A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities

Multi-environment QTL mixed models for drought stress adaptation in wheat

Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance-covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m(-2) was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environment-specific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m(-2)) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m(-2)). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.MTM2014-55966-P Grains Research and Development Corporation (GRDC) of Australia National Institute of Agricultural Technology (INTA) of Argentina, Res. DN 1126/13 Integrated Breeding Progra

Two-mode clustering of genotype by trait and genotype by environment data

In this paper, we demonstrate the use of two-mode clustering for genotype by trait and genotype by environment data. In contrast to two separate (one mode) clusterings on genotypes or traits/environments, two-mode clustering simultaneously produces homogeneous groups of genotypes and traits/environments. For two-mode clustering, we first scan all two-mode cluster solutions with all possible numbers of clusters using k-means. After deciding on the final numbers of clusters, we continue with a two-mode clustering algorithm based on a genetic algorithm. This ensures optimal solutions even for large data sets. We discuss the application of two-mode clustering to multiple trait data stemming from genomic research on tomatoes as well as an application to multi-environment data on barle

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

Genome-wide association mapping for kernel and malting quality traits using hostorical European barley records

Malting quality is an important trait in breeding barley (Hordeum vulgare L.). It requires elaborate, expensive phenotyping, which involves micro-malting experiments. Although there is abundant historical information available for different cultivars in different years and trials, that historical information is not often used in genetic analyses. This study aimed to exploit historical records to assist in identifying genomic regions that affect malting and kernel quality traits in barley. This genome-wide association study utilized information on grain yield and 18 quality traits accumulated over 25 years on 174 European spring and winter barley cultivars combined with diversity array technology markers. Marker-trait associations were tested with a mixed linear model. This model took into account the genetic relatedness between cultivars based on principal components scores obtained from marker information. We detected 140 marker-trait associations. Some of these associations confirmed previously known quantitative trait loci for malting quality (on chromosomes 1H, 2H, and 5H). Other associations were reported for the first time in this study. The genetic correlations between traits are discussed in relation to the chromosomal regions associated with the different traits. This approach is expected to be particularly useful when designing strategies for multiple trait improvements

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

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Genome-wide association studies for Agronomical Traits in a world wide Spring Barley Collection

Background Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS. Results Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions. Conclusions Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversit

Multi-Trait and Multi-Environment QTL Analyses for Resistance to Wheat Diseases

BACKGROUND: Stripe rust, leaf rust, tan spot, and Karnal bunt are economically significant diseases impacting wheat production. The objectives of this study were to identify quantitative trait loci for resistance to these diseases in a recombinant inbred line (RIL) from a cross HD29/WH542, and to evaluate the evidence for the presence loci on chromosome region conferring multiple disease resistance. METHODOLOGY/PRINCIPAL FINDINGS: The RIL population was evaluated for four diseases and genotyped with DNA markers. Multi-trait (MT) analysis revealed thirteen QTLs on nine chromosomes, significantly associated with resistance. Phenotypic variation explained by all significant QTLs for KB, TS, Yr, Lr diseases were 57%, 55%, 38% and 22%, respectively. Marginal trait analysis identified the most significant QTLs for resistance to KB on chromosomes 1BS, 2DS, 3BS, 4BL, 5BL, and 5DL. Chromosomes 3AS and 4BL showed significant association with TS resistance. Significant QTLs for Yr resistance were identified on chromosomes 2AS, 4BL and 5BL, while Lr was significant on 6DS. MT analysis revealed that all the QTLs except 3BL significantly reduce KB and was contributed from parent HD29 while all resistant QTLs for TS except on chromosomes 2DS.1, 2DS.2 and 3BL came from WH542. Five resistant QTLs for Yr and six for Lr were contributed from parents WH542 and HD29 respectively. Chromosome region on 4BL showed significant association to KB, TS, and Yr in the population. The multi environment analysis for KB identified three putative QTLs of which two new QTLs, mapped on chromosomes 3BS and 5DL explained 10 and 20% of the phenotypic variation, respectively. CONCLUSIONS/SIGNIFICANCE: This study revealed that MT analysis is an effective tool for detection of multi-trait QTLs for disease resistance. This approach is a more effective and practical than individual QTL mapping analyses. MT analysis identified RILs that combine resistance to multiple diseases from parents WH542 and/or HD29