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RESUMEN La producción de maíz (Zea Mays L.) ha sido ampliamente beneficiada con la mejora de líneas endocriadas respecto a la resistencia a enfermedades causadas por virus y hongos. Sin embargo, es notable la ausencia de genotipos resistentes a bacteriosis. El objetivo del presente estudio fue identificar regiones genómicas para la mejora de resistencia a Mal de Río Cuarto (MRC) y a bacteriosis (BD) en un germoplasma diverso de maíz. Se evaluó, para ambas enfermedades, una población diversa de líneas de maíz en el ciclo de cultivo 2019-2020 en la región argentina donde la virosis MRC es endémica. Se estimó incidencia y severidad de MRC y BD en cada línea y se realizó un estudio de mapeo por asociación (GWAS) con 78.376 marcadores SNPs. Un modelo multicarácter se utilizó para evaluar simultáneamente la resistencia a MRC y BD en las líneas evaluadas. El germoplasma evidenció alta variabilidad genética tanto para la mejora de la resistencia a MRC como a BD, pero no se observó correlación genética significativa entre la respuesta a ambas enfermedades. Se identificaron regiones genómicas promisorias para resistencia a MRC y a BD, que serán confirmadas en evaluaciones en nuevos ambientes.
ABSTRACT Maize (Zea Mays L.) production has been greatly benefited from the improvement of inbred lines in regard to the resistance to diseases. However, the absence of resistant genotypes to bacteriosis is remarkable. The aim of the study was to identify genomic regions for resistance to Mal de Río Cuarto (MRC) and to bacterial disease (BD) in a diverse maize germplasm evaluated in the Argentinian region where MRC virus is endemic. A maize diverse population was assessed for both diseases during the 2019-2020 crop season. Incidence and severity of MRC and BD were estimated for each line and a genome wide association study (GWAS) was conducted with 78,376 SNP markers. A multi-trait mixed linear model was used for simultaneous evaluation of resistance to MRC and BD in the scored lines. The germplasm showed high genetic variability for both MRC and BD resistance. No significant genetic correlation was observed between the response to both diseases. Promising genomic regions for resistance to MRC and BD were identified and will be confirmed in further trials.
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RESUMEN El Mal de Río Cuarto (MRC) es una de las enfermedades virales más importantes del maíz en Argentina. El índice de severidad de enfermedad (ISE) permite combinar la incidencia y la severidad de una enfermedad en una métrica única. La reacción genotípica a MRC ha sido muy estudiada en poblaciones biparentales, sin embargo este carácter complejo no se ha analizado mediante estudios de mapeo por asociación. El objetivo del presente trabajo es identificar nuevos alelos de resistencia asociados con el ISE de la enfermedad MRC de maíz en un germoplasma exótico del Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT). Una población de líneas de maíz del CIMMYT se evaluó fenotípicamente en ambientes donde la enfermedad es endémica. Los predictores del efecto genotípico (BLUP, best linear unbiased predictor) del ISE de MRC y 78.376 marcadores SNP (Single Nucleotide Polymorphism) se usaron para realizar el mapeo por asociación en 186 líneas de maíz. Los componentes de varianza y los valores de heredabilidad sugieren una amplia variabilidad genotípica en la población de líneas. El mapeo por asociación permitió identificar 11 posibles QTL de resistencia a MRC. La incorporación de germoplasma exótico en los programas de mejoramiento de maíz locales podría contribuir favorablemente a la creación de genotipos híbridos con mayor nivel de resistencia a MRC. La capacidad predictiva de los marcadores asociados con la resistencia a MRC indican que la selección asistida por marcadores es una herramienta recomendable para seleccionar genotipos resistentes a MRC.
ABSTRACT Mal de Río Cuarto (MRC) is one of the most important viral diseases of maize in Argentina. The disease severity index (DSI) allows to combine the incidence and severity of a disease in a single metric. The genotypic reaction to MRC has been extensively studied in biparental populations. However, this complex trait has not been analyzed by genome-wide association studies (GWAS). The aim of this work is to identify new resistance alleles associated with DSI of MRC in an exotic germplasm from the International Maize and Wheat Improvement Center (CIMMYT). A population of maize lines from CIMMYT was phenotypically evaluated in environments in the area where the disease is endemic. The predictors of genetic effects (BLUP, best linear unbiased predictor) and 78,376 SNP markers (Single Nucleotide Polymorphism) were used to perform the GWAS in 186 maize lines. The values of variance components and mean-basis heritability suggest a wide genotypic variability in the population. The GWAS allowed to identify 11 putative QTL of resistance to MRC. The incorporation of exotic germplasm into local maize breeding programs could contribute favorably to the creation of hybrids with a higher level of resistance to MRC. The predictive ability of associated markers with MRC resistance indicates that marker-assisted selection is an advisable tool for selecting MRC resistant genotypes.
