A Study on Genotype-by-Environment Interaction Analysis for Agronomic Traits of Maize Genotypes Across Huang-Huai-Hai Region in China

Facing the trend of increasing population, how to increase maize grain yield is a very important issue to ensure food security. In this study, 28 nationally approved maize hybrids were evaluated across 24 different climatic conditions for two consecutive years (2018–2019). The purpose of this study was to select high-yield with stable genotypes and identify important agronomic traits for maize breeding program improvement. The results of this study showed that the genotype ╳ environment interaction effects of the 12 evaluated agronomic traits was highly significant (P < 0.001). We introduced a novel multi-trait genotype-ideotype distance index (MGIDI) to select genotypes based on multiple agronomic traits. The selection process exhibited by this method is unique and easy to understand, so the MGIDI index will have more and more important applications in future multi-environment trials (METs) research. The genotypes selected by the MGIDI index were G22, G10, G12 and G1 as the high yielding and stable genotypes. The parents of these selected genotypes have the ability to play a greater role as the basic germplasm in the breeding process. A new form of genotype (G) main effects and genotype (G) -by-environment (E) interaction (GGE) technician, genotype*yield*trait (GYT) biplot, based on multiple traits for genotypes selection was also applied in this study. The GYT biplot ranked genotypes by combining grain yield with other evaluated agronomic traits, and displayed the distribution of their traits, namely strengths and weaknesses.     

Mega-environment investigation based on the GGE biplot and genotype selection for high essential oil yield in vetiver grass (Chrysopogon zizanioides L. Roberty)

The genotype and genotype-environment interaction (GEI) significantly differed in the current study, demonstrating that genotype-environment (G × E) interaction heavily influenced genotype yield output. A combined analysis of variance revealed that the genotype and G × E interaction had a significant difference for all eleven characters. Except for the traits Ch1, Ch3, Ch4, Ch5, Ch9, and Ch11, a significant variance was found for all eleven characters over the years, indicating that the environment significantly influenced traits performance. The quality of vetiver essential oil is also affected by genotypic and environmental factors. The GGE biplot model shows that genotypes have a significant G × E interaction, split between the first and second IPCA/interaction principal component axis. In all three contexts, the genotypes V1, V13, and V14 in the center of the biplot graph were determined to be stable. The traits CH1-vs-CH3, CH1-vs-Ch7, and Ch4-vs-CH9 indicated significantly substantial genetic correlations among economic traits over the years. The traits Ch3-vs-Ch4, Ch3-vs-Ch9, Ch4-vs-Ch9, and Ch5-vs-Ch6 were highly significant and positively correlated, but Ch1-vs-Ch11 and CH7-vs-CH9 were negatively related. E1 and E2 were linked together to form one group, and E3 and E4 were linked together. GGE biplot analysis for environment interrelationships V1 and V7 was highly stable and well-performing for essential oil yield from a polygon perspective. Genotypes V1, V7, V10, V11, and V12 are stable and desirable genotypes arrayed in a concentric circle near the center of an ideal genotype. The genotypes V 17, V 41, V 69, and V 70 showed good photosynthetic efficiency. These genotypes might be suitable for large-scale farming.     

Adaptation of safflower genotypes Carthamus tinctorius L.

