In:
Frontiers in Genetics, Frontiers Media SA, Vol. 11 ( 2021-1-20)
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 (Φ PSII ) 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 Φ PSII 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.
Type of Medium:
Online Resource
ISSN:
1664-8021
DOI:
10.3389/fgene.2020.609117
DOI:
10.3389/fgene.2020.609117.s001
DOI:
10.3389/fgene.2020.609117.s002
DOI:
10.3389/fgene.2020.609117.s003
DOI:
10.3389/fgene.2020.609117.s004
DOI:
10.3389/fgene.2020.609117.s005
DOI:
10.3389/fgene.2020.609117.s006
DOI:
10.3389/fgene.2020.609117.s007
DOI:
10.3389/fgene.2020.609117.s008
DOI:
10.3389/fgene.2020.609117.s009
DOI:
10.3389/fgene.2020.609117.s010
DOI:
10.3389/fgene.2020.609117.s011
DOI:
10.3389/fgene.2020.609117.s012
DOI:
10.3389/fgene.2020.609117.s013
DOI:
10.3389/fgene.2020.609117.s014
DOI:
10.3389/fgene.2020.609117.s015
DOI:
10.3389/fgene.2020.609117.s016
DOI:
10.3389/fgene.2020.609117.s017
DOI:
10.3389/fgene.2020.609117.s018
DOI:
10.3389/fgene.2020.609117.s019
DOI:
10.3389/fgene.2020.609117.s020
DOI:
10.3389/fgene.2020.609117.s021
DOI:
10.3389/fgene.2020.609117.s022
DOI:
10.3389/fgene.2020.609117.s023
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2021
detail.hit.zdb_id:
2606823-0
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