Mateo Vargas

Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure

Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker–trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype × environment interaction, thereby making possible the identification of markers contributing to both additive and additive × additive interaction effects of traits.

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.

Quantitative trait loci for yield and correlated traits under high and low soil nitrogen conditions in tropical maize

The first objective of this study was to map and characterize quantitative trait loci (QTL) for grain yield (GY) and for secondary traits under varying nitrogen (N) supply. To achieve this objective, a segregating F2:3 population previously developed for QTL mapping under water-limited conditions was used. The population was evaluated in Mexico under low N conditions in the dry winter season and under low and high N conditions in the wet summer season. From eight QTLs identified for GY under low N conditions, two were also detected under high N conditions. Five QTLs were stable across the two low N environments and five co-localized with QTLs identified for the anthesis-silking interval (ASI) or for the number of ears per plant (ENO) under low N conditions. The percentage of the phenotypic variance expressed by all QTLs for ASI and ENO was quite different when evaluated under low N conditions during the dry winter (40% for ASI and 22% for ENO) and the wet summer seasons (22% for ASI and 46% for ENO). The results suggest optimizing different breeding strategies based on selection index depending on the growing season. Good QTL colocalization was observed for ASI (four QTLs) and ENO (three QTLs) when looking at QTL identified under low N and water-limited conditions in the same population. The results suggest that that both secondary traits can be used in breeding programs for simultaneous improvement of maize against low N and drought stresses.

Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables

Abstract An understanding of the genetic and environmental basis of genotype×environment interaction (GEI) is of fundamental importance in plant breeding. In mapping quantitative trait loci (QTLs), suitable genetic populations are grown in different environments causing QTLs×environment interaction (QEI). The main objective of the present study is to show how Partial Least Squares (PLS) regression and Factorial Regression (FR) models using genetic markers and environmental covariables can be used for studying QEI related to GEI. Biomass data were analyzed from a multi-environment trial consisting of 161 lines from a F3:4 maize segregating population originally created with the purpose of mapping QTLs loci and investigating adaptation differences between highland and lowland tropical maize. PLS and FR methods detected 30 genetic markers (out of 86) that explained a sizeable proportion of the interaction of maize lines over four contrasting environments involving two low-altitude sites, one intermediate-altitude site, and one high-altitude site for biomass production. Based on a previous study, most of the 30 markers were associated with QTLs for biomass and exhibited significant QEI. It was found that marker loci in lines with positive GEI for the highland environments contained more highland alleles, whereas marker loci in lines with positive GEI for intermediate and lowland environments contained more lowland alleles. In addition, PLS and FR models identified maximum temperature as the most-important environmental covariable for GEI. Using a stepwise variable selection procedure, a FR model was constructed for GEI and QEI that exclusively included cross products between genetic markers and environmental covariables. Higher maximum temperature in low- and intermediate-altitude sites affected the expression of some QTLs, while minimum temperature affected the expression of other QTLs.

Physiological factors associated with genotype by environment interaction in wheat

Abstract Wheat cultivars often show highly significant genotype by environment interaction (G×E) for yield, even when comparing different years within a relatively stable location. This study attempts to explain some of the physiological bases of G×E in two experiments: (i) historic yield potential trials (HYPTs) of bread wheat ( Triticum aestivum L.), durum ( T. durum Desf.) and triticale (X Triticosecale Wittmack) cultivars grown under agronomically optimal conditions; (ii) an elite spring wheat yield trial (ESWYT) of 30 bread wheat genotypes cultivated at 27 international locations. For the HYPT, the main objectives were to determine the environmental variables during different phenological stages associated with: (i) G×E among the three crop species, (ii) G×E within each species, and (iii) underlying physiological causes of G×E. For ESWYT, meteorological data were not available and so mean site values of certain crop parameters were used as proxy environmental data to determine whether conditions either pre- or post-anthesis were more influential in determining G×E. Partial least-squares analysis and factorial regression models were used to identify the environmental factors best explaining G×E independent of the main effects. Of the three crops, durums were shown to be the most sensitive to conditions pre-anthesis, requiring higher radiation and cooler average temperatures in order to set high grain number. Triticale, despite having the highest average yield and biomass, performed relatively poor when conditions from spike growth stage onwards were sunny and warm. Bread wheat appeared to be the most robust of the three species. Considering yield, biomass, and yield components, it was apparent that the spike primordia growth stage was generally the most sensitive to environmental factors causing G×E. Results for the ESWYT suggested that conditions post-anthesis were more influential on G×E than conditions pre-anthesis. Implications for how such analysis may assist with both conventional and molecular approaches to breeding are discussed.

