Journal articles 2013
Documents
High-resolution single nucleotide polymorphism genotyping reveals a significant problem among breeder resources
Lucas MR, Huynh B-L, Ehlers JD, Roberts PA, Close TJ (2013). High-resolution single nucleotide polymorphism genotyping reveals a significant problem among breeder resources. The Plant Genome 6(1):1–5. (DOI: 10.3835/plantgenome2012.08.0020). (G6010.02/ G7010.07).
The logistics associated with a modern breeding program can be complex, relying on accuracy and communication between plant breeders, pathologists, quantitative geneticists, and support staff. International and academic facets may bring additional challenges to already error prone activities including the development, maintenance, and distribution of lines. Furthermore, practices such as bulking of seed and the maintenance of within accession variation among landraces must be considered when pursuing marker-assisted approaches to breeding.
Lucas MR, Huynh B-L, Ehlers JD, Roberts PA, Close TJ (2013). High-resolution single nucleotide polymorphism genotyping reveals a significant problem among breeder resources. The Plant Genome 6(1):1–5. (DOI: 10.3835/plantgenome2012.08.0020). (G6010.02/ G7010.07).
The logistics associated with a modern breeding program can be complex, relying on accuracy and communication between plant breeders, pathologists, quantitative geneticists, and support staff. International and academic facets may bring additional challenges to already error prone activities including the development, maintenance, and distribution of lines. Furthermore, practices such as bulking of seed and the maintenance of within accession variation among landraces must be considered when pursuing marker-assisted approaches to breeding.
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OptiMAS: A decision support tool for marker-assisted assembly of diverse alleles
Valente F, Gauthier F, Bardol N, Blanc G, Joets J, Charcosset A, and Moreau L (2013). OptiMAS: A decision support tool for marker-assisted assembly of diverse alleles. Journal of Heredity published online April 10, 2013. (DOI: 10.1093/jhered/est020). (G8009.03.06.02/Subactivity 2.2.6.2).
Current advances in plant genotyping lead to major progress in the knowledge of genetic architecture of traits of interest. It is increasingly important to develop decision support tools to help breeders and geneticists to conduct marker-assisted selection methods to assemble favorable alleles that are discovered. Algorithms have been implemented, within an interactive graphical interface, to 1) trace parental alleles throughout generations, 2) propose strategies to select the best plants based on estimated molecular scores, and 3) efficiently intermate them depending on the expected value of their progenies. With the possibility to consider a multi-allelic context, OptiMAS opens new prospects to assemble favorable alleles issued from diverse parents and further accelerate genetic gain.
Valente F, Gauthier F, Bardol N, Blanc G, Joets J, Charcosset A, and Moreau L (2013). OptiMAS: A decision support tool for marker-assisted assembly of diverse alleles. Journal of Heredity published online April 10, 2013. (DOI: 10.1093/jhered/est020). (G8009.03.06.02/Subactivity 2.2.6.2).
Current advances in plant genotyping lead to major progress in the knowledge of genetic architecture of traits of interest. It is increasingly important to develop decision support tools to help breeders and geneticists to conduct marker-assisted selection methods to assemble favorable alleles that are discovered. Algorithms have been implemented, within an interactive graphical interface, to 1) trace parental alleles throughout generations, 2) propose strategies to select the best plants based on estimated molecular scores, and 3) efficiently intermate them depending on the expected value of their progenies. With the possibility to consider a multi-allelic context, OptiMAS opens new prospects to assemble favorable alleles issued from diverse parents and further accelerate genetic gain.
Plant response to environmental conditions: assessing potential production, water demand, and negative effects of water deficit
Tardieu F (2013). Plant response to environmental conditions: assessing potential production, water demand, and negative effects of water deficit. Frontiers in Plant Physiology 4:17. (DOI: 10.3389/fphys.2013.00017).
This paper reviews methods for analyzing plant performance and its genetic variability under a range of environmental conditions. Biomass accumulation is linked every day to available light in the photosynthetically active radiation (PAR) domain, multiplied by the proportion of light intercepted by plants and by the radiation use efficiency. Total biomass is cumulated over the duration of the considered phase (e.g., plant cycle or vegetative phase). These durations are essentially constant for a given genotype provided that time is corrected for temperature (thermal time). Several ways of expressing thermal time are reviewed. Two alternative equations are presented, based either on the effect of transpiration, or on yield components. Their comparative interests and drawbacks are discussed. The genetic variability of each term of considered equations affects yield under water deficit, via mechanisms at different scales of plant organization and time. The effect of any physiological mechanism on yield of stressed plants acts via one of these terms, although the link is not always straightforward. Finally, I propose practical ways to compare the productivity of genotypes in field environments, and a “minimum dataset”of environmental data and traits that should be recorded for that.
