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Bayesian methods for jointly estimating genomic breeding values of one continuous and one threshold trait

文献类型： 外文期刊

作者： Wang, Chonglong 1 ; Li, Xiujin 2 ; Qian, Rong 1 ; Su, Guosheng 1 ; Zhang, Qin 2 ; Ding, Xiangdong 2 ;

作者机构： 1.Anhui Acad Agr Sci, Inst Anim Husb & Vet Med, Dept Pig Genet & Breeding, Hefei, Peoples R China

2.China Agr Univ, Coll Anim Sci & Technol, Lab Anim Genet Breeding & Reprod, Minist Agr China,Natl Engn Lab Anim Breeding, Beijing, Peoples R China

3.Aarhus Univ, Dept Mol Biol & Genet, Ctr Quantitat Genet & Genom, Tjele, Denmark

4.Sun Yat Sen Univ, Sch Life Sci, State Key Lab Biocontrol, Guangzhou Higher Educ Mega Ctr, North Third Rd, Guangzhou, Guangdong, Peoples R China

期刊名称：PLOS ONE （ 影响因子：3.24； 五年影响因子：3.788 ）

ISSN： 1932-6203

年卷期： 2017 年 12 卷 4 期

页码：

收录情况： SCI

摘要： Genomic selection has become a useful tool for animal and plant breeding. Currently, genomic evaluation is usually carried out using a single-trait model. However, a multi-trait model has the advantage of using information on the correlated traits, leading to more accurate genomic prediction. To date, joint genomic prediction for a continuous and a threshold trait using a multi-trait model is scarce and needs more attention. Based on the previously proposed methods BayesC pi for single continuous trait and BayesTC pi for single threshold trait, we developed a novel method based on a linear-threshold model, i.e., LT-BayesC pi, for joint genomic prediction of a continuous trait and a threshold trait. Computing procedures of LT BayesC pi using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the advantages of LT-BayesC pi over BayesC pi and BayesTC pi with regard to the accuracy of genomic prediction on both traits. Factors affecting the performance of LT-BayesC pi were addressed. The results showed that, in all scenarios, the accuracy of genomic prediction obtained from LT-BayesC pi was significantly increased for the threshold trait compared to that from single trait prediction using BayesTC pi, while the accuracy for the continuous trait was comparable with that from single trait prediction using BayesC pi. The proposed LT-BayesC pi could be a method of choice for joint genomic prediction of one continuous and one threshold trait.

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