Dissecting Maize Productivity: Ideotypes Associated with Grain Yield under Drought Stress and Well‐watered Conditions

To increase maize (Zea mays L.) yields in drought-prone environments and offset predicted maize yield losses under future climates, the development of improved breeding pipelines using a multi-disciplinary approach is essential. Elucidating key growth processes will provide opportunities to improve drought breeding progress through the identification of key phenotypic traits, ideotypes, and donors. In this study, we tested a large set of tropical and subtropical maize inbreds and single cross hybrids under reproductive stage drought stress and well-watered conditions. Patterns of biomass production, senescence, and plant water status were measured throughout the crop cycle. Under drought stress, early biomass production prior to anthesis was important for inbred yield, while delayed senescence was important for hybrid yield. Under well-watered conditions, the ability to maintain a high biomass throughout the growing cycle was crucial for inbred yield, while a stay-green pattern was important for hybrid yield. While new quantitative phenotyping tools such as spectral reflectance (Normalized Difference Vegetation Index, NDVI) allowed for the characterization of growth and senescence patterns as well as yield, qualitative measurements of canopy senescence were also found to be associated with grain yield.

PAPER PRESENTED AT INTERNATIONAL WORKSHOP ON INCREASING WHEAT YIELD POTENTIAL, CIMMYT, OBREGON, MEXICO, 20–24 MARCH 2006 Association of source/sink traits with yield, biomass and radiation use efficiency among random sister lines from three wheat crosses in a high-yield environment

SUMMARY For many years yield improvement reported in wheat was associated with increased dry matter partitioning to grain, but more recently increases in above-ground biomass have indicated a different mechanism for achieving yield potential. The most likely way of increasing crop biomass is by improving radiation use efficiency (RUE); however there is evidence that sink strength is still a critical yield limiting factor in wheat, suggesting that improving the balance between source and sink (source/sink (SS)) is currently the most promising approach for increasing yield, biomass, and RUE. Experiments were designed to establish a more definitive link of SS traits with yield, biomass and RUE in high-yield environments using progeny deriving from parents contrasting in some of those traits. The SS traits formed three main groups relating to (i) phenological pattern of the crop, (ii) assimilation capacity up until shortly after anthesis, and (iii) partitioning of assimilates to reproductive structures shortly after anthesis. The largest genetic gains in performance traits were associated with the second group ; however, traits from the other groups were also identified as being genetically linked to improvement in performance parameters. Because many of these traits are interrelated, principal component analysis (PCA) multiple regression and path analysis were used to expose these relationships more clearly. The trait most consistently associated with performance traits was biomass at anthesis (BMA). The PCA indicated a fairly close association among traits within this group (i.e. assimilation-related traits) while those from the other two groups of SS traits (i.e. phenological and partitioning) appeared to have secondary but independent effects. These conclusions were partially born out by stepwise multiple regression for individual crosses where BMA was often complemented by traits from the two other groups. Taken together, the data suggest that the assimilation traits biomass in vegetative stage (BMV) and BMA have partially independent genetic effects in this germplasm and were complementary to achieving improved performance. The identification of a number of SS traits associated with yield and biomass, which both PCA and multiple regression suggest as being at least partially independent of one another, support the idea that additive gene action could be achieved by adopting a physiological trait based breeding approach where traits from different groups are combined in a single background. A second breeding intervention based on these results would be in selecting progeny for BMA and BMV using spectral reflectance approaches since those traits that lend themselves to large-scale screening. Path analysis confirmed the importance of the spike primordial stage in the genotype by environment interaction for these traits.

