A general statistical framework for dissecting parent-of-origin effects underlying endosperm traits in flowering plants

Genomic imprinting has been thought to play an important role in seed development in flowering plants. Seed in a flowering plant normally contains diploid embryo and triploid endosperm. Empirical studies have shown that some economically important endosperm traits are genetically controlled by imprinted genes. However, the exact number and location of the imprinted genes are largely unknown due to the lack of efficient statistical mapping methods. Here we propose a general statistical variance components framework by utilizing the natural information of sex-specific allelic sharing among sibpairs in line crosses, to map imprinted quantitative trait loci (iQTL) underlying endosperm traits. We propose a new variance components partition method considering the unique characteristic of the triploid endosperm genome, and develop a restricted maximum likelihood estimation method in an interval scan for estimating and testing genome-wide iQTL effects. Cytoplasmic maternal effect which is thought to have primary influences on yield and grain quality is also considered when testing for genomic imprinting. Extension to multiple iQTL analysis is proposed. Asymptotic distribution of the likelihood ratio test for testing the variance components under irregular conditions are studied. Both simulation study and real data analysis indicate good performance and powerfulness of the developed approach.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS323 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Aplicación de índices de tolerancia a la salinidad en plántulas de maíz

Los objetivos de este trabajo fueron: estudiar la aplicación de diferentes índices de tolerancia en caracteres de plántulas de maíz y evaluar su posible utilidad en la identificación de genotipos tolerantes a la salinidad. Se probaron 68 accesiones en dos ambientes (0 y 100m MClNa). Se midieron: largo de raíz, vástago y 3ª hoja y peso seco de raíz y de parte aérea. Se incluyeron 6 índices de tolerancia: índice de susceptibilidad al estrés (SSI), índice de tolerancia al estrés (STI), tolerancia al estrés (TOL), media geométrica de la productividad (GMP), productividad media (MP) e índice de estabilidad del rendimiento (YSI). Debido a la variación espacial relacionada con la concentración de sal en ambientes salinos, sería importante identificar genotipos estables frente a una gama amplia de suelos salinos. El biplot agrupó las accesiones, caracteres medidos en ambientes con y sin estrés e índices de tolerancia a sal, y demostró que los índices GMP, MP y STI fueron los que permitieron identificar los accesiones estables que se caracterizan por tener una alta expresión de estos caracteres en ambos ambientes. La aplicación del método de Componentes Principales (CP) identificó a los caracteres peso seco aéreo y largo de raíz como los de mayor contribución y ambos estuvieron asociados con dichos indices de tolerancia a salinidad. De esta manera, en este estudio las accesiones 1, 7, 30, 33, 43 y 45 fueron los más estables para los caracteres peso seco aéreo y largo de raíz. Entre ellos las accesiones 30 y 33 fueron superiores (pertenecientes a genotipos del Grupo A) porque mostraron los escores más altos sobre el eje CP1 pero sus aportes al CP2 fueron bastantes pequeños, para la mayoría de las variables.The aims of this research were: to study the application of different tolerance Indices in traits measured in maize seedlings and to assess their possible use in the identification of genotypes tolerant to salinity. Sixty eight accessions were tested in two environments (0 and 100mM NaCl). We recorded length for radicle, shoot and third leaf and dry weight for root and shoot. Six stress tolerance indices were included: stress susceptibility (SSI), stress tolerance index (STI), stress tolerance (TOL), geometric mean productivity (GMP), mean productivity (MP) and yield stability index (YSI. Saline environments show a great spatial variation in relation to the salt concentration, for this reason it would be important to identify genotypes with stable behavior in a variety of saline soils. The biplot method allowed clustering accessions, traits measured in stress and non stress environment and salt tolerance Indexes in a same graphic, and showed that GMP, MP and STI indexes were the ones who helped identify the high yielding (group A genotype) and stable accessions, characterized by a high expression of these characters in both environments. Principal Component method showed that shoot dry weight and root length had the highest contribution and both were associated with these above indices in salinity. Therefore, in this study the accessions: 1, 7, 30, 33, 43 and 45 had stable values for the traits root length and shoot dry weight. Within this group the 30 and 33 entries were superior (bellowing to Group A genotypes) because they had the highest PC1 scores but its PC2 scores were rather small for the most of the variables.Fil: Collado, Mónica B.. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Instituto Fitotécnico de "Santa Catalina"; ArgentinaFil: Aulicino, Mónica Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Instituto Fitotécnico de "Santa Catalina"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Arturi, Miguel Jacinto. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Instituto Fitotécnico de "Santa Catalina"; ArgentinaFil: Molina, María del Carmen. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Instituto Fitotécnico de "Santa Catalina"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Genetic Analysis of Tomato Fruit Ripening at Polypeptide Profiles Level through Quantitative and Multivariate Approaches