Tardieu F (2013). Plant response to environmental conditions: assessing potential production, water demand, and negative effects of water deficit. Frontiers in Plant Physiology 4:17. (DOI: 10.3389/fphys.2013.00017).
This paper reviews methods for analyzing plant performance and its genetic variability under a range of environmental conditions. Biomass accumulation is linked every day to available light in the photosynthetically active radiation (PAR) domain, multiplied by the proportion of light intercepted by plants and by the radiation use efficiency. Total biomass is cumulated over the duration of the considered phase (e.g., plant cycle or vegetative phase). These durations are essentially constant for a given genotype provided that time is corrected for temperature (thermal time). Several ways of expressing thermal time are reviewed. Two alternative equations are presented, based either on the effect of transpiration, or on yield components. Their comparative interests and drawbacks are discussed. The genetic variability of each term of considered equations affects yield under water deficit, via mechanisms at different scales of plant organization and time. The effect of any physiological mechanism on yield of stressed plants acts via one of these terms, although the link is not always straightforward. Finally, I propose practical ways to compare the productivity of genotypes in field environments, and a “minimum dataset”of environmental data and traits that should be recorded for that.
Assessment of groundnut under combined heat and drought stress
Hamidou F, Halilou O & Vadez V (2013). Assessment of groundnut under combined heat and drought stress. Journal of Agronomy and Crop Science 199(1):1–11. (DOI:10.1111/j.1439-037X.2012.00518.x). Also published online in 2012.
In semi-arid regions, particularly in the Sahel, water and high-temperature stress are serious constraints for groundnut production. Understanding of combined effects of heat and drought on physiological traits, yield and its attributes is of special significance for improving groundnut productivity. Two hundred and sixty-eight groundnut genotypes were evaluated in four trials under both intermittent drought and fully irrigated conditions, two of the trial being exposed to moderate temperature, while the two other trials were exposed to high temperature. The objectives were to analyse the component of the genetic variance and their interactions with water treatment, year and environment (temperature) for agronomic characteristics, to select genotypes with high pod yield under hot- and moderate-temperature conditions, or both, and to identify traits conferring heat and/or drought tolerance.
Hamidou F, Halilou O & Vadez V (2013). Assessment of groundnut under combined heat and drought stress. Journal of Agronomy and Crop Science 199(1):1–11. (DOI:10.1111/j.1439-037X.2012.00518.x). Also published online in 2012.
In semi-arid regions, particularly in the Sahel, water and high-temperature stress are serious constraints for groundnut production. Understanding of combined effects of heat and drought on physiological traits, yield and its attributes is of special significance for improving groundnut productivity. Two hundred and sixty-eight groundnut genotypes were evaluated in four trials under both intermittent drought and fully irrigated conditions, two of the trial being exposed to moderate temperature, while the two other trials were exposed to high temperature. The objectives were to analyse the component of the genetic variance and their interactions with water treatment, year and environment (temperature) for agronomic characteristics, to select genotypes with high pod yield under hot- and moderate-temperature conditions, or both, and to identify traits conferring heat and/or drought tolerance.
The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis
Malosetti M, Ribaut JM, van Eeuwijk FA (2013). The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Frontiers in Plant Physiology 4:44. (DOI: 10.3389/fphys.2013.00044).
Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”
Malosetti M, Ribaut JM, van Eeuwijk FA (2013). The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Frontiers in Plant Physiology 4:44. (DOI: 10.3389/fphys.2013.00044).
Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”
Phenotyping common beans for adaptation to drought
Beebe SE, Rao IM, Blair MW, Acosta-Gallegos JA(2013). Phenotyping common beans for adaptation to drought. Frontiers in Plant Physiology 4:35. (DOI: 10.3389/fphys.2013.00035).
