The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.

Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.EEA PergaminoFil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaFil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; AustraliaFil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; AustraliaFil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; AustraliaFil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaFil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holand

Shovelomics root traits assessed on the EURoot maize panel are highly heritable across environments but show low genotype-by-nitrogen interaction

Abstract The need for sustainable intensification of agriculture in the coming decades requires a reduction in nitrogen (N) fertilization. One opportunity to reduce N application rates without major losses in yield is breeding for nutrient efficient crops. A key parameter that influences nutrient uptake efficiency is the root system architecture (RSA). To explore the impact of N availability on RSA and to investigate the impact of the growth environment, a diverse set of 36 inbred dent maize lines crossed to the inbred flint line UH007 as a tester was evaluated for N-response over 2 years on three different sites. RSA was investigated by excavating and imaging of the root crowns followed by image analysis with REST software. Despite strong site and year effects, trait heritability was generally high. Root traits showing the greatest heritability (> 0.7) were the width of the root stock, indicative of the horizontal expansion, and the fill factor, a measure of the density of the root system. Heritabilities were in a similar range under high or low N application. Under N deficiency the root stock size decreased, the horizontal expansion decreased and the root stock became less dense. However, there was little differential response of the genotypes to low N availability. Thus, the assessed root traits were more constitutively expressed rather than showing genotype-specific plasticity to low N. In contrast, strong differences were observed for 'stay green' and silage yield, indicating that these highly heritable traits are good indicators for responsiveness to low N

Combining pedigree and genomic information to improve prediction quality: an example in sorghum

Key Message: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. Abstract: Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers

Gene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPs

Quantitative trait locus (QTL) detection is commonly performed by analysis of designed segregating populations derived from two inbred parental lines, where absence of selection, mutation and genetic drift is assumed. Even for designed populations, selection cannot always be avoided, with as consequence varying correlation between genotypes instead of uniform correlation. Akin to linkage disequilibrium mapping, ignoring this type of genetic relatedness will increase the rate of false-positives. In this paper, we advocate using mixed models including genetic relatedness, or ‘kinship’ information for QTL detection in populations where selection forces operated. We demonstrate our case with a three-way barley cross, designed to segregate for dwarfing, vernalization and spike morphology genes, in which selection occurred. The population of 161 inbred lines was screened with 1,536 single nucleotide polymorphisms (SNPs), and used for gene and QTL detection. The coefficient of coancestry matrix was estimated based on the SNPs and imposed to structure the distribution of random genotypic effects. The model incorporating kinship, coancestry, information was consistently superior to the one without kinship (according to the Akaike information criterion). We show, for three traits, that ignoring the coancestry information results in an unrealistically high number of marker–trait associations, without providing clear conclusions about QTL locations. We used a number of widely recognized dwarfing and vernalization genes known to segregate in the studied population as landmarks or references to assess the agreement of the mapping results with a priori candidate gene expectations. Additional QTLs to the major genes were detected for all traits as well

Acción : diario de Teruel y su provincia: Año III Número 633 - (11/12/34)

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.</p

Mixed model association scans of multi-environmental trial data reveal major loci controlling yield and yield related traits in Hordeum vulgare in Mediterranean environments

An association panel consisting of 185 accessions representative of the barley germplasm cultivated in the Mediterranean basin was used to localise quantitative trait loci (QTL) controlling grain yield and yield related traits. The germplasm set was genotyped with 1,536 SNP markers and tested for associations with phenotypic data gathered over 2 years for a total of 24 year × location combinations under a broad range of environmental conditions. Analysis of multi-environmental trial (MET) data by fitting a mixed model with kinship estimates detected from two to seven QTL for the major components of yield including 1000 kernel weight, grains per spike and spikes per m2, as well as heading date, harvest index and plant height. Several of the associations involved SNPs tightly linked to known major genes determining spike morphology in barley (vrs1 and int-c). Similarly, the largest QTL for heading date co-locates with SNPs linked with eam6, a major locus for heading date in barley for autumn sown conditions. Co-localization of several QTL related to yield components traits suggest that major developmental loci may be linked to most of the associations. This study highlights the potential of association genetics to identify genetic variants controlling complex traits

学生との連携による市民社会に開かれた「学び」の創造 : 「在宅での看取り」から持続可能な福祉社会を捉える

千葉大学公共研究センター21世紀COEプログラム「持続可能な福祉社会に向けた公共研究拠点

金融不安定性の分析

研究科: 千葉大学大学院社会文化科学研究科修了年:2000.09学位:経済