|CARLE, SCOTT - Washington State University
Submitted to: Agrosystems, Geosciences & Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2023
Publication Date: 11/9/2023
Citation: Carle, S., Kiszonas, A., Garland Campbell, K.A., Morris, C.F. 2023. STAC: a tool to leverage genetic marker data for crop research and breeding. Agrosystems, Geosciences & Environment. 2023;6:e20436. https://doi.org/10.1002/agg2.20436.
Interpretive Summary: It is important for farmers to have access to crop varieties that are high-yielding, well-adapted, and which produce quality products that consumers want. Crop breeding (the process of developing these crop varieties) has benefited substantially from technology that have given us access to large amounts of genetic data for these crops. However, processing and interpreting that data is still challenging for many applications. In this study we developed two programs within the coding language R that can help in this process; and found that the technique was able to accurately analyze a part of the wheat genome that is important for bread baking quality. These programs can be useful in solving similar problems going forward.
Technical Abstract: As Genotyping By Sequencing (GBS) becomes more prevalent and cost effective, there is benefit in being able to apply the data to solving a variety of problems. However, high degrees of missing data and overreliance in SNPs frequently plague attempts to make full use of it. Here we have developed two R Scripts to serve as a tools in haplotype determination of loci of interest within biparental populations. One of these scripts, STAC (Sparse Tag Allele Caller), provides both automated calling and visual representations of the data around a locus of interest to assist in rapid data compilation decision making. The other script, STAC Integrate, allows automated quality control and logic-based integration of presence/absence data with SNP data, while also rendering global overviews of recombination and coverage across the genome. In this study, we used these tools examine a biparental population of wheat, Triticum aestivum (ND2603 x Butte86) and diagnosis the state of given locus, Glu-B1, from GBS data produced via the Ion Proton. A TASSEL pipeline was used to process the GBS data, the filtered outputs of both SNP markers and presence/absence markers were analyzed via STAC and STAC Integrate, and accurate genotyping of the Glu-B1 locus was demonstrated when compared to previously published SDS-PAGE data. These scripts may serve as a tool for researchers going forward attempting to parse and make better use of GBS data for both research and crop breeding decisions.