Summary Adaptation reactions of 33 genotypes of safflower Carthamus tinctorius L. were studied under 7 different climatic conditions. The genotpyes were divided into two sets. Set I consisted of 15 genotpyes selected from the local populations. Set II had 15 introduced and local varieties. Three control genotpyes, Ute, Ferio, and Local Arak, were common to both sets.Genotpye-environment interaction was not significant for Set I but it was highly significant for Set II. Three environmental indices were obtained and used in the adaptation analyses of the genotpyes in Set II. One of the environmental indices, designated EI, was dependent on the genotypes of Set II. The other two indices, designated EI-1 and EI-2, were independent of the genotpyes of Set II. The methods of Eberhart and Russell (1966) were used in analyses of adaptation by Index EI and the methods of Freeman and Perkins (1971) for Indices EI-1 and EI-2.The mean square associated with genotype-environment interaction was partitioned into two components, heterogeneity of regression and its residual, under EI-1 and EI-2. Both components were highly significant for both cases. However, the mean square of heterogeneity of regression was equal to its residual under EI-1 and even smaller than its residual under EI-2. These observations indicate that a major part of genotpye-environment interaction can not be accounted for by differences in the regressions of the individual genotypes. As well as this overall test, individual regression analyses for single genotypes were also considered. None of the genotypes had significant regression mean square under EI-1. Only two introduced genotypes had significant regression mean squares when EI-2 was used. The overall test of equality of the slopes of the regression for the genotypes of Set II was rejected at the 1% level under EI. This test indicated that genotypes of Set II were significantly different in their association with the EI. The significant differences among the genotypes of Set II were also shown by an F test of the pooled deviation mean square divided by the pooled error mean square. Individual regression analyses for single genotpyes of Set II were considered under EI. Mixed adaptation reactions were observed for different genotypes. Among 18 genotypes of Set II, regression mean squares were significant for only 10 genotpyes. Therefore, it appeared that the dependent environmental indices are more useful than the independent environmental indices when statistical theory of regression is used in the analysis of adaptation. Observations in the present study were not in agreement with the hypothesis that the relation between the performance of different genotypes in the various environments and some measure of these environments is linear or nearly so.Among the 12 introduced genotypes, only one, Ute, was identified as stable and high-yielding. Among the 15 selected from the locally adapted populations, eleven did not differ significantly from Ute in mean yield but four exceeded Ute significantly in mean productivity. The present study thus indicates that the Iranian safflower breeding project has been successful in identifying genotpyes which give high and stable yields under diverse environmental conditions. It does not indicate that introduced and exotic germplasms are unimportant in the breeding projects; it is quite possible that still more desirable genotypes can be developed by incorporating introduced genetic variability into the local germplasm.Associate Professors and Plant Breeder     

An alternative approach to multivariate selection in plant breeding where genotypes are evaluated at many locations

Summary A new approach to genotypic selection in a plant breeding programme where the genotypes under assessment are grown in a number of environments is examined. It is assumed that these environments are a random sub-set of all possible environments where the genotypes are likely to be grown. It involves estimating the probability that each genotype will, if grown at any location, exceed predefined target values for one or more characters. The multi-normal probabilities are estimated from the genotype means and environmental variance of each variate. Where more than a single variate is to be considered, the correlation coefficients between variates are also used in the estimation. It was found that the coefficient obtain by correlating the predicted proportion of locations that genotypes would exceed the set target values, with the observed proportion of locations in a different year, were consistently higher than similar coefficients between observed proportions in different seasons. The latter were high enough to conclude that the approach would be of use in practise. Such a method may therefore be used to identify genotypes which have a high probability of being suitable over a range of locations.     

Efficient localization of mutations by interval haplotype analysis

We have developed a mathematical algorithm to implement a method for localizing mutations using haplotype analysis. Our strategy infers haplotypes based on the determination of genotypes of a proximal and a distal marker for 21 chromosomal intervals distributed across the mouse genome (corresponding to two intervals for Chromosomes (Chrs) 1 and 2 and one for the remaining 17 autosomes). To simulate the analysis of mice homozygous for recessive mutations, we tested the efficacy of our method on over 200 data sets generated from two independent mapping panel data sets containing the genotypes of 46 F₂ progeny of an intercross and 94 F₂ progeny of a backcross. In all cases we were able to identify the chromosomal interval carrying the recessive mutation despite the fact that some of the data sets consisted of as few as 10 meioses. Our strategy proved sensitive and expedient, since the simulated genome-wide screen could be executed by genotype analysis of 40 microsatellite markers in small numbers of intercross or backcross progeny. Received: 2 June 1997 / Accepted: 22 October 1997     

A stage‐wise approach for the analysis of multi‐environment trials

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi‐environment trials can be efficiently analysed by mixed models. A single‐stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two‐stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage‐wise approaches suggested so far are approximate in that they cannot fully reproduce a single‐stage analysis, except in very simple cases, because the variance–covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage‐wise method, which carries forward the full variance–covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single‐stage analysis. Computations are made efficient by a diagonalisation of the residual variance–covariance matrix, which necessitates a corresponding linear transformation of both the first‐stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three‐stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta‐analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta‐analysis.     