Common beans (Phaseolus vulgaris L.) originated in the New World and are the grain legume of greatest production for direct human consumption. Common bean production is subject to frequent droughts in highland Mexico, in the Pacific coast of Central America, in northeast Brazil, and in eastern and southern Africa from Ethiopia to South Africa. This article reviews efforts to improve common bean for drought tolerance, referring to genetic diversity for drought response, the physiology of drought tolerance mechanisms, and breeding strategies. Different races of common bean respond differently to drought, with race Durango of highland Mexico being a major source of genes. Sister species of P. vulgaris likewise have unique traits, especially P. acutifolius which is well adapted to dryland conditions. Diverse sources of tolerance may have different mechanisms of plant response, implying the need for different methods of phenotyping to recognize the relevant traits. Practical considerations of field management are discussed including: trial planning; water management; and field preparation.
Beebe SE, Rao IM, Blair MW, Acosta-Gallegos JA(2013). Phenotyping common beans for adaptation to drought. Frontiers in Plant Physiology 4:35. (DOI: 10.3389/fphys.2013.00035).
Common beans (Phaseolus vulgaris L.) originated in the New World and are the grain legume of greatest production for direct human consumption. Common bean production is subject to frequent droughts in highland Mexico, in the Pacific coast of Central America, in northeast Brazil, and in eastern and southern Africa from Ethiopia to South Africa. This article reviews efforts to improve common bean for drought tolerance, referring to genetic diversity for drought response, the physiology of drought tolerance mechanisms, and breeding strategies. Different races of common bean respond differently to drought, with race Durango of highland Mexico being a major source of genes. Sister species of P. vulgaris likewise have unique traits, especially P. acutifolius which is well adapted to dryland conditions. Diverse sources of tolerance may have different mechanisms of plant response, implying the need for different methods of phenotyping to recognize the relevant traits. Practical considerations of field management are discussed including: trial planning; water management; and field preparation.
Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought
Swamy BPM, Ahmed HU, Henry A, Mauleon R, Dixit S, Vikram P, Tilatto R, Verulkar SB, Perraju P, Mandal NP, Variar M, Robin S, Chandrababu R, Singh ON, Dwivedi JL, Das SP, Mishra KK, Yadaw RB, Aditya TL, Karmakar B, Satoh K, Moumeni A, Kikuchi S, Leung H, Kumar A (2013). Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought. PLoS ONE 8(5):e62795. (DOI: 10.1371/journal.pone.0062795). (G3008.06).
Rice (Oryza sativa L.) is a highly drought sensitive crop, and most semi dwarf rice varieties suffer severe yield losses from reproductive stage drought stress. The genetic complexity of drought tolerance has deterred the identification of agronomically relevant quantitative trait loci (QTL) that can be deployed to improve rice yield under drought in rice. Convergent evidence from physiological characterization, genetic mapping, and multi-location field evaluation was used to address this challenge.
Swamy BPM, Ahmed HU, Henry A, Mauleon R, Dixit S, Vikram P, Tilatto R, Verulkar SB, Perraju P, Mandal NP, Variar M, Robin S, Chandrababu R, Singh ON, Dwivedi JL, Das SP, Mishra KK, Yadaw RB, Aditya TL, Karmakar B, Satoh K, Moumeni A, Kikuchi S, Leung H, Kumar A (2013). Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought. PLoS ONE 8(5):e62795. (DOI: 10.1371/journal.pone.0062795). (G3008.06).
Rice (Oryza sativa L.) is a highly drought sensitive crop, and most semi dwarf rice varieties suffer severe yield losses from reproductive stage drought stress. The genetic complexity of drought tolerance has deterred the identification of agronomically relevant quantitative trait loci (QTL) that can be deployed to improve rice yield under drought in rice. Convergent evidence from physiological characterization, genetic mapping, and multi-location field evaluation was used to address this challenge.
Spatial analysis to support geographic targeting of genotypes to enviroments
Hyman G, Hodson D, Jones P (2013). Spatial analysis to support geographic targeting of genotypes to environments. Frontiers in Plant Physiology 4:40. (DOI: 10.3389/fphys.2013.00040).
Crop improvement efforts have benefited greatly from advances in available data, computing technology, and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions (GEI) to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution, and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps, and assess environment change. Climate, elevation, and vegetation data sets are now widely available, supporting analyses that were much more difficult even 5 or 10 years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.
Hyman G, Hodson D, Jones P (2013). Spatial analysis to support geographic targeting of genotypes to environments. Frontiers in Plant Physiology 4:40. (DOI: 10.3389/fphys.2013.00040).
Crop improvement efforts have benefited greatly from advances in available data, computing technology, and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions (GEI) to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution, and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps, and assess environment change. Climate, elevation, and vegetation data sets are now widely available, supporting analyses that were much more difficult even 5 or 10 years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.
