Analysis of genotype-by-environment interactions in a maize mapping population

Tools and Techniques to Accelerate Crop Breeding

As climate changes and a growing global population continue to escalate the need for greater production capabilities of food crops, technological advances in agricultural and crop research will remain a necessity. While great advances in crop improvement over the past century have contributed to massive increases in yield, classic breeding schemes lack the rate of genetic gain needed to meet future demands. In the past decade, new breeding techniques and tools have been developed to aid in crop improvement. One such advancement is the use of speed breeding. Speed breeding is known as the application of methods that significantly reduce the time between crop generations, thereby streamlining breeding and research efforts. These rapid-generation advancement tactics help to accelerate the pace of crop improvement efforts to sustain food security and meet the food, feed, and fiber demands of the world's growing population. Speed breeding may be achieved through a variety of techniques, including environmental optimization, genomic selection, CRISPR-Cas9 technology, and epigenomic tools. This review aims to discuss these prominent advances in crop breeding technologies and techniques that have the potential to greatly improve plant breeders' ability to rapidly produce vital cultivars.

Molecular breeding of flower load related traits in dioecious autotetraploid Actinidia arguta

Flowering plants exhibit a wide range of sexual reproduction systems, with the majority being hermaphroditic. However, some plants, such as Actinidia arguta (kiwiberry), have evolved into dioecious species with distinct female and male vines. In this study, we investigated the flower load and growth habits of female kiwiberry genotypes to identify the genetic basis of high yield with low maintenance requirements. Owing to the different selection approaches between female and male genotypes, we further extended our study to male kiwiberry genotypes. By combining both investigations, we present a novel breeding tool for dioecious crops. A population of A. arguta seedlings was phenotyped for flower load traits, in particular the proportion of non-floral shoots, proportion of floral shoots, and average number of flowers per floral shoot. Quantitative trait locus (QTL) mapping was used to analyse the genetic basis of these traits. We identified putative QTLs on chromosome 3 associated with flower-load traits. A pleiotropic effect of the male-specific region of the Y chromosome (MSY) on chromosome 3 affecting flower load-related traits between female and male vines was observed in an A. arguta breeding population. Furthermore, we utilized Genomic Best Linear Unbiased Prediction (GBLUP) to predict breeding values for the quantitative traits by leveraging genomic data. This approach allowed us to identify and select superior genotypes. Our findings contribute to the understanding of flowering and fruiting dynamics in Actinidia species, providing insights for kiwiberry breeding programs aiming to improve yield through the utilization of genomic methods and trait mapping.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01476-7.

Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions

Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype-environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha^-1, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha^-1, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

INTRODUCTION

The two most common styles to analyze genotype-by-environment interaction (GEI) and estimate genotypes are additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot. Therefore, the aim of this study was to find the winning genotype(s) under three locations, as well as to investigate the nature and extent of GEI effects on Bambara groundnut production.

METHODS

The experiment was carried out in the fields of three environments with 15 Bambara groundnut accessions using the randomized complete block design (RCBD) with three replications each in Ibadan, Osun, and Odeda. Yield per plant, fresh seed weight, total number of pods per plant, hundred seed weight, length of seeds, and width of seeds were estimated.

RESULTS

According to the combined analysis of variance over environments, genotypes and GEI both had a significant (p < 0.001) impact on Bambara groundnut (BGN) yield. This result revealed that BGN accessions performed differently in the three locations. A two-dimensional GGE biplot was generated using the first two principal component analyses for the pattern of the interaction components with the genotype and GEI. The first two principal component analyses (PCAs) for yield per plant accounted for 59.9% in PCA1 and 40.1% in PCA2. The genotypes that performed best in each environment based on the "which-won-where" polygon were G8, G3, G2, G11, G6, and G4. They were also the vertex genotypes for each environment. Based on the ranking of genotypes, the ideal genotypes were G2 and G6 for YPP, G1 and G5 for FPW, G15 and G13 for TNPP, G3 and GG7 for HSW, G7 and G12 for LOS, and G10 and G7 for WOS. G8 was recorded as the top most-yielding genotype. G8, G4, G7, and G13 were high yielding and the most stable across the environments; G11, G14, and G9 were unstable, but they yielded above-average performance; G14, G12, G15, and G1 were unstable and yielded poorly, as their performances were below average. Bowen was the most discriminating and representative environment and is classified as the superior environment.

