A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities

TESTS AND ESTIMATORS OF MULTIPLICATIVE MODELS FOR VARIETY TRIALS

Some recently obtained results on cross validation, hypothesis test and estimation procedures for multiplicative models applied to multi-site crop variety trials are presented. The PRESS statistic is more sensitive to overfitting and choice of model form than data-splitting cross-validation. Because of their extreme liberality, Gollob F-tests should not be used to test multiplicative terms. FGH tests effectively control Type I error, but are conservative for tests of terms for which the previous term is small. Simulation tests have greater power than FGH tests, but still effectively control Type I error rates. Simulation results and cross validation in two examples suggest that BLUP style shrinkage estimators of multiplicative terms produce fitted models with predictive value at least as good as the best truncated models and would eliminate the need for cross validation as a criterion for model choice. Shrinkage estimators of multiplicative models were better than BLUPs computed under the assumption of random unpatterened interaction in one example and were at least as good in the second example. Both were much better than empirical cell means in both examples. It is suggested that variety performance estimates derived from shrinkage estimators of multiplicative models should replace empirical cell means routinely reported in experiment station crop variety trial bulletins

Stacking tolerance to drought and resistance to a parasitic weed in tropical hybrid maize for enhancing resilience to stress combinations

Open Access JournalMaize is a food security crop cultivated in the African savannas that are vulnerable to the occurrence of drought stress and Striga hermonthica infestation. The co-occurrence of these stresses can severely damage crop growth and productivity of maize. Until recently, maize breeding in International Institute of Tropical Agriculture (IITA) has focused on the development of either drought tolerant or S. hermonthica resistant germplasm using independent screening protocols. The present study was therefore conducted to examine the extent to which maize hybrids simultaneously expressing resistance to S. hermonthica and tolerance to drought (DTSTR) could be developed through sequential selection of parental lines using the two screening protocols. Regional trials involving 77 DTSTR and 22 commercial benchmark hybrids (STR and non-DTSTR) were then conducted under Striga-infested and non-infested conditions, managed drought stress and fully irrigated conditions as well as in multiple rainfed environments for 5 years. The observed yield reductions of 61% under managed drought stress and 23% under Striga-infestation created desirable stress levels leading to the detection of significant differences in grain yield among hybrids at individual stress and non-stress conditions. On average, the DTSTR hybrids out-yielded the STR and non-DTSTR commercial hybrids by 13–19% under managed drought stress and fully irrigated conditions and by −4 to 70% under Striga-infested and non-infested conditions. Among the DTSTR hybrids included in the regional trials, 33 were high yielders with better adaptability across environments under all stressful and non-stressful testing conditions. Twenty-four of the 33 DTSTR hybrids also yielded well across diverse rainfed environments. The genetic correlations of grain yield under managed drought stress with yield under Striga-infestation and multiple rainfed environments were 0.51 and 0.57, respectively. Also, a genetic correlation between yields under Striga-infestation with that recorded in multiple rainfed environments was 0.58. These results suggest that the sequential selection scheme offers an opportunity to accumulate desirable stress-related traits in parents contributing to superior agronomic performance in hybrids across stressful and diverse rainfed field environments that are commonly encountered in the tropical savannas of Africa

SPATIAL ANALYSIS OF YIELD TRIALS USING SEPARABLE ARIMA PROCESSES

Spatial analysis procedures based on one-dimensional and two-dimensional (separable) ARIMA (Auto Regressive Integrated Moving Average) processes were used to analyze several yield trials. Two criteria were used to determine the best spatial model: 1) standard error of the treatment difference (SED) and 2) mean squared error (MSE) of prediction based on a cross-validation approach. It is found that spatial models with two-dimensional exponential covariance functions are frequently the best models regarding SED and MSE. Differenced models are frequently the best models regarding SED and the worst with respect to MSE

Genetic gains in yield and yield related traits under drought stress and favorable environments in a maize population improved using marker assisted recurrent selection

The objective of marker assisted recurrent selection (MARS) is to increase the frequency of favorable marker alleles in a population before inbred line extraction. This approach was used to improve drought tolerance and grain yield (GY) in a biparental cross of two elite drought tolerant lines. The testcrosses of randomly selected 50 S1 lines from each of the three selection cycles (C0, C1, C2) of the MARS population, parental testcrosses and the cross between the two parents (F1) were evaluated under drought stress (DS) and well watered (WW) well as under rainfed conditions to determine genetic gains in GY and other agronomic traits. Also, the S1 lines derived from each selection types were genotyped with single nucleotide polymorphism (SNP) markers. Testcrosses derived from C2 produced significantly higher grain field under DS than those derived from C0 with a relative genetic gain of 7% per cycle. Also, the testcrosses of S1 lines from C2 showed an average genetic gain of 1% per cycle under WW condition and 3% per cycle under rainfed condition. Molecular analysis revealed that the frequency of favorable marker alleles increased from 0.510 at C0 to 0.515 at C2, while the effective number of alleles (Ne) per locus decreased from C0 (1.93) to C2 (1.87). Our results underscore the effectiveness of MARS for improvement of GY under DS condition

