Abstract
Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (ΦPSII) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both ΦPSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.
Highlights
Due to breakthroughs in DNA sequencing technology over the past decade, high-throughput genotyping is a routine practice in plant breeding (Rimbert et al, 2018)
As complex traits of study, we focused on photosynthetic light use efficiency of photosystem II ( PSII) and projected leaf area (PLA) in Arabidopsis thaliana
As we intend to use this population to explore the utility of biological knowledge in genomic prediction, we combined projected leaf area (PLA), another indicator of plant growth, with PSII
Summary
Due to breakthroughs in DNA sequencing technology over the past decade, high-throughput genotyping is a routine practice in plant breeding (Rimbert et al, 2018). Phenotyping is undergoing a similar revolution: large phenomics facilities are being developed that can rapidly score large germplasm collections of plants in a range of different environments (Flood et al, 2016; Crain et al, 2018) These technological developments have made it possible to acquire datasets describing genotypes and phenotypes for large numbers of individuals at an extended temporal scale. GP accuracy, usually defined as the correlation (Pearson’s r) between observed phenotypes and predicted breeding values, is generally lower for complex traits than for simpler ones (Morgante, 2018) This is because such traits are affected by many loci with small to moderate effects, along with non-additive genetic (dominance, epistasis) and genotype-by-environment (GxE) interactions (Falconer and Mackay, 1996). Incorporating epistasis into GP models has been reported to improve performance in selfing plant species but may not work for outcrossing species; additive GP models are still the primary choice (Jiang and Reif, 2015)
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