scholarly journals Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Vipin Tomar ◽  
Daljit Singh ◽  
Guriqbal Singh Dhillon ◽  
Yong Suk Chung ◽  
Jesse Poland ◽  
...  
Keyword(s):  
Complex Traits ◽  
Predictive Accuracy ◽  
Model Performance ◽  
Environmental Variation ◽  
Triticum Aestivum L ◽  
Wheat Breeding ◽  
Environmental Variance ◽  
Breeding Programs ◽  
Breeding Lines ◽  
The Impact

Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

Genotype×environment interactions and some considerations of their implications for wheat breeding in Australia This review is one of a series commissioned by the Advisory Committee of the Journal.

1998 ◽  
Vol 49 (2) ◽  
pp. 153 ◽  
Author(s):  
K. E. Basford ◽  
M. Cooper
Keyword(s):  
Genetic Material ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Specific Adaptation ◽  
New Concepts ◽  
Statistical Studies ◽  
Definition Of ◽  
Level Of Understanding ◽  
The Impact

Genotype×environment (G×E) interactions complicate selection forbroad adaptation, while their nature and causes need to be understood toutilise and exploit them in selection for specific adaptation. This invitedreview combines an assessment of the literature with the experience we havegained from involvement in wheat breeding and associated research programs toassess (1) the implications of G×E interactions for wheat breeding inAustralia, (2) the impact that research into G E interactions has had onbreeding strategy, and (3) the evidence for impact from this research efforton genetic improvement of crop adaptation. The role of analytical methodologyin this process is considered and some important issues are discussed.There are sufficient examples drawn from wheat breeding in Australia tosuggest that progress in dealing with G×E interactions can be made andseveral of these are presented. They show that impact in plant breedingfollows from achieving an appropriate level of understanding of theenvironmental and genetic factors causing the interactions as well as anassessment of their importance in the target genotype-environment system. Anaccurate definition of the environmental factor(s) contributing to theG×E interactions has been particularly important in determining therelevance of observed differences in plant adaptation to the target populationof environments. From the combination of biological and statistical studies, amore comprehensive understanding of G×E interactions has emerged andcontributed to new concepts and procedures for dealing with them.Distinguishing between what are repeatable and non-repeatable interactions isa key step. Genuine cases of positive specific adaptation observed inmulti-environment trials (METs) can be exploited by appropriately targetedselection strategies, while non-repeatable interactions are accommodated byselection for broad adaptation.The investigation of G×E interactions for grain yield of wheat inAustralia has matured to the point where an understanding of some of theircauses has enabled wheat breeders to exploit positive components of specificadaptation. The experience that has been gained in achieving these advancesindicates the importance of establishing a MET system that is relevant to thetarget population of environments of the breeding program. The investment ofadequate resources into effective design, conduct, analysis, andinterpretation of METs remains critical to continued progress from selectionin complex genotype-environment systems that present large G× Einteractions. Wheat breeders who understand their genetic material and thetarget population of environments can then use the generated information baseto achieve impact from their breeding programs.

scholarly journals Development of new PCR-based markers specific for chromosome arms of rye (Secale cereale L.)

Genome ◽  
2016 ◽  
Vol 59 (3) ◽  
pp. 159-165 ◽  
Author(s):  
Ling Qiu ◽  
Zong-xiang Tang ◽  
Meng Li ◽  
Shu-lan Fu
Keyword(s):  
Secale Cereale ◽  
Triticum Aestivum L ◽  
Wheat Breeding ◽  
Addition Lines ◽  
Breeding Programs ◽  
Effective Utilization ◽  
Secale Cereale L ◽  
Monosomic Addition ◽  
Monosomic Addition Lines ◽  
Disomic Addition Lines

