A novel phenotype imputation method with copula model.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Jianjun Zhang, Jane Zizhen Zhao, Samantha Gonzales, Xuexia Wang, Qiuying Sha
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引用次数: 0

Abstract

Background: Jointly analyzing multiple phenotype/traits may increase power in genetic association studies by aggregating weak genetic effects. The chance that at least one phenotype is missing increases exponentially as the number of phenotype increases especially for a real dataset. It is a common practice to discard individuals with missing phenotype or phenotype with a large proportion of missing values. Such a discarding method may lead to a loss of power or even an insufficient sample size for analysis. To our knowledge, many existing phenotype imputing methods are built on multivariate normal assumptions for analysis. Violation of these assumptions may lead to inflated type I errors or even loss of power in some cases. To overcome these limitations, we propose a novel phenotype imputation method based on a new Gaussian copula model with three different loss functions to address the issue of missing phenotype.

Results: In a variety of simulations and a real genetic association study for lung function, we show that our method outperforms existing methods and can also increase the power of the association test when compared to other comparable phenotype imputation methods. The proposed method is implemented in an R package available at https://github.com/jane-zizhen-zhao/CopulaPhenoImpute1.0 CONCLUSIONS: We propose a novel phenotype imputation method with a new Gaussian copula model based on three loss functions. Results of the simulation studies and real data analyses illustrate that the proposed method outperforms comparable methods.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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