Journal articles 2013
Documents
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.
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Performance of nine cassava (Manihot esculanta Crantz) clones across three environments
Peprah BB, Ofori K, Asante IK, Parkes E (2013). Performance of nine cassava (Manihot esculanta Crantz) clones across three environments. Journal of Plant Breeding and Crop Science 5(4):48–53. (DOI:10.5897/JPBCS12.027). (G7010.01.05).
The study was carried out to quantify the genotype × environment interaction (G × E) and to estimate the phenotypic stability by genotype genotype × environment (GGE) biplot of nine cassava clones comprising 5 hybrids, 3 parent checks and 1 improved variety. The study was planted across three different environments; Fumesua, Pokuase and Ejura representing forest, coastal savanna and forest transition zones, respectively. Genotype main effect was significant (P < 0.001) for fresh root yield and dry matter content, G × E interaction effect was significant (P < 0.001) for fresh root yield only and environment main effect was significant (P < 0.01) for only fresh root yield. The most stable clone for fresh root yield with above average performance was La02/026 (hybrid). The high genotype and low environment effects, and the relatively low interaction on dry matter content imply that evaluation and selection can be effectively done in fewer environments to select clones with high performance for the trait whiles fresh root yield requires multiple environments to identify clones with broad and specific adaptation.
Peprah BB, Ofori K, Asante IK, Parkes E (2013). Performance of nine cassava (Manihot esculanta Crantz) clones across three environments. Journal of Plant Breeding and Crop Science 5(4):48–53. (DOI:10.5897/JPBCS12.027). (G7010.01.05).
The study was carried out to quantify the genotype × environment interaction (G × E) and to estimate the phenotypic stability by genotype genotype × environment (GGE) biplot of nine cassava clones comprising 5 hybrids, 3 parent checks and 1 improved variety. The study was planted across three different environments; Fumesua, Pokuase and Ejura representing forest, coastal savanna and forest transition zones, respectively. Genotype main effect was significant (P < 0.001) for fresh root yield and dry matter content, G × E interaction effect was significant (P < 0.001) for fresh root yield only and environment main effect was significant (P < 0.01) for only fresh root yield. The most stable clone for fresh root yield with above average performance was La02/026 (hybrid). The high genotype and low environment effects, and the relatively low interaction on dry matter content imply that evaluation and selection can be effectively done in fewer environments to select clones with high performance for the trait whiles fresh root yield requires multiple environments to identify clones with broad and specific adaptation.
Association studies and legume synteny reveal haplotypes determining seed size in Vigna unguiculata
Lucas MR, Huynh B-L, da Silva Vinholes P, Cisse N, Drabo I, Ehlers JD, Roberts PA and Close TJ (2013). Association studies and legume synteny reveal haplotypes determining seed size in Vigna unguiculata. Frontiers in Plant Science 4:95. (DOI: 10.3389/fpls.2013.00095). (G6010.02/ G7010.07).
Highly specific seed market classes for cowpea and other grain legumes exist because grain is most commonly cooked and consumed whole. Size, shape, color, and texture are critical features of these market classes and breeders target development of cultivars for market acceptance. Resistance to biotic and abiotic stresses that are absent from elite breeding material are often introgressed through crosses to landraces or wild relatives. When crosses are made between parents with different grain quality characteristics, recovery of progeny with acceptable or enhanced grain quality is problematic. Thus genetic markers for grain quality traits can help in pyramiding genes needed for specific market classes.
Lucas MR, Huynh B-L, da Silva Vinholes P, Cisse N, Drabo I, Ehlers JD, Roberts PA and Close TJ (2013). Association studies and legume synteny reveal haplotypes determining seed size in Vigna unguiculata. Frontiers in Plant Science 4:95. (DOI: 10.3389/fpls.2013.00095). (G6010.02/ G7010.07).
