“Bending” and beyond: Better estimates of quantitative genetic parameters?

Meyer, Karin

doi:10.1111/jbg.12386

Cited by 8 publications

(6 citation statements)

References 47 publications

(67 reference statements)

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“…Estimation of genetic parameters is a fundamental task in quantitative genetics (Meyer, 2019) As far as we know, genetic parameters of the aforementioned traits have not yet been studied in Qingyuan partridge chickens. Variance components of body weight have been widely investigated in domestic animals, and it was reported that maternal effects significantly contribute to phenotypes (Alves et al., 2018; Kushwaha et al., 2009; Roy et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

Genetic parameters and direct and maternal effects for important traits in late‐feathering Qingyuan partridge hens in China

Kang

et al. 2020

J Animal Breeding Genetics

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Qingyuan partridge chicken is an important indigenous chicken in China. In its breeding schemes, chickens are usually selected at the age of 105‐day‐old for five traits, including body weight (BW), shank length (SL), shank girth (SG), comb height (CH) and feather maturity (FM). At present, genetic parameters of the aforementioned traits have still not been studied in Qingyuan partridge chickens. The objectives of this study were the following: (1) to investigate whether the optimal statistical models of these traits need to consider maternal genetic and permanent environmental effects in late‐feathering Qingyuan partridge hens, and (2) to estimate genetic parameters for these traits based on the optimal models. The numbers of records for BW, SL, SG, CH and FM were 13,721, 13,671, 13,670, 13,669 and 13,672, respectively. Variance components were estimated using average information‐restricted maximum likelihood method, and the optimal model was determined based on Bayesian information criterion. More specifically, the optimal model for BW considered maternal genetic and permanent environmental effects in addition to direct additive genetic effect; SL, SG and FM considered direct and maternal genetic effects; and CH considered direct and maternal genetic effects, and the covariance between them. The direct heritabilities of these traits estimated using the optimal models were 0.21 ± 0.04, 0.30 ± 0.05, 0.40 ± 0.05, 0.59 ± 0.09 and 0.09 ± 0.04, respectively; the maternal heritabilities were 0.01 ± 0.04, 0.05 ± 0.05, 0.04 ± 0.05, 0.09 ± 0.09 and 0.03 ± 0.04, respectively. Maternal genetic effect evidently played an important part in FM and maternal heritability accounted for 30 per cent of total heritability. Furthermore, the direct and maternal genetic effects for CH were estimated to be negatively and moderately correlated (−0.51 ± 0.11). For all traits, neglecting existent maternal effects biased the estimation of direct heritability. Therefore, to implement optimum breeding strategies for improvement of these traits in Qingyuan partridge hens, maternal effects should be taken into consideration.

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Section: Introductionmentioning

confidence: 99%

Genetic parameters and direct and maternal effects for important traits in late‐feathering Qingyuan partridge hens in China

Kang

et al. 2020

J Animal Breeding Genetics

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“…There exists previous work on penalized estimation of genetic covariances (e.g., Meyer et al , 2011; Meyer, 2016, 2019) that also uses Bayesian principles and scale-free penalty functions to reduce variation of the estimates from small datasets and for large numbers of traits. Our proposed priors and expert knowledge reduced variation of estimates in the simulated case study.…”

Section: Discussionmentioning

confidence: 99%

“…Future research could expand the hierarchical decomposition prior framework to other settings. For example, to multiple traits or modelling genotype-by-environment interactions, which are notoriously noisy, and we aim to find parsimonious models (e.g., Meyer, 2016, 2019; Tolhurst et al , 2019). Also, expand to model macro- and micro-environmental effects (e.g., Selle et al , 2019) and to model multiple layers of sparse, yet high-dimensional, “omic” data from modern biological experiments using network-like models (Damianou and Lawrence, 2013).…”

Section: Discussionmentioning

confidence: 99%

Robust Modelling of Additive and Non-additive Variation with Intuitive Inclusion of Expert Knowledge

Hem

Selle

Fuglstad

et al. 2020

Preprint

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A major challenge with modelling non-additive genetic variation is that it is hard to separate nonadditive variation from additive and environmental variation. In this paper, we describe how to alleviate this issue, and improve genomic modelling of additive and non-additive variation, by leveraging the ample expert knowledge available about the relative magnitude of the sources of phenotypic variation. The method is Bayesian and uses the recently introduced penalized complexity and hierarchical decomposition prior frameworks, where priors can be specified and visualized in an intuitive way, and be used to induce parsimonious modelling. We evaluate the potential impact for plant breeding through a simulated case study of a wheat breeding program. We compare different models and different priors with varying amounts of expert knowledge. The results show that the proposed priors and expert knowledge improved the robustness of the genomic modelling and the selection of the genetically best individuals in the breeding program. We observed this improvement in both variety selection on genetic values and parent selection on additive values, but the variety selection benefited the most. An improvement was not observed in the overall accuracy of estimating genetic values for all individuals and variance components. Finally, we discuss the importance of expert-knowledge priors for genomic modelling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modelling.

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“…A parameter expanded EM algorithm (Liu, Rubin, & Wu, ) is implemented in ASReml for this case but does not quickly produce an acceptable result when the maximum of the likelihood corresponds to parameter values outside the parameter space. Meyer () presents an approach which seeks to bend a genetic variance matrix towards the covariance structure of the phenotypic variance matrix in the context of multivariate analysis. Another approach is to use a more parsimonious parameterization.…”

Section: Methodsmentioning

confidence: 99%

Average information residual maximum likelihood in practice

Gilmour¹

2019

J Animal Breeding Genetics

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Gilmour, Thompson, and Cullis (Biometrics, 1995, 51, 1440) presented the average information residual maximum likelihood (REML) algorithm for efficient variance parameter estimation in the linear mixed model. That paper dealt specifically with traditional variance component models, but the algorithm was quickly applied to more general models and implemented in several REML packages including ASReml (Gilmour et al., Biometrics, 2015, 51, 1440). This paper outlines the theory with respect to these more general models, describes the main issues encountered in fitting these models and how they have been addressed in the ASReml software. The issues covered are the basics steps in the implementation of the algorithm, keeping parameters within the parameter space, maximizing sparsity, avoiding issues associated with unstructured variance matrices by using the factor‐analytic structure and handling singularities in marker‐based relationship matrices and current work.

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“Bending” and beyond: Better estimates of quantitative genetic parameters?

Cited by 8 publications

References 47 publications

Genetic parameters and direct and maternal effects for important traits in late‐feathering Qingyuan partridge hens in China

Genetic parameters and direct and maternal effects for important traits in late‐feathering Qingyuan partridge hens in China

Robust Modelling of Additive and Non-additive Variation with Intuitive Inclusion of Expert Knowledge

Average information residual maximum likelihood in practice

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