Journal articles 2006
Documents
Relationship between carbon isotope discrimination, ash content and grain yield in wheat in the Peninsular Zone of India
Misra SC, Randive R, Rao VS, Sheshshayee MS, Serraj R and Monneveux P (2006). Relationship between carbon isotope discrimination, ash content and grain yield in wheat in the Peninsular Zone of India. Journal of Agronomy and Crop Science 192(5):352–362. (DOI: 10.1111/j.1439-037X.2006.00225.x). Not open access: view abstract
Misra SC, Randive R, Rao VS, Sheshshayee MS, Serraj R and Monneveux P (2006). Relationship between carbon isotope discrimination, ash content and grain yield in wheat in the Peninsular Zone of India. Journal of Agronomy and Crop Science 192(5):352–362. (DOI: 10.1111/j.1439-037X.2006.00225.x). Not open access: view abstract
Sampling strategies for conserving diversity when forming core subsets using genetic marker
Franco J, Crossa J, Warburton M and Taba S (2006). Sampling strategies for conserving diversity when forming core subsets using genetic markers. Crop Science 46(2):854–864. (DOI: 10.2135/cropsci2005.07-0201). Not open access: view abstract
Franco J, Crossa J, Warburton M and Taba S (2006). Sampling strategies for conserving diversity when forming core subsets using genetic markers. Crop Science 46(2):854–864. (DOI: 10.2135/cropsci2005.07-0201). Not open access: view abstract
Sequencing multiple and diverse rice varieties: Connecting whole-genome variation with phenotypes
McNally KL, Bruskiewich R, Mackill D, Leach JE, Buell CR, Leung H (2006). Sequencing multiple and diverse rice varieties: Connecting whole-genome variation with phenotypes. Plant Physiology 141(1):26–31. (DOI: 10.1104/pp.106.077313).
The International Rice Functional Genomics Consortium (IRFGC) has initiated a project to provide the rice research community with access to extensive information on genetic variation present within and between diverse rice cultivars and landraces, as well as the genetic resources to exploit that information. Among crop plants, rice is uniquely positioned to achieve this goal due to the release of a high-quality, whole-genome sequence; advances in the use of high-density arrays to compare complex genomes; and the availability of large collections of genetic materials rich in trait variation. In this project, the international rice research community will collaborate with Perlegen Sciences to identify a large fraction of the single nucleotide polymorphisms (SNPs) present in cultivated rice through whole-genome comparisons of 21 rice genomes, including cultivars, germplasm lines, and landraces.
McNally KL, Bruskiewich R, Mackill D, Leach JE, Buell CR, Leung H (2006). Sequencing multiple and diverse rice varieties: Connecting whole-genome variation with phenotypes. Plant Physiology 141(1):26–31. (DOI: 10.1104/pp.106.077313).
The International Rice Functional Genomics Consortium (IRFGC) has initiated a project to provide the rice research community with access to extensive information on genetic variation present within and between diverse rice cultivars and landraces, as well as the genetic resources to exploit that information. Among crop plants, rice is uniquely positioned to achieve this goal due to the release of a high-quality, whole-genome sequence; advances in the use of high-density arrays to compare complex genomes; and the availability of large collections of genetic materials rich in trait variation. In this project, the international rice research community will collaborate with Perlegen Sciences to identify a large fraction of the single nucleotide polymorphisms (SNPs) present in cultivated rice through whole-genome comparisons of 21 rice genomes, including cultivars, germplasm lines, and landraces.
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SSR analysis of near isogenic lines (NILs) for P deficiency tolerance
Collard BCY, Thomson M, Penarubia M, Lu X, Heuer S, Wissuwa M, Mackill DJ and Ismail AM (2006). SSR analysis of near isogenic lines (NILs) for P deficiency tolerance. SABRAO Journal of Breeding and Genetics 38:131–138. Not open access: view journal website
Collard BCY, Thomson M, Penarubia M, Lu X, Heuer S, Wissuwa M, Mackill DJ and Ismail AM (2006). SSR analysis of near isogenic lines (NILs) for P deficiency tolerance. SABRAO Journal of Breeding and Genetics 38:131–138. Not open access: view journal website
The Generation Challenge Programme (GCP): Standards for crop data
Bruskiewich R, Davenport G, Hazekamp T, Metz T, Ruiz M, Simon R, Takeya M, Lee J, Senger M, McLaren G, and van Hintum T (2006). The Generation Challenge Programme (GCP): Standards for crop data. OMICS: A Journal of Integrative Biology. 10(2):215–219.
