Genotype Main Effect Research Articles

Evaluation of Indian ginseng [Withania somnifera (L.) Dunal] breeding lines and genotype-by-environment interaction across production environments in western India.

In order to find location-specific and broadly adapted genotypes for total root alkaloid content and dry root yield along with additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction in Indian ginseng (Withania somnifera (L.) Dunal), (GGE) biplot analyses were used in the current study. Trials were carried out in a randomized complete block design (RCBD) over three succeeding years viz., 2016-2017, 2017-2018 and 2018-2019 at three different locations (S. K. Nagar, Bhiloda and Jagudan). Analysis of variance (ANOVA) for AMMI for dry root yield revealed that the environment, genotype, and GE interaction, respectively, accounted for significant sums of squares of 35.31%, 24.89%, and 32.96%. For total root alkaloid content, a significance of 27.59% of total sum of squares was justified by environment, 17.72% by genotype and 43.13% by GEI. Nine experimental trials in total were taken into consideration as contexts for the GEI analysis in 16 genotypes, including one check. AMMI analysis showed that genotypes, SKA-11, SKA-27, SKA-23 and SKA-10 were superior for mean dry root yield and SKA-11, SKA-27 and SKA-21 had better performance for total root alkaloid content across environment. The GGE biplot analysis showed genotypes SKA-11, SKA-27, SKA-10 desirable for dry root yield and SKA-26, SKA-27, SKA-11 for total root alkaloid content. As a result of the GGE and AMMI biplot techniques, SKA-11 and SKA-27 were determined to be the most desired genotypes for both total root alkaloid content and dry root yield. Further, simultaneous stability index or SSI statistics identified SKA-6, SKA-10, SKA-27, SKA-11 and AWS-1 for higher dry root yield, whilst SKA-25, SKA-6, SKA-11, SKA-12 and AWS-1 for total alkaloid content from root. Based on trait variation, GGE biplot analysis identified two mega-environments for dry root yield and a total of four for total root alkaloid content. Additionally, two representative and discriminating environments-one for dry root production and the other for total root alkaloid content were found. Location-specific and breeding for broad adaptation could be advocated for improvement and release of varieties for Indian ginseng.

Genotypic stability analysis among Lilium genotypes for growth and yield contributing traits

Genotypes grown in multi-environmental trials may respond differently to a range of climatic factors, soil characteristics and cultural management practices. As such, the proportion of the variation in the phenotypic traits due to the main effects of genotype, environment and their interaction is routinely assessed when selecting best-performing lines. Keeping this in mind, the present study was carried out to find the stable Lilium genotypes evaluated over two locations for two years in a Randomized Block Design along with three replications. Eighteen diverse genotypes of Lilium were assessed for twenty different vegetative, flowering and bulb parameters. Genotype “Eyeliner” indicated stability for the majority of parameters, including days to bulb sprout emergence, bud length, number of flowers/stem, weight of bulblets, and vase life, while genotype “Yelloween” exhibited stability for a variety of parameters, including leaf length, bud length, size of flower, and days to first flower, and is suitable for a variety of environments.

Crop model determined mega-environments for cassava yield trials on paddy fields following rice

The Cropping System Model (CSM)-MANIHOT-Cassava provides the opportunity to determine target environments for cassava (Manihot esculenta Crantz) yield trials by simulating growth and yield data for various environments. The aim of this research was to investigate whether cassava production on paddy fields in Northeast, Thailand could be grouped into mega-environments using the model. Simulations for four different cassava genotypes grown on paddy field following rice harvest was conducted for various soil types and the weather data from 1988 to 2017. The genotype main effect plus genotype by environment interaction (GGE biplot) technique was used to group the mega-environments. The analyses of yearly data showed inconsistent results across years for environment grouping and for the winning genotypes of the individual environment group. An analysis using GGE biplot with the average value of the simulated storage root dry weight (SDW) for 30 years indicated that all 41 environments were grouped into two different mega-environments. This study demonstrated the ability of the CSM-MANIHOT-Cassava to help identify the mega-environments for cassava yield trials on paddy field during off-season of rice that could help reduce both time and resources.