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La selección genómica (SG) es usada para predecir el mérito de un genotipo respecto a un carácter cuantitativo a partir de datos moleculares o genómicos. Estadísticamente, la SG requiere ajustar un modelo de regresión con múltiples variables predictoras asociadas a los estados de los marcadores moleculares (MM). El modelo se calibra en una población en la que hay datos fenotípicos y genómicos. La abundancia y la correlación de la información de los MM dificultan la estimación, y por ello existen distintas estrategias para el ajuste del modelo basadas en: mejor predictor lineal insesgado (BLUP), regresiones Bayesianas y aprendizaje automático. La correlación entre el fenotipo observado y el mérito genético predicho por el modelo ajustado, provee una medida de eficiencia (capacidad predictiva) de la SG. El objetivo de este trabajo fue realizar un meta-análisis de la eficiencia de la SG en cereales. Se realizó una revisión sistemática de estudios relacionados a SG y se llevó a cabo un meta-análisis, para obtener una medida global de la eficiencia de la SG en trigo y maíz, bajo diferentes escenarios (cantidad de MM y método estadístico usado para la SG). El metaanálisis indicó un coeficiente de correlación promedio de 0,61 entre los méritos genéticos predichos y los fenotipos observados. No se observaron diferencias significativas en la eficiencia de la SG realizada con modelos basados en BLUP (RR-BLUP y GBLUP), enfoque estadístico más comúnmente usado. El incremento de MM no cambia significativamente la eficiencia de la SG.
Genomic selection (GS) is used to predict the merit of a genotype with respect to a quantitative trait from molecular or genomic data. Statistically, GS requires fitting a regression model with multiple predictors associated with the molecular markers (MM) states. The model is calibrated in a population with phenotypic and genomic data. The abundance and correlation of MM information make model estimation challenging. For that reason there are diverse strategies to adjust the model: based on best linear unbiased predictors (BLUP), Bayesian regressions and machine learning methods. The correlation between the observed phenotype and the predicted genetic merit by the fitted model provides a measure of the efficiency (predictive ability) of the GS. The objective of this work was to perform a metaanalysis on the efficiency of GS in cereals. A systematic review of related GS studies and a meta-analysis, in wheat and maize, was carried out to obtain a global measure of GS efficiency under different scenarios (MM quantity and statistical models used in GS). The meta-analysis indicated an average correlation coefficient of 0.61 between observed and predicted genetic merits. There were no significant differences in the efficiency of the GS based on BLUP (RR-BLUP and GBLUP), the most common statistical approach. The increase of MM data, make GS efficiency do not vary widely.
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La diversidad genómica, expresada en las diferencias entre haplotipos moleculares de un conjunto de individuos, puede dividirse en componentes de variabilidad entre y dentro de algún factor de clasificación de los individuos. Para tal partición de varianzas, se usa análisis molecular de la varianza (AMOVA), el cual se construye a partir de las distancias multivariadas entre pares de haplotipos. El AMOVA clásico permite evaluar la significancia estadística de dos o más factores jerárquicos y consecuentemente no existe prueba de interacción entre factores. Sin embargo, existen situaciones donde los factores que clasifican a los individuos están cruzados y no anidados, es decir todos los niveles de un factor se encuentran representados en cada nivel del otro factor. Este trabajo propone una prueba estadística para evaluar la interacción entre factores cruzados en un AMOVA No-Jerárquico. La hipótesis nula de interacción establece que las diferencias moleculares entre individuos de distintos niveles de un factor son las mismas para todos los niveles del otro factor que los clasifica. La propuesta de análisis de interacción de factores a partir de distancias en un AMOVA No-Jerárquico comprende: cálculo de la matriz de distancia y partición de la misma en bloques, posterior cálculo de residuos y análisis de varianza no-paramétrico sobre los residuos. Su implementación es ilustrada en escenarios simulados y real. Los resultados sugieren que la prueba de interacción propuesta para el AMOVA No- Jerárquico presenta alta potencia.
