Genetics of grain dormancy in a white wheat

A white-grained wheat accession, AUS1408, is a current major source of pre-harvest sprouting (PHS) tolerance in Australian breeding programs. This study has located 2 significant quantitative trait loci (QTLs) for its grain dormancy on 4AL and 5BL. Their associations with seed dormancy have been determined from population-level marker-trait associations (with 3 years of phenotype data) and confirmed by transmission/disequilibrium test on selected advanced breeding lines. The 4AL QTL was expressed in all years of testing, with phenotypic variance ranging from 5 to 15%, indicating a strong genotype × environment interaction. This QTL has been reported in wheat cultivars of diverse origin and was also found to be strongly influenced by the environment. The 5BL QTL was found to have a remarkably consistent effect on the trait at a phenotypic variance of around 10%. The successful outcome in this study was facilitated by high throughput DArT mapping, which complemented mapping with microsatellite markers for critical QTL identification. Identification of these QTLs from AUS1408 should enable sprouting tolerance derived from this source to be incorporated into advanced breeding lines, with the use of molecular markers reducing the requirement for multi-year field testing.

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Breeding for improved blanchability in peanut: phenotyping, genotype × environment interaction and selection

Crop and Pasture Science ◽

10.1071/cp18156 ◽

2018 ◽

Vol 69 (12) ◽

pp. 1237 ◽

Cited By ~ 2

Author(s):

G. C. Wright ◽

M. G. Borgognone ◽

D. J. O Connor ◽

R. C. N. Rachaputi ◽

R. J. Henry ◽

...

Keyword(s):

Low Cost ◽

Peanut Butter ◽

Environment Interaction ◽

Breeding Lines ◽

Seed Supply ◽

Genotype Environment Interaction ◽

Effective Selection ◽

The Usa ◽

Speed Up ◽

Very High

Breeding for improved blanchability—the propensity of the testa (skin) to be removed from the kernel following rapid heat treatment—is a priority for improvement in the Australian Peanut Breeding Program (APBP). Easy removal of the testa by blanching is required for processing of peanuts into peanut butter and various other confectionary products. Thus, blanchability is an economically important trait in any newly released cultivar in Australia. A better understanding of the range of genetic variation, nature of inheritance and genotype×environment (G×E) interactions, and the development of a low-cost method to phenotype in early generations, could speed up breeding for this trait. Studies were conducted to develop a low-cost, rapid method utilising minimal amounts of seed to phenotype in early generations, along with an assessment of G×E interactions over a range of years and environments to derive optimal selection protocols. Use of a smaller kernel sample size than standard (50 vs 200g) was effective for accurately assessing blanchability in breeding lines and could allow selection in early generations (e.g. in seed produced from a single F2 plant where seed supply is adequate). G×E interaction for blanchability was shown to be very low. Genotypic variance explained 62–100% of the total variance for blanchability, assessed in two diverse germplasm pools including 107 accessions in the USA mini-core over three environments and multiple APBP breeding lines grown over nine different years–environments. Genotypic correlations between all environments were very high (~0.60–0.96), with heritability for the blanchability trait estimated to be very high (0.74–0.97) across the 13 trials. The results clearly demonstrate that effective selection for improved blanchability can be conducted in early generations and in a limited number of contrasting environments to ensure consistency of results.

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Genotype environment interaction and stability for yield and its components in advanced breeding lines of red rice (Oryza sativa L.)

Bangladesh Journal of Botany ◽

10.3329/bjb.v49i3.49328 ◽

2020 ◽

Vol 49 (3) ◽

pp. 425-435

Author(s):

BM Dushyantha Kumar ◽

AP Purushottam ◽

P Raghavendra ◽

T Vittal ◽

KN Shubha ◽

...

