Dataset
Dataset to accompany genomics combined with UAS data enhances prediction of grain yield in winter wheat
Washington State University
2022
DOI:
https://doi.org/10.7273/000004567
Abstract
With the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best prediction performance when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. Interestingly, using only phenotypic information was slightly worse in some cases than the combination of both sources, whereas in other cases, using only phenotypic information provided the best prediction performance. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating more related inputs in the models.
Included here are:
A .csv file with field trait and drone data from 2018 through 2022 used in model analysis.
A .vcf file with genotype by sequencing (gbs) data of all tested wheat lines between 2015 and 2022. This data was also used in model analysis.
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Details
- Title
- Dataset to accompany genomics combined with UAS data enhances prediction of grain yield in winter wheat
- Creators
- Osval A. Montesinos-López (Author) - Universidad de ColimaAndrew W. Herr (Author) - Washington State UniversityJose Crossa (Author) - International Maize and Wheat Improvement Center, Carretera México-Veracruz, MéxicoArron H. Carter (Author) - Washington State University, Crop and Soil Sciences, Department of
- Academic Unit
- Crop and Soil Sciences, Department of
- Publisher
- Washington State University
- Identifiers
- 99900914641301842
- Resource Type
- Dataset