Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis

Liu, Nannan; Du, Yuanhao; Warburton, Marilyn L.; Xiao, Yingjie; Yan, Jianbing

doi:10.1093/molbev/msaa283

Cited by 40 publications

(47 citation statements)

References 60 publications

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“…This was supported by testing the interaction between the detected QTLs and the five sites, where 80.0% of the QTLs were found to be significantly interacting with the sites (Table S7; Materials and methods). This demonstrated the dynamic genetic effects of mean QTLs across sites and highlighted partial overlap for QTLs regulating mean and plasticity as reported in the previous studies 24,25,27 .…”

Section: Loci Associated With the Variation Of Remaining Traits-a Complex Genetic Architecture Involving Multiallelic Pleiotropy And Genosupporting

confidence: 79%

“…Previous studies showed that the highly selected region during maize adaptation to temperate climate explained less G-by-E variation than the selected region 12 and the allele frequency of plasticity QTLs were changed between temperate and tropical lines 27 , suggesting that directional selection may have shaped their genetic diversity. Here, we explored whether the 93 plasticity QTLs were selected during intense artificial selection by evaluating their allele frequency changes using two collections of breeding materials, one collection from China that has predominantly been deployed in the 1960s, 1980s, and early 2000s, and a second collection from US before and after 2003 50 .…”

Section: Plasticity Qtl May Have Been Subjected To Directional Selection During the Breeding Programmentioning

confidence: 96%

“…Further investigations demonstrated the role of plastic QTLs in heterosis and adaptation from tropical to temperate zone, paving the way to genomic-promised crop improvement by manipulating the phenotypic plasticity 27,28 .…”

Section: Introductionmentioning

confidence: 99%

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Complex genetic architecture underlying the plasticity of maize agronomic traits

Jin

Liu

Guo

et al. 2022

Preprint

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Phenotypic plasticity is the property of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant for future agriculture under changing climate. Here, we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a maize diverse population, planted at five sites with distinct environmental conditions and genotyped with ~ 6.60 million SNPs. We found that altitude-related environmental factors were main drivers for across site variation in flowering time traits but not plant architecture and yield traits. For 23 traits, we detected 109 QTLs, of which 29 was for mean, 66 was for plasticity, and 14 for both parameters, besides, 80% of the QTLs were interreacted with the environment. The effects of several QTLs changed in magnitude or sign, driving variation in phenotype plasticity, and we further experimentally validated one plastic gene ZmTPS14.1 whose effect was likely mediated by the compensation effect of ZmSPL6 which was from the downstream pathway probably. By integrating genetic diversity, environmental variation, and their interaction in a joint model, we could provide site-specific predictions with increased accuracy by as much as 15.5%, 3.8%, and 4.4% for DTT, PH, and EW, respectively. Overall, we revealed a complex genetic architecture involving multiallelic, pleiotropy, and genotype by environment interaction underlying maize complex trait mean and plasticity variation. Our study thus provided novel insights into the dynamic genetic architectures of agronomic traits in response to changing environments, paving a practical route to precision agriculture.

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Section: Loci Associated With the Variation Of Remaining Traits-a Complex Genetic Architecture Involving Multiallelic Pleiotropy And Genosupporting

confidence: 79%

Section: Plasticity Qtl May Have Been Subjected To Directional Selection During the Breeding Programmentioning

confidence: 96%

See 1 more Smart Citation

Complex genetic architecture underlying the plasticity of maize agronomic traits

Jin

Liu

Guo

et al. 2022

Preprint

Self Cite

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“…The potential of phenotypic plasticity to evolve [ 61 ] in maize lines endowed with native resistance to FAW foliar damage due to the strong influence of genotype by environment (GXE) interaction underscores the importance of conducting multi-year germplasm evaluations when screening maize germplasm for resistance to FAW foliar damage. Previous studies have also concluded that the analysis of genotype by environment interactions could facilitate the identification of cultivars whose yield-stability are related to the linear effect of an environmental index [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Genomic Analysis of Resistance to Fall Armyworm (Spodoptera frugiperda) in CIMMYT Maize Lines

Kamweru

Anani

Beyene

et al. 2022

Genes

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The recent invasion, rapid spread, and widescale destruction of the maize crop by the fall armyworm (FAW; Spodoptera frugiperda (J.E. Smith)) is likely to worsen the food insecurity situation in Africa. In the present study, a set of 424 maize lines were screened for their responses to FAW under artificial infestation to dissect the genetic basis of resistance. All lines were evaluated for two seasons under screen houses and genotyped with the DArTseq platform. Foliar damage was rated on a scale of 1 (highly resistant) to 9 (highly susceptible) and scored at 7, 14, and 21 days after artificial infestation. Analyses of variance revealed significant genotypic and genotype by environment interaction variances for all traits. Heritability estimates for leaf damage scores were moderately high and ranged from 0.38 to 0.58. Grain yield was negatively correlated with a high magnitude to foliar damage scores, ear rot, and ear damage traits. The genome-wide association study (GWAS) revealed 56 significant marker–trait associations and the predicted functions of the putative candidate genes varied from a defense response to several genes of unknown function. Overall, the study revealed that native genetic resistance to FAW is quantitative in nature and is controlled by many loci with minor effects.

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“…A two-step approach has been widely used to study the genetics underlying hybrid performance, where the first step consists of estimating the GCA, SCA and the MPH ( Guo et al, 2014 ; Zhou et al, 2018 ; Yi et al, 2019 ) and the second step represents the QTL mapping step with the GCA, SCA and MPH treated as the traits of interest. In a previous genome-wide association study (GWAS) with an NCII population, different coding schemes for the genotypes were applied, namely the additive, dominance and recessive coding ( Hyun et al, 2008 ; Liu et al, 2021 ). However, the additive model was usually not sufficient to explain hybrid performance and MPH.…”

Section: Introductionmentioning

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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Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis

Cited by 40 publications

References 60 publications

Complex genetic architecture underlying the plasticity of maize agronomic traits

Complex genetic architecture underlying the plasticity of maize agronomic traits

Genomic Analysis of Resistance to Fall Armyworm (Spodoptera frugiperda) in CIMMYT Maize Lines

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

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