Investigation of Genotype by Environment Interactions for Seed Zinc and Iron Concentration and Iron Bioavailability in Common Bean

Iron and zinc malnutrition are global public health concerns afflicting mostly infants, children, and women in low- and middle-income countries with widespread consumption of plant-based diets. Common bean is a widely consumed staple crop around the world and is an excellent source of protein, fiber, and minerals including iron and zinc. The development of nutrient-dense common bean varieties that deliver more bioavailable iron and zinc with a high level of trait stability requires a measurement of the contributions from genotype, environment, and genotype by environment interactions. In this research, we investigated the magnitude of genotype by environment interaction for seed zinc and iron concentration and seed iron bioavailability (FeBIO) using a set of nine test genotypes and three farmers’ local check varieties. The research germplasm was evaluated for two field seasons across nine on-farm locations in three agro-ecological zones in Uganda. Seed zinc concentration ranged from 18.0 to 42.0 μg g–1 and was largely controlled by genotype, location, and the interaction between location and season [28.0, 26.2, and 14.7% of phenotypic variability explained (PVE), respectively]. Within a genotype, zinc concentration ranged on average 12 μg g–1 across environments. Seed iron concentration varied from 40.7 to 96.7 μg g–1 and was largely controlled by genotype, location, and the interaction between genotype, location, and season (25.7, 17.4, and 13.7% of PVE, respectively). Within a genotype, iron concentration ranged on average 28 μg g–1 across environments. Seed FeBIO ranged from 8 to 116% of Merlin navy control and was largely controlled by genotype (68.3% of PVE). The red mottled genotypes (Rozi Koko and Chijar) accumulated the most seed zinc and iron concentration, while the yellow (Ervilha and Cebo Cela) and white (Blanco Fanesquero) genotypes had the highest seed FeBIO and performed better than the three farmers’ local check genotypes (NABE-4, NABE-15, and Masindi yellow). The genotypes with superior and stable trait performance, especially the Manteca seed class which combine high iron and zinc concentrations with high FeBIO, would serve as valuable parental materials for crop improvement breeding programs aimed at enhancing the nutritional value of the common bean.

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Genotype by Environment Interaction Effect on Grain Iron and Zinc Concentration of Indian and Mediterranean Lentil Genotypes

Agronomy ◽

10.3390/agronomy11091761 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1761

Author(s):

Soma Gupta ◽

Shouvik Das ◽

Harsh Kumar Dikshit ◽

Gyan Prakash Mishra ◽

Muraleedhar S. Aski ◽

...

Keyword(s):

Zinc Concentration ◽

Selection Index ◽

Stability Index ◽

Genotype By Environment Interaction ◽

Pooled Analysis ◽

Environment Interaction ◽

Breeding Lines ◽

Genotype By Environment ◽

Iron And Zinc ◽

The Stability

Lentil grains with high nutritional value qualify as a promising candidate for alleviation of micronutrient malnutrition in South Asia and North Africa. Genetic variation for micronutrient concentration in germplasm is prerequisite for biofortification of this crop. In the present study, ninety-six lentil genotypes consisting of Indian (released varieties, advanced breeding lines and germplasm lines) and Mediterranean (germplasm lines and landraces) line were evaluated for grain iron (Fe) and zinc (Zn) concentrations and the stability of these traits was studied across three different locations in India. The pooled analysis of variance revealed significant genotype, environment and genotype by environment interaction (GEI) mean squares for both the micronutrients. Stability analysis employing the AMMI model elucidated the first two interaction principal components as significant and cumulatively explained 100% of GEI variation. The first two components explained 55.9% and 44.1% of the GEI sum of squares for grain iron and 50.8% and 49.2% for grain zinc concentration, respectively. No correlation between grain iron and zinc concentration was observed. Among 96 lines, genotypes IG 49, P 16214, ILL 147 and P 2118 were found to be relatively stable, having higher mean iron and zinc concentrations with low modified AMMI stability value (MASV), modified AMMI stability index (MASI) and genotype selection index (GSI). The identified promising genotypes (high Fe: P16214, IG 115, P 2127 and IC 560812 and high Zn: P 8115, P3234, LL 461 and IC 560812) can be utilized for studying the genetics of grain Fe and Zn concentration by developing mapping populations and for biofortification of Indian lentil.