Genotype-by-diagnosis interaction influences self-control in human cocaine addiction

Not everyone who uses drugs loses control over their intake, which is a hallmark of addiction. Although familial risk studies suggest significant addiction heritability, the genetic basis of vulnerability to drug addiction remains largely unknown. In the present study, we investigate the relationship between self-control, cocaine use, and the rs36024 single nucleotide polymorphism of the noradrenaline transporter gene (SLC6A2). We hypothesize that C-allele-carrying adults show impaired self-control, as measured by the stop-signal task and demonstrated previously in adolescents, and further exacerbated by chronic cocaine use. Patients with cocaine use disorder (CUD, n = 79) and healthy unrelated participants with no history of drug abuse (n = 54) completed the stop-signal task. All participants were genotyped for rs36024 allelic variants (CC/TT homozygotes, CT heterozygotes). We measured mean stop-signal reaction time, reflecting the ability to inhibit ongoing motor responses, reaction times to go stimuli, and the proportion of successful stops. CUD patients showed prolonged stop-signal reaction time, however, there was no main effect of rs36024 genotype. Importantly, there was a significant genotype-by-diagnosis interaction such that CUD patients with CC genotype had longer stop-signal reaction time and fewer successful stops compared with CC healthy controls and TT CUD patients. CT CUD patients showed an intermediate performance. Self-control deficits were associated with cocaine use disorder diagnosis, which interacts with the noradrenaline transporter rs36024 polymorphism. Our findings suggest that rs36024 may represent a potential genetic vulnerability marker, which facilitates the transition from first cocaine use to addiction by weakening the inhibitory control over behavior.

Genotype by Temperature Interaction for Plasma Physiological Indexes in Turbot (Scophthalmus maximus) under Acute Heat Stress─Exploring a Method for Screening Physiological Biomarkers of Nontoxic Stress in Aquatic Environments.

This paper presents a method for exploring the genetic mechanism underlying the plasma physiological indexes under heat stress in aquatic environments and for screening reliable stress biomarkers based on split-split-plot analysis (SSP), additive main effects and multiplicative interaction (AMMI) analysis, and genotype main effects and genotype × environment interaction (GGE) biplot analysis. The methodology developed was illustrated by applying it to a specific turbot heat stress case study. Five plasma physiological indexes (epinephrine, cortisol, alkaline phosphatase, superoxide dismutase, and blood glucose levels) were measured in turbot (Scophthalmus maximus) under acute heat stress at four temperatures (18, 21, 24, and 27 °C) for various exposure times (3, 6, 9, 12, 24, 48, and 72 h). The SSP analysis showed that exposure time and temperature × gene interactions had significant (P < 0.01) effects on the activity/content of turbot plasma physiological indexes. The AMMI analysis showed the following: (1) that at each exposure time, the genotype effect > the genotype × temperature interaction > the temperature effect; (2) that during the whole experiment, the change trend of the contribution of the genotype × temperature interactions was similar to that of the temperature effect, and the changing trends of the contributions of the genotype × temperature interaction and the genotype effect were clearly completely reversed; and (3) that the 3-24-h period was the key period for the changes in the physiological indexes due to acute heat stress. The GGE biplot analysis showed that blood glucose and cortisol levels were reliable biomarkers and could be used as early warning markers for numerical simulations of physiological behavior.

Mega‐environment analysis and breeding for specific adaptation

AbstractMega‐environment (ME) analysis is analysis of multi‐year, multi‐location crop variety trial data conducted in a target region of a crop to understand the magnitude and nature of genotype‐by‐environment interaction (GE) of the crop in the region. If repeatable GE patterns are identified, then the target region must be divided into subregions or MEs. Breeding and utilizing ME‐specific cultivars will convert the repeatable GE into genotypic main effect (G) within ME, thereby improving heritability (selection reliability) and selection gain and maximize regional and overall productivity. If no repeatable GE is found, then the target region must be treated as a single ME and the GE must be accommodated by testing adequately, that is, at a sufficient number of locations in a sufficient number of years. This paper presents a theoretical framework of ME analysis, describes graphical tools to reveal the which‐won‐where patterns in a genotype‐by‐environment dataset, and demonstrates LG (location‐grouping) biplot analysis for revealing repeatable GE patterns and delineating MEs. The concept of G + GE or GGE, that is, GE relative to G, is emphasized. It is the relative GE that is the basis for ME analysis and breeding for specific adaptation; absolute magnitude of GE has little relevance for these purposes. Breeding ME‐specific oat cultivars in Canada is demonstrated with a real‐world example.