Keyword(s):

Grain Yield ◽

Oryza Sativa L ◽

Environment Interaction ◽

Gge Biplot ◽

Red Rice ◽

Yield Performance ◽

Breeding Lines ◽

Genotype Environment Interaction ◽

Ammi Analysis ◽

Total Variability

Effects of genotype, environment and their interaction for grain yield and yield attributing characters in 20 advanced breeding lines of rice across six environments was investigated. Yield stability and adaptability of yield performance were analyzed by Eberhart and Russel model and (GGE) bi-plot. The AMMI analysis of variance indicated that mean squares due to genotypes, location and genotype location contributed per cent 59.08, 5.79 and 21.63, respectively for total variability in grain yield per hectare. Estimates of GGE bi-plot revealed that the lines G1, G3, G11, G13, G15, G12, G16, G7 and G10 were positioned near GGL bi-plot origin indicating wider adaptation for the trait grain yield per hectare. Eberhart and Russel Model and GGE biplot model showed the advanced breeding lines viz., JB 1-11-7 (G1) and JA 6-2 (G15) exhibited wider adaptability across the tested environments for number of productive tillers per plant and yield per hectare.

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DEVELOPMENTAL STABILITY OF DROSOPHILA MELANOGASTER UNDER ARTIFICIAL AND NATURAL SELECTION IN CONSTANT AND FLUCTUATING ENVIRONMENTS

Genetics ◽

10.1093/genetics/95.4.1033 ◽

1980 ◽

Vol 95 (4) ◽

pp. 1033-1042

Author(s):

Brian P Bradley

Keyword(s):

Drosophila Melanogaster ◽

Natural Selection ◽

Developmental Stability ◽

Stabilizing Selection ◽

Phenotypic Variance ◽

Environment Interaction ◽

Total Phenotypic Variance ◽

Environmental Variance ◽

Genotype Environment Interaction ◽

Fluctuating Environments

ABSTRACT Populations of Drosophila melanogaster in constant 25λ and fluctuating 20/29λ environments showed increases in developmental stability, indicated by decreases in bilateral asymmetry of sterno-pleural chaeta number. In both environments, rates of decrease in asymmetry were greater under natural selection (control lines) than under artificial stabilizing selection. Overall mean asymmetry was greater in the fluctuating environment.—There was no evidence that decreased asymmetry was due to heterozygosity, and the decline in asymmetry was not explained by the decline in chaeta number in the lines under only natural selection. However, the decline was consistent with changes in total phenotypic variance and environmental variance.— The divergence between lines after 39 generations of selection was seen in differences in asymmetry and also in the genotype-environment interaction expressed in cross-culturing experiments.

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Statistical and graphical (GGE biplot) evaluation of the adaptive ability and stability of winter barley breeding lines

Vavilov Journal of Genetics and Breeding ◽

10.18699/vj19.469 ◽

2019 ◽

Vol 23 (1) ◽

pp. 110-118 ◽

Cited By ~ 5

Author(s):

V. N. Gudzenko

Keyword(s):

Optimal Combination ◽

Winter Barley ◽

Environment Interaction ◽

Gge Biplot ◽

Statistical Parameters ◽

Breeding Lines ◽

Barley Breeding ◽

Yield Variation ◽

Genotype Environment Interaction ◽

Sowing Dates

Due to current global climate changes, the issue of improving adaptive capacity of crops is of high importance. It is important to create winter crop varieties with both ecological adaptability and yield stability in years with different hydrothermal conditions. In order to develop winter barley varieties with a combination of yield and stability, 14 promising breeding lines have been evaluated in the conditions of the V.M. Remeslo Myronovka Institute of Wheat of NAAS of Ukraine in 2012/2013–2014/2015 using four different sowing dates. The ANOVA revealed a reliable part in yield variation: 64.59 % for environment, 16.84 % for genotype–environment interaction, and 15.57 % for genotype. The sowing dates significantly increased the yield variation of the breeding lines. The differences between the average yields of the lines depending on sowing date within the year were 1.05 t/ha in 2012/2013, 0.90 t/ha in 2013/2014, and 1.25 t/ha in 2014/2015. For genotype–environment interaction interpretation and ranking lines by yield a number of the most known statistical parameters of adaptability, stability, and plasticity and GGE biplot were applied. The use of different sowing dates at the final stage of the winter barley breeding process is a simple but effective approach that allows a more detailed assessment of the adaptive potential of breeding lines in various growing conditions. As compared to statistical parameters, GGE biplot has some advantages for interpretation of genotype–environment interaction. This graphic model allows ranking environments to be visualized for their discriminating ability and representativeness, as well as both specifically adapted genotypes and the ones with the optimal combination of yield potential and stability to be identified in a set of environments (mega-environment). The breeding line Pallidum 4816 with the optimal combination of yield and stability, as well as the high-yielding breeding lines Pallidum 4857 and Pallidum 4659 were identified and submitted to the State Variety Testing of Ukraine as the new winter barley varieties MIP Yason, MIP Oskar and MIP Hladiator.