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Genetic Variability and Genotype X Environment Interactions Effect on Grain Iron (Fe) and Zinc (Zn) Concentration in Lentils and Their Characterization under Terai Environments of Nepal

Advances in Nutrition & Food Science ◽

10.33140/anfs.05.01.01 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 1

Keyword(s):

Analysis Of Variance ◽

Zinc Concentration ◽

Animal Feed ◽

Zn Deficiency ◽

Environment Interaction ◽

Human Beings ◽

Zn Concentration ◽

Iron And Zinc ◽

Tract Infections ◽

Location Interaction

Billions of peoples are directly affected from the micronutrient malnutrition called hidden hunger affecting one in three people. Micronutrient Iron (Fe), and zinc (Zn) deficiencies affect large numbers of people worldwide. Iron (Fe) deficiency leads to maternal mortality, mental damage and lower disease resistant of children. Likely Zinc (Zn) deficiency is responsible for stunting, lower respiratory tract infections, and malaria and diarrhea disease in human beings. Nepalese lentils are in fact rich sources of proteins and micronutrients (Fe, Zn) for human health and straws as a valuable animal feed. It has ability to sequester N and C improves soil nutrient status, which in turn provides sustainable production systems. Twenty five lentil genotypes were evaluated to analyze genotype × environment interaction for iron and zinc concentration in the grains. Analysis of variance (ANOVA) indicated that the accessions under study were found varied significantly (P=<0.001) for both seed Fe and Zn concentrations at all the three locations. Pooled analysis of variance over locations displayed highly significant (at P=<0.001) differences between genotypes, locations and genotype × location interaction for Zn micronutrient but insignificant genotype x location interaction was found in Fe micronutrient. Among 25 genotypes, the ranges for seed Fe concentration were 71.81ppm (ILL-2712)-154.03 ppm (PL-4) (mean 103.34 ppm) at Khajura, 79.89 ppm (ILL-3490)-128.14 ppm (PL-4) (mean 95.43 ppm) at Parwanipur, and 83.92 ppm (ILL-7979) -137.63 ppm (ILL-6819) (mean 103.11ppm) at Rampur, while the range across all the three locations was 82.53 ppm (ILL-7979) -133.49 ppm (PL-4) (mean 101.04 ppm). Likely the range for seed Zn concentration was 53.76 ppm (ILL-7723) – 70.15 ppm (ILL-4605) (mean 61.84 ppm) at Khajura, while the ranges for Parwanipur and Rampur were 54.21 ppm (ILL-7723) -91,94 ppm (ILL-4605) (mean 76.55 ppm) and 46.41 ppm (LG-12) – 59.95 ppm (ILL-4605) (mean 54.27 ppm) , respectively. The range across the three environments was 54.03 ppm (ILL-7723) – 75.34 ppm (HUL-57) (mean 64.22 ppm). Although both the micronutrients were influenced by environment, seed Fe was more sensitive to environmental fluctuations in comparison to seed Zn concentration. The G × E study revealed that it was proved that genotypes Sagun, RL-6 and LG-12 were more stable for seed Fe concentration and genotypes WBL-77, ILL-7164, RL-11 were found more stable for seed Zn concentration. In the AMMI analysis employing Gollob’s test, first two PC explained 100% of the G × E variation. PC 1 and PC 2 explained 87.19% and 12.81% of total G × E interactions for Fe concentration and likely for Zn concentration; PC1 and PC2 explained 70.11% and 29.88%, respectively. The critical perusal of biplot revealed that Parawnipur locations was found to discriminating power for Fe concentration while for Zn concentration Khajura location was found to be most discriminative. The critical analysis of pedigree vis-à-vis micronutrient concentration did not reveal any correlation. This is probably the first report on iron and zinc concentration in lentil from Nepal.

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Meta-QTL analysis of seed iron and zinc concentration and content in common bean (Phaseolus vulgaris L.)

