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

Liu, Hailan; Chen, Guo-Bo

doi:10.1038/s41437-018-0099-5

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…In other conditions, both methods performed similarly (for example, when a training population size was 20,000 r 2 GBLUP = 0:653 ± 0:017 vs. r 2 RHEPCG = 0:648 ± 0:021). With the enlargement of the training population, the predictive accuracy approximated the true heritability, which as some studies have demonstrated, is the upper bound of predictive accuracy (de los Campos et al, 2013;Liu and Chen, 2018). Meanwhile, RHEPCG was significantly faster than GBLUP (for example, when a training population size was 20,000 T GBLUP =53666s vs T RHEPCG =1237s ).…”

Section: Resultsmentioning

confidence: 85%

An efficient genomic prediction method without the direct inverse of the genomic relationship matrix

2022

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GBLUP, the most widely used genomic prediction (GP) method, consumes large and increasing amounts of computational resources as the training population size increases due to the inverse of the genomic relationship matrix (GRM). Therefore, in this study, we developed a new genomic prediction method (RHEPCG) that avoids the direct inverse of the GRM by combining randomized Haseman–Elston (HE) regression (RHE-reg) and a preconditioned conjugate gradient (PCG). The simulation results demonstrate that RHEPCG, in most cases, not only achieves similar predictive accuracy with GBLUP but also significantly reduces computational time. As for the real data, RHEPCG shows similar or better predictive accuracy for seven traits of the Arabidopsis thaliana F2 population and four traits of the Sorghum bicolor RIL population compared with GBLUP. This indicates that RHEPCG is a practical alternative to GBLUP and has better computational efficiency.

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Section: Resultsmentioning

confidence: 85%

An efficient genomic prediction method without the direct inverse of the genomic relationship matrix

2022

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“…Dominance effects include incomplete dominance, complete dominance and overdominance, and these effects vary for different traits. Typical genomic prediction models with dominance genetic effects assume that dominance genetic effects of all loci have the same variance and directly use genomic markers to construct a dominance genetic relationship matrix between individuals using the same matrix for all traits (Alves et al 2020 ; Liu and Chen 2018 ; Su et al 2012 ). In the present study, the SNP loci are weighted according to the degree of deviation between heterozygous and homozygous loci, which differentiates the degrees of dominance effects on the trait between loci.…”

Section: Discussionmentioning

confidence: 99%

Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities

Liu

Luo

et al. 2022

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The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.

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“…Nonadditive variance, which includes dominance and epistatic effects that generally consist of various interaction effects, such as AA, additive-by-dominance (AD), and dominance-by-dominance (DD) interaction effects, has long been recognized as essential component for dissecting the genetic architecture of target traits and understanding the genetic basis of quantitative traits (Su et al, 2012; Da et al, 2014; Muñoz et al, 2014; Azevedo et al, 2015; Jiang and Reif, 2015; Zhao et al, 2015; Bouvet et al, 2016; Dias et al, 2018; Liu and Chen, 2018; Varona et al, 2018). Several studies were performed using TRMs to test the effect of various combinations of nonadditive effects in extended GBLUP models.…”

Section: Discussionmentioning

confidence: 99%

Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize

et al. 2019

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Genomic selection (GS), a tool developed for molecular breeding, is used by plant breeders to improve breeding efficacy by shortening the breeding cycle and to facilitate the selection of candidate lines for creating hybrids without phenotyping in various environments. Association and linkage mapping have been widely used to explore and detect candidate genes in order to understand the genetic mechanisms of quantitative traits. In the current study, phenotypic and genotypic data from three experimental populations, including data on six agronomic traits (e.g., plant height, ear height, ear length, ear diameter, grain yield per plant, and hundred-kernel weight), were used to evaluate the effect of trait-relevant markers (TRMs) on prediction accuracy estimation. Integrating information from mapping into a statistical model can efficiently improve prediction performance compared with using stochastically selected markers to perform GS. The prediction accuracy can reach plateau when a total of 500–1,000 TRMs are utilized in GS. The prediction accuracy can be significantly enhanced by including nonadditive effects and TRMs in the GS model when genotypic data with high proportions of heterozygous alleles and complex agronomic traits with high proportion of nonadditive variancein phenotypic variance are used to perform GS. In addition, taking information on population structure into account can slightly improve prediction performance when the genetic relationship between the training and testing sets is influenced by population stratification due to different allele frequencies. In conclusion, GS is a useful approach for prescreening candidate lines, and the empirical evidence provided by the current study for TRMs and nonadditive effects can inform plant breeding and in turn contribute to the improvement of selection efficiency in practical GS-assisted breeding programs.

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

Cited by 11 publications

References 31 publications

An efficient genomic prediction method without the direct inverse of the genomic relationship matrix

An efficient genomic prediction method without the direct inverse of the genomic relationship matrix

Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities

Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize

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