The genomic diversity, expressed in the differences between molecular haplotypes of a group of individuals, can be divided into components of variability between and within some factor of classification of the individuals. For such variance partitioning, molecular analysis of variance (AMOVA) is used, which is constructed from the multivariate distances between pairs of haplotypes. The classical AMOVA allows the evaluation of the statistical significance of two or more hierarchical factors and consequently there is no interaction test between factors. However, there are situations where the factors that classify individuals are crossed rather than nested, that is, all the levels of a factor are represented in each level of the other one. This paper proposes a statistical test to evaluate the interaction between crossed factors in a Non-Hierarchical AMOVA. The null hypothesis of interaction establishes that the molecular differences between individuals of different levels of a factor are the same for all the levels of the other factor that classifies them. The proposed analysis of interaction in a Non- Hierarchical AMOVA includes: calculation of the distance matrix and partition of it into blocks, subsequent calculation of residuals and analysis of non-parametric variance on the residuals. Its implementation is illustrated in simulated and real scenarios. The results suggest that the proposed interaction test for the Non-Hierarchical AMOVA presents high power.
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The effect of the drying conditions on the retention quality for dried chicory roots (Cichorium intibyus L.) was investigated. Cubes of chicory roots were dried using hot air and vacuum dryers at 60 and 80 °C. Two different air velocities (0.2 and 0.7 m/s) were used in the hot air dryer, and two vacuum pressures (25 and 50 mmHg absolute) were set in the vacuum chamber. An exhaustive three dimensional mathematical model to describe mass transfer during drying of chicory roots of 1 cm of side was presented considering a polynomial functionality for the contraction kinetics. Experimental data obtained at laboratory scale were used to validate the proposed model showing good agreement between the experimental and estimated moisture profiles for both drying procedures. Moisture diffusivity was found to increase with the air drying temperature, velocity and vacuum pressure depending on the drying method. However, higher moisture diffusivity coefficients and lower activation energy values were obtained for the vacuum drying method. Samples dried using the vacuum drier at 60 °C and 25 mmHg presented better retention quality attributes, i.e., better rehydration, lower shrinkage and higher total phenolic content. The proposed mathematical model was able to satisfactorily predict the described behavior.
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Las pruebas de asociación entre marcadores moleculares y variables fenotípicas son cruciales para la identificación de QTL (Quantitative Trait Loci). Los avances biotecnológicos incrementaron la disponibilidad de marcadores genéticos y consecuentemente el número de pruebas de la asociación fenotipo-genotipo. El incremento de pruebas de significancia estadística a realizar en simultaneo (multiplicidad) demanda correcciones de los valores-p obtenidos para cada prueba de hipótesis de manera de mantener acotada las tasas de error para la familia de pruebas de asociación. Las correcciones estadísticas clásicas para el problema de multiplicidad, como Bonferroni, el método de control de la tasa de falsos descubrimientos (FDR) y el número efectivo de pruebas (Meff), son ampliamente usadas, pero fueron desarrolladas para datos independientes. Sin embargo, cuando las poblaciones de mapeo están genéticamente estructuradas los datos dejan de ser independientes. En este trabajo, proponemos un método de corrección por multiplicidad basado en estimación del número efectivo de pruebas desde un modelo que ajusta por la estructura de correlación subyacente. Se evalúa el desempeño del procedimiento propuesto a través del análisis de los valores-p obtenidos para un conjunto de QTL simulados. Los resultados sugieren que el método propuesto provee control de la tasa de falsos positivos y presenta mayor potencia que otros métodos de corrección por multiplicidad usados en mapeo asociativo.
The association tests between molecular markers and phenotypic traits are crucial for the Quantitative Trait Loci (QTL) identification. Biotechnological advances increased the molecular marker information; consequently, the number of genotype-phenotype association tests required incremented too. The multiple statistical inferences (multiplicity) demand corrections of the p-values obtained for each comparison in order to keep limited the error rates for the family of association tests. However, classic statistical correction methods such as Bonferroni, False Discovery Rate (FDR) and the Effective Number of Independent Test (Meff) were developed in the context of independent data. Wherever, when the population genetic structure is present, the data are no longer independent. In this paper, we propose a method of correction for multiplicity based on estimation of the effective number of tests from a model that adjust for the underlying correlation structure. We evaluate the performance of the proposed procedure in the estimation of p-values for a set of simulated QTL. The results suggest that the proposed method provides control of FDR and has more power than other methods for multiplicity correction used in association mapping.
