Iván Matus

Chapter 10 - Genetic diversity for quantitatively inherited agronomic and malting quality traits

This chapter reviews diversity in agronomic traits, diversity in malting quality traits, and the current status of Quantitative Trait Loci (QTL) analysis in barley and the application of QTL tools to the analysis of genetic diversity in barley and crop improvement. Agronomic and quality traits were undoubtedly key issues for the domesticators of barley. Crop productivity would clearly have been an attribute of key interest, and the selection of shattering-resistant mutants probably led to a quantum leap in yield. Because barley has been used both as a food and as a principal ingredient of fermented beverages from the earliest times, there may well have been conscious selection for end-use properties. The selection of hull-less mutants in areas of the world where barley was a principal foodstuff underscores the importance of end-use properties in domestication. The malting and brewing properties of wild barley accessions and landraces have not been welldescribed and are, in fact, extremely difficult to measure. Plant breeding efforts are directed primarily at traits exhibiting quantitative variation. Breeders and geneticists were now able to collaborate in developing and testing hypotheses regarding the number, location, effect, and interactions of genes influencing quantitative traits.

Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance.

A collection of 368 advanced lines and cultivars of spring wheat (Triticum aestivum L.) from Chile, Uruguay, and CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo), with good agronomic characteristics were evaluated under the Mediterranean conditions of central Chile. Three different water regimes were assayed: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation around booting), and full irrigation (FI; four irrigations: at tillering, flag leaf appearance, heading, and middle grain filling). Traits evaluated were grain yield (GY), agronomical yield components, days from sowing to heading, carbon isotope discrimination (Δ(13) C) in kernels, and canopy spectral reflectance. Correlation analyses were performed for 70 spectral reflectance indices (SRI) and the other traits evaluated in the three trials. GY and Δ(13) C were the traits best correlated with SRI, particularly when these indices were measured during grain filling. However, only GY could be predicted using a single regression, with Normalized Difference Moisture Index (NDMI2: 2,200; 1,100) having the best fit to the data for the three trials. For Δ(13) C, only individual regressions could be forecast under FI (r(2): 0.25-0.37) and MWS (r(2): 0.45-0.59) but not under SWS (r(2): 0.03-0.09). NIR-based SRI proved to be better predictors than those that combine visible and NIR wavelengths.

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

Genetic Progress in Winter Wheat Cultivars released in Chile from 1920 to 2000

El trigo (Triticum aestivum L.) es el cultivo mas importante en Chile en terminos de superficie sembrada y areas geograficas en las cuales se siembra, cubriendo una gran diversidad de condiciones climaticas. Este estudio evaluo los cambios de varias caracteristicas agronomicas de variedades de trigo de invierno liberadas en el pais entre 1920 y 2000. Un total de 117 genotipos de trigos de invierno, que representa 45 cultivares antiguos y 72 cultivares modernos, se evaluaron en un clima humedo de tipo mediterraneo, en condiciones de riego, en el ano 2003. Los cultivares antiguos corresponden a aquellos liberados antes del ano 1960 y los modernos a los liberados despues del ano 1960. Mediante un analisis de componentes principales (CP) usando 10 caracteristicas agronomicas, permitio separar claramente los cultivares modernos de los antiguos. Al comparar los cultivares modernos con los antiguos se determino que la altura de la planta se ha reducido un 25,6%, pero en otras caracteristicas se produjo un aumento, como el indice de cosecha (21,1%), numero de granos por espiga (42,6%), valor de sedimentacion (103%) y la dureza del grano (32,0%). La variacion en la altura de la planta se correlaciono negativamente con el indice de cosecha (r = -0,30, p < 0,001). El rendimiento de grano, una caracteristica no incluida en el analisis de PC, estuvo altamente correlacionado con el segundo PC (r = 0,81, p < 0,0001). Correlaciones significativas (p < 0,01) se encontraron entre el ano de la liberacion de los cultivares y las caracteristicas agronomicas: altura de planta (r = -0,82), indice de cosecha (r = 0,40), numero de granos por espiga (0,69), valor de sedimentacion (r = 0,64), y peso del grano (r = -0,46). Estas correlaciones fueron en su mayoria una consecuencia de ausencia o presencia de genes de enanismo en el germoplasma. Finalmente el avance en rendimiento, calculado a partir de datos de rendimiento de ensayos en los que se probaron entre 15 y 25 cultivares y lineas avanzadas de trigo de invierno evaluados casi todos los anos entre 1965 y hasta 2001, no mostro ningun aumento en el rendimiento entre 1965 y 1975, pero si un aumento de 246 kg ha-1 por ano entre 1976 y 1998, lo que representa un incremento anual de 2,6%.

