Genetic and Genotype × Environment Interaction Effects for Appearance Quality of Rice

This study was conducted to generate genetic information in rice varieties based on a complete diallel crosses over two years. The results indicated that genotype effect was significant for all traits. Genotype × environment interaction effects were significant only for cooked grain length （CGL） and cooked grain shape （CGSH）. General combining ability （GCA） and specific combining ability （SCA） effects were significant for entire traits, which indicated the important roles of both additive and non-additive gene actions. GCA x environment interaction effects were significant for CGL, CGSH and grain elongation index （GEI）. In the controlling of the inheritance of milled grain shape （GSH）, milled grain width （MGW）, GEI, milled grain length （MGL）, CGSH and cooked grain width （CGW）, the additive gene effects were more important than non-additive one. The average degree of dominance was within the range of partial dominance for all of the traits. The narrow-sense heritability was ranged from 0.65 （GSH） to 0.36 （CGL）. GCA effects were significant for all of the parents in milled grain length and it was significant for some of the parents in other traits. The crosses of Deilmani × IRFAON-215 exhibited significant SCA for GEI. The positive mean of heterosis was observed for CGW. The highest maximum values of heterosis were revealed in GEI, flowed by GSH, MGW and CGW. GCA and MPV were significantly and positively correlated together for all traits.

Interaction of genotype and environment effects on important traits of cassava(Manihot esculenta Crantz)

General and specific environmental adaptation of genotypes is the main goal of breeders.However, genotype-by-environment(G x E) interaction complicates the identification of genotypes for release. This study aimed at analyzing the effects of G x E interaction on the expression of important cassava traits using two multivariate analyses: additive main effects and multiplicative interaction(AMMI) and genotype stability index(GSI). Total carotene content(TCC), postharvest physiological deterioration(PPD), and reaction to viral diseases were significantly affected by G x E interaction effects. The low percent(%)variation due to genotype for cassava brown streak disease(GBSD) explained the influence of environment on CBSD expression. The % variation due to genotype for TCC was higher(96%) than variation due to environment(1.7%) and G x E interaction(2.4%) indicating a low interaction effect of environment on TCC accumulation. The % variation due to genotype was higher than % variation due to environment for all traits but CBSD root necrosis and CBSD on stems, indicating the influence of environment on the severity of the viral diseases. These findings indicate that screening for disease resistance requires multi-environment trials, whereas a single-environment trial suffices to screen for total carotene content.

Effects of erect panicle genotype and environment interactions on rice yield and yield components

The dense and erect panicle (EP) genotype conferred by DEP1 has been widely used in the breeding of high-yield Chinese japonica rice varieties.However,the breeding value of the EP genotype has rarely been determined at the plant population level.Therefore,the effects of the interaction of EP genotype and the environment at different locations and times on rice yield and its various components were investigated in this study.Two sets of near-isogenic lines (NILs)of EP and non-EP (NEP) genotypes with Liaojing 5 (LG5) and Akitakomachi (AKI) backgrounds were grown in the field in 2016 and 2017 in Shenyang,China,and Kyoto,Japan.In 2018,these sets were grown only in Kyoto,Japan.The average yields of the EP and NEP genotypes were 6.67 and 6.13 t ha^(-1)for the AKI background,and 6.66 and 6.58 t ha^(-1)for the LG5 background,respectively.The EP genotype positively affected panicle number (PN) and grain number per square meter (GNPM),mostly resulting in a positive effect on harvest index (HI).In contrast,the EP genotype exerted a negative effect on thousand-grain weight (KGW).The ratio of the performance of the EP genotype relative to the NEP genotype in terms of yield and total biomass correlated positively with mean daily solar radiation during a 40-day period around heading.These results indicate that the effectiveness of the EP genotype depends on the availability of solar radiation,and the effect of this genotype is consistently positive for sink formation,conditional in terms of source capacity,and positive in a high-radiation environment.

Characterization of the Main Effects, Epistatic Effects and Their Environmental Interactions of QTL on the Genetic Basis of Plant Height and Heading Date in Rice

Main-effect QTL, epistatic effects and their interactions with environment are important genetic components of quantitativetraits. In this study, we analyzed the QTL, epistatic effects and QTL by environment interactions (QE) underlying plantheight and heading date, using a doubled-haploid ( DH) population consisting of 190 lines from the cross between anindica parent Zhenshan 97 and a japonica parent Wuyujing 2, and tested in two-year replicated field trials. A geneticlinkage map with 179 SSR (simple sequence repeat) marker loci was constructed. A mixed linear model approach wasapplied to detect QTL, digenic interactions and QEs for the two traits. In total, 20 main-effect QTLs, 9 digenic interactionsinvolving 18 loci, and 5 QTL by environment interactions were found to be responsible for the two traits. No interactionswere detected between the digenic interaction and environment. The amounts of variations explained by QTLs of maineffect were 53.9% for plant height and 57.8% for heading date, larger than that explained by epistasis and QEs. However,the epistasis and QE interactions sometimes accounted for a significant part of phenotypic variation and should not bedisregarded.

