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A hidden two-locus disease association pattern in genome-wide association studies.

Yang C, Wan X, Yang Q, Xue H, Tang NL, Yu W - BMC Bioinformatics (2011)

Bottom Line: 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.Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

Availability: The software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip.

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Related in: MedlinePlus

Analysis result of the local region of the CAD data set located by rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478. (a) The enriched signal after imputation: The -log10P value given by the joint regression. (b) The LD structure (r2) in the same region. (c) The -log10P of single SNP analysis. (d) The locations of the genotyped SNPs rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478.
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Figure 5: Analysis result of the local region of the CAD data set located by rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478. (a) The enriched signal after imputation: The -log10P value given by the joint regression. (b) The LD structure (r2) in the same region. (c) The -log10P of single SNP analysis. (d) The locations of the genotyped SNPs rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478.

Mentions: Here we also show the enriched signals obtained from the imputation. Figure 5(a) shows the -log10P given by the joint regression. Figure 5(b) shows the LD structure (r2) in that region. Figure 5(c) shows the -log10P of single SNP analysis. Figure 5(d) shows the locations of the genotyped SNPs which are listed in Table 2. Again, the marginal effects of the imputed SNPs are weak. We see clearly that the signal of unfaithfulness appears in the block-like manner.


A hidden two-locus disease association pattern in genome-wide association studies.

Yang C, Wan X, Yang Q, Xue H, Tang NL, Yu W - BMC Bioinformatics (2011)

Analysis result of the local region of the CAD data set located by rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478. (a) The enriched signal after imputation: The -log10P value given by the joint regression. (b) The LD structure (r2) in the same region. (c) The -log10P of single SNP analysis. (d) The locations of the genotyped SNPs rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3116488&req=5

Figure 5: Analysis result of the local region of the CAD data set located by rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478. (a) The enriched signal after imputation: The -log10P value given by the joint regression. (b) The LD structure (r2) in the same region. (c) The -log10P of single SNP analysis. (d) The locations of the genotyped SNPs rs7162070, rs1876853, rs8029602, rs16969475 and rs16969478.
Mentions: Here we also show the enriched signals obtained from the imputation. Figure 5(a) shows the -log10P given by the joint regression. Figure 5(b) shows the LD structure (r2) in that region. Figure 5(c) shows the -log10P of single SNP analysis. Figure 5(d) shows the locations of the genotyped SNPs which are listed in Table 2. Again, the marginal effects of the imputed SNPs are weak. We see clearly that the signal of unfaithfulness appears in the block-like manner.

Bottom Line: 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.Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

Availability: The software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip.

Show MeSH
Related in: MedlinePlus