STAC: A tool to leverage genetic marker data for crop research and breeding
Abstract As genotyping by sequencing (GBS) becomes more prevalent and cost‐effective, there is a benefit in being able to apply the data to solve a variety of problems. However, high degrees of missing data and overreliance on single nucleotide polymorphisms (SNPs), while ignoring other forms of gen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | Agrosystems, Geosciences & Environment |
| Online Access: | https://doi.org/10.1002/agg2.20436 |
| _version_ | 1827582925634273280 |
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| author | Scott Carle Alecia Kiszonas Kimberly Garland‐Campbell Craig F. Morris |
| author_facet | Scott Carle Alecia Kiszonas Kimberly Garland‐Campbell Craig F. Morris |
| author_sort | Scott Carle |
| collection | DOAJ |
| description | Abstract As genotyping by sequencing (GBS) becomes more prevalent and cost‐effective, there is a benefit in being able to apply the data to solve a variety of problems. However, high degrees of missing data and overreliance on single nucleotide polymorphisms (SNPs), while ignoring other forms of genetic variation, frequently plague attempts to make full use of GBS sequence data. Here we have developed two R scripts to serve as a tool in haplotype determination at loci of interest within biparental populations. One of these scripts, Sparse Tag Allele Caller (STAC), 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. These scripts are designed to be used together to maximize the utility of the available data. These tools were validated on a biparental population of wheat that was genotyped through GBS. They successfully enabled haplotype determination of a locus that was difficult to directly genotype, and their systemic accuracy was demonstrated in multiple populations and species. These scripts may serve as a tool for researchers attempting to make better use of GBS and other genetic marker data for both research and crop breeding decisions. |
| first_indexed | 2024-03-08T22:58:33Z |
| format | Article |
| id | doaj.art-819261c164b648c590913fecb80b7039 |
| institution | Directory Open Access Journal |
| issn | 2639-6696 |
| language | English |
| last_indexed | 2024-03-08T22:58:33Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Agrosystems, Geosciences & Environment |
| spelling | doaj.art-819261c164b648c590913fecb80b70392023-12-16T02:28:30ZengWileyAgrosystems, Geosciences & Environment2639-66962023-12-0164n/an/a10.1002/agg2.20436STAC: A tool to leverage genetic marker data for crop research and breedingScott Carle0Alecia Kiszonas1Kimberly Garland‐Campbell2Craig F. Morris3Department of Crop and Soil Sciences Washington State University Pullman Washington USAUSDA‐ARS Western Wheat Quality Laboratory Washington State University Pullman Washington USAUSDA‐ARS Wheat Health, Genetics and Quality Research Unit Washington State University Pullman Washington USAUSDA‐ARS Western Wheat Quality Laboratory Washington State University Pullman Washington USAAbstract As genotyping by sequencing (GBS) becomes more prevalent and cost‐effective, there is a benefit in being able to apply the data to solve a variety of problems. However, high degrees of missing data and overreliance on single nucleotide polymorphisms (SNPs), while ignoring other forms of genetic variation, frequently plague attempts to make full use of GBS sequence data. Here we have developed two R scripts to serve as a tool in haplotype determination at loci of interest within biparental populations. One of these scripts, Sparse Tag Allele Caller (STAC), 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. These scripts are designed to be used together to maximize the utility of the available data. These tools were validated on a biparental population of wheat that was genotyped through GBS. They successfully enabled haplotype determination of a locus that was difficult to directly genotype, and their systemic accuracy was demonstrated in multiple populations and species. These scripts may serve as a tool for researchers attempting to make better use of GBS and other genetic marker data for both research and crop breeding decisions.https://doi.org/10.1002/agg2.20436 |
| spellingShingle | Scott Carle Alecia Kiszonas Kimberly Garland‐Campbell Craig F. Morris STAC: A tool to leverage genetic marker data for crop research and breeding Agrosystems, Geosciences & Environment |
| title | STAC: A tool to leverage genetic marker data for crop research and breeding |
| title_full | STAC: A tool to leverage genetic marker data for crop research and breeding |
| title_fullStr | STAC: A tool to leverage genetic marker data for crop research and breeding |
| title_full_unstemmed | STAC: A tool to leverage genetic marker data for crop research and breeding |
| title_short | STAC: A tool to leverage genetic marker data for crop research and breeding |
| title_sort | stac a tool to leverage genetic marker data for crop research and breeding |
| url | https://doi.org/10.1002/agg2.20436 |
| work_keys_str_mv | AT scottcarle stacatooltoleveragegeneticmarkerdataforcropresearchandbreeding AT aleciakiszonas stacatooltoleveragegeneticmarkerdataforcropresearchandbreeding AT kimberlygarlandcampbell stacatooltoleveragegeneticmarkerdataforcropresearchandbreeding AT craigfmorris stacatooltoleveragegeneticmarkerdataforcropresearchandbreeding |