PCR-based rye (Secale cereale L.) chromosome-specific markers can contribute to the effective utilization of elite genes of rye in wheat (Triticum aestivum L.) breeding programs. In the present study, 578 new PCR-based rye-specific markers have been developed by using specific length amplified fragment sequencing (SLAF-seq) technology, and 76 markers displayed different polymorphism among rye Kustro, Imperial, and King II. A total of 427 and 387 markers were, respectively, located on individual chromosomes and chromosome arms of Kustro by using a set of wheat–rye monosomic addition lines and 13 monotelosomic addition lines, which were derived from T. aestivum L. ‘Mianyang11’ × S. cereale L. ‘Kustro’. In addition, two sets of wheat–rye disomic addition lines, which were derived from T. aestivum L. var. Chinese Spring × S. cereale L. var. Imperial and T. aestivum L. ‘Holdfast’ × S. cereale L. var. King II, were used to test the chromosomal specificity of the 427 markers. The chromosomal locations of 281 markers were consistent among the three sets of wheat–rye addition lines. The markers developed in this study can be used to identify a given segment of rye chromosomes in wheat background and accelerate the utilization of elite genes on rye chromosomes in wheat breeding programs.

scholarly journals Identification of prospective sources of agronomically-valuable traits of bread wheat (Triticum aestivum L.) among breeding lines in the condition of Forest-Steppe of Ukraine

2020 ◽  
Vol 10 (5) ◽  
pp. 253-258
Author(s):  
S.O. Kovalchuk ◽  
S.I. Voloschuk ◽  
N.A. Kozub
Keyword(s):  
Bread Wheat ◽  
Seed Proteins ◽  
Seed Storage ◽  
Triticum Aestivum L ◽  
Wheat Breeding ◽  
Brown Rust ◽  
Forest Steppe ◽  
Breeding Lines ◽  
Biochemical Compositions ◽  
Moderate Resistance

The aim of work was the estimation of valuable traits of bread wheat breeding lines, obtained from interspecies crosses with wild Aegilops and Triticum species growing in a condition of the Forest-Steppe of Ukraine. We used the seed proteins electrophoresis in PAAG for confirmation of the presence of rye seed storage components in the wheat parental lines genomes. The biochemical compositions of seeds had determined by the infrared spectroscopy method. As a result of researching from the set of 600 breeding lines were selected best lines with increased grain yield from 1 m2, with high protein content in grain, disease resistance, and winter hardiness significantly exceeded the standard variety Polesskaya-90. All lines have high and moderate resistance against diseases: Powdery Mildew, Brown Rust, Septoria Blotch. Based on obtained data had selected breeding lines, which were promising sources of single and complex agronomically valuable traits for bread wheat breeding and genetic researches.

scholarly journals Comparison of Genomic Selection Models for Exploring Predictive Ability of Complex Traits in Breeding Programs

2021 ◽  
Author(s):  
Lance F. Merrick ◽  
Arron H. Carter
Keyword(s):  
Complex Traits ◽  
Seedling Emergence ◽  
Predictive Ability ◽  
Complex Trait ◽  
Parametric Models ◽  
Support Vector ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Mapping Panel ◽  
Increase In Accuracy

AbstractTraits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait’s genetic basis. One such trait, seedling emergence of wheat (Triticum aestivumL.) from deep planting, presents a unique opportunity to explore the best method to use and implement GS models to predict a complex trait. 17 GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel (DP) phenotyped from 2015-2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross-validations. However, the consistent moderate accuracy of cBLUP and other parametric models indicates no need to implement computationally demanding non-parametric models for complex traits. There was an increase in accuracy using cross-validations from 0.40 to 0.41 and independent validations from 0.10 to 0.17 using diversity panels lines to breeding lines. The environmental effects of complex traits can be overcome by combining years of the same populations. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using parametric models within their own breeding programs with increased accuracy as they combine training populations over the years.

scholarly journals Fifty years of wheat breeding in Southern Brazil: yield improvement and associated changes

2007 ◽  
Vol 42 (6) ◽  
pp. 817-825 ◽  
Author(s):  
Osmar Rodrigues ◽  
Julio César Barreneche Lhamby ◽  
Agostinho Dirceu Didonet ◽  
José Abramo Marchese
Keyword(s):  
Grain Yield ◽  
Harvest Index ◽  
Wheat Breeding ◽  
Wheat Cultivars ◽  
Southern Brazil ◽  
Breeding Programs ◽  
The Past ◽  
Biological Yield ◽  
Grain Yield Components ◽  
The Impact

