FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm
Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low c...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-11-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.721600/full |
| _version_ | 1798034811170848768 |
|---|---|
| author | Wenwu Xu Xiaodong Liu Mingfu Liao Shijun Xiao Min Zheng Tianxiong Yao Zuoquan Chen Lusheng Huang Zhiyan Zhang |
| author_facet | Wenwu Xu Xiaodong Liu Mingfu Liao Shijun Xiao Min Zheng Tianxiong Yao Zuoquan Chen Lusheng Huang Zhiyan Zhang |
| author_sort | Wenwu Xu |
| collection | DOAJ |
| description | Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913). |
| first_indexed | 2024-04-11T20:49:33Z |
| format | Article |
| id | doaj.art-dd6f54cb775f4aaca283fc720c074252 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-11T20:49:33Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-dd6f54cb775f4aaca283fc720c0742522022-12-22T04:03:53ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-11-011210.3389/fgene.2021.721600721600FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithmWenwu XuXiaodong LiuMingfu LiaoShijun XiaoMin ZhengTianxiong YaoZuoquan ChenLusheng HuangZhiyan ZhangGenomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).https://www.frontiersin.org/articles/10.3389/fgene.2021.721600/fullgenomic selectionmodelbig data-orientedGEBVFMixFN |
| spellingShingle | Wenwu Xu Xiaodong Liu Mingfu Liao Shijun Xiao Min Zheng Tianxiong Yao Zuoquan Chen Lusheng Huang Zhiyan Zhang FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm Frontiers in Genetics genomic selection model big data-oriented GEBV FMixFN |
| title | FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm |
| title_full | FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm |
| title_fullStr | FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm |
| title_full_unstemmed | FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm |
| title_short | FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm |
| title_sort | fmixfn a fast big data oriented genomic selection model based on an iterative conditional expectation algorithm |
| topic | genomic selection model big data-oriented GEBV FMixFN |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.721600/full |
| work_keys_str_mv | AT wenwuxu fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT xiaodongliu fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT mingfuliao fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT shijunxiao fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT minzheng fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT tianxiongyao fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT zuoquanchen fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT lushenghuang fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm AT zhiyanzhang fmixfnafastbigdataorientedgenomicselectionmodelbasedonaniterativeconditionalexpectationalgorithm |