Highly specific seed market classes for cowpea and other grain legumes exist because grain is most commonly cooked and consumed whole. Size, shape, color, and texture are critical features of these market classes and breeders target development of cultivars for market acceptance. Resistance to biotic and abiotic stresses that are absent from elite breeding material are often introgressed through crosses to landraces or wild relatives. When crosses are made between parents with different grain quality characteristics, recovery of progeny with acceptable or enhanced grain quality is problematic. Thus genetic markers for grain quality traits can help in pyramiding genes needed for specific market classes.
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.
Massive sorghum collection genotyped with SSR markers to enhance use of global genetic resources
Billot C, Ramu P, Bouchet S, Chantereau J, Deu M, Gardes L, Noyer J-L, Rami J-F, Rivallan R, Li Y, Lu P, Wang T, Folkertsma RT, Arnaud E, Upadhyaya HD, Glaszmann J-C, Hash CT (2013). Massive sorghum collection genotyped with SSR markers to enhance use of global genetic resources. PLoS One 8(4): e59714. (DOI: 10.1371/journal.pone.0059714). (G4005.01.03/ G4007.01).
Large ex situ collections require approaches for sampling manageable amounts of germplasm for in-depth characterization and use. We present here a large diversity survey in sorghum with 3367 accessions and 41 reference nuclear SSR markers. Of 19 alleles on average per locus, the largest numbers of alleles were concentrated in central and eastern Africa. Cultivated sorghum appeared structured according to geographic regions and race within region. A total of 13 groups of variable size were distinguished. The peripheral groups in western Africa, southern Africa and eastern Asia were the most homogeneous and clearly differentiated.
Billot C, Ramu P, Bouchet S, Chantereau J, Deu M, Gardes L, Noyer J-L, Rami J-F, Rivallan R, Li Y, Lu P, Wang T, Folkertsma RT, Arnaud E, Upadhyaya HD, Glaszmann J-C, Hash CT (2013). Massive sorghum collection genotyped with SSR markers to enhance use of global genetic resources. PLoS One 8(4): e59714. (DOI: 10.1371/journal.pone.0059714). (G4005.01.03/ G4007.01).
Large ex situ collections require approaches for sampling manageable amounts of germplasm for in-depth characterization and use. We present here a large diversity survey in sorghum with 3367 accessions and 41 reference nuclear SSR markers. Of 19 alleles on average per locus, the largest numbers of alleles were concentrated in central and eastern Africa. Cultivated sorghum appeared structured according to geographic regions and race within region. A total of 13 groups of variable size were distinguished. The peripheral groups in western Africa, southern Africa and eastern Asia were the most homogeneous and clearly differentiated.
Integrated consensus map of cultivated peanut and wild relatives reveals structures of the A and B genomes of Arachis and divergence of the legume genomes
Shirasawa K, Bertioli DJ, Varshney RK, Moretzsohn MC, Leal-Bertioli SCM, Thudi M, Pandey MK, Rami J-F, Foncéka D, Gowda MVC, Qin H, Guo B, Hong Y, Liang X, Hirakawa H, Tabata S and Isobe S (2013). Integrated consensus map of cultivated peanut and wild relatives reveals structures of the A and B genomes of Arachis and divergence of the legume genomes. DNA Research 20(2):173–184 (DOI: 10.1093/dnares/dss042). (G6010.01)
Abtract: The complex, tetraploid genome structure of peanut (Arachis hypogaea) has obstructed advances in genetics and genomics in the species. The aim of this study is to understand the genome structure of Arachis by developing a high-density integrated consensus map. Three recombinant inbred line populations derived from crosses between the A genome diploid species, Arachis duranensis and Arachis stenosperma; the B genome diploid species, Arachis ipaënsis and Arachis magna; and between the AB genome tetraploids, A. hypogaea and an artificial amphidiploid (A. ipaënsis × A. duranensis)4×, were used to construct genetic linkage maps: 10 linkage groups (LGs) of 544 cM with 597 loci for the A genome; 10 LGs of 461 cM with 798 loci for the B genome; and 20 LGs of 1442 cM with 1469 loci for the AB genome. The resultant maps plus 13 published maps were integrated into a consensus map covering 2651 cM with 3693 marker loci which was anchored to 20 consensus LGs corresponding to the A and B genomes. The comparative genomics with genome sequences of Cajanus cajan, Glycine max, Lotus japonicus, and Medicago truncatula revealed that the Arachis genome has segmented synteny relationship to the other legumes. The comparative maps in legumes, integrated tetraploid consensus maps, and genome-specific diploid maps will increase the genetic and genomic understanding of Arachis and should facilitate molecular breeding.
