As part of NIAB's research various resources, tools and programmes have been developed and are available below.
The full sequence of the CIMMYT spring wheat variety Weebill 1, sequenced as part of the Wheat10+ Genomes Project
The wheat pedigree provides information on parentage, country of origin, year of use and breeding company for an extensive set of UK wheat varieties
The NIAB online repository for marker-assisted selection assays.
Runs gene dropping simulations for any supplied pedigree structure and genotype data.
Information and files relevant to the NIAB MAGIC elite population
Information and files relevant to NIAB wheat NAM populations.
This dataset consists of 26,017 SNPs across 480 bread wheat (Triticum aestivum L.) accessions. SNP genotyping was performed using the wheat 90k Illumina iSelect SNP array (Wang et al., 2014. Doi: 10.1111/pbi.12183). Genotypes were predominantly sourced from the UK, France, Germany, and the Netherlands, but also includes accessions from Belgium, Canada, Denmark, Sweden, Switzerland and the USA. NOTE: large Excel file (40MB)
Germplasm is available from NIAB under standard material transfer agreement (MTA). For germplasm requests contact Sigrid Heuer.
Differentially penalised regression to predict agronomic traits from metabolites and markers. (DiPR)
DiPR is a simple modification to ridge regression which allows two or more sets of variables to be penalised separately. These are the files used to demonstrate its use to predicting field phenotypes by combining marker and metabolite data.
Differentially penalised regression to predict agronomic traits from metabolites and markers in wheat. Ward et al (2015). BMC genetics 16.1:19.
Marker and phenotype data from the TriticeaeGenome panel of 376 elite lines
This is analysed in the paper Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Bentley et al. (2014). Theoretical and Applied Genetics 127:2619-2633
An R script which uses genetic algorithms to select a subset of lines from a larger collection. The algorithm searches for the subset with either the greatest genetic diversity or which captures the greatest number of alleles. This is a replacement for similar functions previously available in the package PowerMarker.
The method is described in the paper Maximising the potential of multi-parental populations in crop breeding, Ladejobi et al. 2016