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The objectives of this study were to evaluate the reproductive and productive performance of dairy cows with and without puerperal metritis and to evaluate the effectiveness of using a long-acting ceftiofur preparation. Dairy cows in one dairy farm, calving from July 2009 to January 2010, were examined between 3 and 14 days postpartum and classified on the basis of vaginal discharge into three groups: cows with normal discharge (control; C); cows with a bloody mucus purulent or pathologic nonfetid discharge (PnFD), and cows with bloody mucopurulent or purulent fetid discharge (PFD). Cows in C and PnFD groups were not treated, whereas those in the PFD group were randomly allocated to receive 2.2 mg/kg of ceftiofur subcutaneously behind the ear (PFD-T) or remain untreated (PFD-No T). From the 640 cows examined, 58.2% formed the C group, 13.4% formed the PnFD group, and 28.4% formed the PFD group. Survival curves differed between cows in the C group and PFD-No T group (P = 0.0013) and between PFD-No T versus PFD-T group (P = 0.0006). Survival curves of PnFD were intermediate and did not differ from those in the C group (P = 0.2) and PFD-T group (P = 0.1) but tended to be different from the PFD-No T group (P = 0.056). The postpartum interval to achieve a 25% pregnancy rate was 72 days for cows in the C group, 73 days for the PFD-T group, 83 days for PnFD group, and 95 days for the PFD-No T group. The chance of pregnancy in a cow in the C group was 1.98 times higher (95% confidence interval = 1.33, 3.08) and in cows in the PFD-T group was 2.16 times higher (95% confidence interval = 1.37, 3.50) than that in the PFD-No T group. Finally, the chance of pregnancy in cows in the PnFD group tended to be higher (P = 0.08) than that in the PFD-No T group but did not differ from the other two groups. Cumulative 305-day milk production was higher (P < 0.0001) in C group than those with vaginal discharge, regardless of fetidness and regardless of treatment. It is concluded that puerperal metritis affects the reproductive and productive performance of dairy cows and the treatment with ceftiofur was effective in reducing the adverse effects on reproductive performance but not on milk production.
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Doenças dos Bovinos/fisiopatologia , Bovinos , Endometrite/fisiopatologia , Lactação/fisiologia , Complicações Infecciosas na Gravidez/fisiopatologia , Infecção Puerperal/fisiopatologia , Reprodução/fisiologia , Animais , Argentina/epidemiologia , Doenças dos Bovinos/epidemiologia , Indústria de Laticínios/estatística & dados numéricos , Endometrite/complicações , Endometrite/epidemiologia , Feminino , Gravidez , Complicações Infecciosas na Gravidez/epidemiologia , Complicações Infecciosas na Gravidez/veterinária , Taxa de Gravidez , Infecção Puerperal/epidemiologia , Infecção Puerperal/veterinária , Descarga Vaginal/complicações , Descarga Vaginal/epidemiologia , Descarga Vaginal/fisiopatologia , Descarga Vaginal/veterináriaRESUMO
The objective of this study was to estimate the relative contribution of factors affecting how quickly cattle become pregnant in Argentine dairy herds. Data from 76,401 cows from 249 dairy herds were analyzed. A hazard model was used to explore days open (DO). The factors considered were milk yield, lactation number, calving season, and breeding technique (i.e., type of service: artificial insemination [AI], or combined service). Cows with lower milk yield had 1.09 to 1.38 higher likelihood to become pregnant than those with higher milk yield (P < 0.0001). The number of DO increased linearly with an increasing number of lactations (P < 0.0001). Cows calving in fall-winter had a shorter interval to conception than those calving in summer. The hazard rate for combined service was 1.27; therefore, cows with combined service were more likely to become pregnant during the observation period than those bred by AI. The difference in DO between cows of high versus low milk yield was smaller when dairies used AI as the main breeding technique than when they used combined service. Furthermore, dairies using mainly combined service had lower milk yield (5693.7 L) than those using mainly AI (7684.4 L). Although lactation number and calving season contributed to explain the number of DO, the influence of production level, the type of service, and the interaction between them was also associated with reproductive efficiency in Argentine dairy herds.
