Genomic prediction of maize yield across European environmental conditions

Millet, Émilie; Kruijer, Willem; Coupel‐Ledru, Aude; Prado, Santiago Alvarez; Cabrera‐Bosquet, Llorenç; Lacube, Sébastien; Charcosset, Alain; Welcker, Claude; Eeuwijk, Fred van; Tardieu, François

doi:10.1038/s41588-019-0414-y

Cited by 204 publications

(239 citation statements)

References 43 publications

Supporting

Mentioning

235

Contrasting

Order By: Relevance

“…First we analyze the field trials described in Millet et al (2016) and Millet et al (2019), involving 254 hybrids of maize ( Zea mays ). We consider a subset of four trials, representing four (out of a total of five) different environmental scenarios described in Millet et al (2016).…”

Section: Resultsmentioning

confidence: 99%

Reconstruction of networks with direct and indirect genetic effects

Kruijer

Behrouzi

Korts

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

1Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e. through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example when trying to improve crop yield and simultaneously controlling plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, current methods require all genetic variance to be explained by a small number of QTLs with fixed effects. Only few authors considered the 'missing heritability' case, where contributions of many undetectable QTLs are modelled with random effects. Usually, these are treated as nuisance terms, that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, leading to the PCgen algorithm, which can analyze many more traits (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used. We have implemented the PCgen-algorithm in the R-package pcgen. 2 3 4 5 6 7 8 9 10 11 12 13 14 15KEYWORDS Structural Equation Models, multivariate mixed models, causal inference 16 17 18 22 the genomic prediction applications are based on linear mixed-23 or Bayesian models that predict the phenotype for the target 24 trait (yield) as a function of a multivariate distribution for SNP 25 effects. In these models, the physiological mechanisms and traits 26 that modulate the genotypic response to the environment over 27 time are modeled implicitly via the SNP effects on the target 28 trait (Zhou and Stephens 2014; Calus and Veerkamp 2011). The 29 availability of high throughput phenotyping technologies has 30 enabled breeders to characterize additional traits and monitor 31 growth and development during the season. This opens new 32 opportunities in breeding strategies, in which better-adapted 33 genotypes result from combining loci that regulate complemen-34 tary physiological mechanisms. This kind of breeding strategy 35 is called physiological breeding (Reynolds and Langridge 2016). 36In physiological breeding, prediction accuracy for the target 37 trait benefits from modeling the target trait and its underly-38 ing traits simultaneously (van Eeuwijk et al. 2018) because of a 39 larger power to estimate its underlying effects (Stephens 2013).40The challenge of...

show abstract

Section: Resultsmentioning

confidence: 99%

Reconstruction of networks with direct and indirect genetic effects

Kruijer

Behrouzi

Korts

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thereafter, the correspondence relationships between plasticity-related genetic loci and environmental factors or scenarios can be investigated, and the favorable alleles of these genetic loci can be utilized in MAS to cultivate environmental-specific cultivars. In addition, the rice grain ionome recorded in these studies can also be used to perform GS across varied environments, just as the prediction of maize yield under G×E interaction (Millet et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Lower plasticity in disease resistance is crucial to broadly-adaptability cultivars, while phenotypic plasticity can be harnessed to improve the cultivars' yield performance in determined environmental scenarios with an adequate supply of water and fertilizer. For traits show G×E, incorporating G×E in the genomic prediction can boost its accuracy, especially in field experiments performed in a wide range of environmental scenarios (Lopez-Cruz et al, 2015;Malosetti et al, 2016;Millet et al, 2019). The prerequisite for utilizing phenotypic plasticity in breeding practice is investigating the effect of phenotypic plasticity on phenotypic variance and dissecting the genetic architecture for phenotypic plasticity.…”

Section: Introductionmentioning

confidence: 99%

The Genetic Architecture for Phenotypic Plasticity of the Rice Grain Ionome

et al. 2020

View full text Add to dashboard Cite

The ionome of the rice grain is crucial for the health of populations that consume rice as a staple food. However, the contribution of phenotypic plasticity to the variation of rice grain ionome and the genetic architecture of phenotypic plasticity are poorly understood. In this study, we investigated the rice grain ionome of a rice diversity panel in up to eight environments. A considerable proportion of phenotypic variance can be attributed to phenotypic plasticity. Then, phenotypic plasticity and mean phenotype were quantified using Bayesian Finlay-Wilkinson regression, and a significant correlation between them was observed. However, the genetic architecture of mean phenotype was distinct from that of phenotypic plasticity. Also, the correlation between them was mainly attributed to the phenotypic divergence between rice subspecies. Furthermore, the results of wholegenome regression analysis showed that the genetic loci related to phenotypic plasticity can explain a considerable proportion of the phenotypic variance in some environments, especially for Cd, Cu, Mn, and Zn. Our study not only sheds light on the genetic architecture of phenotypic plasticity of the rice grain ionome but also suggests that the genetic loci which related to phenotypic plasticity are valuable in rice grain ionome improvement breeding.

show abstract

“…A test of the model was performed based on two field datasets. In Dataset E ( Table 1 ), the final leaf lengths and widths of all leaves of the reference hybrid B73×UH007 were measured in both water deficit and well-watered conditions in 14 experiments presented in Millet et al (2019) , in eight field sites from 2011 to 2013 spread along a climatic transect in Europe ( Supplementary Table S3 ). In Dataset F ( Table 1 ), the 14 parameterized hybrids were analysed in Mauguio in 2016, with the same measurements (hybrid names in Supplementary Table S4 ).…”

Section: Methodsmentioning

confidence: 99%

“…The rapid development of sensor networks and of environmental grids makes it possible to characterize environmental conditions in any field ( Chenu et al , 2013 ; Harrison et al , 2014 ). This information can be combined with the genomic prediction of the sensitivity of individual genotypes to environmental conditions, thereby making possible the prediction of the yield of hundreds of genotypes in hundreds of fields ( Millet et al , 2019 ). However, poor prediction of leaf area is often a cause of inaccurate simulations, as shown by comparison of 27 crop models ( Martre et al , 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios

Lacube

Manceau

Welcker

et al. 2020

Journal of Experimental Botany

Self Cite

View full text Add to dashboard Cite

The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.

show abstract

Genomic prediction of maize yield across European environmental conditions

Cited by 204 publications

References 43 publications

Reconstruction of networks with direct and indirect genetic effects

Reconstruction of networks with direct and indirect genetic effects

The Genetic Architecture for Phenotypic Plasticity of the Rice Grain Ionome

Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios

Contact Info

Product

Resources

About