Ionomics Workflow
Workflow used by David Salt and Magnus Nordborg
From David Salt
The workflow steps described below capture the process used in the attached papers (see below). This type of workflow can be used with any phenotype of interest, ionomics is just an example.
- Identify natural variation in the shoot ionome using data stored at www.ionomicshub.org.
- Use this data to perform GWAS mapping (in Baxter et al., 2010 we did it on shoot Na) or search existing GWA dataset (currently prototyped at http://ibnkhaldun.cs.purdue.edu:8348/ionomicsatlas/ for ionomics and http://arabidopsis.gmi.oeaw.ac.at:5000/DisplayResults for phenotypes described in Atwell et al., 2010)
- Pick a pair of accessions that have alternative alleles at the peak associated SNP for the phenotype of interest and make an experimental F2 population (we used Col-0 and Tsu-1 for Na in Rus et al., 2006).
- Perform bulk segregant analysis (BSA) to confirm the original GWA, using chips to genotype [either ATH1 for single feature polymorphisms (Rus et al., 2006) or SNPTILING array (Becker et al., 2011)]. The BSA can also be done using NGS.
- Search sequence under confirmed GWA QTL (in Arabidopsis this is 40kb or ~ 10 - 20 genes) for candidate genes and search www.ionomicshub.org for phenotyped knockout allele. If no data in www.ionomicshub.org order T-DNA alleles and phenotype all.
- Perform genetic complementation
- Search landscape distribution of peak associated SNP linked to phenotype at http://ibnkhaldun.cs.purdue.edu:8348/ionomicsatlas/ to build up hypothesis about potential selective benefit.
- Perform genome analysis to look for signals of selection around the gene of interested (e.g. Gunther and Schmid 2011)
Brain Dilkes (bdilkes@purdue.edu) has got GWAS analysis running in his lab and has already been in contact with iPlant about getting this analysis incorporated into the discovery environment.
Ivan Baxter (IBaxter@danforthcenter.org) has the chip-based BSA scripts.