Evaluation of yield performance of early maturing soybean (Glycine max L. Merill) genotypes in Ethiopia by using GGE Biplot model

Genotype main effect and genotype by environment interaction biplot analysis is the best fit model for which-won-where pattern analysis, genotype, and test environment evaluation. Hence, the aim of this study was to identify stable and high-yielding soybean genotypes for production in diverse environments by using the genotype main effect and genotype by environment biplot stability model. Eighteen soybean genotypes were evaluated across six environments during the 2019 cropping season by using a randomized complete block design with four replications. Among evaluated environments and genotypes, Tiro-afeta gave the highest yield (3.71 t ha -1); while Humera gave the lowest yield (1.37 t ha-1), and genotype JM-HAR/PR142-15-SB gave the highest mean grain yield of 2.9 t ha -1 across the six locations. Based on the information generated from the GGE biplot, Tiro Afeta and Areka were identified as ideal environments, whereas genotypes PR-143-(14), JM-HAR/G99-15-SD-2 and JM-HAR/PR142-15-SB were ideal genotype. The ‘which won where’ biplot of the GGE analysis revealed that the six environments grouped into three different mega-environments with different winning genotypes. Among the testing environments, Areka, Sirinka and Humera grouped into one mega environment; while Tiro afeta grouped into the second mega environment and Jimma and Hawasa were classified into the third mega environment with the winning genotypes JM-HAR/PR142-15-SB, PR-143-(14) and KS4895 for each mega environment, respectively. Based on the GGE biplot stability model used in the study, JM-HAR/G99-15-SD-2, JM-HAR/PR142-15SB, and PR-143-(14) were high yielder and stable genotypes. Hence, these genotypes were recommended for variety verification and release after additional evaluation for more seasons. Int. J. Agril. Res. Innov. Tech. 12(2): 101-110, December 2022

A regression approach to identify discriminating locations

AbstractSelection of planting locations is an important issue in plant breeding and may significantly impact the probability of correctly identifying the desired genotype based on noisy data. This is especially true for early‐stage experimentation where a set of experimental genotypes is observed in very few locations. This study aims to identify locations that are most helpful for discriminating the main effects of two or more genotypes. A regression‐based model is proposed that characterizes the sensitivity of location to the genotype main effect, and locations with significant positive sensitivity are termed discriminative. Comparing or ranking genotypes using these locations results in exaggerated expected differences between genotype main effects, allowing for more accurate comparisons in the presence of noise. Based on the proposed model, an expression for the probability of making correct genotype selection is derived given a set of locations with known location sensitivities. The probability of correct selection is then used to characterize an optimal set of location that maximizes the probability of selecting the genotype with better main effect. These locations are independent of the genotypes being compared and only depend on the number of locations being considered and the sensitivities of these locations to genotype effects. The theoretical implications of the model are validated using data obtained from a commercial soybean breeding program. The results show that by using discriminating locations correct selection is made 76% of the time, versus 67% for all locations and only 63% of the time for non‐discriminating locations.

Studies of Oat-Maize Hybrids Tolerance to Soil Drought Stress

The ontogenesis and yield formation in crop plants are modified by environmental conditions. Due to climatic change detected over two decades, the harmful influence of abiotic factors is increasing. One of the most threatening issues reducing plant productivity is drought stress. The strength of plant response to water shortages could differ depending on the strength of the drought stress, type of crop, genetic background, presence of additional stresses, and stage of plant development. There are examples of sexual hybridization between crop plants like oat (Avena sativa L.) and maize (Zea mays L.) with which stable fertile hybrids were generated. Additional maize chromosomes in oat plants (oat × maize addition, OMA) often infer morphological and physiological (e.g., PS II photosystem activity and chlorophyll production) changes modulated by the interaction of certain maize chromosomes added to the oat genome. The aim of the research was to evaluate the chosen physiological, biochemical, and agronomic parameters of OMA plants subjected to soil drought. Analysis of variance indicated that the main effects of genotype as well as treatment × genotype interaction were significant for all the traits studied (photosynthetic pigment content, selected PSII indices, mass of stem, number of grains/plant, mass of grains/plant). Most of the examined lines severely reduced PSII photosystem parameters, pigment content, and yield-related traits under drought stress. The results indicated that two lines (9 and 78b) retained high yielding potential under drought stress compared to commercial cv. Bingo.