Mapping epistasis and environment × QTX interaction based on four-omics genotypes for the detected QTX loci controlling complex traits in tobacco

Using newly developed methods and software, association mapping was conducted for chromium content and total sugar in tobacco leaf, based on four-omics datasets. Our objective was to collect data on genotype and phenotype for 60 leaf samples at four developmental stages, from three plant architectural positions and for three cultivars that were grown in two locations. Association mapping was conducted to detect genetic variants at quantitative trait SNP(QTS) loci, quantitative trait transcript(QTT) differences,quantitative trait protein(QTP) variability, and quantitative trait metabolite(QTM) changes,which can be summarized as QTX locus variation. The total heritabilities of the four-omics loci for both traits tested were 23.60% for epistasis and 15.26% for treatment interaction.Epistasis and environment × treatment interaction had important impacts on complex traits at all-omics levels. For decreasing chromium content and increasing total sugar in tobacco leaf, six methylated loci can be directly used for marker-assisted selection, and expression of ten QTTs, seven QTPs and six QTMs can be modified by selection or cultivation.

Genotype-by-environment interaction for grain yield among novel cowpea(Vigna unguiculata L.) selections derived by gamma irradiation

This study determined the effects of genotype-by-environment(G × E) interaction and stability of yield among elite cowpea(Vigna unguiculata L.) selections derived by gamma irradiation. The study was conducted in Namibia at three selected sites: Bagani, Mannheim,and Omahenene, during 2014/2015 and 2015/2016. Thirty-four newly developed mutant genotypes and three local checks were evaluated using a randomized complete block design with three replications. Grain yield data were analyzed using the additive main effects and multiplicative interaction(AMMI) and the genotype main effect plus genotype-by-environment interaction(GGE) biplot methods. The AMMI and GGE biplot models explained 77.49% and 75.57% of total observed genotypic variation, respectively.Bagani and Omahenene were the environments best discriminating the test genotypes during 2014/2015 and 2015/2016, respectively. Four promising mutant genotypes: G9(Sh L3 P74), G10(Sh R3 P4), G12(Sh R9 P5), and G4(Sh L2 P4), showed wide adaptation and grain yields of 2.83, 2.06, 1.99, and 1.95 t ha^(-1), respectively. The novel mutant lines are useful genetic resources for production or future cowpea breeding programs in Namibia or similar environments.

Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia

To explore the effect of genotype and genotype x environment interaction on Fe concentration in rice grains, Fe concentrations of 10 genotypes were analyzed across eight paddy field environments during 2007-2008 using the AMMI-biplot method. Experiments were conducted using a randomized completely block design with three replications in eight environments. Results indicated that environment （E）, genotype （G） and genotype x environment interaction （GE） significantly affected Fe concentration in rice grains. Environment explained 74.43 % of total （G＋E＋GE） variation, whereas G and GE captured 5.60% and 19.67%, respectively. Rice genotype Barumun was desirable in terms of the highest ability and stability for Fe concentration in rice grains. Environment in genotype Cilongok was the best representative of the overall environments and the most powerful to discriminate rice genotypes.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

RGxE: An R Program for Genotype x Environment Interaction Analysis

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. Univariate stability statistics calculated are regression slope (bi), deviation from regression (S2d), Shukla’s variance (σi2), S square Wricke’s ecovalence (Wi), and Kang’s yield stability (YSi). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://github.com/mahendra1/RGxE, http://cucurbitbreeding.com/todd-wehner/publications/software-sas-r-project/? and http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html.

Additive Main Effect and Multiplicative Interaction Stability Analysis of Grain Yield Performance in Cowpea Genotypes across Locations

