Advances in genomic selection in domestic animals

Zhang, Zhe; Zhang, Qin; Ding, Xiangdong

doi:10.1007/s11434-011-4632-7

Cited by 25 publications

(14 citation statements)

References 57 publications

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“…From genomic selection point of view, significance tests may not be required because all markers should be applied to predict the whole genome effect for each line, regardless how small their effects are (Meuwissen et al 2001). Recently, genomic selection has become a hot topic for animal and plant breeding Hayes 2007, 2009;Goddard et al 2010;Hayes et al 2009;Heffner et al 2009;Jannink et al 2010;Meuwissen et al 2001;Nielsen et al 2009;Schaeffer 2006;Sonesson and Meuwissen 2009;Xu 2003;Xu and Hu 2010;Zhang et al 2011). Genomic selection using multiple environmental data should be a useful subject for further investigation.…”

Section: Discussionmentioning

confidence: 99%

An expectation and maximization algorithm for estimating Q × E interaction effects

Zhao

2012

Theor Appl Genet

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A Markov chain Monte Carlo (MCMC) implemented Bayesian method has been developed to detect quantitative trait loci (QTL) effects and Q x E interaction effects. However, the MCMC algorithm is time consuming due to repeated samplings of QTL parameters. We developed an expectation and maximization (EM) algorithm as an alternative method for detecting QTL and Q x E interaction. Simulation studies and real data analysis showed that the EM algorithm produced comparable result as the Bayesian method, but with a speed many magnitudes faster than the MCMC algorithm. We used the EM algorithm to analyze a well known barley dataset produced by the North American Barley Genome Mapping Project. The dataset contained eight quantitative traits collected from 150 doubled-haploid (DH) lines evaluated in multiple environments. Each line was genotyped for 495 polymorphic markers. The result showed that all eight traits exhibited QTL main effects and Q x E interaction effects. On average, the main effects and Q x E interaction effects contributed 34.56 and 16.23% of the total phenotypic variance, respectively. Furthermore, we found that whether or not a locus shows Q x E interaction does not depend on the presence of main effect.

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Section: Discussionmentioning

confidence: 99%

An expectation and maximization algorithm for estimating Q × E interaction effects

Zhao

2012

Theor Appl Genet

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“…Prediction of yet-to-be observed phenotypes for complex quantitative traits in agricultural species [1,2] or for disease status in medicine [3] exploits connections between phenotypes, genealogies, and DNA variations potentially representing functional diversity of organisms. Systems biology approaches have uncovered abundant epistasis in model organisms, including the mouse and the rat [4], Drosophila melanogaster [5], and Saccharomyces cerevisiae [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

et al. 2013

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BackgroundArguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel.ResultsWe sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible.ConclusionsIt is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance.

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“…With the rapid development of high-throughput molecular marker techniques, such as single nucleotide polymorphisms (SNPs) and statistical approaches, genomic prediction first proposed by Meuwissen et al (2001) has been successfully applied to genetic improvement of complex traits that are controlled by polygenic effects-numerous smalleffect quantitative trait loci (QTL) (Schaeffer, 2006;Hayes et al 2009;Jannink et al 2010;Zhang et al 2011;Riedelsheimer et al 2012). Compared to the conventional marker-assisted selection (MAS), genomic prediction is far more accurate by utilizing all molecular marker information to estimate the breeding values of each individual in a candidate population (Heffner et al 2009;Arruda et al 2016).…”

Section: Introductionmentioning

confidence: 99%

A new genomic prediction method with additive-dominance effects in the least-squares framework

Liu

Chen

2018

Heredity

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In our previous work, we proposed a genomic prediction method combing identical-by-state-based Haseman-Elston regression and best linear prediction with additive variance component only (HEBLP|A herein), the most essential component of genetic variation. Since the dominance effects contribute significantly in heterosis, it is desirable to incorporate the HEBLP with dominance variance component that is expected to enhance the predictive accuracy as we move to the further development: HEBLP|AD, a paralleled implementation of genomic prediction compared with genomic best linear unbiased prediction (GBLUP). The simulation results indicated that when the dominance effects contributed to a large proportion of genetic variation, HEBLP|AD and GBLUP|AD, having similar accuracy, both outperformed HEBLP|A; but when the dominance variation was none or little, HEBLP|A, HEBLP|AD, and GBLUP|AD had similar predictability. The analysis of real data from Arabidopsis thaliana F2 population also demonstrated the latter situation. In summary, HEBLP|AD performed stable whether a trait was controlled by dominance effects or not.

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Advances in genomic selection in domestic animals

Cited by 25 publications

References 57 publications

An expectation and maximization algorithm for estimating Q × E interaction effects

An expectation and maximization algorithm for estimating Q × E interaction effects

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

A new genomic prediction method with additive-dominance effects in the least-squares framework

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