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Cruzamento/métodos , Bovinos/fisiologia , Animais , Argentina , Indústria de Laticínios , Feminino , Fertilidade , Fertilização , Inseminação Artificial/veterinária , Lactação , Gravidez , Modelos de Riscos Proporcionais , Estações do Ano , Fatores de TempoRESUMO
BACKGROUND: The low (LF) vs. high (HF) frequency energy ratio, computed from the spectral decomposition of heart beat intervals, has become a major tool in cardiac autonomic system control and sympatho-vagal balance studies. The (statistical) distributions of response variables designed from ratios of two quantities, such as the LF/HF ratio, are likely to non-normal, hence preventing e.g., from a relevant use of the t-test. Even using a non-parametric formulation, the solution may be not appropriate as the test statistics do not account for correlation and heteroskedasticity, such as those that can be observed when several measures are taken from the same patient. OBJECTIVES: The analyses for such type of data require the application of statistical models which do not assume a priori independence. In this spirit, the present contribution proposes the use of the Generalized Linear Mixed Models (GLMMs) framework to assess differences between groups of measures performed over classes of patients. METHODS: Statistical linear mixed models allow the inclusion of at least one random effect, besides the error term, which induces correlation between observations from the same subject. Moreover, by using GLMM, practitioners could assume any probability distribution, within the exponential family, for the data, and naturally model heteroskedasticity. Here, the sympatho-vagal balance expressed as LF/HF ratio of patients suffering neurogenic erectile dysfunction under three different body positions was analyzed in a case-control protocol by means of a GLMM under gamma and Gaussian distributed responses assumptions. RESULTS: The gamma GLMM model was compared with the normal linear mixed model (LMM) approach conducted using raw and log transformed data. Both raw GLMM gamma and log transformed LMM allow better inference for factor effects, including correlations between observations from the same patient under different body position compared to the raw LMM. The gamma GLMM provides a more natural distribution assumption of a response expressed as a ratio. CONCLUSIONS: A gamma distribution assumption intrinsically models quadratic relationships between the expected value and the variance of the data avoiding prior data transformation. SAS and R source code are available on request.
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Disfunção Erétil/etiologia , Frequência Cardíaca/fisiologia , Sistema Nervoso Autônomo/fisiopatologia , Eletrocardiografia , Disfunção Erétil/fisiopatologia , Humanos , Modelos Lineares , Masculino , Neurônios/fisiologiaRESUMO
Species dispersal studies provide valuable information in biological research. Restricted dispersal may give rise to a non-random distribution of genotypes in space. Detection of spatial genetic structure may therefore provide valuable insight into dispersal. Spatial structure has been treated via autocorrelation analysis with several univariate statistics for which results could dependent on sampling designs. New geostatistical approaches (variogram-based analysis) have been proposed to overcome this problem. However, modelling parametric variograms could be difficult in practice. We introduce a non-parametric variogram-based method for autocorrelation analysis between DNA samples that have been genotyped by means of multilocus-multiallele molecular markers. The method addresses two important aspects of fine-scale spatial genetic analyses: the identification of a non-random distribution of genotypes in space, and the estimation of the magnitude of any non-random structure. The method uses a plot of the squared Euclidean genetic distances vs. spatial distances between pairs of DNA-samples as empirical variogram. The underlying spatial trend in the plot is fitted by a non-parametric smoothing (LOESS, Local Regression). Finally, the predicted LOESS values are explained by segmented regressions (SR) to obtain classical spatial values such as the extent of autocorrelation. For illustration we use multivariate and single-locus genetic distances calculated from a microsatellite data set for which autocorrelation was previously reported. The LOESS/SR method produced a good fit providing similar value of published autocorrelation for this data. The fit by LOESS/SR was simpler to obtain than the parametric analysis since initial parameter values are not required during the trend estimation process. The LOESS/SR method offers a new alternative for spatial analysis.
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Modelos Genéticos , Alelos , Animais , Variação Genética , Análise Multivariada , Dinâmica Populacional , RatosRESUMO
The aim of this work was to use the Partial Least Squares Regression (PLS) technique to fit simple models for the interpretation of an underlying complex process. In this study, the technique was used to build a statistical model for molecular kinetic data obtained from hemodialyzed patients. By using PLS we derived statistical linear models for the prediction of the equilibrated urea concentration which would be reached 30-60 min after the end of the dialysis session. Models with an average relative prediction error (RPE) of less than 0.05% were achieved. The model predictive accuracy was evaluated in a cross-center study yielding an RPE < 3%. The chosen model was robust to variations such as sampling extraction time demonstrating a high capacity for modeling kinetics. It also was found to be useful for bedside monitoring. Finally, the PLS technique allowed identification of the most important co-variables in the model and of those patients with outlier patterns in their molecular dynamics.