Drought-resistance of local wheat varieties in Shanxi Province of China: A comprehensive evaluation by using GGE biplot and subordinate function
Yang J-W, Zhu J-G, Wang S-G, Sun D-Z, Shi Y-G and Chen W-G (2013). Drought-resistance of local wheat varieties in Shanxi Province of China: A comprehensive evaluation by using GGE biplot and subordinate function. Chinese Journal of Applied Ecology 24(4):1031−1038. (G7010.02.01)
Abstract: Taking 7 local wheat varieties in Shanxi Province of China and two other control varieties as test materials, this paper studied their morphological and physiological traits under normal and water stress field conditions. The drought-resistance coefficient of each index of the traits was calculated. On the basis of principal component analysis, the correlations between the drought-resistance indices and their relationships with the drought-resistance of different varieties were analyzed by GGE biplot, and the drought resistance of the wheat varieties was comprehensively evaluated with the combination of subordinate function and drought resistance index analysis. The main morphological and physiological factors affecting the drought-resistance of the wheat varieties were uppermost internode length, plant height, internode length, leaf area, leaf POD and SOD activities, and leaf relative water content and relative electric conductivity. There existed different degrees of correlation between these indices, and each index had different effects on the drought resistance of the varieties, being the main cause for the different drought resistance of the wheat varieties. Based on the drought-resistance, the test varieties could be classified into three groups, i.e., drought-resistance group, intermediate group, and sensitive group. Two highly drought-resistance cultivars, Baiheshangtou and Zhuganqing, whose drought-resistance was similar to that of drought-resistant Jinmai 47, could be used as the parent materials for breeding drought-resistance wheat.
Yang J-W, Zhu J-G, Wang S-G, Sun D-Z, Shi Y-G and Chen W-G (2013). Drought-resistance of local wheat varieties in Shanxi Province of China: A comprehensive evaluation by using GGE biplot and subordinate function. Chinese Journal of Applied Ecology 24(4):1031−1038. (G7010.02.01)
Abstract: Taking 7 local wheat varieties in Shanxi Province of China and two other control varieties as test materials, this paper studied their morphological and physiological traits under normal and water stress field conditions. The drought-resistance coefficient of each index of the traits was calculated. On the basis of principal component analysis, the correlations between the drought-resistance indices and their relationships with the drought-resistance of different varieties were analyzed by GGE biplot, and the drought resistance of the wheat varieties was comprehensively evaluated with the combination of subordinate function and drought resistance index analysis. The main morphological and physiological factors affecting the drought-resistance of the wheat varieties were uppermost internode length, plant height, internode length, leaf area, leaf POD and SOD activities, and leaf relative water content and relative electric conductivity. There existed different degrees of correlation between these indices, and each index had different effects on the drought resistance of the varieties, being the main cause for the different drought resistance of the wheat varieties. Based on the drought-resistance, the test varieties could be classified into three groups, i.e., drought-resistance group, intermediate group, and sensitive group. Two highly drought-resistance cultivars, Baiheshangtou and Zhuganqing, whose drought-resistance was similar to that of drought-resistant Jinmai 47, could be used as the parent materials for breeding drought-resistance wheat.
Resistance of αAI-1 transgenic chickpea (Cicer arietinum) and cowpea (Vigna unguiculata) dry grains to bruchid beetles (Coleoptera: Chrysomelidae)
Lüthi C, Álvarez-Alfageme F, Ehlers JD, Higgins TJV and Romeis J (2013). Resistance of αAI-1 transgenic chickpea (Cicer arietinum) and cowpea (Vigna unguiculata) dry grains to bruchid beetles (Coleoptera: Chrysomelidae). Bulletin of Entomological Research, available on CJO2013, pp1–9. (DOI: 10.1017/S0007485312000818). (G6010.02/G7010.07.01). Not open access: view online
Lüthi C, Álvarez-Alfageme F, Ehlers JD, Higgins TJV and Romeis J (2013). Resistance of αAI-1 transgenic chickpea (Cicer arietinum) and cowpea (Vigna unguiculata) dry grains to bruchid beetles (Coleoptera: Chrysomelidae). Bulletin of Entomological Research, available on CJO2013, pp1–9. (DOI: 10.1017/S0007485312000818). (G6010.02/G7010.07.01). Not open access: view online