The association of genetic variation in CACNA1C with resting-state functional connectivity in youth bipolar disorder

BackgroundCACNA1C rs1006737 A allele, identified as a genetic risk variant for bipolar disorder (BD), is associated with anomalous functional connectivity in adults with and without BD. Studies have yet to investigate the association of CACNA1C rs1006737 with resting-state functional connectivity (rsFC) in youth BD.MethodsParticipants included 139 youth with BD-I, -II, or -not otherwise specified, ages 13–20 years, including 27 BD A-carriers, 41 BD non-carriers, 32 healthy controls (HC) A-carriers, and 39 HC non-carriers. Anterior cingulate cortex (ACC), amygdala, and orbitofrontal cortex (OFC) were examined as regions-of-interest in seed-to-voxel analyses. General linear models included main effects of diagnosis and rs1006737, and an interaction term, controlling for age, sex, and race.ResultsWe observed a main effect of BD diagnosis on rsFC between the right amygdala and the right occipital pole (p = 0.02), and a main effect of rs1006737 genotypes on rsFC between the right OFC and bilateral occipital cortex (p < 0.001). Two significant BD diagnosis-by-CACNA1C rs1006737 interactions were also identified. The A allele was associated with positive rsFC between the right ACC and right amygdala in BD but negative rsFC in HC (p = 0.01), and negative rsFC between the left OFC and left putamen in BD but positive rsFC in HC (p = 0.01).ConclusionThis study found that the rs1006737 A allele, identified as a genetic risk variant for BD in adults, was differentially associated with rsFC in youth with BD in regions relevant to emotion, executive function, and reward. Future task-based approaches are warranted to better understand brain connectivity in relation to CACNA1C in BD.

Leveraging genetic diversity in mice to inform individual differences in brain microstructure and memory.

In human Alzheimer's disease (AD) patients and AD mouse models, both differential pre-disease brain features and differential disease-associated memory decline are observed, suggesting that certain neurological features may protect against AD-related cognitive decline. The combination of these features is known as brain reserve, and understanding the genetic underpinnings of brain reserve may advance AD treatment in genetically diverse human populations. One potential source of brain reserve is brain microstructure, which is genetically influenced and can be measured with diffusion MRI (dMRI). To investigate variation of dMRI metrics in pre-disease-onset, genetically diverse AD mouse models, we utilized a population of genetically distinct AD mice produced by crossing the 5XFAD transgenic mouse model of AD to 3 inbred strains (C57BL/6J, DBA/2J, FVB/NJ) and two wild-derived strains (CAST/EiJ, WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI) to probe neural microanatomy in 83 regions of interest (ROIs). At 5 months of age, these mice underwent contextual fear conditioning (CFC). Strain had a significant effect on dMRI measures in most ROIs tested, while far fewer effects of sex, sex*strain interactions, or strain*sex*5XFAD genotype interactions were observed. A main effect of 5XFAD genotype was observed in only 1 ROI, suggesting that the 5XFAD transgene does not strongly disrupt neural development or microstructure of mice in early adulthood. Strain also explained the most variance in mouse baseline motor activity and long-term fear memory. Additionally, significant effects of sex and strain*sex interaction were observed on baseline motor activity, and significant strain*sex and sex*5XFAD genotype interactions were observed on long-term memory. We are the first to study the genetic influences of brain microanatomy in genetically diverse AD mice. Thus, we demonstrated that strain is the primary factor influencing brain microstructure in young adult AD mice and that neural development and early adult microstructure are not strongly altered by the 5XFAD transgene. We also demonstrated that strain, sex, and 5XFAD genotype interact to influence memory in genetically diverse adult mice. Our results support the usefulness of the 5XFAD mouse model and convey strong relationships between natural genetic variation, brain microstructure, and memory.

