Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants
Abstract Background With the advance of next-generation sequencing, various gene-based rare variant association tests have been developed, particularly for binary and continuous phenotypes. In contrast, fewer methods are available for traits not following binomial or normal distributions. To address...
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-06029-5 |
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| author | Xiaomin Liu Yi-Ju Li Qiao Fan |
| author_facet | Xiaomin Liu Yi-Ju Li Qiao Fan |
| author_sort | Xiaomin Liu |
| collection | DOAJ |
| description | Abstract Background With the advance of next-generation sequencing, various gene-based rare variant association tests have been developed, particularly for binary and continuous phenotypes. In contrast, fewer methods are available for traits not following binomial or normal distributions. To address this, we previously proposed a set of burden- and kernel-based rare variant tests for count data following zero-inflated Poisson (ZIP) distributions, referred to as ZIP-b and ZIP-k tests. We sought to extend the methods to accommodate negative binomial distribution and implemented these tests in a new R package. Results We introduce ZIM4rv, an R package designed to analyze the association of rare variants with zero-inflated counts outcomes. Our package offers two novel models developed by our team: our previously proposed ZIP-b and ZIP-k tests, and the newly derived Negative Binomial Burden and Kernel Test (ZINB-b, ZINB-k). Additionally, we include an ad-hoc two-stage analysis, testing zero and non-zero as a binary outcome and non-zero as a continuous outcome, respectively. To showcase the utility of our platform, we applied this program to analyze neuritic plaque count data from the ROSMAP cohort. Conclusion The R package ZIM4rv presents an integrated workflow for conducting association tests on a set of rare variants with zero-inflated counts data. |
| format | Article |
| id | doaj-art-64b074e9573d4f0888208fb098c63612 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Bioinformatics |
| spelling | doaj-art-64b074e9573d4f0888208fb098c636122025-01-19T12:41:00ZengBMCBMC Bioinformatics1471-21052025-01-0126111010.1186/s12859-024-06029-5Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variantsXiaomin Liu0Yi-Ju Li1Qiao Fan2Centre for Quantitative Medicine, Duke-NUS Medical School, National University of SingaporeCentre for Quantitative Medicine, Duke-NUS Medical School, National University of SingaporeCentre for Quantitative Medicine, Duke-NUS Medical School, National University of SingaporeAbstract Background With the advance of next-generation sequencing, various gene-based rare variant association tests have been developed, particularly for binary and continuous phenotypes. In contrast, fewer methods are available for traits not following binomial or normal distributions. To address this, we previously proposed a set of burden- and kernel-based rare variant tests for count data following zero-inflated Poisson (ZIP) distributions, referred to as ZIP-b and ZIP-k tests. We sought to extend the methods to accommodate negative binomial distribution and implemented these tests in a new R package. Results We introduce ZIM4rv, an R package designed to analyze the association of rare variants with zero-inflated counts outcomes. Our package offers two novel models developed by our team: our previously proposed ZIP-b and ZIP-k tests, and the newly derived Negative Binomial Burden and Kernel Test (ZINB-b, ZINB-k). Additionally, we include an ad-hoc two-stage analysis, testing zero and non-zero as a binary outcome and non-zero as a continuous outcome, respectively. To showcase the utility of our platform, we applied this program to analyze neuritic plaque count data from the ROSMAP cohort. Conclusion The R package ZIM4rv presents an integrated workflow for conducting association tests on a set of rare variants with zero-inflated counts data.https://doi.org/10.1186/s12859-024-06029-5Rare variantsZero-inflated countsZIM4rv R packageRegional-based test |
| spellingShingle | Xiaomin Liu Yi-Ju Li Qiao Fan Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants BMC Bioinformatics Rare variants Zero-inflated counts ZIM4rv R package Regional-based test |
| title | Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants |
| title_full | Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants |
| title_fullStr | Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants |
| title_full_unstemmed | Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants |
| title_short | Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants |
| title_sort | zim4rv an r package to modeling zero inflated count phenotype on regional based rare variants |
| topic | Rare variants Zero-inflated counts ZIM4rv R package Regional-based test |
| url | https://doi.org/10.1186/s12859-024-06029-5 |
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