Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models

Felleki, Majbritt; Lee, Dongwhan; Lee, Youngjo; Gilmour, A.; Rönnegård, Lars

doi:10.1017/s0016672312000766

Cited by 46 publications

(90 citation statements)

References 27 publications

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“…To estimate genetic parameters for body weight and its uniformity, the sire-dam DHGLM was used [33, 34] because it is expected to provide unbiased (co)variance components for uniformity [14]. Body weight records were treated in two different ways.…”

Section: Methodsmentioning

confidence: 99%

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation

et al. 2017

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BackgroundIn farmed Atlantic salmon, heritability for uniformity of body weight is low, indicating that the accuracy of estimated breeding values (EBV) may be low. The use of genomic information could be one way to increase accuracy and, hence, obtain greater response to selection. Genomic information can be merged with pedigree information to construct a combined relationship matrix ( matrix) for a single-step genomic evaluation (ssGBLUP), allowing realized relationships of the genotyped animals to be exploited, in addition to numerator pedigree relationships ( matrix). We compared the predictive ability of EBV for uniformity of body weight in Atlantic salmon, when implementing either the or matrix in the genetic evaluation. We used double hierarchical generalized linear models (DHGLM) based either on a sire-dam (sire-dam DHGLM) or an animal model (animal DHGLM) for both body weight and its uniformity.ResultsWith the animal DHGLM, the use of instead of significantly increased the correlation between the predicted EBV and adjusted phenotypes, which is a measure of predictive ability, for both body weight and its uniformity (41.1 to 78.1%). When log-transformed body weights were used to account for a scale effect, the use of instead of produced a small and non-significant increase (1.3 to 13.9%) in predictive ability. The sire-dam DHGLM had lower predictive ability for uniformity compared to the animal DHGLM.ConclusionsUse of the combined numerator and genomic relationship matrix () significantly increased the predictive ability of EBV for uniformity when using the animal DHGLM for untransformed body weight. The increase was only minor when using log-transformed body weights, which may be due to the lower heritability of scaled uniformity, the lower genetic correlation of transformed body weight with its uniformity compared to the untransformed traits, and the small number of genotyped animals in the reference population. This study shows that ssGBLUP increases the accuracy of EBV for uniformity of body weight and is expected to increase response to selection in uniformity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0308-3) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation

et al. 2017

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“…We also assumed that p and p v followed a multivariate normal distribution, Rönnegård et al (2010) to include correlations between random effects for mean and residual variance models. However, as explained below, the variance components estimation software used for this study did not allow implementation of the extended algorithm by Felleki et al (2012). Furthermore, previous studies showed, based on their own data, that variance component estimates were approximately the same if the model with heterogeneous residual variance did or did not include a genetic correlation between u and u v (e.g., Rönnegård et al, 2010).…”

Section: Statistical Modelmentioning

confidence: 97%

“…These iterations between these 2 models were performed until convergence. Although REMLF90 was modified to implement the DHGLM method by Rön-negård et al (2010), these modifications did not allow the implementation of the extended DHGLM method proposed by Felleki et al (2012). The reason is that the BLUPF90 family of programs (Misztal, 2012) allows the use of a single weight for all traits included in the model, whereas the bivariate extended DGHLM method requires different weights for the mean and residual variance models.…”

Section: Statistical Modelmentioning

confidence: 99%

“…At convergence, the 2 univariate estimations of variance components for both mean and residual variance models were equivalent to a bivariate estimation of variance components for both mean and residual variance models for which correlations were fixed to zero between p and p v and between u and u v . Therefore, the adjusted profile h-likelihood (APHL) can be approximated from the log REML-likelihood (logL) of the bivariate model taking into account the fact that the adjusted squared residuals from the mean model were fitted for the residual variance model (Felleki et al, 2012;Mulder et al, 2013): Akaike, 1973) was estimated based on the APHL :…”

Section: Statistical Modelmentioning

confidence: 99%

“…They showed that this model can be implemented on large data sets through software used by the current dairy industry. The DHGLM method was later extended by Felleki et al (2012) to estimate correlations between random effects for the mean and residual variance parts. Using such models, selection for uniformity in the dairy industry can be performed through breed-ing values explaining genetic heterogeneity in residual variance.…”

Section: Introductionmentioning

confidence: 99%

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Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle

Vandenplas

Bastin

Gengler

et al. 2013

Journal of Dairy Science

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Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/ dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × testday had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01 × 10 −3 and 4.17 × 10 −3 for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × testday effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contributed substantially to micro-environmental sensitivity. Addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model.

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Gene × gene and genotype × environment interactions in wheat

Raffo

Jensen

2023

Crop Science

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Accelerating the rate of genetic gain of major crops is required to meet the projected global demand for food. Genomic selection (GS) has made valuable contributions to the genetic progress of different species and is currently a routine procedure in most wheat (Triticum aestivum L.) breeding programs (WBP). Accounting for gene × gene (G × G) and genotype × environment interactions (G × E) in GS can significantly enhance genetic improvement by increasing the rate of genetic gain and adaptation of wheat cultivars. However, a comprehensive and updated knowledge of G × G and G × E methodologies is required to define the appropriate breeding strategy. In this review, we start by briefly describing the current situation and relevance of wheat breeding, then we focus on the central concepts of G × G and G × E and discuss the implications, challenges, and opportunities for the implementation of interactions in GS and wheat breeding. This review is primarily intended to be a reference material for wheat breeders and researchers. Nevertheless, most of the concepts exposed are transferable to any other autogamous or clonal crops, and are therefore useful in a much broader scope. We expect to encourage and facilitate the implementation of GS models accounting for interactions in plant breeding.

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Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models

Cited by 46 publications

References 27 publications

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle

Gene × gene and genotype × environment interactions in wheat

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