Multi-environment Trials Research Articles

Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Californian multi-environment trials (2017-2022) were assessed using liquid chromatography-mass spectrometry. In total, 3104 proteins were identified across all of the samples. Location, genotype, and year explained 26.7, 17.1, and 14.3% of the variance in the relative abundance of individual proteins, respectively. Sixteen proteins with storage, DNA/RNA binding, or enzymatic functions were significantly higher/lower in abundance (compared to the overall mean) in the Yolo 3 and Imperial Valley locations, Butta 12 and LCS Odyssey genotypes, and the 2017-18 and 2021-22 years. Individual protein abundances were reasonably predictive (RMSECV = 1.25-2.04%) for total, alcohol-soluble, and malt protein content and malt fine extract. This study illustrates the role of the environment in the barley proteome and the utility of proteomics and machine learning to predict grain/malt quality.

Leveraging genomic prediction to surpass current yield gains in spring barley

Key messageGenetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines.Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22–60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019–2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.

Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.

In plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations. Various sparse testing methods have been explored to optimize resource use. This study employed Incomplete Block Design (IBD) to allocate lines to environments, ensuring not all lines were tested in every environment. We compared IBD to Random line allocation, maintaining a consistent number of environments per line across both methods. The primary objective was to estimate grain yield performance of lines using Genomic Estimated Breeding Values (GEBVs) computed through six Genomic Best Linear Unbiased Predictor (GBLUP) methods. In the first five methods, missing values were predicted before cross-environment adjustment; in the sixth, adjustment was performed directly. Using the Bayesian GBLUP model, we analyzed genotype performance under both IBD and random allocation. Results indicate that computing GEBVs for a target population of environments (TPE) using available phenotype and marker data is effective for selection. The IBD method showed superior performance with less variability compared to random allocation. These findings suggest that using IBD designs can enhance selection accuracy and efficiency, and that pre-adjustment prediction of missing lines may not necessarily improve selection outcomes.

MegaLMM improves genomic predictions in new environments using environmental covariates.

Multi-environment trials (METs) are crucial for identifying varieties that perform well across a target population of environments (TPE). However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize Genome-To-Fields dataset, consisting of 4402 varieties cultivated in 195 trials with 87.1\% of phenotypic values missing, and demonstrated its high accuracy in genomic prediction under various breeding scenarios. Furthermore, we showcased MegaLMM's superiority over univariate GBLUP in predicting trait performance of experimental genotypes in new environments. Finally, we explored the use of higher-dimensional quantitative ECs and discussed when and how detailed environmental data can be leveraged for genomic prediction from METs. We propose that MegaLMM can be applied to plant breeding of diverse crops and different fields of genetics where large-scale linear mixed models are utilized.

Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.

Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials' optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial's allocation considering the counties' economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.

Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.

Maximizing genetic gains across agronomic and consumer preference traits in St. Augustinegrass breeding

AbstractCombining large multi‐environment trial (MET) datasets to decide which genotypes to move forward in the breeding process can be challenging, especially when dealing with negatively correlated traits. The use of a selection index has long been identified as an effective strategy in these situations. However, the method has found limited application in turfgrass breeding. The objective of this study was to use MET data for St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] breeding lines evaluated across the southern United States to compare genetic gains achieved with the additive additive genetic index (AI) versus the turf performance index (TPI) incorporating agronomic as well as consumer preference traits. The use of either selection index produced more positive genetic gains across traits than direct selection even in the presence of negative correlations. However, the higher genetic gains obtained with AI versus TPI indicate that the use of an index that weighs traits according to their importance is a better approach for selection. Moreover, under a more stringent selection intensity, none of the best lines identified with AI would have been selected with TPI emphasizing the importance of choosing selection criteria that provide a more nuanced ranking of lines. Additionally, higher heritability values and gains from selection were obtained for turfgrass quality under stress (drought and shade) than under normal conditions indicating that selection under stress environments might be more efficient. Most of the evaluated St. Augustinegrass lines outperformed the checks, further supporting the value of cross‐institutional breeding collaborations.

Selection of biomass sorghum genotypes based on multi-environment trials and multiple traits for 2G ethanol purpose

Selection of biomass sorghum genotypes based on multi-environment trials and multiple traits for 2G ethanol purpose

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.

Genotype-by-environment interactions for the growth and survival of Litopenaeus vannamei under normal and low-salinity environments

Litopenaeus vannamei is the primary shrimp species farmed worldwide, as it can tolerate salinities ranging from near freshwater to hypersaline seawater. Inland shrimp farming in low-salinity waters expanded in the Americas and Asia following the domestication of L. vannamei and may be primed for further growth. Salinity is a key production parameter affecting the growth and survival of shrimp and varies between seasons and locations. This study aimed to compare the growth and survival rates of L. vannamei under normal (NS; 30–32 ppt) and low-salinity (LS; 10 ppt) environments and estimate genetic parameters and genotype-by-environment interactions (G x E) between growth or survival rates and salinity. Over approximately seven generations, 1028 full-sib shrimp families were evaluated for growth (ADG) and survival in a nucleus breeding center under NS and LS environments. Parental models were used for single and multi-environment trial (MET) analyses to estimate genetic parameters and G x E. Growth (ADG) (0.32 ± 0.05 g vs. 0.29 ± 0.05 g) and survival (82.6 ± 10.8 % vs. 72.2 ± 19.1 %) were significantly better under the NS versus LS environment, respectively. Heritability estimates were low for growth (0.04–0.14) and low to moderate for survival (0.09–0.23). Genetic correlations (rg) between salinity environments were strong (0.87 ± 0.07) for growth and moderate (0.66 ± 0.14) for survival, indicating weak G x E for growth and moderate G x E for survival.

