Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies

Cesarani, Alberto; Pocrnić, Ivan; Macciotta, N.P.P.; Fragomeni, B. O.; Misztal, I.; Lourenço, Daniela

doi:10.1111/jbg.12367

Cited by 26 publications

(23 citation statements)

References 36 publications

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“…The selective genotyping strategy can increase accuracy of selection and genetic gain compared to random genotyping strategies [24]. However, the selective genotyping can create bias and lead to overestimation of genetic variances when single-step genomic BLUP (ssGBLUP) is used to estimate variance components [25]. To exploit genomic information, as well as pedigree data and phenotypes of non-genotyped birds in genetic evaluation, ssGBLUP models [22] can be used.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Background The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment. Methods A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only. Results Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds. Conclusions The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Compared to the random genotyping strategy, the selective genotyping strategy leads to higher genetic gain . However, the selective genotyping leads to an overestimation of genetic variances with single step GBLUP (ssGBLUP) (Cesarani et al, 2019).…”

Section: Genomic Informationmentioning

confidence: 99%

“…The selective genotyping strategy can increase accuracy of selection and genetic gain compared to random genotyping strategies . However, the selective genotyping can create bias and lead to overestimation of genetic variances when ssGBLUP is employed to estimate variance components (Cesarani et al, 2019). To utilize genomic information, as well as pedigree and phenotypes of non-genotyped birds in genetic evaluation, ssGBLUP models ) can be used.…”

Section: Referencesmentioning

confidence: 99%

“…However, accurate prediction of breeding values requires accurate estimates of variance components. An animal model using the pedigree relationship matrix is currently recommended for estimation of variance components in the situation of selective genotyping (Cesarani et al, 2019).…”

Section: Referencesmentioning

confidence: 99%

“…Variance components were estimated using the PBLUP multivariate model (2.2) for two reasons. One reason is selective genotyping applied to birds in B that might lead to overestimation and bias of variance components estimates with a ssGBLUP model (Cesarani et al, 2019). Another reason is that the complexity of the models and large amount of genotyped animals and non-genotyped animals in the pedigree would make ssGBLUP computationally demanding.…”

Section: Statistical Modelsmentioning

confidence: 99%

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Genotype by environment interactions in poultry breeding programs

Tuan

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Environmental differences between the breeding (B) and commercial production (C) environments may lead to genotype-by-environment interactions (GxE) i.e. re-ranking of breeding values of animals in the two environments. A substantial re-ranking implies genetic progress achieved in breeding programs is not realized in performance of production animals. The issues of GxE are not new and several solutions exist, however, there has not been much focus on solutions for breeding programs for poultry. This PhD-project investigated GxE interactions in breeding programs for poultry and solutions to improve genetic progress in these breeding programs. A strong GxE interaction for body weight (BW) traits was found in broilers that were raised in B and C environments. Indications of GxE were significant re-ranking of breeding values, heterogeneous variances and different heritability for BW under B and C conditions. The genetic correlations between BW traits measured in B and C environments were in the range 0.48-0.54. Genetic variances of C traits were more than 2 times higher than those of B traits. Heritability of C traits (0.31-0.37) were higher than those of B traits (0.27-0.30). In this thesis, several approaches to improve genetic gains of the poultry breeding programs in the presence of GxE have been investigated: phenotyping strategies, optimal modelling of traits, use of group records, and the use of genomic information. Different phenotyping strategies were compared in a breeding program for broilers that used genomic selection. It was found that when the genetic correlations between traits measured in B and C were 0.5 and 0.7, allocation of 70% and 30% hatched birds to B and C environments, respectively, for phenotype testing led to the highest genetic gains among the compared phenotyping strategies. When the genetic correlation was 0.9, moving birds to C did not improve genetic gains of the breeding scheme due to reduced selection intensity. Increasing proportion of birds moved to C (from 15 to 45%) could reduce rate of inbreeding of the breeding program. Optimal modelling of traits was explored in a genetic analysis that was carried out for BW in broilers at different ages raised in a commercial environment. A statistical model was developed with the aim to increase predictive ability of the model for the traits affected by maternal effects. A criterion for the development of the statistical model was based on correlation between EBVs and corrected phenotypes of half-sib individuals. The statistical model also accounted for heterogeneous variances between sexes. In breeding programs for village chicken, where strong GxE interactions are expected, the use of group records was a good option to increase genetic gain of the breeding programs. The use of group records from villages significantly improved genetic gains compared to the scheme without birds tested in the village although group records led to a slightly lower genetic gain compared to individual records. In addition, the use of genomic information was ex...

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Impact and utility of shallow pedigree using single-step genomic BLUP for prediction of unbiased genomic breeding values

Gowane

Alex

Mukherjee

et al. 2022

Trop Anim Health Prod

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Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies

Cited by 26 publications

References 36 publications

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

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

Genotype by environment interactions in poultry breeding programs

Impact and utility of shallow pedigree using single-step genomic BLUP for prediction of unbiased genomic breeding values

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