The area under the function: an index for selecting desirable genotypes

The linear regression approach has been widely used for selecting high-yielding and stable genotypes targeted to several environments. The genotype mean yield and the regression coefficient of a genotype's performance on an index of environmental productivity are the two main stability parameters. Using both can often complicate the breeder's decision when comparing high-yielding, less-stable genotypes with low-yielding, stable genotypes. This study proposes to combine the mean yield and regression coefficient into a unified desirability index (D _i). Thus, D _i is defined as the area under the linear regression function divided by the difference between the two extreme environmental indexes. D _i is equal to the mean of the i ^th genotype across all environments plus its slope multiplied by the mean of the environmental indexes of the two extreme environments (symmetry). Desirable genotypes are those with a large D _i. For symmetric trials the desirability index depends largely on the mean yield of the genotype and for asymmetric trials the slope has an important influence on the desirability index. The use of D _i was illustrated by a 20-environments maize yield trial and a 25-environments wheat yield trial. Three maize genotypes out of nine showed values of D _i 's that were significantly larger than a hypothetical, stable genotype. These were considered desirable, even though two of them had slopes significantly greater than 1.0. The results obtained from ranking wheat genotypes on mean yield differ from a ranking based on D _i .     

A simple method to approximate gene content in large pedigree populations: application to the myostatin gene in dual-purpose Belgian Blue cattle

Gene content is the number of copies of a particular allele in a genotype of an animal. Gene content can be used to study additive gene action of candidate gene. Usually genotype data are available only for a part of population and for the rest gene contents have to be calculated based on typed relatives. Methods to calculate expected gene content for animals on large complex pedigrees are relatively complex. In this paper we proposed a practical method to calculate gene content using a linear regression. The method does not estimate genotype probabilities but these can be approximated from gene content assuming Hardy-Weinberg proportions. The approach was compared with other methods on multiple simulated data sets for real bovine pedigrees of 1 082 and 907 903 animals. Different allelic frequencies (0.4 and 0.2) and proportions of the missing genotypes (90, 70, and 50%) were considered in simulation. The simulation showed that the proposed method has similar capability to predict gene content as the iterative peeling method, however it requires less time and can be more practical for large pedigrees. The method was also applied to real data on the bovine myostatin locus on a large dual-purpose Belgian Blue pedigree of 235 133 animals. It was demonstrated that the proposed method can be easily adapted for particular pedigrees.     

SPATIAL DISTRIBUTION OF GENETIC INDIVIDUALS IN THICKETS OF ALNUS INCANA SSP. RUGOSA,A CLONAL SHRUB

Thickets of speckled alder (Alnus incana ssp. rugosa (Du Roi) Clausen) consist of numerous discrete clumps of stems. Presumably all stems in a single clump are part of a single genetic individual, but a genet could comprise more than one clump. Starch-gel electrophoresis was used to identify genetic individuals in four alder populations in central New York. A single genetic marker, a tetramer with three alleles, could discriminate five genotypes. Nearest neighbor analysis revealed that genotypes were distributed randomly. That is, the pattern of genotypes was statistically indistinguishable from a model where each clump is considered a unique individual and where clump genotypes are randomly distributed. Calculation of Morisita's index of dispersion confirmed that clumps of a single genotype were not aggregated. Although alder is capable of forming root suckers and offsets, lateral expansion of genets is apparently ineffective. Apparently, spatial distribution of genetic individuals within alder thickets is not influenced by clonal growth or by other factors acting to cause patterns in the genetic structure of plant populations.     