DISCUSSION

Based on the performance of accessions in each region, we recommend TVSU 455 (G8) and TVSU 458 (G3) in Bowen, TVSU 455 (G8) and TVSU 939 (G6) and TVSU 454 (G1) in Ibadan, and TVSU 158 (G2) and TVSU 2096 (G10) in Odeda. The variety that performed best in the three environments was TVSU 455 (G8). They could also be used as parental lines in breeding programs.

Evaluation of grain yield performance and its stability in various spring barley accessions under condition of different agroclimatic zones of Ukraine

Two extremely urgent problems of biological and agronomic research nowadays are ensuring an optimal balance between usage of natural resources to meet rapidly growing needs for food production and preservation of biodiversity. It is also important to extend the genetic diversity of the main crop varieties in agroecosystems. At the same time, modern varieties should be characterized by a combination of high yield and preserving yield stability under variable conditions. Solving the outlined tasks requires comprehensive research and involvement in breeding process of the genetical diversity concentrated in genebanks of the world. Barley (Hordeum vulgare L.) is one of the most important crops that satisfy the various needs of humanity. In respect to this, in 2020–2022, a multi-environment trial was conducted in three agroclimatic zones of Ukraine (Forest-Steppe, Polissia, and Northern Steppe). We studied 44 spring barley collection accessions of different ecological and geographical origin, different subspecies and groups of botanical varieties which were obtained from the National Center for Plant Genetic Resources of Ukraine. Statistical indices (Hom, Sc) and graphical models (GGE biplot, AMMI) were used to interpret the yield performance and its stability. Both individual ecological sites in different years and combinations of different sites and years of trials were characterized for productivity, discriminating power and representativeness. The environments differed quite strongly among themselves in terms of these indicators. It was established that most of the genotypes were characterized by higher adaptability to individual environmental conditions (stability in different years), compared to adaptability for all agroclimatic zones (wide adaptation). A strong cross-over genotype by environment interaction was found for most studied accessions. Nevertheless, both genotypes with very high stability in only one agroclimatic zone (Amil (UKR), Gateway (CAN)) and genotypes with a combination of high adaptability to one or two ecological niches and relatively higher wide adaptability (Stymul (UKR), Ly-1064 (UKR), Rannij (KAZ), Shedevr (UKR), and Arthur (CZE)) were identified. There were also the accessions which did not show maximum performance in the individual sites, but had relatively higher wide adaptability (Ly-1059 (UKR), Ly-1120 (UKR), Diantus (UKR), and Danielle (CZE)). In general, the naked barley genotypes were inferior to the covered ones in terms of yield potential and wide adaptability, but at the same time, some of them (CDC ExPlus (CAN), CDC Gainer (CAN), and Roseland (CAN)), accordingly to the statistical indicators, had increased stability in certain ecological sites. Among naked barley accessions relatively better wide adaptability according to the graphical analysis was found in the accession CDC McGwire (CAN), and by the statistical parameters CDC ExPlus (CAN) was better than standard. The peculiarities of yield manifestation and its variability in different spring barley genotypes in the multi-environment trial revealed in this study will contribute to the complementation and deepening of existing data in terms of the genotype by environment interaction. Our results can be used in further studies for developing spring barley variety models both with specific and wide adaptation under conditions of different agroclimatic zones of Ukraine. The disitnguished accessions of different origin and botanical affiliation are recommended for creating a new breeding material with the aim of simultaneously increasing yield potential and stability, as well as widening the genetic basis of spring barley varieties.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.