Genomic prediction for targeted populations of environments in oat (Avena sativa)

Pablo Sandro; Madhav Bhatta; Alisha Bower; Sarah Carlson; Jean-Luc Jannink; David J. Waring; Clay Birkett; Kevin Smith; Jochum Wiersma; Melanie Caffe; Jonathan Kleinjan; Michael S. McMullen; Lydia English; Lucia Gutierrez

doi:10.1071/CP23126

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 75(5)

Genomic prediction for targeted populations of environments in oat (Avena sativa)

Pablo Sandro ^A , Madhav Bhatta ^A ^B , Alisha Bower ^C , Sarah Carlson ^C , Jean-Luc Jannink ^D , David J. Waring

^D , Clay Birkett ^D , Kevin Smith ^E , Jochum Wiersma ^E , Melanie Caffe ^F , Jonathan Kleinjan ^F , Michael S. McMullen ^G , Lydia English ^C and Lucia Gutierrez

^A ^*

+ Author Affiliations

- Author Affiliations

^A Department of Plant and Agroecosystem Sciences, University of Wisconsin – Madison, 1575 Linden Drive, Madison, WI 53706, USA.

^B Bayer Crop Science, Chesterfield, MO 63017, USA.

^C Practical Farmers of Iowa, 1615 Golden Aspen Drive, Suite 101, Ames, IA, USA.

^D School of Integrative Plant Science Plant Breeding and Genetics Section, Cornell University, 258 Emerson Hall, Ithaca, NY 14853, USA.

^E Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN 55108, USA.

^F Department of Agronomy, Horticulture, and Plant Sciences, South Dakota State University, Research Parkway, University Station, Brookings, SD 57006, USA.

^G Plant Sciences, North Dakota State University, Loftsgard 370D Dept 7670, PO Box 6050 Fargo, ND 58108-6050, USA.

^* Correspondence to: gutierrezcha@wisc.edu

Handling Editor: Chengdao Li

Crop & Pasture Science 75, CP23126 https://doi.org/10.1071/CP23126

Submitted: 10 May 2023 Accepted: 7 April 2024 Published: 30 April 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs.

Aims

The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders.

Methods

The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers.

Key results

Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs.

Conclusions

Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs.

Implications

We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets.

Keywords: genomic best linear unbiased predictions (GBLUP), genomic prediction, genomic selection, genotype by environment interaction (GEI), genotypic performance, multi-environment trials (METs), targeted populations of environments (TPE), unbalanced dataset.

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