The Generation Challenge Programme (GCP) is an international research consortium striving to apply molecular biological advances to crop improvement for developing countries. Central to its activities is the creation of a next generation global crop information platform and network to share genetic resources, genomics, and crop improvement information. This system is being designed based on a comprehensive scientific domain object model and associated shared ontology. This model covers germplasm, genotype, phenotype, functional genomics, and geographical information data types needed in GCP research. This paper provides an overview of this modelling effort.
Bruskiewich R, Davenport G, Hazekamp T, Metz T, Ruiz M, Simon R, Takeya M, Lee J, Senger M, McLaren G, and van Hintum T (2006). The Generation Challenge Programme (GCP): Standards for crop data. OMICS: A Journal of Integrative Biology. 10(2):215–219.
The Generation Challenge Programme (GCP) is an international research consortium striving to apply molecular biological advances to crop improvement for developing countries. Central to its activities is the creation of a next generation global crop information platform and network to share genetic resources, genomics, and crop improvement information. This system is being designed based on a comprehensive scientific domain object model and associated shared ontology. This model covers germplasm, genotype, phenotype, functional genomics, and geographical information data types needed in GCP research. This paper provides an overview of this modelling effort.
The genetic architecture of disease resistance in maize: a synthesis of published studies
Wisser RJ, Balint-Kurti PJ and Nelson RJ (2006). The genetic architecture of disease resistance in maize: a synthesis of published studies. Phytopathology 96(2):120–129. (DOI: 10.1094/PHYTO-96-0120).
Fifty publications on the mapping of maize disease resistance loci were synthesized. These papers reported the locations of 437 quantitative trait loci (QTL) for disease (dQTL), 17 resistance genes (R-genes), and 25 R-gene analogs. A set of rules was devised to enable the placement of these loci on a single consensus map, permitting analysis of the distribution of resistance loci identified across a variety of maize germplasm for a number of different diseases. The confidence intervals of the dQTL were distributed over all 10 chromosomes and covered 89% of the genetic map to which the data were anchored. Visual inspection indicated the presence of clusters of dQTL for multiple diseases. Clustering of dQTL was supported by statistical tests that took into account genome-wide variations in gene density. Several novel clusters of resistance loci were identified. Evidence was also found for the association of dQTL with maturity-related QTL. It was evident from the distinct dQTL distributions for the different diseases that certain breeding schemes may be more suitable for certain diseases. This review provides an up-to-date synthesis of reports on the locations of resistance loci in maize.
Wisser RJ, Balint-Kurti PJ and Nelson RJ (2006). The genetic architecture of disease resistance in maize: a synthesis of published studies. Phytopathology 96(2):120–129. (DOI: 10.1094/PHYTO-96-0120).
Fifty publications on the mapping of maize disease resistance loci were synthesized. These papers reported the locations of 437 quantitative trait loci (QTL) for disease (dQTL), 17 resistance genes (R-genes), and 25 R-gene analogs. A set of rules was devised to enable the placement of these loci on a single consensus map, permitting analysis of the distribution of resistance loci identified across a variety of maize germplasm for a number of different diseases. The confidence intervals of the dQTL were distributed over all 10 chromosomes and covered 89% of the genetic map to which the data were anchored. Visual inspection indicated the presence of clusters of dQTL for multiple diseases. Clustering of dQTL was supported by statistical tests that took into account genome-wide variations in gene density. Several novel clusters of resistance loci were identified. Evidence was also found for the association of dQTL with maturity-related QTL. It was evident from the distinct dQTL distributions for the different diseases that certain breeding schemes may be more suitable for certain diseases. This review provides an up-to-date synthesis of reports on the locations of resistance loci in maize.