Stability assessment of selected clones of Neolamarckia cadamba under the multi environmental trials using AMMI and GGE biplot analysis

Stability assessment of selected clones of Neolamarckia cadamba under the multi environmental trials using AMMI and GGE biplot analysis

Multi‐Environment Analysis of Nutritional and Grain Quality Traits in Relation to Grain Yield Under Drought and Terminal Heat Stress in Bread Wheat and Durum Wheat

ABSTRACTHeat and drought are two important constraints to global wheat productivity; understanding the genotypic responses for quality parameters under harsh production conditions (drought and heat) is very important for developing nutrient‐dense wheat varieties. A set of 15 modern bread wheat (Triticum aestivum L. subsp. aestivum) and durum wheat (Triticum turgidum subsp. durum) cultivars were tested in nine environments, including three different production conditions (normal, heat and drought) during 2020–21. Genotype stability performance for yield, nutrition and quality parameters is assessed using multienvironment trials through AMMI and GGE Biplot analysis. We discovered intriguing stress dynamics in grain zinc content (Zn) and grain iron content (Fe). Under heat stress, zinc concentration increases but decreases under drought stress, while iron does the opposite. Selecting zinc, starch and kernel weight under terminal heat stress can boost yield. Protein content and yield are inversely related, making it difficult for breeders to optimise both traits. G × E interactions and stability indices across all environments have found genotypes with high‐yielding stable genotypes, G12 (MP1358) (42.09 ppm) and G5 (HI1544) (42.41 ppm) have high Fe content. G12 (MP1358) (14.98%) ranked highest in protein concentration. Meanwhile, for Zn content, G11 (MACS 4058) (45.23 ppm) and G15 (WH730) (42.44 ppm) were top performers across environments. G7 (HI 1636) and G12 (MP1358) stand out as a win‐win genotype for their high potential and stability in yield, protein, Zn and Fe content. Our study shows the complex relationships and possible suggestions for targeted breeding programmes under heat and drought stress conditions to improve wheat grain quality and micronutrient profiles without yield loss.

Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria

Genotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha−1 and 3645 kg ha−1 with a mean of 2324 kg ha−1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive.Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.

Adaptability, stability, and productivity of potato breeding clones and cultivars at high latitudes in Europe

The aim of potato breeding is to release cultivars that exhibit high and stable performance across the target population of environments. The objective of this research was therefore to investigate the use of various methods (site regression [SREG], coefficient of variation and regression deviation [σ2δ]) for determining the adaptability and stability of productive and quality traits in the Nordic region of Europe. The multi-environment trials included 256 breeding clones and released cultivars grown by EU farmers at three distinct testing sites over two years in Sweden. There was significant (P < 0.001) variation in tuber yield, starch percentage and reducing sugars in the tuber flesh among the breeding clones and cultivars, testing environments and the genotype by environment interaction (GEI). The environments were very diverse, as revealed by the SREG biplots and particularly for the GEI patterns noted in terms of their productive and quality characteristics. The percentage of stable high-tuber yielding germplasm was greater for breeding clones (23%) than for released European cultivars (2%), thus revealing the advantage of potato breeding in the target population of environments. SLU 1415001 and SLU 1314015 were the most promising breeding clones due to their stable high tuber yield. This characteristic was best for the starch potato cultivars, although none of them exhibited a significant different σ2δ. ‘Talent’ shows an almost stable good performance among low reducing sugar cultivars and breeding clones, which are often unstable in terms of their scoring across environments. Neither a breeding clone nor a cultivar was at the top for stable tuber yield, tuber flesh starch or reducing sugars in the tuber flesh, which shows the challenge faced by potato breeding while addressing the needs of different markets.

Analysis of genotype-by-environment interaction effect in barley genotypes using AMMI and GGE biplot methods