Variability in glutenin subunit composition of Mediterranean durum wheat germplasm and its relationship with gluten strength

The allelic composition at five glutenin loci was assessed by one-dimensional sodium dodecyl sulphate polyacrylamide gel electrophoresis (1D SDS–PAGE) on a set of 155 landraces (from 21 Mediterranean countries) and 18 representative modern varieties. Gluten strength was determined by SDS-sedimentation on samples grown under rainfed conditions during 3 years in north-eastern Spain. One hundred and fourteen alleles/banding patterns were identified (25 at Glu-1 and 89 at Glu-2/Glu-3 loci); 0·85 of them were in landraces at very low frequency and 0·72 were unreported. Genetic diversity index was 0·71 for landraces and 0·38 for modern varieties. All modern varieties exhibited medium to strong gluten type with none of their 13 banding patterns having a significant effect on gluten-strength type. Ten banding patterns significantly affected gluten strength in landraces. Alleles Glu-B1e (band 20), Glu-A3a (band 6), Glu-A3d (bands 6+11), Glu-B3a (bands 2+4+15+19) and Glu-B2a (band 12) significantly increased the SDS-value, and their effects were associated with their frequency. Two alleles, Glu-A3b (band 5) and Glu-B2b (null), significantly reduced gluten strength, but only the effect of the latter locus could be associated with its frequency. Only three rare banding patterns affected gluten strength significantly: Glu-B1a (band 7), found in six landraces, had a negative effect, whereas banding patterns 2+4+14+15+18 and 2+4+15+18+19 at Glu-B3 had a positive effect. Landraces with outstanding gluten strength were more frequent in eastern than in western Mediterranean countries. The geographical pattern displayed from the frequencies of Glu-A1c is discussed.R. Nazco was recipient of a Ph.D. grant from the Comissionat per Universitats i Investigació del Departament d’Innovació, Universitats i Empresa of the Generalitat of Catalonia and the Fondo Social Europeo. This study was partially funded by CICYT under projects AGL2006-09226-C02-01, AGL2009- 11187 and AGL2012-37217 and was developed within the framework of the agreement between INIA Spain and CIMMYT. The Centre UdL-IRTA is part of the Centre CONSOLIDER INGENIO 2010 on Agrigenomics funded by the Spanish Ministry of Education and Science. CRF, ICARDA and USDA Germplasm Bank are acknowledged for providing accessions for the present study.info:eu-repo/semantics/publishedVersio

Dominance and G×E interaction effects improvegenomic prediction and genetic gain inintermediate wheatgrass (Thinopyrumintermedium)

Genomic selection (GS) based recurrent selection methods were developed to accelerate the domestication of intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey]. A subset of the breeding population phenotyped at multiple environments is used to train GS models and then predict trait values of the breeding population. In this study, we implemented several GS models that investigated the use of additive and dominance effects and G×E interaction effects to understand how they affected trait predictions in intermediate wheatgrass. We evaluated 451 genotypes from the University of Minnesota IWG breeding program for nine agronomic and domestication traits at two Minnesota locations during 2017–2018. Genet-mean based heritabilities for these traits ranged from 0.34 to 0.77. Using fourfold cross validation, we observed the highest predictive abilities (correlation of 0.67) in models that considered G×E effects. When G×E effects were fitted in GS models, trait predictions improved by 18%, 15%, 20%, and 23% for yield, spike weight, spike length, and free threshing, respectively. Genomic selection models with dominance effects showed only modest increases of up to 3% and were trait-dependent. Crossenvironment predictions were better for high heritability traits such as spike length, shatter resistance, free threshing, grain weight, and seed length than traits with low heritability and large environmental variance such as spike weight, grain yield, and seed width. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

Citation: Rutkoski, J., . . . Singh, R. (2016). Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. G3-Genes Genomes Genetics, 6(9), 2799-2808. https://doi.org/10.1534/g3.116.032888Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots

Threshold Models for Genome-Enabled Prediction of Ordinal Categorical Traits in Plant Breeding

Categorical scores for disease susceptibility or resistance often are recorded in plant breeding. The aim of this study was to introduce genomic models for analyzing ordinal characters and to assess the predictive ability of genomic predictions for ordered categorical phenotypes using a threshold model counterpart of the Genomic Best Linear Unbiased Predictor (i.e., TGBLUP). The threshold model was used to relate a hypothetical underlying scale to the outward categorical response. We present an empirical application where a total of nine models, five without interaction and four with genomic x environment interaction (G·E) and genomic additive x additive x environment interaction (GxGxE), were used. We assessed the proposed models using data consisting of 278 maize lines genotyped with 46,347 single-nucleotide polymorphisms and evaluated for disease resistance [with ordinal scores from 1 (no disease) to 5 (complete infection)] in three environments (Colombia, Zimbabwe, and Mexico). Models with GxE captured a sizeable proportion of the total variability, which indicates the importance of introducing interaction to improve prediction accuracy. Relative to models based on main effects only, the models that included GxE achieved 9–14% gains in prediction accuracy; adding additive x additive interactions did not increase prediction accuracy consistently across locations