Herbage yield stability of cocksfoot (Dactylis glomerata L.) genotypes across rain-fed environments

Cocksfoot (Dactylis glomerata L.) is a cool-season perennial grass that naturally grows in semi-steppe rangelands in Iran. In recent years, as a result of climate change, coupled with high livestock grazing, rangeland productivity has decreased. This study aimed to analyse the stability of forage dry matter (DM) production of 36 cocksfoot genotypes across four rain-fed environments (Ardebil, Zanjan, Hamadan, and Kermanshah) in Iran. At each location, an experiment was conducted using a randomised complete-block design (RCBD) with three replications over 2 years. The result of combined analysis of variance (ANOVA) across locations showed significant effects of environment (E), genotype (G), and GE interaction (P &lt; 0.01) for DM yield. The E, G and GE interaction effects accounted for 33.19%, 17.98% and 40.52% of the total variance respectively. The GE interaction was subsequently investigated using regression stability, Additive Main effects and Multiplicative Interaction (AMMI), and genotype main effect (G) plus genotype–environment (GE) interaction (GGE) biplot analysis. According to the regression method, genotypes G2, G3, G11 and G12 with a slope close to unity, coupled with high production, had good general stability in all locations. In addition, some genotypes were specifically identified for poor- and high-performing environments. According to the AMMI-2, IPC1 vs IPC2 biplot, genotypes G2, G10, G11, G14 and G15, placed close to the origin of the biplot coupled with higher production, showed general stability in all environments. The best genotypes for the respective environments were also determined. Using the GGE biplot, genotypes G2, G3, G11 and G12 were more stable in all environments. Therefore, on the basis of all analytical methods, three local genotypes, G2 (Karaj), G3 (Marand) and G11 (Qazvin), and a foreign genotype, G14 (from, USA), were identified as most suitable genotypes for breeding improved varieties and cultivation in the study locations and similar areas.

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease.

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson's disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease. Forty-eight Parkinson's disease patients and 39 matched healthy controls underwent genotyping and 7T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson's disease diagnosis. We found that, in Parkinson's disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein (SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson's disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson's disease.

Caracterização agronômica e interação genótipo ambiente de quinoa nas condições do Brasil e da Colômbia

ABSTRACT Quinoa is a highly adaptable crop due to its considerable genetic variability, making it an important trait for cultivation under different soil and climatic conditions. To achieve crop-wide adaptation, it is essential to identify variability based on morpho-agronomic differences and genotype x environment (GxE) interaction. This study aimed to characterize eight quinoa progenies in Brazil and Colombia. The experiments in Brazil were conducted in an irrigated area of the Fazenda Água Limpa, Universidade de Brasília, at 1,100 m, on two dates: March to July 2018 and May to August 2019. In Colombia, experiments were carried out in Santander de Quilichao and Popayán at 1,100 and 1,800 m, respectively. The treatments consisted of five progenies selected in Brazil, one from Colombia, and two from Ecuador. The experiments followed a complete randomized block design, with eight progenies and four replications. For statistical analysis, the F test was used with p≤0.01 and p≤0.05. Means were grouped by the Scott-Knott test. The AMMI (Additive Main effects and Multiplicative Interaction) analysis was performed, combining analysis of variance and analysis of principal components, to adjust the main effects of genotypes (G) and environments (E) and the GxE interaction. Significant differences were found at p≤0.01 and p≤0.05 for environments, genotypes, and the interaction of GxE. The progenies with wide adaptation to environments were BRX2, BRX5, BRX6 (selected from BRS Syetetuba) and PRIX (selected from Piartal), with average yields above 3,151.95 kg/ha. The genotypes showed differences at the same location in different periods, expressing the need to carry out selection for specific periods and locations. Genotypes BRX5 and BRX6 showed high agronomic potential in all evaluated environments, being promising for future genetic improvement programs.