甘蔗品种主要性状的基因型与环境及其互作效应分析

用AMMI模型双标图对国家第六轮甘蔗品种区域试验5个试点的12个甘蔗品种试验数据进行分析,研究甘蔗区试中不同品种的产量稳定性问题。结果表明,参试品种的6个产量性状在品种间和地点间差异显著,品种与地点的互作效应差异显著;FN30、YG16蔗茎产量和含糖量高,稳定性强,属于高产、稳产性较好的品种。AMMI模型很好地解释了甘蔗品种产量性状的基因型效应、环境效应和GE互作效应。     

Imputation of missing genotypes from sparse to high density using long-range phasing

Related individuals share potentially long chromosome segments that trace to a common ancestor. We describe a phasing algorithm (ChromoPhase) that utilizes this characteristic of finite populations to phase large sections of a chromosome. In addition to phasing, our method imputes missing genotypes in individuals genotyped at lower marker density when more densely genotyped relatives are available. ChromoPhase uses a pedigree to collect an individual's (the proband) surrogate parents and offspring and uses genotypic similarity to identify its genomic surrogates. The algorithm then cycles through the relatives and genomic surrogates one at a time to find shared chromosome segments. Once a segment has been identified, any missing information in the proband is filled in with information from the relative. We tested ChromoPhase in a simulated population consisting of 400 individuals at a marker density of 1500/M, which is approximately equivalent to a 50K bovine single nucleotide polymorphism chip. In simulated data, 99.9% loci were correctly phased and, when imputing from 100 to 1500 markers, more than 87% of missing genotypes were correctly imputed. Performance increased when the number of generations available in the pedigree increased, but was reduced when the sparse genotype contained fewer loci. However, in simulated data, ChromoPhase correctly imputed at least 12% more genotypes than fastPHASE, depending on sparse marker density. We also tested the algorithm in a real Holstein cattle data set to impute 50K genotypes in animals with a sparse 3K genotype. In these data 92% of genotypes were correctly imputed in animals with a genotyped sire. We evaluated the accuracy of genomic predictions with the dense, sparse, and imputed simulated data sets and show that the reduction in genomic evaluation accuracy is modest even with imperfectly imputed genotype data. Our results demonstrate that imputation of missing genotypes, and potentially full genome sequence, using long-range phasing is feasible.     

A reliable multiplex genotyping assay for HCV using a suspension bead array

The genotyping of the hepatitis C virus (HCV) plays an important role in the treatment of HCV because genotype determination has recently been incorporated into the treatment guidelines for HCV infections. Most current genotyping methods are unable to detect mixed genotypes from two or more HCV infections. We therefore developed a multiplex genotyping assay to determine HCV genotypes using a bead array. Synthetic plasmids, genotype panels and standards were used to verify the target‐specific primer (TSP) design in the assay, and the results indicated that discrimination efforts using 10 TSPs in a single reaction were extremely successful. Thirty‐five specimens were then tested to evaluate the assay performance, and the results were highly consistent with those of direct sequencing, supporting the reliability of the assay. Moreover, the results from samples with mixed HCV genotypes revealed that the method is capable of detecting two different genotypes within a sample. Furthermore, the specificity evaluation results suggested that the assay could correctly identify HCV in HCV/human immunodeficiency virus (HIV) co‐infected patients. This genotyping platform enables the simultaneous detection and identification of more than one genotype in a same sample and is able to test 96 samples simultaneously. It could therefore provide a rapid, efficient and reliable method of determining HCV genotypes in the future.     

A multiplex PCR for unequivocal differentiation of all encapsulated and non-encapsulated genotypes of Trichinella

We have developed a single PCR test for the simple and unequivocal differentiation of all currently recognised genotypes of Trichilnella. Partial DNA sequence data were generated from internal transcribed spacers ITS1 and ITS2, and from the expansion segment V region of the ribosomal DNA repeat from five species of Trichinella and two additional genotypes, designated T5 and T6. Five different PCR primer sets were identified which, when used simultaneously in a multiplex PCR, produce a unique electrophoretic DNA banding pattern for each species and genotype including three distinct genotypes of Trichinella pseudospiralis. The banding patterns for each parasite genotype consist of no more than two well-defined DNA fragments, except isolates of T. pseudospiralis which generate multiple, closely migrating bands. The expansion segment V-derived primer set contributes at least one fragment to each genotypic pattern and, therefore, functions both as a means for differentiation as well as an internal control for the PCR. The reliability and reproducibility of each DNA banding pattern were verified using multiple geographical isolates of each Trichinella genotype. The technique was developed further to distinguish genotypes at the level of single muscle larvae using a nested, multiplex PCR, whereby the entire internal transcribed spacer region as well as the gap region of the expansion segment V of the large subunit ribosomal DNA are amplified concurrently in a first-round PCR using primer sets specific for each region, followed by the multiplex PCR for final diagnosis.     

