Implementing within‐cross genomic prediction to reduce oat breeding costs

Abstract

A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrowbase biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow-base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

“…We also assessed prediction accuracy while using the other biparental families as the training population for the focal family (i.e., using 11 families to predict the 12th), where some families were unrelated, and others had HSF in the training set. Our prediction accuracies were lower than those reported by Mellers et al (2020), where prediction accuracy of multiple oat traits (seed quality, agronomic performance; not fatty acid traits) was evaluated in a single oat biparental family across generations. This difference is likely due to the higher relatedness from using one family in Mellers et al (2020).…”

“…As the biosynthetic process of fatty acids is well-conserved across plant species (Li-Beisson et al, 2013), these results are promising for employing similar methods in other grain and seed crops. This work complements the growing number of examples of genomic prediction of agronomic, quality and metabolite traits in oat (Brzozowski et al, 2022;Haikka, Knürr et al, 2020;Mellers et al, 2020;Rio et al, 2021), and provides a foundation for incorporating flavor (Günther-Jordanland et al, 2020;Lapveteläinen & Rannikko, 2000;Lapveteläinen et al, 2001) and aroma (Dach & Schieberle, 2021;Schuh & Schieberle, 2005) traits in a genomics-enabled oat breeding program. Broadly, developing effective genomic prediction methods for nutritional traits within families will contribute to efficient plant breeding for more nutritious staple crops.…”

“…Oat seeds are unique among small grains in their high percent composition of fat (e.g., 7% in oat vs 3% in wheat), and >70% of fats in oat seeds are unsaturated, which is beneficial in reducing heart disease (Gulvady et al., 2013). There is ongoing work with genomic prediction within oat breeding programs (Haikka, Knürr et al., 2020; Haikka, Manninen et al., 2020), populations (Rio et al., 2021) and a biparental family (Mellers et al., 2020), and this work will be further enabled by advances in oat genome sequencing (Kamal et al., 2022). As such, testing approaches to leverage existing data from well‐characterized germplasm panels for genomic prediction in breeding programs could advance application of genomic prediction in oat breeding.…”

Evaluation of strategies to optimize training populations for genomic prediction in oat (Avena sativa)

“…Although GS in plant breeding was considered challenging (Desta & Ortiz, 2014), it has been successfully implemented in cereal crops, for example, wheat and barley (Ankamah‐Yeboah et al, 2020; Larkin et al, 2019), and has great potential for improved selection for yield and disease resistance in oats (Haikka, Knürr, et al, 2020; Haikka, Manninen, et al, 2020; Mellers et al, 2020). Genomics‐resources and marker systems were for a long time limited in oats due to the complexity of the oat genome and reduced research investments compared with other major crops (Latta et al, 2019).…”

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

“…Additionally, time is saved by using GS because it is no longer necessary to wait for late filial generations to phenotype complex quantitative traits such as yield, biotic and abiotic stresses, among others. The genotypic data can be obtained from the seed of early generations and used to predict phenotypic performance of later generation individuals without the need for extensive phenotyping evaluation over years and environments (Mellers et al, 2020). Furthermore, it highlights the potential to increase the speed of varietal development across crop species (Bhat et al, 2016;Crossa et al, 2017).…”