Theoretical and Applied Genetics ◽

10.1007/s00122-018-3104-8 ◽

2018 ◽

Vol 131 (8) ◽

pp. 1645-1658 ◽

Cited By ~ 10

Author(s):

Paulo Izquierdo ◽

Carolina Astudillo ◽

Matthew W. Blair ◽

Asif M. Iqbal ◽

Bodo Raatz ◽

...

Keyword(s):

Phaseolus Vulgaris ◽

Common Bean ◽

Qtl Analysis ◽

Zinc Concentration ◽

Phaseolus Vulgaris L ◽

Iron And Zinc

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On-farm multi-location evaluation of genotype by environment interactions for seed yield and cooking time in common bean

Scientific Reports ◽

10.1038/s41598-020-60087-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Dennis N. Katuuramu ◽

Gabriel B. Luyima ◽

Stanley T. Nkalubo ◽

Jason A. Wiesinger ◽

James D. Kelly ◽

...

Keyword(s):

Common Bean ◽

Seed Yield ◽

Cooking Time ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

On Farm

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Analysis of genotype by environment interactions in a maize mapping population

10.1101/2021.07.21.453280 ◽

2021 ◽

Author(s):

Asher I Hudson ◽

Sarah G Odell ◽

Pierre Dubreuil ◽

Marie-Helene Tixier ◽

Sebastien Praud ◽

...

Keyword(s):

Genome Wide Association Study ◽

Genetic Correlations ◽

Genotype By Environment Interaction ◽

Environment Interaction ◽

Crop Breeding ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Genome Wide ◽

A Genome ◽

Genetic Covariances

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interaction in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.

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Accuracies of univariate and multivariate genomic prediction models in African Cassava

10.1101/116301 ◽

2017 ◽

Author(s):

Uche Godfrey Okeke ◽

Deniz Akdemir ◽

Ismail Rabbi ◽

Peter Kulakow ◽

Jean-Luc Jannink

Keyword(s):

Genomic Selection ◽

Mixed Model ◽

Genetic Evaluation ◽

Environment Interaction ◽

Genotype By Environment Interactions ◽

Breeding Values ◽

Genotype By Environment ◽

Step Model ◽

One Step ◽

Estimated Breeding Values

List of abbreviationsGSGenomic SelectionBLUPBest Linear Unbiased PredictionEBVsEstimated Breeding ValuesEGVsEstimated genetic ValuesGEBVsGenomic Estimated Breeding ValuesSNPsSingle Nucleotide polymorphismsGxEGenotype-by-environment interactionsGxEGenotype-by-environment interactionsGxGGene-by-gene interactionsGxGxEGene-by-gene-by-environment interactionsuTUnivariate single environment one-step modeluEUnivariate multi environment one-step modelMTMulti-trait single environment one-step modelMEMultivariate single trait multi environment modelAbstractBackgroundGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.MethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.ResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.ConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

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QTL for seed iron and zinc concentration and content in a Mesoamerican common bean (Phaseolus vulgaris L.) population

Theoretical and Applied Genetics ◽

10.1007/s00122-010-1371-0 ◽

2010 ◽

Vol 121 (6) ◽

pp. 1059-1070 ◽

Cited By ~ 56

Author(s):

Matthew W. Blair ◽

Juliana I. Medina ◽

Carolina Astudillo ◽

Judith Rengifo ◽

Steve E. Beebe ◽

...

Keyword(s):

Phaseolus Vulgaris ◽

Common Bean ◽

Zinc Concentration ◽

Phaseolus Vulgaris L ◽

Iron And Zinc

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A molecular investigation of genotype by environment interactions.