Genotype-by-environment interaction (GEI) analysis play a key role in any breeding program involving the development of new varieties for cultivation across various environments or in a specific region. The additive main effects and multiplicative interaction (AMMI) method and the GGE biplot are the two main statistical tools that have emerged to analyze GEI in multi-environment trials (METs). The main goal of the present study was to identify the best-performing and stable barley genotypes for the warm regions of Iran. For this purpose, 18 new advanced barley genotypes were investigated in five warm locations in Iran during two cropping seasons (2021–2023). In all experiments, test genotypes were evaluated in a randomized complete block design (RCBD) with three replications. Based on results, grain yield was significantly dependent on environments (E), genotypes (G), and GEI. The GEI effect was further divided into three principal component axes (IPCAs). The AMMI method identified genotypes G3, G9, G10, and G14 as ideal genotypes due to their low IPCA scores and high performances. In the GGE biplot analysis, the initial two PCAs accounted for 49.36 % of the total variation of grain yield, including both G and GEI effects. Based on averaged two-year data, genotypes G3, G4, G10, and G14 showed particular adaptability in the Zabol and Moghan regions. Moreover, the ranking of test environments showed good discriminatory and representative abilities for the Zabol and Moghan regions, so these environments constituted a mega-environment in Iran's warm climate. The genotype ranking indicated G3, G10 and G14 genotypes as the superior genotypes with the highest grain yield and stability in different test environments. Moreover, these results were confirmed by the results obtained by WAASB and WAASBY biplots. In conclusion, genotypes G3, G10 and G14 can be suggested for commercial usage and cultivation in various regions in Iran's warm climate.

Canonical correlations between wheat traits in multienvironment trials

ABSTRACT Wheat is a crop of great food importance for food production in Brazil. However, the improvement of this crop is still concentrated in isolated areas, which hinders advances in new cultivation regions due to the extensive demand for existing information. In this investigation, four wheat genotypes analysed to study associations and interrelationships between wheat traits across nine environments during the agricultural years of 2016, 2017 and 2018. The experiment was conducted using a randomised block design with four repetitions, and the analysis was divided into two groups: morphological and productive traits of wheat, comprising three and eleven characteristics, respectively. Canonical correlations between the morphological and productive groups were found to be dependent in most environments. Taller plants with longer main spikes and longer tiller spikes can be indirectly selected to increase grain yield under different sowing conditions. The information generated in this study indicate that some care must be taken when using the interrelations between trait groups to select or recommend genotypes for broader regions, as these relationships are influenced by the specific conditions of each region and sub-region.

Elucidation of the genetic architecture of water absorption capacity in hard winter wheat through genome wide association study.

Water absorption capacity (WAC) influences various aspects of bread making, such as loaf volume, bread yield, and shelf life. Despite its importance in the baking process and end-product quality, its genetic determinants are less explored. To address this limitation, a genome-wide association study was conducted on 337 hard wheat (Triticum aestivum L.) genotypes evaluated over 5 years in multi-environmental trials. Phenotyping was done using the solvent retention capacity (SRC) test with water (SRC-water), sucrose (SRC-sucrose), lactic acid (SRC-lactic acid), and sodium carbonate (SRC-carbonate) as solvents. Individuals were genotyped using genotyping-by-sequencing to detect single nucleotide polymorphisms across the wheat genome. To detect the genomic regions that underline the SRCs and gluten performance index (GPI), a genome-wide association study was performed using six multi-locus models using the mrMLM package in R. Adjusted means for SRC-water ranged from 54.1% to 66.5%, while SRC-carbonate exhibited a narrow range from 84.9% to 93.9%. Moderate to high genomic heritability values were observed for SRCs and GPI, ranging from h2=0.61 to 0.88. The genome-wide association study identified a total of 42 quantitative trait nucleotides (QTNs), of which five explained over 10% of the phenotypic variation (R2 ≥ 10%). Most of the QTNs were detected on chromosomes 1A, 1B, 3B, and 5B. Few QTNs, such as S1A_5190318, S1B_3282665, S4D_472908721, and S7A_37433960, were located near gliadin, glutenin starch synthesis, and galactosyltransferase genes. Overall, these results show WAC to be under polygenic genetic control, with genes involved in the synthesis of key flour components influencing overall water absorption.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

A framework for simulating genotype-by-environment interaction using multiplicative models

Key messageThe simulation of genotype-by-environment interaction using multiplicative models provides a general and scalable framework to generate realistic multi-environment datasets and model plant breeding programmes.Plant breeding has been historically shaped by genotype-by-environment interaction (GEI). Despite its importance, however, many current simulations do not adequately capture the complexity of GEI inherent to plant breeding. The framework developed in this paper simulates GEI with desirable structure using multiplicative models. The framework can be used to simulate a hypothetical target population of environments (TPE), from which many different multi-environment trial (MET) datasets can be sampled. Measures of variance explained and expected accuracy are developed to tune the simulation of non-crossover and crossover GEI and quantify the MET-TPE alignment. The framework has been implemented within the R package FieldSimR, and is demonstrated here using two working examples supported by R code. The first example embeds the framework into a linear mixed model to generate MET datasets with low, moderate and high GEI, which are used to compare several popular statistical models applied to plant breeding. The prediction accuracy generally increases as the level of GEI decreases or the number of environments sampled in the MET increases. The second example integrates the framework into a breeding programme simulation to compare genomic and phenotypic selection strategies over time. Genomic selection outperforms phenotypic selection by ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}50–70% in the TPE, depending on the level of GEI. These examples demonstrate how the new framework can be used to generate realistic MET datasets and model plant breeding programmes that better reflect the complexity of real-world settings, making it a valuable tool for optimising a wide range of breeding methodologies.

Modeling QTL-by-environment interactions for multi-parent populations.

Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.