Development of improved genotypes for extra early maturity, higher yield and Mungbean Yellow Mosaic India Virus (MYMIV) resistance in soybean (Glycine max)

Context Breeding for early maturity and higher yield is the principal objective in genetic improvement of Indian soybean. Yellow Mosaic Disease caused by Mungbean Yellow Mosaic India Virus (MYMIV) causes 80% yield loss in soybean. Aims This study aimed to develop early maturing, MYMIV resistant and high yielding soybean genotypes for enhancing soybean production and expanding the land area under cropping. Methods MYMIV resistance was introgressed from G. soja in to a widely adaptable cultivar JS 335 through a series of four generations of backcrosses and by evaluating derived progeny against MYMIV at a disease hot spot. Key results An extra-early maturing (71 days) genetic stock called NRC 252 was developed, which can be a potential gene donor in breeding for early maturing soybean varieties. Introgression lines YMV 1, YMV 2, YMV 11 and YMV 16 with MYMIV resistance and higher yield performance over recurrent parent and other check varieties were identified and characterised. Biplot analysis, assessing the main effect of genotype and the interaction of genotype with environment, revealed an ideal genotype with respect to 100-seed weight and grain yield that was also promising under sugarcane-soybean intercropping system in spring season. Conclusions Alleles from wild type soybean could improve yield attributing traits and MYMIV resistance in cultivated soybean. Improved genotypes such as YMV 1, YMV 2, YMV 11 and YMV 16 were found superior to the recurrent parent JS 335 as well as other check varieties. Implications The genotypes developed in the present study will help in reducing the damage caused by MYMIV disease and expansion of the area of soybean cultivation through intercropping with sugarcane.

AWOT and CWOT for genotype and genotype by treatment interaction joint analysis in pharmacogenetics GWAS.

Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant-drug-response association discoveries are not limited to common variants with extremely large effect. In this paper, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy weighted joint test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test p-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e., with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II phase 3 trials. The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html. Supplementary data are available at Bioinformatics online.

Dissecting the Genotype × Environment Interaction for Potato Tuber Yield and Components

In the Mediterranean Basin, potato (Solanum tuberosum L.) is a major staple crop, the yield performance of which can vary dramatically based on pedo-climatic conditions and genotype. Hence, dissecting the genotype × environment interaction (GEI) in this region is mandatory for the setup of high-yielding and stable potato genotypes, also considering its importance for local potato breeding and the development of protected geographical identifications. Therefore, this research evaluated the marketable tuber yield (MY) and several yield components of five potato genotypes (Arizona, Generosa, Levante, Paradiso, and Vogue) over 4 years (2019, 2020, 2021, and 2022) and three locations in Sicily (Southern Italy) by additive main effects and multiplicative interactions (AMMI) and genotype main effects + genotype × environment interaction (GGE) biplot analyses. From combined ANOVA emerged a high significance of GEI, with the environment that provided the most considerable extent of variation for the most of the productive traits. The AMMI and GGE analyses identified Arizona as the best leading genotype in the studied area by virtue of its high productivity (44.5 t ha−1 of mean MY) coupled with stability, followed by Generosa (46.5 t ha−1). Ideal environments (location × year) were highly dependent on the productive trait, but most of them belonged to Acireale, characterized by fertile soils. According to our results, this approach could be recommended for breeding programs and commercial cultivation in the studied regions, along with the setup of potato protected geographical identifications.

GENOTYPE BY ENVIRONMENT INTERACTION AND STABILITY ANALYSIS OF ETHIOPIAN COMMERCIAL DURUM WHEAT (Triticum turgidum L.) CULTIVARS IN SOUTHERN ETHIOPIA

&lt;p class="abstrakinggris"&gt;The use of improved varieties with wider adaptability and stability is necessary to maximize the productivity of durum wheat. However, due to genotype by environment interaction (GEI) effect, superior genotypes in one environment could be inferior in other environments. Hence, a multi-environmental trial (MET) was commenced to determine the magnitude of GEI effect and identify stable durum wheat genotypes across environments. The MET was conducted using nine durum wheat cultivars in randomized completely block design with three replications across four contrasting locations in 2020 crop seasons. The result of combined analysis of variance showed the presence of significant differences among the main effects; environments, genotypes, and GEI effects for grain yield. The additive main effects and multiplicative interaction (AMMI) combined ANOVA for main effects of environment, genotype, and GEI revealed highly significant differences among cultivars with 66.2%, 26.4%, and 7.3% share of sum square variation, respectively, of the total variation. AMMI and genotype plus genotype by environment (GGE) Bi-plot analysis identified the cultivars Fetan, Denbi, and Mangudo as high yielders and adaptive to the favourable locations. AMMI stability value and yield stability index identified Mangudo as the most stable and adaptive cultivar across locations. The AMMI Bi-plot analysis showed that the first two interaction principal component analysis (IPCAs) captured 90.45% of the total interaction sum of squares (ISS), where IPCA1 took 55.61% and IPCA2 accounted for 34.84% of GEI effects. This study identified Mangudo as the most stable cultivar with acceptable yield while Fetan was the top yielder genotype across locations.&lt;/p&gt;