Genetics ◽

10.1093/genetics/139.1.19 ◽

1995 ◽

Vol 139 (1) ◽

pp. 19-33 ◽

Cited By ~ 1

Author(s):

A M Dean

Keyword(s):

Genotype By Environment Interaction ◽

Metabolic Control Analysis ◽

Environment Interaction ◽

Control Analysis ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Natural Isolates ◽

Selection Intensities ◽

Beta Galactosidase ◽

Control Coefficients

Abstract The fitnesses conferred by seven lactose operons, which had been transduced into a common genetic background from natural isolates of Escherichia coli, were determined during competition for growth rate-limiting quantities of galactosyl-glycerol, a naturally occurring galactoside. The fitnesses of these same operons have been previously determined on lactose and three artificial galactosides, lactulose, methyl-galactoside and galactosyl-arabinose. Analysis suggests that although marked genotype by environment interactions occur, changes in the fitness rankings are rare. The relative activities of the beta-galactosidases and the permeases were determined on galactosyl-glycerol, lactose, lactulose and methyl-galactoside. Both enzymes display considerable kinetic variation. The beta-galactosidase alleles provide no evidence for genotype by environment interactions at the level of enzyme activity. The permease alleles display genotype by environment interactions with a few causing changes in activity rankings. The contributions to fitness made by the permeases and the beta-galactosidases were partitioned using metabolic control analysis. Most of the genotype by environment interaction at the level of fitness is generated by changes in the distribution of control among steps in the pathway, particularly at the permease where large control coefficients ensure that its kinetic variation has marked fitness effects. Indeed, changes in activity rankings at the permease account for the few changes in fitness rankings. In contrast, the control coefficients of the beta-galactosidase are sufficiently small that its kinetic variation is in, or close to, the neutral limit. The selection coefficients are larger on the artificial galactosides because the control coefficients of the permease and beta-galactosidase are larger. The flux summation theorem requires that control coefficients associated with other steps in the pathway must be reduced, implying that the selection at these steps will be less intense on the artificial galactosides. This suggests that selection intensities need not be greater in novel environments.

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The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkaa050 ◽

2021 ◽

Author(s):

Anna R Rogers ◽

Jeffrey C Dunne ◽

Cinta Romay ◽

Martin Bohn ◽

Edward S Buckler ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Genomic Prediction ◽

Environmental Data ◽

Environment Interaction ◽

Hybrid Performance ◽

Interaction Patterns ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Main Effect

Abstract High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

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Genotype by environment interaction analysis of wheat (Triticum aestivum L.) grain yield under rain-fed conditions in Zambia

SABRAO Journal of Breeding and Genetics ◽

10.54910/sabrao2021.53.4.5 ◽

2021 ◽

Vol 53 (4) ◽

pp. 609-619

Author(s):

B. Tembo

Keyword(s):

Grain Yield ◽

Agricultural Research ◽

Interaction Analysis ◽

Crop Improvement ◽

Genotype By Environment Interaction ◽

Research Station ◽

Environment Interaction ◽

Wheat Genotypes ◽

Lattice Design ◽

Genotype By Environment

Understanding genotype by environment interaction (GEI) is important for crop improvement because it aids in the recommendation of cultivars and the identification of appropriate production environments. The objective of this study was to determine the magnitude of GEI for the grain yield of wheat grown under rain-fed conditions in Zambia by using the additive main effects and multiplicative interaction (AMMI) model. The study was conducted in 2015/16 at Mutanda Research Station, Mt. Makulu Research Station and Golden Valley Agricultural Research Trust (GART) in Chibombo. During2016/17, the experiment was performed at Mpongwe, Mt. Makulu Research Station and GART Chibombo, Zambia. Fifty-five rain-fed wheat genotypes were evaluated for grain yield in a 5 × 11 alpha lattice design with two replications. Results revealed the presence of significant variation in yield across genotypes, environments, and GEI indicating the differential performance of genotypes across environments. The variance due to the effect of environments was higher than the variances due to genotypes and GEI. The variances ascribed to environments, genotypes, and GEI accounted for 45.79%, 12.96%, and 22.56% of the total variation, respectively. These results indicated that in rain-fed wheat genotypes under study, grain yield was more controlled by the environment than by genetics. AMMI biplot analysis demonstrated that E2 was the main contributor to the GEI given that it was located farthest from the origin. Furthermore, E2 was unstable yet recorded the highest yield. Genotype G47 contributed highly to the GEI sum of squares considering that it was also located far from the origin. Genotypes G12 and G18 were relatively stable because they were situated close to the origin. Their position indicated that they had minimal interaction with the environment. Genotype 47 was the highest-yielding genotype but was unstable, whereas G34 was the lowest-yielding genotype and was unstable.

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