NIAB DIVERSE MAGIC GENOTYPES AND PHENOTYPES

dataset

posted on 2021-04-12, 10:57 authored by Richard MottRichard Mott, Michael Scott, James Cockram, Nick FradgleyNick Fradgley, Ian Mackay, Keith Gardener, Funmi Ladejobi, Alison Bentley

SNP Genotype and Phenotype datasets for the NIAB DIVERSE MAGIC wheat population and its founders.

The diverse MAGIC wheat population was developed at the National Institute for Applied Botany (NIAB), from whom germplasm is available (contact James Cockram).

Summary of the Data Sets available here:

(i) Founder_Consensus_Genotypes.calls.adjusted.txt, All_MAGIC_Consensus_Genotypes.calls.adjusted.txt: Tab-delimited genotypes of the 16 founders of the NIAB DIVERSE MAGIC wheat population and for 550 MAGIC lines, obtained using the 35k Wheat Breeders' Array. Calls were made using the Axiom Best Practices Genotyping Analysis workflow with an inbreeding penalty of 4. The released genotypes have consensus calls where multiple samples were genotyped from the same line. In addition, the genotypes at sites with no minor homozygous calls have been adjusted.

(ii) FOUNDERS.tar, MAGIC_PLINK.tar: Genotypes in PLINK format of 1.1M imputed SNPs from exome capture in the 16 founders and and low -coverage sequencing in 505 MAGIC lines.

(iii) MAGIC_PLINK_PRUNED.tar 55k tagging SNP genotypes of 505 MAGIC lines, suitable for GWAS

(iv) MAGIC_PHENOTYPES.txt Phenotypes for the MAGIC lines and founders.

(v) BASIC_GWAS.tar contains the genotypes and phenotypes and analysis scripts packaged into one file. We provide a simple pipeline for genetic mapping with these data.

Once unpacked, the 'DATA' subdirectory contains the phenotypic data and the tagging set of ~55k SNP sites called in 504 inbred lines. In this directory, we include R functions for association mapping (file mixed.model.functions.r), including a mixed model transformation to remove the inflationary effects of unequal relatedness on genetic associations. Association mapping can be run on the basis of SNPs or the inferred founder haplotype dosages. To run, follow the steps in the R script example.analysis.r (this will run without modification if the downloaded directory is used as the R working directory). We also include a function for plotting the results as a manhattan plot (plot.functions.r).