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A gene-based method for detecting gene-gene co-association in a case-control association study.

ABSTRACT: Association study (especially the genome-wide association study) now has a key function in identification and characterization of disease-predisposing genetic variant(s), which customarily involve multiple single nucleotide polymorphisms (SNPs) in a candidate region or across the genome. Case-control association design remains the most popular and a challenging issue in the statistical analysis is the optimal use of all information contained in these SNPs. Previous approaches often treated gene-gene interaction as deviation from additive genetic effects or replaced it with SNP-SNP interaction. However, these approaches are limited for their failure of consideration of gene-gene interaction or gene-gene co-association at gene level. Although the co-association of the SNPs within a candidate gene can be detected by principal component analysis-based logistic regression model, the detection of co-association between genes in genome remains uncertain. Here, we proposed a canonical correlation-based U statistic (CCU) for detecting gene-based gene-gene co-association in the case-control design. We explored its type I error rates and power through simulation and analyzed two real data sets. By treating gene as a functional unit in analysis, we found that CCU was a strong alternative to previous approaches. We discussed the performance of CCU as a gene-based gene-gene co-association statistic and the prospect of further improvement.

SUBMITTER: Peng Q

PROVIDER: S-EPMC2987308 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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A gene-based method for detecting gene-gene co-association in a case-control association study.

Peng Qianqian Q Zhao Jinghua J Xue Fuzhong F

European journal of human genetics : EJHG 20091223 5

Association study (especially the genome-wide association study) now has a key function in identification and characterization of disease-predisposing genetic variant(s), which customarily involve multiple single nucleotide polymorphisms (SNPs) in a candidate region or across the genome. Case-control association design remains the most popular and a challenging issue in the statistical analysis is the optimal use of all information contained in these SNPs. Previous approaches often treated gene- ...[more]

PMID: 20029457

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Project description:BackgroundThe rapid advance in large-scale SNP-chip technologies offers us great opportunities in elucidating the genetic basis of complex diseases. Methods for large-scale interactions analysis have been under development from several sources. Due to several difficult issues (e.g., sparseness of data in high dimensions and low replication or validation rate), development of fast, powerful and robust methods for detecting various forms of gene-gene interactions continues to be a challenging task.Methodology/principal findingsIn this article, we have developed an evolution-based method to search for genome-wide epistasis in a case-control design. From an evolutionary perspective, we view that human diseases originate from ancient mutations and consider that the underlying genetic variants play a role in differentiating human population into the healthy and the diseased. Based on this concept, traditional evolutionary measure, fixation index (Fst) for two unlinked loci, which measures the genetic distance between populations, should be able to reveal the responsible genetic interplays for disease traits. To validate our proposal, we first investigated the theoretical distribution of Fst by using extensive simulations. Then, we explored its power for detecting gene-gene interactions via SNP markers, and compared it with the conventional Pearson Chi-square test, mutual information based test and linkage disequilibrium based test under several disease models. The proposed evolution-based method outperformed these compared methods in dominant and additive models, no matter what the disease allele frequencies were. However, its performance was relatively poor in a recessive model. Finally, we applied the proposed evolution-based method to analysis of a published dataset. Our results showed that the P value of the Fst -based statistic is smaller than those obtained by the LD-based statistic or Poisson regression models.Conclusions/significanceWith rapidly growing large-scale genetic association studies, the proposed evolution-based method can be a promising tool in the identification of epistatic effects.

Dataset Information

A gene-based method for detecting gene-gene co-association in a case-control association study.

Publications

A gene-based method for detecting gene-gene co-association in a case-control association study.

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