Physiological Traits Associated with Wheat Yield Potential and Performance under Water-Stress in a Mediterranean Environment

Different physiological traits have been proposed as key traits associated with yield potential as well as performance under water stress. The aim of this paper is to examine the genotypic variability of leaf chlorophyll, stem water-soluble carbohydrate content and carbon isotope discrimination (Δ13C), and their relationship with grain yield (GY) and other agronomical traits, under contrasting water conditions in a Mediterranean environment. The study was performed on a large collection of 384 wheat genotypes grown under water stress (WS, rainfed), mild water stress (MWS, deficit irrigation), and full irrigation (FI). The average GY of two growing seasons was 2.4, 4.8, and 8.9 Mg ha−1 under WS, MWS, and FI, respectively. Chlorophyll content at anthesis was positively correlated with GY (except under FI in 2011) and the agronomical components kernels per spike (KS) and thousand kernel weight (TKW). The WSC content at anthesis (WSCCa) was negatively correlated with spikes per square meter (SM2), but positively correlated with KS and TKW under WS and FI conditions. As a consequence, the relationships between WSCCa with GY were low or not significant. Therefore, selecting for high stem WSC would not necessary lead to genotypes of GY potential. The relationship between Δ13C and GY was positive under FI and MWS but negative under severe WS (in 2011), indicating higher water use under yield potential and MWS conditions.

Effect of soil depth and increasing fertilization rate on yield and its components of two durum wheat varieties

Agronomic practices, climatic variables, and soil conditions are key factors in crop productivity. Although the effects of soil chemical properties and water and agronomic crop management are known, there is little information about effective soil depth and its influence on crop productivity. Since most crop fertilization systems are based on the productive potential associated with climatic conditions and chemical properties of the first 20 cm of soil depth, the objective of this study was to determine the importance of effective depth in terms of increasing fertilization rates on durum wheat ( Triticum turgidum L. var. durum) productivity. Two experiments were conducted in the 2006-2007 season in the Santa Rosa Experimental Station (71°54’ S, 36°31’ W, 220 m a.s.l.) of the Instituto de Investigaciones Agropecuarias (INIA) located in south central Chile. We used the hard wheat cvs. Llareta-INIA and Corcolen-INIA because both respond differently to soil physicochemical properties. Each cultivar was sown in sectors with different depths: 1) 0.45 m depth, silt loam on river material and 2) 1.0 m depth, loam on deep sediments. Fertilization was: 1) control without fertilization, 2) basal fertilization (BF) based on P, K, Ca, Mg, S, B, and Zn plus 90 kg N ha-1, and 3) BF plus 210 kg N ha-1. Grain yield, plant height, and number of stems m-2 were positively affected by increasing depth of soil profile. The increasing fertilization rate affected grain yield and plant height. Grain yield for cv. Corcolen-INIA had a greater response than cv. Llareta-INIA when soil depth was increased.

Drought Tolerance in Recombinant Chromosome Substitution Lines (RCSLs) Derived from the Cross Hordeum vulgare subsp. spontaneum (Caesarea 26-24) × Hordeum. vulgare subsp. vulgare cv. Harrington

Se estudio el rendimiento de grano (GY) y la tolerancia a la sequia de lineas recombinantes con substitucion de cromosomas (RCSLs), provenientes de la cruza entre Hordeum vulgare L. subsp. spontaneum (K. Koch) Thell y H. vulgare L. subsp. vulgare, en dos ambientes contrastantes, uno con estres hidrico (WWS) y otro sin estres hidrico (NWS), durante tres temporadas de crecimiento, 2004-2005, 2005-2006 y 2006-2007. En la primera temporada se establecieron 80 RCSLs y en las siguientes se evaluo una seleccion de 13 RCSLs. Se utilizo un diseno a-latice en todos los experimentos. Con los datos de GY obtenidos en los sitios WWS y NWS se calculo el indice de sensibilidad a la sequia (DSI). Durante la temporada 2004-2005, el GY vario ampliamente entre localidades, reflejando las diferencias en la disponibilidad de agua. El GY promedio de las 80 RCSLs fue 4,4 y 8,0 Mg ha-1 en los sitios WWS y NWS, respectivamente. El DSI vario ampliamente entre genotipos, desde 0,24 hasta 1,53. Ademas, el DSI se correlaciono negativa y significativamente con el rendimiento de grano en el ambiente WWS y permitio seleccionar un grupo de genotipos tolerantes y otro de sensibles a la sequia. El grupo de genotipos tolerantes a sequia mostro en el sitio WWS un rendimiento de grano promedio de 18, 12 y 7% superior al de los no tolerantes, en las temporadas 2004-2005, 2005-2006 y 2006-2007, respectivamente.

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.

Effect of soil cadmium concentration on three Chilean durum wheat cultivars in four environments

ABSTRACT Durum wheat (Triticum turgidum L. var durum) is a species that accumulates cadmium (Cd). Durum wheat cultivars differ in their absorption ability of Cd; therefore, identifying and selecting genetic material with low Cd accumulation reduces human exposure to this toxic element. In the present study, Cd concentration was evaluated in three Chilean durum wheat cultivars (Llareta-INIA, Corcolén-INIA, and Lleuque-INIA) grown in four Chilean locations with varying concentrations of Cd in soils. The objective of this study was to evaluate the response of these durum wheat cultivars to different doses of cadmium in terms of grain yield; Cd concentration in different plant tissues (grain, straw, roots); soil Cd concentration was also evaluated. Results show that grain yield was not affected by soil Cd; differences in Cd concentration in plant tissues were generally associated with location, cultivar, and soil Cd concentration. Grain Cd concentration in all three cultivars was classified in the low accumulation category for this metal; ‘Lleuque-INIA’ noted as having a very low accumulation.

Ascertainment bias from imputation methods evaluation in wheat.

BackgroundWhole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.ResultsIn this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available.ConclusionsPoorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.