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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.

Show MeSH
The performance comparison of four methods: Marginal association tests, Lasso, BEAM and the proposed exhaustive two-locus joint analysis. 100 data sets are generated under each parameter setting. 1000 samples (500 cases and 500 controls) are simulated in each data set. The power is calculated as the proportion of the 100 data sets in which the disease associated SNPs are detected.
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Figure 2: The performance comparison of four methods: Marginal association tests, Lasso, BEAM and the proposed exhaustive two-locus joint analysis. 100 data sets are generated under each parameter setting. 1000 samples (500 cases and 500 controls) are simulated in each data set. The power is calculated as the proportion of the 100 data sets in which the disease associated SNPs are detected.

Mentions: The results in Figure 2 indicate that it is difficult for existing methods to detect the association masked by unfaithfulness while our proposed method achieves reasonable performance. Specifically, the poor performance of the marginal association test is not surprising since the marginal effects are weak in the presence of unfaithfulness. Although Lasso can simultaneously analyze all SNPs, it still suffers from the difficulty of detecting associations masked by unfaithfulness. This agrees with the analysis result in [11]. BEAM has a better performance, which should be attributed to its first order Markov chain designed for the accommodation of correlation. But its performance is still not comparable with the performance of our proposed method in most settings.


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)

The performance comparison of four methods: Marginal association tests, Lasso, BEAM and the proposed exhaustive two-locus joint analysis. 100 data sets are generated under each parameter setting. 1000 samples (500 cases and 500 controls) are simulated in each data set. The power is calculated as the proportion of the 100 data sets in which the disease associated SNPs are detected.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: The performance comparison of four methods: Marginal association tests, Lasso, BEAM and the proposed exhaustive two-locus joint analysis. 100 data sets are generated under each parameter setting. 1000 samples (500 cases and 500 controls) are simulated in each data set. The power is calculated as the proportion of the 100 data sets in which the disease associated SNPs are detected.
Mentions: The results in Figure 2 indicate that it is difficult for existing methods to detect the association masked by unfaithfulness while our proposed method achieves reasonable performance. Specifically, the poor performance of the marginal association test is not surprising since the marginal effects are weak in the presence of unfaithfulness. Although Lasso can simultaneously analyze all SNPs, it still suffers from the difficulty of detecting associations masked by unfaithfulness. This agrees with the analysis result in [11]. BEAM has a better performance, which should be attributed to its first order Markov chain designed for the accommodation of correlation. But its performance is still not comparable with the performance of our proposed method in most settings.

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