Genotype × environment interactions of potato tuber quality characteristics by AMMI and GGE biplot analysis

Potato is the most important non-grain crop worldwide, whose quality characteristics are always affected by genotype × environment interaction (GEI). Knowledge about the influence of GEI on potato quality could be very important to develop new or existing potato genotypes with high quality characteristics coupled with stability at varying environmental conditions. In this research, for the first time, additive main effects and multiplicative interaction (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction (GGE) biplot analyses were used to dissect GEI in potato quality traits by evaluating six potato genotypes (Arizona, Cayenne, Generosa, Laudine, Salad Blue and Vogue) over four locations and two consecutive years (eight environments). Pooled ANOVA revealed a P ≤ 0.001 significance of ANOVA components for all traits under study, with G that attributed the highest variation on dry matter, citric acid, DPPH and washability, and E that contributed more on total phenolics, vitamin C, reducing sugars ad FRAP. After assessing a small but significant proportion to total variance of GEI for all qualitative traits, it was found that almost all of the variance was explained by the cumulative contribution of IPC1 and IPC2 for AMMI and by PC1 and PC2 for GGE. From AMMI and GGE biplots clearly emerged that, despite qualitative performances of genotypes fluctuated considerably across the environments, Salad Blue was the most suitable genotype for dry matter, total phenolics, vitamin C and FRAP throughout the 8 environments due to its above-average mean values and stability. On the contrary, no clear differentiations were found between environments, indicating that the choice of the “ideal” environment depended on the quality trait. Our results suggested the importance of AMMI and GGE analysis in identifying qualitatively high and stable genotypes over multi-environmental trials. This approach could be useful in providing scientific strength for the setup of potato protected geographical identifications.

Assessing the Suitability of Multivariate Analysis for Stress Tolerance Indices, Biomass, and Grain Yield for Detecting Salt Tolerance in Advanced Spring Wheat Lines Irrigated with Saline Water under Field Conditions

Successfully evaluating and improving the salt tolerance of genotypes requires an appropriate analysis tool to allow simultaneous analysis of multiple traits and to facilitate the ranking of genotypes across different growth stages and salinity levels. In this study, we evaluate the salt tolerance of 56 recombinant inbred lines (RILs) in the presence of salt-tolerant and salt-sensitive control genotypes using multivariate analysis of plant dry weight, measured at 75 (PDW-75) and 90 (PDW-90) days from sowing, biological yield (BY), grain yield (GY), and their salt tolerance indices (STIs). All RILs and genotypes were evaluated under the control and 15 dS m−1 for two consecutive years (2019/2020 and 2020/2021). Results showed significant main effects of salinity and genotype as well as their interactions on four plant traits. Significant genotypic differences were also found for all calculated STIs. STIs exhibited moderate to strong relationships with the four plant traits when measured under either the control or salinity conditions and between each other. The principal component analysis (PCA) showed that the most variation among all analyzed variables was explained by the first two PCs, with the PC1 and PC2 explained at 61.8–71.8% and at 28.0–38.2% of the total variation, respectively. The PC1 had positive and strong correlations with the four plant traits measured under salinity conditions and STI, YI, REI, SWPI, MRPI, MPI, GMPI, and HMPI. The PC2 had strong correlations with BY and GY measured under the control conditions and SSI, TOL, RSE, and YSI. The PC1 was able to identify the salt-tolerant genotypes, while the PC2 was able to isolate the salt-sensitive ones. Cluster analysis based on multiple traits organized 64 genotypes into four groups varied from salt-tolerant to salt-sensitive genotypes, with the salt-tolerant group attaining higher value for plant traits under salinity conditions and the STIs related to the PC1. In conclusion, the use of multivariate analysis together with the STIs that evaluated the performance of genotypes under contrasting environmental conditions will help breeders to distinguish salt-tolerant genotypes from salt-sensitive ones, even at the early growth stages of plant development.