Multi-environment BSA-seq using large F3 populations is able to achieve reliable QTL mapping with high power and resolution: An experimental demonstration in rice

Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F_(3)populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F_(3)population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F_(3)populations and multi-environment experiments can achieve high power, resolution, and reliability.

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.

Statistical Method for Block in Replication Design and Its SAS Program

Large-scale genetic population used for genetic breeding researches covers a large area in the field experiment,and the effect of local control would be gradually weakened.The block in replication(BIR)design is suitable for large population,which is applied to the field experiment of genetic population.The statistical methods of analysis of variance(ANOVA)and heritability estimation in single and multiple environments were derived and implemented using the statistical analysis system(SAS)program for the analysis of BIR.As a work example,a comparison of statistical analysis between BIR design and the completely random block(CRB)design were conducted for the protein content from a panel containing 455 soybean germplasms.The results indicated the different estimates of average heritability in multiple environments.The research results provided technical support for the application of BIR design in genetics and breeding studies.

The Application of GGE Biplot Analysis for Evaluating Test Locations and Mega-Environment Investigation of Cotton Regional Trials

In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction（GGE） biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley（YaRV）, China. Four traits（cotton lint yield, fiber length, lint breaking tenacity, micronaire） and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments： a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.

Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran

Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.

Yield Stability of Maize Hybrids Evaluated in National Maize Cultivar Regional Trials in Southwestern China Using Parametric Methods

Assessment of yield stability is an important issue for maize （Zea mays L.） cultivar evaluation and recommendation. Many parametric procedures are available for stability analysis, each of them allowing for different interpretations. The objective of the present study was to assess yield stability of maize hybrids evaluated in the National Maize Cultivar Regional Trials in southwestern China using 20 parametric stability statistics proposed by various authors at different times, and to investigate their interrelationships. Two yield datasets were obtained from the 2003 and 2004 national maize cultivar regional trials in southwestern China. A combined analysis of variance, stability statistics, and rank correlations among these stability statistics were determined. Effects of location, cultivar, and cultivar by location interaction were highly significant （P〈0.01）. Different stability statistics were used to determine the stability of the studied cultivars. Cultivar mean yield （Y） was significantly correlated to the Lin and Binns stability statistic （LP, r=0.98^＊＊ and 0.97^＊＊ for 2003 and 2004 trials, respectively） and desirability index （HD, r=0.38 and 0.84^＊＊ for the 2003 and 2004 trials, respectively）. The statistics LP and HD would be useful for simultaneously selecting for high yield and stability. Based on a principal component analysis, the parametric stability statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.

Yield Stability of Maize Hybrids Evaluated in Maize Regional Trials in Southwestern China Using Nonparametric Methods

Analysis of multi-environment trials （METs） of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction （GEI） to 14 maize （Zea mays L.） genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEl and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability. Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX168 as desirable and ND108, CM12, CN36, and NK6661 as undesirable genotypes.

GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield

The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize food production,new varieties are developed by selecting superior lines of seeds suitable for a specific environment.Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop.A large number of statistical and machine learning models are employed for single environment trials,where it is assumed that the environment does not have any effect on the quantitative traits.However,it is essential to consider both genomic and environmental data to develop a new variety,as these strong assumptions may lead to failing to select top lines for an environment.Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment.In the process,we also developed a new technique for identifying environmentspecific markers that can be useful in many applications of environment-specific genomic selection.The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario.Furthermore,the feature importance analysis shows that environmental information,followed by genomic information,is the driving factor in predicting environment-specific yield for a line.We also demonstrate a way to extend our framework for new data types,such as text or soil data.The extended model also shows the potential to be useful in genomic selection.

Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions

Genotyping and phenotyping large natural populations provide opportunities for population genomic analysis and genome-wide association studies(GWAS). Several rice populations have been re-sequenced in the past decade;however, many major Chinese rice cultivars were not included in these studies. Here, we report large-scale genomic and phenotypic datasets for a collection mainly comprised of 1,275 rice accessions of widely planted cultivars and parental hybrid rice lines from China. The population was divided into three indica/Xian and three japonica/Geng phylogenetic subgroups that correlate strongly with their geographic or breeding origins. We acquired a total of 146 phenotypic datasets for 29 agronomic traits under multi-environments for different subpopulations. With GWAS, we identified a total of 143 significant association loci, including three newly identified candidate genes or alleles that control heading date or amylose content. Our genotypic analysis of agronomically important genes in the population revealed that many favorable alleles are underused in elite accessions, suggesting they may be used to provide improvements in future breeding efforts. Our study provides useful resources for rice genetics research and breeding.