Erratum to “Physiological factors associated with genotype by environment interaction in wheat”

Wheat cultivars often show highly significant genotype by environment interaction ðGEÞ for yield, even when comparing different years within a relatively stable location. This study attempts to explain some of the physiological bases of GE in two experiments: (i) historic yield potential trials (HYPTs) of bread wheat (BW) (Triticum aestivum L.), durum (T. durum Desf.) and triticale (TCL) (X Triticosecale Wittmack) cultivars grown under agronomically optimal conditions; (ii) an elite spring wheat yield trial (ESWYT) of 30 BW genotypes cultivated at 27 international locations. For the HYPT, the main objectives were to determine the environmental variables during different phenological stages associated with: (i) GE among the three crop species, (ii) GE within each species, and (iii) underlying physiological causes of GE. For ESWYT, meteorological data were not available and so mean site values of certain crop parameters were used as proxy environmental data to determine whether conditions either pre- or post-anthesis were more influential in determining GE. Partial least squares analysis (PLS) and factorial regression (FR) models were used to identify the environmental factors best explaining GE independent of the main effects. Of the three crops, durums were shown to be the most sensitive to conditions pre-anthesis, requiring higher radiation and cooler average temperatures (Txs) in order to set high grain number. TCL, despite having the highest average yield and biomass, performed relatively poor when conditions from spike growth stage onwards were sunny and warm. BW appeared to be the most robust of the three species. Considering yield, biomass, and yield components, it was apparent that the spike primordia growth stage was generally the most sensitive to environmental factors causing GE. Results for the ESWYT suggested that conditions post-anthesis were more influential on GE than conditions pre-anthesis. Implications for how such analysis may assist with both conventional and molecular approaches to breeding are discussed. # 2003 Elsevier B.V. All rights reserved.

Structural equation modelling for studying genotype x environment interactions of physiological traits affecting yield in wheat

SUMMARY In plant physiology and breeding, it is important to understand the causes of genotyperenvironment interactions (GEIs) of complex traits such as grain yield. It is difficult to study the underlying sequential biological processes of such traits, their components and other intermediate traits, as well as the main environmental factors affecting those processes. The structural equation models (SEMs) used in the present study allow the external and internal factors affecting GEI of various traits and their interrelations to be accounted for. The study included 86 wheat genotypes derived from three different crosses and evaluated over 3 years. Several attributes, as well as grain yield and yield components, were measured during five crop development stages. Environmental data for the five development stages were averaged. The SEM approach facilitated comprehensive understanding of GEI effects among the different traits, and decomposed the total effects of grain yield components and cross-product covariates on grain yield GEI into direct and indirect effects. External climatic variables were related mostly to main final yield components, and only more intermediate endogenous variables, such as spikes/m 2 , were affected by minimum temperature and radiation in the early stages of plant development.

Genetic yield gains in CIMMYT’s international elite Spring Wheat yield trials by modeling the Genotype X environment interaction

We calculated the annual genetic gains for grain yield (GY) of wheat (Triticum aestivum L.) achieved over 8 yr of international Elite Spring Wheat Yield Trials (ESWYT), from 2006–2007 (27th ESWYT) to 2014–2015 (34th ESWYT). In total, 426 locations were classified within three main megaenvironments (MEs): ME1 (optimally irrigated environments), ME4 (drought-stressed environments), and ME5 (heat-stressed environments). By fitting a factor analytical structure for modeling the genotype × environment (G × E) interaction, we measured GY gains relative to the widely grown cultivar Attila (GYA) and to the local checks (GYLC). Genetic gains for GYA and GYLC across locations were 1.67 and 0.53% (90.1 and 28.7 kg ha–1 yr–1), respectively. In ME1, genetic gains were 1.63 and 0.72% (102.7 and 46.65 kg ha–1 yr–1) for GYA and GYLC, respectively. In ME4, genetic gains were 2.7 and 0.41% (88 and 15.45 kg ha–1 yr–1) for GYA and GYLC, respectively. In ME5, genetic gains were 0.31 and 1.0% (11.28 and 36.6 kg ha–1 yr–1) for GYA and GYLC, respectively. The high GYA in ME1 and ME4 can be partially attributed to yellow rust races that affect Attila. When G × E interactions were not modeled, genetic gains were lower. Analyses showed that CIMMYT’s location at Ciudad Obregon, Mexico, is highly correlated with locations in other countries in ME1. Lines that were top performers in more than one ME and more than one country were identified. CIMMYT’s breeding program continues to deliver improved and widely adapted germplasm for target environments.