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Genotype × environment interaction and stability analysis on barley (Hordeum vulgare L.) lines in Algeria

Acta Agronomica Hungarica ◽

10.1556/aagr.59.2011.4.4 ◽

2011 ◽

Vol 59 (4) ◽

pp. 325-336 ◽

Cited By ~ 1

Author(s):

L. Hanifi-Mekliche ◽

A. Mekliche ◽

P. Monneveux

Keyword(s):

Hordeum Vulgare ◽

Environment Interaction ◽

Stability Studies ◽

Heading Time ◽

Stem Height ◽

Thousand Grain Weight ◽

Hordeum Vulgare L ◽

Breeding Lines ◽

Growing Seasons ◽

Genotype Environment Interaction

The objectives of this research were to assess the genotype × environment interaction and to identify barley (Hordeum vulgare L.) genotypes with high stability for grain yield, yield components, straw height, ear length, heading time, straw yield and harvest index. Eighteen barley breeding lines and one commercial cultivar were evaluated under field conditions during five growing seasons. The trials were carried out under both rainfed and irrigated conditions during the first four years and under rainfed conditions only during the last growing season. Stability studies showed significant differences between the environments for all the traits and between the genotypes for thousand-grain weight, heading time and ear length. The genotype × environment interaction was, however, not significant according to the Finlay-Wilkinson analysis. The analysis of correlations between the various traits showed the importance of selecting for earliness, high number of grains/ear, stem height and ear length in order to obtain acceptable grain yields under drought-stressed conditions.

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Identification of core locations for soybean breeding in Southern Brazil

Pesquisa Agropecuária Brasileira ◽

10.1590/s1678-3921.pab2019.v54.00991 ◽

2019 ◽

Vol 54 ◽

Author(s):

Gilvani Matei ◽

Andrei Daniel Zdziarski ◽

Leomar Guilherme Woyann ◽

Rodrigo Zanella ◽

Vinícius de Bitencourt Bez Batti ◽

...

Keyword(s):

Block Design ◽

The State ◽

Phenotypic Variance ◽

Environment Interaction ◽

Genotypic Effect ◽

Environment Effect ◽

Mato Grosso ◽

Genotype Environment Interaction ◽

Main Effects ◽

Selection Of

Abstract: The objective of this work was to identify core locations in two soybean macroregions in Brazil for the evaluation and selection of soybean (Glycine max) lineages. Twenty-two cultivars were tested in 23 locations in these two macroregions (MR1 and MR2), during four years, from 2012 to 2015. Trials were conducted in a randomized complete block design with three replicates. All analyses were performed using the GGEbiplot software. The genotype main effects plus genotype × location interaction + genotype main effects plus genotype × environment interaction and genotypic effect vs. genotype x environment effect analyses were used to identify core locations, i.e., locations with high representativeness and consistency of results. Chapada, in the state of Rio Grande do Sul, and Maracaju, in the state of Mato Grosso do Sul, were the core locations in MR1 and MR2, respectively. These locations were the most representative and consistent over the years, and the genotypic effect explained a high proportion of phenotypic variance.

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An investigation of genotype-environment interaction and stability for pea (Pisum sativum L) seed yield

International Journal of Horticultural Science ◽

10.31421/ijhs/6/1/67 ◽

2000 ◽

Vol 6 (1) ◽

Author(s):

L. Csizmadia ◽

F. Kormos

Keyword(s):

Linear Regression ◽

Pisum Sativum ◽

Seed Yield ◽

Regression Technique ◽

Environment Interaction ◽

Stability Parameters ◽

Breeding Lines ◽

Genotype Environment Interaction ◽

Pisum Sativum L ◽

Stability Variance

Eighteen pea breeding lines and three check cultivars were tested in two years with and without irrigation. The linear regression can explain only a small part of GEI so the use of regression technique was not possible for interpreting the data. In this case the ecovalence, stability variance and superiority measure stability parameters cannot describe properly the genotype's response. With the AMMI method it is possible to group properly the genotypes according to their response.

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