John Bingham Laboratory
John Bingham Laboratory
Hunt H.V.,University of Cambridge |
Moots H.M.,University of Cambridge |
Graybosch R.A.,University of Nebraska - Lincoln |
Jones H.,John Bingham Laboratory |
And 6 more authors.
Molecular Biology and Evolution | Year: 2013
Waxy mutants, in which endosperm starch contains ∼100% amylopectin rather than the wild-type composition of ∼70% amylopectin and ∼30% amylose, occur in many domesticated cereals. The cultivation of waxy varieties is concentrated in east Asia, where there is a culinary preference for glutinous-textured foods that may have developed from ancient food processing traditions. The waxy phenotype results from mutations in the GBSSI gene, which catalyzes amylose synthesis. Broomcorn or proso millet (Panicum miliaceum L.) is one of the world's oldest cultivated cereals, which spread across Eurasia early in prehistory. Recent phylogeographic analysis has shown strong genetic structuring that likely reflects ancient expansion patterns. Broomcorn millet is highly unusual in being an allotetraploid cereal with fully waxy varieties. Previous work characterized two homeologous GBSSI loci, with multiple alleles at each, but could not determine whether both loci contributed to GBSSI function. We first tested the relative contribution of the two GBSSI loci to amylose synthesis and second tested the association between GBSSI alleles and phylogeographic structure inferred from simple sequence repeats (SSRs). We evaluated the phenotype of all known GBSSI genotypes in broomcorn millet by assaying starch composition and protein function. The results showed that the GBSSI-S locus is the major locus controlling endosperm amylose content, and the GBSSI-L locus has strongly reduced synthesis capacity. We genotyped 178 individuals from landraces from across Eurasia for the 2 GBSSI and 16 SSR loci and analyzed phylogeographic structuring and the geographic and phylogenetic distribution of GBSSI alleles. We found that GBSSI alleles have distinct spatial distributions and strong associations with particular genetic clusters defined by SSRs. The combination of alleles that results in a partially waxy phenotype does not exist in landrace populations. Our data suggest that broomcorn millet is a system in the process of becoming diploidized for the GBSSI locus responsible for grain amylose. Mutant alleles show some exchange between genetic groups, which was favored by selection for the waxy phenotype in particular regions. Partially waxy phenotypes were probably selected against-this unexpected finding shows that better understanding is needed of the human biology of this phenomenon that distinguishes cereal use in eastern and western cultures. © 2012 The Author.
Papini A.,University of Florence |
Mosti S.,University of Florence |
Bandara N.L.,John Bingham Laboratory
Pakistan Journal of Botany | Year: 2011
The Genus Rebutia K. Schum. is a taxonomically complex genus of Cactaceae subfamily Cactoideae. This genus was subjected to splitting and lumpering treatments through the years. Molecular data revealed that Rebutia sensu lato must be divided in a clade corresponding to former Rebutia section Rebutia (Rebutia sensu stricto), related to the clade formed by Weingartia/Cintia/Sulcorebutia. Only this clade corresponds to genus Rebutia in our results. The other sections of Rebutia s. l. (Aylostera, Digitorebutia, Cylindrorebutia) cluster together to form another clade not directly related to Rebutia s. str. For priority reason this clade is recombined as genus Aylostera Speg. An analytical key is provided, to identify genera Rebutia K. Schum., Aylostera Speg., and Weingartia Werderm. (including Sulcorebutia Backeb. and Cintia Knize & Riha) and in Aylostera, at infrageneric level, the subgenera Aylostera and Mediolobivia. Further investigations are needed to assume taxonomic decisions about the clade Weingartia/Sulcorebutia/Cintia that, as a whole, should be assigned to genus Weingartia Werderm. A list of taxa belonging to genera Rebutia K. Schum. and Aylostera Speg., is provided. In this treatment the necessary combinations following the separation of Aylostera as a genus autonomous from Rebutia are proposed. The ATPB-rbcL IGS fragment revealed to be enough variable to be used for Barcoding of species among Cactaceae. All species here considered are in CITES appendix II, with frequent determination difficulties.
Bentley A.R.,John Bingham Laboratory |
Horsnell R.,John Bingham Laboratory |
Werner C.P.,KWS |
Turner A.S.,John Innes Center |
And 9 more authors.
Journal of Experimental Botany | Year: 2013
Flowering is a critical period in the life cycle of flowering plant species, resulting in an irreversible commitment of significant resources. Wheat is photoperiod sensitive, flowering only when daylength surpasses a critical length; however, photoperiod insensitivity (PI) has been selected by plant breeders for >40 years to enhance yield in certain environments. Control of flowering time has been greatly facilitated by the development of molecular markers for the Photoperiod-1 (Ppd-1) homeoloci, on the group 2 chromosomes. In the current study, an allelic series of BC2F4 lines in the winter wheat cultivars 'Robigus' and 'Alchemy' was developed to elucidate the influence on flowering of eight gene variants from the B- and D-genomes of bread wheat and the A-genome of durum wheat. Allele effects were tested in short, natural, and extended photoperiods in the field and controlled environments. Across genetic back-ground and treatment, the D-genome PI allele, Ppd-D1a, had a more potent effect on reducing flowering time than Ppd-B1a. However, there was significant donor allele effect for both Ppd-D1a and Ppd-B1a, suggesting the presence of linked modifier genes and/or additional sources of latent sensitivity. Development of Ppd-A1a BC2F4 lines derived from synthetic hexaploid wheat provided an opportunity to compare directly the flowering time effect of the A-genome allele from durum with the B- and D-genome variants from bread wheat for the first time. Analyses indicated that the reducing effect of Ppd-A1a is comparable with that of Ppd-D1a, confirming it as a useful alternative source of PI. © The Author .
Ward J.,Rothamsted Research |
Rakszegi M.,Hungarian Academy of Sciences |
Bedo Z.,Hungarian Academy of Sciences |
Shewry P.R.,Rothamsted Research |
Mackay I.,John Bingham Laboratory
BMC genetics | Year: 2015
BACKGROUND: Genomic prediction of agronomic traits as targets for selection in plant breeding programmes is increasingly common. The methods employed can also be applied to predict traits from other sources of covariates, such as metabolomics. However, prediction combining sets of covariates can be less accurate than using the best of the individual sets.RESULTS: We describe a method, termed Differentially Penalized Regression (DiPR), which uses standard ridge regression software to combine sets of covariates while applying independent penalties to each. In a dataset of wheat varieties, field traits are better predicted, on average, by seed metabolites than by genetic markers, but DiPR using both sets of predictors is best.CONCLUSION: DiPR is a simple and accessible method of using existing software to combine multiple sets of covariates in trait prediction when there are more predictors than observations and the contribution to accuracy from each set differs.
PubMed | John Bingham Laboratory, Rothamsted Research and Hungarian Academy of Sciences
Type: | Journal: BMC genetics | Year: 2015
Genomic prediction of agronomic traits as targets for selection in plant breeding programmes is increasingly common. The methods employed can also be applied to predict traits from other sources of covariates, such as metabolomics. However, prediction combining sets of covariates can be less accurate than using the best of the individual sets.We describe a method, termed Differentially Penalized Regression (DiPR), which uses standard ridge regression software to combine sets of covariates while applying independent penalties to each. In a dataset of wheat varieties, field traits are better predicted, on average, by seed metabolites than by genetic markers, but DiPR using both sets of predictors is best.DiPR is a simple and accessible method of using existing software to combine multiple sets of covariates in trait prediction when there are more predictors than observations and the contribution to accuracy from each set differs.