A novel phenotype imputation method with copula model
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...
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| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05990-5 |
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| author | Jianjun Zhang Jane Zizhen Zhao Samantha Gonzales Xuexia Wang Qiuying Sha |
| author_facet | Jianjun Zhang Jane Zizhen Zhao Samantha Gonzales Xuexia Wang Qiuying Sha |
| author_sort | Jianjun Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1ba9f6a769a4e7c824390eb8e368826 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-e1ba9f6a769a4e7c824390eb8e3688262024-12-01T12:47:37ZengBMCBMC Bioinformatics1471-21052024-11-0125112110.1186/s12859-024-05990-5A novel phenotype imputation method with copula modelJianjun Zhang0Jane Zizhen Zhao1Samantha Gonzales2Xuexia Wang3Qiuying Sha4Department of Mathematics, University of North TexasDepartment of Psychology and Neuroscience, The University of North Carolina at Chapel HillDepartment of Biostatistics, Florida International UniversityDepartment of Biostatistics, Florida International UniversityDepartment of Mathematical Sciences, Michigan Technological UniversityAbstract 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.https://doi.org/10.1186/s12859-024-05990-5Genetic studiesLoss functionInflated type I errorGaussian copulaPhenotype imputation |
| spellingShingle | Jianjun Zhang Jane Zizhen Zhao Samantha Gonzales Xuexia Wang Qiuying Sha A novel phenotype imputation method with copula model BMC Bioinformatics Genetic studies Loss function Inflated type I error Gaussian copula Phenotype imputation |
| title | A novel phenotype imputation method with copula model |
| title_full | A novel phenotype imputation method with copula model |
| title_fullStr | A novel phenotype imputation method with copula model |
| title_full_unstemmed | A novel phenotype imputation method with copula model |
| title_short | A novel phenotype imputation method with copula model |
| title_sort | novel phenotype imputation method with copula model |
| topic | Genetic studies Loss function Inflated type I error Gaussian copula Phenotype imputation |
| url | https://doi.org/10.1186/s12859-024-05990-5 |
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