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Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA.

ABSTRACT: Summary:Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, a Bayesian hierarchical model that integrates multiple GWAS datasets to boost statistical power for the identification of risk variants and to estimate pleiotropic architecture within a unified framework. Here we propose a novel improvement of graph-GPA which incorporates external knowledge about phenotype-phenotype relationship to guide the estimation of genetic correlation and the association mapping. The application of graph-GPA to GWAS datasets for 12 complex diseases with a prior disease graph obtained from a text mining of biomedical literature illustrates its power to improve the identification of risk genetic variants and to facilitate understanding of genetic relationship among complex diseases. Availability and implementation:graph-GPA is implemented as an R package 'GGPA', which is publicly available at http://dongjunchung.github.io/GGPA/. DDNet, a web interface to query diseases of interest and download a prior disease graph obtained from a text mining of biomedical literature, is publicly available at http://www.chunglab.io/ddnet/. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Kim HJ

PROVIDER: S-EPMC6248798 | biostudies-other | 2018 Jun

REPOSITORIES: biostudies-other

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Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA.

Kim Hang J HJ Yu Zhenning Z Lawson Andrew A Zhao Hongyu H Chung Dongjun D

Bioinformatics (Oxford, England) 20180601 12

<h4>Summary</h4>Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, ...[more]

PMID: 29432514

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Project description:BackgroundFive main methods, commonly applied in genomic selection, were used to estimate the GEBV on the 15th QTLMAS workshop dataset: GBLUP, LASSO, Bayes A and two Bayes B type of methods (BBn and BBt). GBLUP is a mixed model approach where GEBV are obtained using a relationship matrix calculated from the SNP genotypes. The remaining methods are regression-based approaches where the SNP effects are first estimated and, then GEBV are calculated given the individuals' genotypes.MethodsThe differences between the regression-based methods are in their prior distributions for the SNP effects. The prior distribution for LASSO is a Laplace distribution, for Bayes A is a scaled Student-t distribution, and the Bayes B type methods have a Spike and Slab prior where only a proportion (?) of SNP has an effect, following a given distribution. In this study, two different distributions were considered for the Bayes B type methods: (i) normal and (ii) scaled Student-t. They are referred here as the BBn and BBt methods, respectively. These prior distributions are defined by one or more parameters controlling their scale/rate (?), shape (df) or proportion of SNP with effect (?). LASSO requires one (?); two for Bayes A (?, df) and Bayes Bn (?, ?); and three for Bayes Bt (?, df, ?). In this study, all parameters were estimated from the data. An extra scenario for Bayes A and BBt was included where df was not estimated but fixed to 4 (suffixed _4df). The implementation of GBLUP was done using ASREML, the heritability was also estimated from the data. All other methods were implemented using a MCMC approach.ResultsAll Bayes A and B methods showed accuracy (correlation between True and Estimated BV) as high as 0.94 except for BA_4df (r = 0.91). Compared to the traditional BLUP using pedigree information, these methods improved the accuracy between 50 and 55%. GBLUP and LASSO were less accurate (0.81 and 0.85 respectively) and the improvements were 34 and 40% compared to BLUP.ConclusionsResults of all methods were consistent and the accuracies for GEBV ranged between 0.81 and 0.94. When all parameters were estimated the results were similar for the Bayes A and Bayes B methods. Results showed that Bayes A was more sensitive to the changes in the shape parameter, and the parameter changes led to change in the accuracy of GEBV. However BBt was more robust to the change in this parameter. This may be explained by the fact that BBt estimates one extra parameter and it can buffer against a non-proper shape parameter.

Dataset Information

Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA.

Publications

Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA.

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