Genetic relatedness and the ratio of subpopulation‐common alleles are related in genomic prediction across structured subpopulations in maize

Li, Dongdong; Wang, Pingxi; Gu, Rong; Fu, Junjie; Xu, Zhenxiang; Lyle, Demar; Peng, Yunling; Wang, Guoying; Zhang, Hongwei

doi:10.1111/pbr.12717

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…For cross-validation scheme 1, just two of 10 traits, namely EH and KL showed slight improvement. We guess that TC and TM populations had different genetic backgrounds ( Li et al, 2019 ) and those QTL made different effect within two different populations. So the QTL effect was estimated biasedly when only one tester population as training population.…”

Section: Discussionmentioning

confidence: 99%

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

Zhou

et al. 2021

Front. Plant Sci.

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Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

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Section: Discussionmentioning

confidence: 99%

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

Zhou

et al. 2021

Front. Plant Sci.

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“…The lower genetic relationship between training populations consisting of in-house developed lines and externally developed selection candidates has, however, a strong influence on the accuracy of genomic predictions (Clark et al, 2012;Ly et al, 2013;Pszczola et al, 2012). A larger genetic distance is usually associated with a lower prediction accuracy as has been observed in the prediction across germplasm groups in maize (Zea mays L.) (Guo et al, 2014;Li et al, 2019;Morais et al, 2020;Rio et al, 2019), sorghum [Sorghum bicolor (L.) Moench] (Sapkota et al, 2020), and wheat (Bentley et al, 2014;Norman et al, 2018). This observation was likewise made when predicting germplasm developed outside the given breeding program with an in-house training population that did not cover the entire genetic space of the other breeding programs (Figure 2), even though all lines were adapted to Central European growing conditions and more closely related to each other in comparison to the mentioned maize and sorghum germplasm groups.…”

Section: Discussionmentioning

confidence: 99%

Genomic selection of parents and crosses beyond the native gene pool of a breeding program

Michel

Löschenberger²,

Ametz³

et al. 2021

The Plant Genome

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Genomic selection has become a valuable tool for selecting cultivar candidates in many plant breeding programs. Genomic selection of elite parents and crossing combinations with germplasm developed outside a breeding program has, however, hardly been explored until now. The aim of this study was to assess the potential of this method for commonly ranking and selecting elite germplasm developed within and beyond a given breeding program. A winter wheat (Triticum aestivum L.) population consisting of 611 in-house and 87 externally developed lines was used to compare training population compositions and statistical models for genomically predicting baking quality in this framework. Augmenting training populations with lines from other breeding programs had a larger influence on the prediction ability than adding in-house generated lines when aiming to commonly rank both germplasm sets. Exploiting preexisting information of secondary correlated traits resulted likewise in more accurate predictions both in empirical analyses and simulations. Genotyping germplasm developed beyond a given breeding program is moreover a convenient way to clarify its relationships with a breeder's own germplasm because pedigree information is oftentimes not available for this purpose. Genomic predictions can thus support a more informed diversity management, especially when integrating simply to phenotype correlated traits to partly circumvent resource reallocations for a costly phenotyping of germplasm from other programs.

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Genomic Prediction across Structured Hybrid Populations and Environments in Maize

et al. 2021

Plants

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Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.

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Genetic relatedness and the ratio of subpopulation‐common alleles are related in genomic prediction across structured subpopulations in maize

Cited by 3 publications

References 38 publications

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

Genomic selection of parents and crosses beyond the native gene pool of a breeding program

Genomic Prediction across Structured Hybrid Populations and Environments in Maize

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