Leveraging Transcriptomics Data for Genomic Prediction Models in Cassava
This study prioritised SNPs in close proximity to genome regions with biological importance for a given trait
Abstract
Genomic prediction models were, in principle, developed to include all the available marker information; with this approach, these models have shown in various crops moderate to high predictive accuracies. Previous studies in cassava have demonstrated that, even with relatively small training populations and low-density genotyping by sequencing (GBS) markers, prediction models are feasible for genomic selection. In the present study, the author prioritized single nucleotide polymorphisms (SNPs) in close proximity to genome regions with biological importance for a given trait.
This work is part of the “Next Generation Cassava Breeding Project” which is supported by the UK Department for International Development, in partnership with the Bill & Melinda Gates Foundation.
Citation
Roberto Lozano, Dunia Pino Del Carpio, Siraj Ismail Kayondo, Teddy Amuge, Ozimati Alfred Adebo, Morag Ferguson, Jean-Luc Jannink. Leveraging Transcriptomics Data for Genomic Prediction Models in Cassava bioRxiv 208181; doi: https://doi.org/10.1101/208181
Links
Leveraging Transcriptomics Data for Genomic Prediction Models in Cassava