Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model. / Villumsen, Trine Michelle; Su, Guosheng; Cai, Zexi; Guldbrandtsen, Bernt; Asp, Torben; Sahana, Goutam; Lund, Mogens Sandø.

Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2. 2018. 11.618.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Villumsen, TM, Su, G, Cai, Z, Guldbrandtsen, B, Asp, T, Sahana, G & Lund, MS 2018, Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model. in Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2., 11.618, The 11th World Congress on Genetics Applied to Livestock Production , Auckland, New Zealand, 11/02/2018.

APA

Villumsen, T. M., Su, G., Cai, Z., Guldbrandtsen, B., Asp, T., Sahana, G., & Lund, M. S. (2018). Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model. In Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2 [11.618]

CBE

Villumsen TM, Su G, Cai Z, Guldbrandtsen B, Asp T, Sahana G, Lund MS. 2018. Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model. In Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2.

MLA

Villumsen, Trine Michelle et al. "Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model". Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2. 2018.

Vancouver

Villumsen TM, Su G, Cai Z, Guldbrandtsen B, Asp T, Sahana G et al. Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model. In Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2. 2018. 11.618

Author

Villumsen, Trine Michelle ; Su, Guosheng ; Cai, Zexi ; Guldbrandtsen, Bernt ; Asp, Torben ; Sahana, Goutam ; Lund, Mogens Sandø. / Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model. Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Methods and Tools - Prediction 2. 2018.

Bibtex

@inproceedings{39b34edaa2394a029e7529844d742030,
title = "Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model",
abstract = "The accuracy of genomic prediction for mink was compared for single-trait and multiple-trait GBLUP models and Bayesian models that allowed for heterogeneous (co)variance structure over the genome. The mink population consisted of 2,103 brown minks genotyped with the method of genotyping by sequencing. Four live grading traits and four traits on dried pelts for size and quality were analysed. GWAS analysis detected significant SNPs for all the traits. The single-trait Bayesian model resulted in higher accuracies for the genomic predictions than the single-trait GBLUP model, especially for the traits measured on dried pelts. We expected the multiple-trait models to be superior to the single trait models since the multiple-trait model can make use of information when traits are correlated. However, we did not find a general improvement in accuracies with the multiple-trait models compared to the single-trait models. Keywords: GWAS, GBLUP, BayesAS, heterogeneous (co)variances",
author = "Villumsen, {Trine Michelle} and Guosheng Su and Zexi Cai and Bernt Guldbrandtsen and Torben Asp and Goutam Sahana and Lund, {Mogens Sand{\o}}",
year = "2018",
language = "English",
booktitle = "Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018",

}

RIS

TY - GEN

T1 - Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model

AU - Villumsen, Trine Michelle

AU - Su, Guosheng

AU - Cai, Zexi

AU - Guldbrandtsen, Bernt

AU - Asp, Torben

AU - Sahana, Goutam

AU - Lund, Mogens Sandø

PY - 2018

Y1 - 2018

N2 - The accuracy of genomic prediction for mink was compared for single-trait and multiple-trait GBLUP models and Bayesian models that allowed for heterogeneous (co)variance structure over the genome. The mink population consisted of 2,103 brown minks genotyped with the method of genotyping by sequencing. Four live grading traits and four traits on dried pelts for size and quality were analysed. GWAS analysis detected significant SNPs for all the traits. The single-trait Bayesian model resulted in higher accuracies for the genomic predictions than the single-trait GBLUP model, especially for the traits measured on dried pelts. We expected the multiple-trait models to be superior to the single trait models since the multiple-trait model can make use of information when traits are correlated. However, we did not find a general improvement in accuracies with the multiple-trait models compared to the single-trait models. Keywords: GWAS, GBLUP, BayesAS, heterogeneous (co)variances

AB - The accuracy of genomic prediction for mink was compared for single-trait and multiple-trait GBLUP models and Bayesian models that allowed for heterogeneous (co)variance structure over the genome. The mink population consisted of 2,103 brown minks genotyped with the method of genotyping by sequencing. Four live grading traits and four traits on dried pelts for size and quality were analysed. GWAS analysis detected significant SNPs for all the traits. The single-trait Bayesian model resulted in higher accuracies for the genomic predictions than the single-trait GBLUP model, especially for the traits measured on dried pelts. We expected the multiple-trait models to be superior to the single trait models since the multiple-trait model can make use of information when traits are correlated. However, we did not find a general improvement in accuracies with the multiple-trait models compared to the single-trait models. Keywords: GWAS, GBLUP, BayesAS, heterogeneous (co)variances

M3 - Article in proceedings

BT - Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018

ER -