Algorithm for haplotype inference via galled-tree networks with simple galls

The problem of determining haplotypes from genotypes has gained considerable prominence in the research community. Here the focus is on determining sets of SNP values on individual chromosomes since such information captures the genetic causes of diseases. The most efficient algorithmic tool for haplotyping is based on perfect phylogenetic trees. A drawback of this method is that it cannot be applied in situations when the data contains homoplasies (multiple mutations of the same character) or recombinations. Recently, Song et al. ( 2005 ) studied the two cases: haplotyping via imperfect phylogenies with a single homoplasy and via galled-tree networks with one gall. In Gupta et al. ( 2010 ), we have shown that the haplotyping via galled-tree networks is NP-hard, even if we restrict to the case when every gall contains at most 3 mutations. We present a polynomial algorithm for haplotyping via galled-tree networks with simple galls (each having two mutations) for genotype matrices which satisfy a natural condition which is implied by presence of at least one 1 in each column that contains a 2. In the end, we give the experimental results comparing our algorithm with PHASE on simulated data.     

Differentiation of Trichinella genotypes by polymerase chain reaction using sequence-specific primers.

A method was developed to identify species and genotypes within the genus Trichinella using polymerase chain reaction (PCR) and specific primers. Enzymatic amplification of 2 partially conserved and repetitive genomic DNA sequences that have been shown to be variable in length within the different Trichinella genotypes form the basis of this test. Within these regions of the genome, 4 sets of primers were evaluated from which 2 were chosen for their ability to differentiate among the genotypes under stringent primer annealing conditions while maintaining high yields of amplification product. Differences in the size of PCR products from multiple isolates of each genotype indicate sufficient variation to identify 7 of the 8 parasite groups within this genus. One primer set can differentiate among some genotypes working from a single larva. Identification of Trichinella genotypes will assist in distinguishing between sylvatic and synanthropic life cycles. Such information will be critical in tracing sources of trichinellosis by easily and unambiguously identifying likely host reservoirs and will provide valuable information for instituting methods of control.     

Analysis of genetic marker-phenotype relationships by jack-knifed partial least squares regression (PLSR)

The utility of a relatively new multivariate method, bi-linear modelling by cross-validated partial least squares regression (PLSR), was investigated in the analysis of QTL. The distinguishing feature of PLSR is to reveal reliable covariance structures in data of different types with regard to the same set objects. Two matrices X (here: genetic markers) and Y (here: phenotypes) are interactively decomposed into latent variables (PLS components, or PCs) in a way which facilitates statistically reliable and graphically interpretable model building. Natural collinearities between input variables are utilized actively to stabilise the modelling, instead of being treated as a statistical problem. The importance of cross-validation/jack-knifing as an intuitively appealing way to avoid overfitting, is emphasized. Two datasets from chromosomal mapping studies of different complexity were chosen for illustration (QTL for tomato yield and for oat heading date). Results from PLSR analysis were compared to published results and to results using the package PLABQTL in these data sets. In all cases PLSR gave at least similar explained validation variances as the reported studies. An attractive feature is that PLSR allows the analysis of several traits/replicates in one analysis, and the direct visual identification of individuals with desirable marker genotypes. It is suggested that PLSR may be useful in structural and functional genomics and in marker assisted selection, particularly in cases with limited number of objects.     