Shirasawa K, Bertioli DJ, Varshney RK, Moretzsohn MC, Leal-Bertioli SCM, Thudi M, Pandey MK, Rami J-F, Foncéka D, Gowda MVC, Qin H, Guo B, Hong Y, Liang X, Hirakawa H, Tabata S and Isobe S (2013). Integrated consensus map of cultivated peanut and wild relatives reveals structures of the A and B genomes of Arachis and divergence of the legume genomes. DNA Research 20(2):173–184 (DOI: 10.1093/dnares/dss042). (G6010.01)
Abtract: The complex, tetraploid genome structure of peanut (Arachis hypogaea) has obstructed advances in genetics and genomics in the species. The aim of this study is to understand the genome structure of Arachis by developing a high-density integrated consensus map. Three recombinant inbred line populations derived from crosses between the A genome diploid species, Arachis duranensis and Arachis stenosperma; the B genome diploid species, Arachis ipaënsis and Arachis magna; and between the AB genome tetraploids, A. hypogaea and an artificial amphidiploid (A. ipaënsis × A. duranensis)4×, were used to construct genetic linkage maps: 10 linkage groups (LGs) of 544 cM with 597 loci for the A genome; 10 LGs of 461 cM with 798 loci for the B genome; and 20 LGs of 1442 cM with 1469 loci for the AB genome. The resultant maps plus 13 published maps were integrated into a consensus map covering 2651 cM with 3693 marker loci which was anchored to 20 consensus LGs corresponding to the A and B genomes. The comparative genomics with genome sequences of Cajanus cajan, Glycine max, Lotus japonicus, and Medicago truncatula revealed that the Arachis genome has segmented synteny relationship to the other legumes. The comparative maps in legumes, integrated tetraploid consensus maps, and genome-specific diploid maps will increase the genetic and genomic understanding of Arachis and should facilitate molecular breeding.
Breeding of new wheat variety Yunhan 618 with strong gluten and drought tolerance
Chai Y, Li X, Zhao Z, Sun L and Shao X (2013). Breeding of new wheat variety Yunhan 618 with strong gluten and drought tolerance. Shaanxi Journal of Agricultural Sciences 2013(3):51–53,78. Article in Chinese. Not open access; view journal website. (G7010.02.01)
Chai Y, Li X, Zhao Z, Sun L and Shao X (2013). Breeding of new wheat variety Yunhan 618 with strong gluten and drought tolerance. Shaanxi Journal of Agricultural Sciences 2013(3):51–53,78. Article in Chinese. Not open access; view journal website. (G7010.02.01)
Aluminum tolerance in maize is associated with higher MATE1 gene copy number
Maron LG, Guimarães CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, Danilova TV, Kudrna D, Magalhaes JV, Piñeros MA, Schatz MC, Wing RA and Kochian LV (2013). Aluminum tolerance in maize is associated with higher MATE1 gene copy number. PNAS 110(13): 5241–5246 (DOI: 10.1073/pnas.1220766110). Not open access; view abstract. (G3008.02 and G7010.03.02)
Maron LG, Guimarães CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, Danilova TV, Kudrna D, Magalhaes JV, Piñeros MA, Schatz MC, Wing RA and Kochian LV (2013). Aluminum tolerance in maize is associated with higher MATE1 gene copy number. PNAS 110(13): 5241–5246 (DOI: 10.1073/pnas.1220766110). Not open access; view abstract. (G3008.02 and G7010.03.02)
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.
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.”
