Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

Chu, Thinh Tuan; Bastiaansen, J.W.M.; Berg, Peer; Romé, Hélène; Marois, Danye; Henshall, John; Jensen, Just

doi:10.1186/s12711-019-0513-3

Cited by 2 publications

(2 citation statements)

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“…Their parents were raised in a bio‐secure environment where selection was done on the relatives of birds in our study. Pedigree covered 23 selection rounds (SR), which corresponds to when a decision of selection was done, back to when the line was formed and included birds from both the commercial and bio‐secure environments (Chu, Bastiaansen, et al., 2019 ; Chu, Madsen, et al., 2019 ; Mebratie et al., 2019 ). However, the phenotypes and genotypes used in this study were only commercially reared birds and were focussed on 12 SR.…”

Section: Methodsmentioning

confidence: 99%

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Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

Romé

Chu

Marois³

et al. 2021

J Animal Breeding Genetics

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BLUP (best linear unbiased prediction) is the standard for predicting breeding values, where different assumptions can be made on variance–covariance structure, which may influence predictive ability. Herein, we compare accuracy of prediction of four derived‐BLUP models: (a) a pedigree relationship matrix (PBLUP), (b) a genomic relationship matrix (GBLUP), (c) a weighted genomic relationship matrix (WGBLUP) and (d) a relationship matrix based on genomic features that consisted of only a subset of SNP selected on a priori information (GFBLUP). We phenotyped a commercial population of broilers for body weight (BW) in five successive weeks and genotyped them using a 50k SNP array. We compared predictive ability of univariate models using conservative cross‐validation method, where each full‐sib group was divided into two folds. Results from cross‐validation showed, with WGBLUP model, a gain in accuracy from 2% to 7% compared with GBLUP model. Splitting the additive genetic matrix into two matrices, based on significance level of SNP (Gf: estimated with only set of SNP selected on significance level, Gr: estimated with the remaining SNP), led to a gain in accuracy from 1% to 70%, depending on the proportion of SNP used to define Gf. Thus, information from GWAS in models improves predictive ability of breeding values for BW in broilers. Increasing the power of detection of SNP effects, by acquiring more data or improving methods for GWAS, will help improve predictive ability.

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Section: Methodsmentioning

confidence: 99%

“…We computed standard errors of those estimates using formulas presented in the Appendix S1 of Chu, et al. ( 2019 ); Chu, et al. ( 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

Romé

Chu

Marois³

et al. 2021

J Animal Breeding Genetics

Self Cite

View full text Add to dashboard Cite

show abstract

435. Optimizing sampling strategies for sib test schemes in a broiler breeder program

Hollander¹,

Breen²,

Henshall³

et al. 2022

Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)

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Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

Abstract: After publication of this work [1], we noticed that there was an error: the formula to calculate the standard error of the estimated correlation

Cited by 2 publications

References 1 publication

Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

435. Optimizing sampling strategies for sib test schemes in a broiler breeder program

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