The objective of this study was to assess the impact of genetic breeding on grain yield, and to identify the physiological traits associated to the increment in yield and their related growth processes, for wheat cultivars grown in Southern Brazil, in the past five decades. Seven wheat cultivars released between 1940 and 1992, were compared for physiological aspects associated with grain yield. Grain yield, biological yield, biomass partitioning, harvest index and grain yield components were also determined. The number of grains per square meter was more affected by plant breeding and was better correlated with grain yield (r = 0.94, p<0.01) than with grain weight (r = -0.39ns). The higher number of grains per square meter was better correlated with the number of grains per spike in the modern cultivars than in the older ones. The genetic gain in grain yield was 44.9 kg ha-1 per year, reflecting important efforts of the breeding programs carried out in Southern Brazil. Grain yield changes, during the period of study, were better associated with biomass production (r = 0.78, p<0.01) than with harvest index (r = 0.65, p<0.01).

A linked SNP marker to genotype Fr-B2 in wheat

2018 ◽  
Vol 69 (9) ◽  
pp. 859
Author(s):  
H. A. Eagles ◽  
J. Hyles ◽  
Jayne Wilson ◽  
Karen Cane ◽  
K. L. Forrest ◽  
...  
Keyword(s):  
Triticum Aestivum L ◽  
Frost Tolerance ◽  
Large Deletion ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Breeding Lines ◽  
Specific Pcr ◽  
Chromosome 5B ◽  
Allele Specific ◽  
Specific Pcr Assay

Fr-B2 is a complex locus on chromosome 5B that affects frost tolerance, days to heading, grain yield and probably other traits of commercial importance in wheat (Triticum aestivum L.). It interacts epistatically with other major genes, especially VRN1. There are two known alleles of Fr-B2: an intact, wild-type allele, and an allele with a large deletion. Published methods for identifying these alleles are slow and expensive, making the development of a high-throughput, co-dominant SNP (single-nucleotide polymorphism) marker highly desirable, especially for commercial wheat breeding. A diverse panel of cultivars and breeding lines was characterised for SNPs and alleles of Fr-B2. Four SNP markers co-segregated as a haplotype block with Fr-B2 across unrelated cultivars and related backcrosses differing for alleles of Fr-B2. A robust KASP (Kompetitive allele-specific PCR) assay was developed for one of the SNPs, KASP_IWB26333, which should facilitate the inclusion of Fr-B2 on genotyping platforms for breeding and research.

Of growing importance: combining greater early vigour and transpiration efficiency for wheat in variable rainfed environments

2015 ◽  
Vol 42 (12) ◽  
pp. 1107 ◽  
Author(s):  
P. B. Wilson ◽  
G. J. Rebetzke ◽  
A. G. Condon
Keyword(s):  
Triticum Aestivum L ◽  
Wheat Breeding ◽  
Transpiration Efficiency ◽  
Average Yield ◽  
Breeding Programs ◽  
Grain Yields ◽  
Complex Genetics ◽  
Early Vigour ◽  
Potential Benefits ◽  
Efficient Water Use

Increasing climate variability, particularly variability in the timing and amount of soil water, means that breeding wheat (Triticum aestivum L.) varieties with stable high grain yields is increasingly more challenging. Changing environmental conditions in water-limited rainfed environments will alter genotype ranking to reduce confidence in the identification of consistently higher yielding performers. Greater early vigour (EV) and transpiration efficiency (TE) are two physiological traits that have demonstrated benefits as breeding targets for efficient water-use in Mediterranean in-season water and monsoonal stored water environments, respectively. This Perspective discusses the hypothesis that combining higher TE and greater EV will broaden the adaptation and increase grain yields for wheats grown across most rainfed environments. We examine the physiology underpinning adaptation with greater EV and higher TE, as well as the challenges and potential benefits of deploying these traits in combination. We then discuss how these two traits interact with different environments and, in particular, the different wheat-growing regions of Australia. We conclude that the combination of these two traits is genetically and physiologically feasible, as well theoretically beneficial to average yield in most rainfed environments. Hence, we suggest a strategy for reliably managing the complex genetics underpinning EV and TE when phenotyping and selecting both traits in commercial wheat breeding programs.