Crops are largely influenced by climatic conditions during the growing season and therefore, minor deviation from optimal conditions can seriously threaten yield. In view of this, knowledge on the effect of environmental factors on crop growth and development could reduce the possibilities of significant yield loss. There have been statistical methods which have been developed in respect to characterizing crops but the additive main effect and multiplicative interaction (AMMI) method integrates analysis of variance and principal components analysis into a unified approach. AMMI has been used in the analysis of G × E interaction with greater precision in many crops. The objective of this study was to assess the extent of genotype x environment interaction and to select the stable cowpea genotypes in Ghanaian environments over seasons using AMMI model. Eight genotypes of cowpea released by Crops Research Institute of Ghana over two decades were selected for evaluation in two locations and two seasons using RCBD with 3 replications in forest and transitional zones of Ghana. When the mean yields of various genotypes were subjected to the AMMI model, the results showed that, a highly significant (P 0.001) genotype by location and by year interaction effects for cowpea grain yield was recorded with 63.1% of the total variation attributable to environmental effects. The AMMI Bi-plot of PC1 and GGE Bi-plot gave 80.8% and 89.3% respectively. Genotype Asontem (G3) had the highest yield and was adapted to all the environments and seasons. Genotypes Asetenapa (G1) and Soronko (G6) were however not stable with consistently low yield across all the environments. It is recommended that farmers in Forest and transitional zones of Ghana should cultivate the highly stable cowpea genotypes in order to get stable yields across environments due to climatic change.

Effects of genetic and environmental factors and gene-environment interaction on expression variations of genes related to stroke in rat brain

To determine if genetic and environmental (dietary) factors and gene-environment interaction impact on the expression variation of genes related to stroke, we conducted microarray experiments using two homozygous rat strains SHRSR and SHRSP fed with high and low dietary salt levels. We obtained expression data of 8,779 genes and performed the ranking analysis of microarray data. The results show that the genetic difference for stroke in rat brain has a strong effect on expression variations of genes. At false discovery rate (FDR) ≤ 5%, 534 genes were found to be differentially expressed between the genotypes resistant and prone to stroke, among which 304 genes were up-regulated in the resistant genotype and down-regulated in the prone genotype and 230 were down-regulated in the former and up-regulated in the latter. In addition, 365 were functional genes for transcription and translation, receptors (in particular, neurotransmitter receptor), channels of ions, transportation, metabolism and enzymes, and functional and structural proteins. Some of these genes are pivotal genes that cause stroke. However, dietary salt levels and GE interaction do not strongly impact on the expression variations of these genes detected on arrays.

Genotype x Environment Interaction of Carotene Content of Yellow-Fleshed Cassava Genotypes in Nigeria

Cassava is an important starchy root crop and a major staple for more than 70 million people in Nigeria. New yellow-fleshed genotypes are being developed to combat vitamin A deficiency. Trials of 18 yellow-fleshed genotypes and two officially released white-fleshed clones, used as checks for 2008/2009 and 2009/2010 seasons in five major cassava growing agroecological zones of Nigeria. The trial locations were Ikenne （humid forest）, Ibadan （forest-savanna transition）, Ubiaja （subhumid forest）, Mokwa （southern Guinea savanna） and Zaria （northern Guinea savanna）. At each location, the trial was established in a randomized complete block design with four replications. The objective of the study was to assess genotype performance and genotype ~ environment interaction for total carotene concentration （TCC）, total carotene content per root （TC-R）, and total carotene content per plant （TC-P）. Significant differences （P 〈 0.001） among genotypes, environments and genotype x environment interaction for all the traits evaluated were observed. For TCC, TC-R and TC-P, the best genotypes across the 10 environments were TMS I051601, TMS 1050311, and TMS 1050998. Variation among genotypes accounted for most of the Total Sum of Squares for TCC （67.9%）, TC-R （39.0%） and TC-P （35.9%）. These characteristics of total carotene were also highly correlated. This study revealed that cassava with total carotene concentration can be assessed using either the TCC, or the TC-R, or the TC-P.

AMMI analysis of interaction of genotype and environment in chalkiness of indica rice

Chalkiness is an unpleasant trait for rice con-sumer,which is known to be controlled geneti-cally and affected by environment during grainmaturing.We used the model of Additive Main

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci （QTLs） for plant height in wheat （Triticum aestivum L.） were studied using a set of 168 doubled haploid （DH） lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai＇an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions （QEs）. Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection （MAS）.

Characterization of early maturing elite genotypes based on MTSI and MGIDI indexes:an illustration in upland cotton(Gossypium hirsutum L.)

Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of growth.Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate change.In the current study,a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early maturity.The study aimed to identify genotype(s)that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index(MTSI)and multi-trait genotype-ideotype distance index(MGIDI).Results MTSI analysis revealed that out of the 20 genotypes,three genotypes,viz.,NNDC-30,A-2,and S-32 accomplished well in terms of early maturity traits in two seasons.Furthermore,three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%.The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll opening.The selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second pickings.However,there were negative gains for traits related to flowering and boll opening.Conclusion The study identified three genotypes exhibiting early maturity and accomplished well under different planting densities.The multivariate methods(MTSI and MGIDI)serve as novel approaches for selecting desired genotypes in plant breeding programs,especially across various growing environments.These methods offer exclusive benefits and can easily construe and minimize multicollinearity issues.