Genotypechecker: an interactive tool for checking the inheritance consistency of genotyped pedigrees

Datapoint errors in pedigree genotype data sets are difficult to identify and adversely affect downstream genetic analyses. We present GenotypeChecker, a desktop software tool for assisting data cleansing. The application identifies likely data errors in pedigree/genotype data sets by performing an inheritance-checking algorithm for each marker across the pedigree, and highlights inconsistently inherited genotypes in an exploratory user interface. By 'masking' suspect datapoints and rechecking inheritance consistency, erroneous datapoints can be confirmed and cleansed from the data set. The software, examples and documentation are freely available at http://bioinformatics.roslin.ac.uk/genotypechecker.     

Conditional likelihood methods for haplotype-based association analysis using matched case-control data

Genetic epidemiologists routinely assess disease susceptibility in relation to haplotypes, that is, combinations of alleles on a single chromosome. We study statistical methods for inferring haplotype-related disease risk using single nucleotide polymorphism (SNP) genotype data from matched case-control studies, where controls are individually matched to cases on some selected factors. Assuming a logistic regression model for haplotype-disease association, we propose two conditional likelihood approaches that address the issue that haplotypes cannot be inferred with certainty from SNP genotype data (phase ambiguity). One approach is based on the likelihood of disease status conditioned on the total number of cases, genotypes, and other covariates within each matching stratum, and the other is based on the joint likelihood of disease status and genotypes conditioned only on the total number of cases and other covariates. The joint-likelihood approach is generally more efficient, particularly for assessing haplotype-environment interactions. Simulation studies demonstrated that the first approach was more robust to model assumptions on the diplotype distribution conditioned on environmental risk variables and matching factors in the control population. We applied the two methods to analyze a matched case-control study of prostate cancer.     

Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier's algorithm

When sampling locations are known, the association between genetic and geographic distances can be tested by spatial autocorrelation or regression methods. These tests give some clues to the possible shape of the genetic landscape. Nevertheless, correlation analyses fail when attempting to identify where genetic barriers exist, namely, the areas where a given variable shows an abrupt rate of change. To this end, a computational geometry approach is more suitable because it provides the locations and the directions of barriers and because it can show where geographic patterns of two or more variables are similar. In this frame we have implemented Monmonier's (1973) maximum difference algorithm in a new software package to identify genetic barriers. To provide a more realistic representation of the barriers in a genetic landscape, we implemented in the software a significance test by means of bootstrap matrices analysis. As a result, the noise associated with genetic markers can be visualized on a geographic map and the areas where genetic barriers are more robust can be identified. Moreover, this multiple matrices approach can visualize the patterns of variation associated with different markers in the same overall picture. This improved Monmonier's method is highly reliable and can be applied to nongenetic data whenever sampling locations and a distance matrix between corresponding data are available.     

Individual identification from genetic marker data: developments and accuracy comparisons of methods

Genetic marker‐based identification of distinct individuals and recognition of duplicated individuals has important applications in many research areas in ecology, evolutionary biology, conservation biology and forensics. The widely applied genotype mismatch (MM) method, however, is inaccurate because it relies on a fixed and suboptimal threshold number (T_M) of mismatches, and often yields self‐inconsistent pairwise inferences. In this study, I improved MM method by calculating an optimal T_M to accommodate the number, mistyping rates, missing data and allele frequencies of the markers. I also developed a pairwise likelihood relationship (LR) method and a likelihood clustering (LC) method for individual identification, using poor‐quality data that may have high and variable rates of allelic dropouts and false alleles at genotyped loci. The 3 methods together with the relatedness (RL) method were then compared in accuracy by analysing an empirical frog data set and many simulated data sets generated under different parameter combinations. The analysis results showed that LC is generally one or two orders more accurate for individual identification than the other methods. Its accuracy is especially superior when the sampled multilocus genotypes have poor quality (i.e. teemed with genotyping errors and missing data) and highly replicated, a situation typical of noninvasive sampling used in estimating population size. Importantly, LC is the only method that guarantees to produce self‐consistent results by partitioning the entire set of multilocus genotypes into distinct clusters, each cluster containing one or more genotypes that all represent the same individual. The LC and LR methods were implemented in a computer program COLONY for free download from the Internet.