Inheritance of vernalization response in three populations of spring wheat

1994 ◽  
Vol 74 (4) ◽  
pp. 753-757 ◽  
Author(s):  
P. E. Jedel
Keyword(s):  
Spring Wheat ◽  
Triticum Aestivum L ◽  
Wheat Breeding ◽  
Vernalization Response ◽  
Breeding Programs ◽  
Reciprocal Crosses ◽  
Vrn Genes ◽  
Selection For ◽  
Early Late ◽  
Made In

Vernalization responses are known to differ among spring wheat (Triticum aestivum L.) genotypes. Three crosses were made to determine the inheritance of vernalization response in the spring wheat cultivars Cajeme 71, Yecora 70, Glenlea, Pitic 62 and Neepawa. Segregation analyses of days to anthesis were made of the F2 generation in a growth room (25/15 °C, 16/8 h). Segregation analysis of the F3 generation was made in a summer greenhouse. Reciprocal crosses between Neepawa and Pitic 62 indicated an early/late/transgressively late ratio of 12:3:1 in the F2 generation. The F3 generation results fitted an early/late/transgressively late/segregating ratio of 4:1:1:10. Based on the segregation of transgressively late types from both crosses, it was concluded that the genes for spring habit in Pitic 62 and Neepawa were different and not maternally inherited. The Glenlea/Pitic 62 cross produced one transgressively late segregant in an F2 population of 97 plants. The data fitted an early/late/transgressively late ratio of 60:3:1, indicating that Glenlea may differ from Pitic at three Vrn loci. Therefore, either Glenlea or Pitic 62 may carry two dominant Vrn alleles. The reciprocal crosses between Yecora 70 and Cajeme 71 did not segregate transgressively late types in the F2 generation. Therefore, those cultivars had a Vrn allele in common. Selection for vernalization response might be useful when introducing exotic germplasm into spring wheat breeding programs and in manipulating maturity responses. Key words: Vernalization, spring wheat, Vrn genes

scholarly journals Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

2021 ◽  
Vol 12 ◽  
Author(s):  
Harsimardeep S. Gill ◽  
Jyotirmoy Halder ◽  
Jinfeng Zhang ◽  
Navreet K. Brar ◽  
Teerath S. Rai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Agronomic Traits ◽  
Wheat Breeding ◽  
Successful Implementation ◽  
Limited Information ◽  
Multiple Traits ◽  
Breeding Lines

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Haplotype analyses in wheat for complex traits: tracking the chromosome 3B and 7B regions associated with late maturity alpha amylase (LMA) in breeding programs

2009 ◽  
Vol 60 (5) ◽  
pp. 463 ◽  
Author(s):  
M. D. McNeil ◽  
D. Diepeveen ◽  
R. Wilson ◽  
I. Barclay ◽  
R. McLean ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Relationships ◽  
Artificial Chromosome ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Discriminating Power ◽  
Late Maturity ◽  
Australian Wheat ◽  
Wheat Lines ◽  
Chromosome 3B

The quantitative trait loci (QTLs) on chromosomes 7BL and 3BS from Halberd have been used as a major source of tolerance to late maturity α amylase (LMA) within Australian wheat breeding programs. New simple sequence repeat (SSR) markers identified from the sequencing of Bacterial Artificial Chromosome (BAC) clones from the wheat cv. Renan library, and known SSRs, were used to characterise these major QTLs. The reduction or elimination of the LMA defect in wheat cultivars is a major goal for wheat breeding programs and is confounded by the complexity in measuring the trait unambiguously. In this haplotyping study focussing on two significant chromosomal regions, markers and combinations of markers were investigated for their ability to discriminate between 39 Australian and Mexican wheat lines differing in levels of LMA. Genetic relationships among these wheat lines estimated by cluster analysis of molecular marker data were combined with phenotypic information in order to calibrate the genotypes of the wheat lines against their LMA phenotype. It was evident that some SSRs from the respective QTLs had greater discriminating power than others to identify LMA phenotypes. Discrimination was not, however, absolute and a statistical analysis of the data defined a risk factor associated with particular combinations of alleles, for use in early selection or backcrossing.

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