Analysis on Interaction Effects Between Variety and Site of Silage Maize Regional Test in Guizhou Province

Taking the yield in the second group of Guizhou silage maize regional test in 2019 as data information, 8 experimental sites and 12 silage maize varieties as experimental objects, the interaction effect between gene and environment was analyzed by using AMMI model. The results showed that the average fresh weight yield of each variety was 3 199.5~3 976.6 kg/667m^(2), among them, 5 varieties had an increase in the yield. Variety variation accounted for 10.51% of the total variation;experimental site variation accounted for 63.22% of the total variation;interaction effect variation between gene and environment accounted for 26.28% of the total variation;IPCA1 and IPCA2 variation accounted for 50.7% and 31.2% of the interaction variation, respectively;IPCA3 variation accounted for 7.25% of the interaction variation. g_4, g_8, g_9, g_10, g_11 and g_12 had better adaptability to e_1, e_2, e_6 and e_7;while g_1, g_2, g_3, g_5, g_6 and g_7 had better adaptability to e_3, e_4, e_5 and e_8. In consideration of yield, g_1(Huinongqing 2) and g_9(Xinyu 666) were silage maize varieties with high and stable yield;g_3(Hemuyu 905), g_8(Wuhuayu 3) and g_11(Liangdu 191) had general stability, and their yield was higher than that of the control;g_12(Jinduyu 999) had the worst stability and low yield.

Genotype × environment interaction patterns of dry matter yield in meadow brome, orchardgrass, tall fescue, and timothy evaluated at harsh winter sites

Background:Genotype×environment interaction(GEI)slows genetic gains and complicates selection decisions in plant breeding programs.Forage breeding program seed sales often encompass large geographic regions to which the cultivars may not be adapted.An understanding of the extent of GEI in perennial,cool-season forage grasses will facilitate improved selection decisions and end-use in areas with harsh winters.Methods:We evaluated the dry matter yield of nine meadow brome(Bromus biebersteinii Roemer&J.A.Schultes),nine orchardgrass(Dactylis glomerata L.),seven tall fescue(Lolium arundinaceum(Schreb.)Darbysh.),and 10 timothy(Phleum pratense L.)cultivars or breeding populations at seven high latitude and/or elevation locations in Canada and the United States from 2019 to 2021.Results:For each of the species,we found significant differences among the genotypes for dry matter yield across environments and found significant levels of GEI.Using site regression analysis and GGE biplot visualizations,we then characterized the extent of the interactions in each species.Except for tall fescue,there was little evidence for the broad adaptation of genotypes across locations.Conclusions:This research adds further evidence to the limitations of perennial,forage breeding programs to develop widely adapted cultivars and the need to maintain regional breeding efforts.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

GENETIC MODELS AND ANALYSIS METHODS FOR SEX-LINKED AND MATERNAL GENE EFFECTS

Genetic models are proposed for analyzing sex-linked and maternal effects as well as autosomal gene effects.For the model with no genotype×environment interaction,the total genetic effect is partitioned into direct additive （A）,direct dominance （D）,sexlinked （L）,maternal additive （Am） and maternal dominance （Dm） genetic components.For the model including genotype×environment interaction （GE）,GE can also be partitioned into components of direct additive by environment interaction （AE）,direct dominance by environment interaction （DE）,sex-linked by environment interaction （LE）,maternal additive by environment interaction （AmE ）,and maternal dominance by environment interaction （DmE）.Linear functions of genetic components are listed for parent,F1,and F2.A set of parents,their reciprocal F1’s and F2’s is applicable for efficient analysis.Variance and covariance components can be well mated by MINQUE（O/l） with the jackknife procedure.The t-test conducted by the jackknife procedure is applicable for detecting significance of variation.Adjusted Unbiased Prediction （AUP） method is suggested for predicting genetic effects.

Effects of salinity, light and temperature on growth rates of two species of Gracilaria (Rhodophyta)

Effects of temperature, salinity and light intensity on growth rates of Gracilaria lichenoides and G. tenuistipitata var. liui Zhang et Xia were tested. Eight to ten levels of each factor were first tested separately. The best growth rate was obtained under the conditions of 32℃, 30 and 240 μmol/（m^2·s） for G. lichenoides, and 24℃, 20 and 200 μmol/（m^2·s） for G. tenuistipitata, respectively. Then a uniform design was used to evaluate the optimal combinations of the three factors. The best conditions for the highest daily specific growth rates （% increase in wet weight） are determined to be 31.30℃, 32.10, and 287.23 lamol/（m^2·s） for G. lichenoides （16.26%/d）, and 25.38℃, 21.10, and 229.07 lamol/（m^2·s） for G tenuistipitata （14.83%/d）, respectively.