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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Wenwu Xu, Xiaodong Liu, Mingfu Liao, Shijun Xiao, Min Zheng, Tianxiong Yao, Zuoquan Chen, Lusheng Huang, Zhiyan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	genomic selection model big data-oriented GEBV FMixFN Genetics QH426-470
Acceso en línea:	https://doaj.org/article/dd6f54cb775f4aaca283fc720c074252
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:dd6f54cb775f4aaca283fc720c074252
record_format	dspace
spelling	oai:doaj.org-article:dd6f54cb775f4aaca283fc720c0742522021-11-18T09:56:43ZFMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm1664-802110.3389/fgene.2021.721600https://doaj.org/article/dd6f54cb775f4aaca283fc720c0742522021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.721600/fullhttps://doaj.org/toc/1664-8021Genomic 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).Wenwu XuXiaodong LiuMingfu LiaoShijun XiaoMin ZhengTianxiong YaoZuoquan ChenLusheng HuangZhiyan ZhangFrontiers Media S.A.articlegenomic selectionmodelbig data-orientedGEBVFMixFNGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	genomic selection model big data-oriented GEBV FMixFN Genetics QH426-470
spellingShingle	genomic selection model big data-oriented GEBV FMixFN Genetics QH426-470 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
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).
format	article
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
title	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_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_sort	fmixfn: a fast big data-oriented genomic selection model based on an iterative conditional expectation algorithm
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/dd6f54cb775f4aaca283fc720c074252
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
_version_	1718420875805458432

FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm

Ejemplares similares