Multivariate analysis became essential in functional and structural Genomics because of the large quantity of biological data provided by these new research areas. Diallel mating design was widely applied to analyze the heritability of quantitative traits but it was recently used for approaching to the inheritance patterns of other levels of gene expression such as transcript profiles. Investigating the inheritance pattern of total polypeptide profiles with a diallel design remains as a vacancy subject. The objective of the present research was to infer the inheritance of total polypeptides profiles from tomato pericarp tissue at four different ripening stages in a diallel mating design including five recombinant inbred lines (RILs) and their ten second cycle hybrids (SCH). To achieve this objective, a multivariate analysis was applied to identify eventual inheritance patterns through a data mining approach and then univariate analyses were used to verify these patterns. Mainly dominance and also overdominance, though in a minor percentage, contributed to the gene actions involved in their genetic basis. Multivariate analysis was efficient in identifying inheritance patterns of total polypeptide profiles through a data mining approach, and univariate analyses largely verified the identified gene actionsFil: Marchionni Basté, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario. Facultad de Cs.agrarias. Departamento de Biologia. Cat.de Genetica; ArgentinaFil: Pereira Da Costa, Javier Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario. Facultad de Cs.agrarias. Departamento de Biologia. Cat.de Genetica; ArgentinaFil: Rodríguez, Gustavo Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario. Facultad de Cs.agrarias. Departamento de Biologia. Cat.de Genetica; ArgentinaFil: Zorzoli, Roxana. Universidad Nacional de Rosario. Facultad de Cs.agrarias. Departamento de Biologia. Cat.de Genetica; Argentina. Universidad Nacional de Rosario. Consejo de Investigaciones; ArgentinaFil: Pratta, Guillermo Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario. Facultad de Cs.agrarias. Departamento de Biologia. Cat.de Genetica; Argentin

Genetic variation and quantitative trait loci associated with developmental stability and the environmental correlation between traits in maize

There is limited experimental information about the genetic basis of micro-environmental variance (V-E) (developmental stability) and environmental correlations. This study, by using a population of maize recombinant inbred lines (RIL) and simple sequence repeat (SSR) polymorphic markers, aims at the following: firstly, to quantify the genetic component of the V-E or developmental stability for four traits in maize and the environmental correlation between these traits, and secondly, to identify quantitative trait loci (QTLs) that influence these quantities. We found that, when estimating variances and correlations and testing their homogeneity, estimates and tests are needed that are not highly dependent on normality assumptions. There was significant variation among the RILs in V-E and in the environmental correlation for some of the traits, implying genetic heterogeneity in the V-E and environmental correlations. The genetic coefficient of variation of the environmental variance (GCVV(E)) was estimated to be 20%, which is lower than estimates obtained for other species. A few genomic regions involved in the stability of one trait or two traits were detected, and these did not have an important influence on the mean of the trait. One region that could be associated with the environmental correlations between traits was also detected.</p

Genetic research in a public-private research consortium: prospects for indirect use of Elige breeding germplasm in academic research

The creation of a public¿private research partnership between plant breeding industry and academia can be beneficial for all parties involved. Academic partners benefit from the material contributions by industry and a practically relevant research focus, while industry benefits from increased insights and methodology tailored to a relevant set of data. However, plant breeding industry is highly competitive and there are obvious limits to the data and material partners are willing and able to share. This will usually include current and historic released cultivated materials, but will very often not include the elite germplasm used in-house to create new cultivars. Especially for crops where hybrid cultivars dominate the market, parental lines of hybrid cultivars are considered core assets that are never provided to outside parties. However, this limitation often does not apply to DNA or genetic fingerprints of these parental lines. We developed a procedure to take advantage of elite breeding materials for the creation of new promising research populations, through indirect selection of parents. The procedure starts with the identification of a number of traits for further study based on the presence of marker-trait associations and a priori knowledge within the participating companies about promising traits for quality improvement. Next, regression-based multi-QTL models are fitted to hybrid cultivar data to identify QTLs. Fingerprint data of parental lines of a limited number of specific hybrids are then used to predict parental phenotypes using the multi-QTL model fitted on hybrid data. The specific hybrids spanned the whole of the sensory space adequately. Finally, a choice of parental lines is made based on the QTL model predictions and new promising line combinations are identified. Breeding industry is then asked to create and provide progeny of these line combinations for further research. This approach will be illustrated with a case study in tomato

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