An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.
- Authors
- Yang, James J; Li, Jia; Williams, L Keoki; Buu, Anne
- Year
- 2016
- Journal
- BMC bioinformatics
- PMID
- 26729364
- DOI
- 10.1186/s12859-015-0868-6
- PMCID
- PMC4704475
BACKGROUND: In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data. RESULTS: Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct. CONCLUSIONS: The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.
The relationship between the covariance Ξ΄ (y-axis) and the correlation Ο (x-axis)
The distributions of phenotypes for alcohol, nicotine, marijuana and cocaine dependence. The x-axis is the number of symptoms, and the y-axis is the frequency
The QQ-plots of p-values for the marginal tests of association between the SNPs and each of the four addiction symptomatology variables. The x-axis is the expected βlog10(p-value), and the y-axis is the observed βlog10(p-value). The diagonal gray straight lines have the slope 1 and intercept 0
The QQ-plot of p-values for the Fisher combination test of association between the SNPs and the multivariate phenotype of addiction. The x-axis is the expected βlog10(p-value), and the y-axis is the observed βlog10(p-value). The diagonal gray straight lines have the slope 1 and intercept 0
| # | Section | Preview |
|---|---|---|
| 40 | Results β Real data analysis β SAGE data analysis results | For each trait, we conducted a genome-wide association test using the hurdle model [34] because ofβ¦ |
| 41 | Results β Real data analysis β SAGE data analysis results | findings: (1) For the nicotine symptoms, the p-values fall on the diagonal line and this indicatesβ¦ |
| 42 | Results β Real data analysis β SAGE data analysis results | To identify the SNPs associated with the phenotypes, we adopted the commonly used significance levelβ¦ |
| 43 | Discussion | In GWAS for complex diseases, the association between a SNP and each phenotype is usually weak.β¦ |
| 44 | Discussion | We conducted a simulation study to compare the performance of the competing methods. The GEE and the⦠|
| 45 | Discussion | In our simulation study, we only considered continuous multivariate phenotypes. Future studies may⦠|
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In this knowledge base
| Title | Year | PMID |
|---|---|---|
| A Brief Critique of the TATES Procedure. | 2018 | 29468442 |
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