some of the
heritability of common diseases [9]. Most reported interactions are
currently found through candidate approaches, which incorporate
prior biological knowledge. Moreover, very few interactions have
been confirmed in an independent population.
Genome-wide interaction-based association (GWIBA) analysis
uses markers to conduct genome-wide screens without prior
candidate selection. In addition, GWIBA incorporates interaction
effects among genetic variants. Many interaction-based methods
for GWIBA are currently available, including a logistic
regressionbased method [10]; in addition, several methods have been
recently developed [1115]. However, no studies on real data have
successfully identified novel disease-associated loci. Two studie (...truncated)