Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

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.

Meta-analysis across Nellore cattle populations identifies common metabolic mechanisms that regulate feed efficiency-related traits

“…Thus, these results permit a better interpretation of the biological control of cattle feed efficiency through metabolic aspects and neural control mediating the catabolic and anabolic pathways, with effects on energy balance leading to specific physiological signals. Overall, the results obtained can be used to search for causal mutations or as a strategy to pre-select SNP markers for use in genomic selection approaches aiming to reduce the number of markers and calculation time and avoid overfitting the model [ 77 , 78 ].…”

Genomic prediction in plants: opportunities for ensemble machine learning based approaches

“…This further explains that their performance can be improved by removing unrelated markers from the GRM, for instance using biological knowledge about markers. 31,53 The parametric LMM equations can be solved using a Bayesian framework. Bayesian methods define prior SNP effects distributions to model different genetic architectures.…”

“…For the first dataset, we selected a natural population with minimal structure, balanced LD, genotyped at roughly equal genomic spacing and mostly inbred lines: the 360 accessions in the core set of the Arabidopsis thaliana HapMap population. 29 Genotype data of 344 out of the 360 core accessions was obtained from Farooq, van Dijk, 31 containing 207,981 SNPs. The phenotypes were simulated using one of the scenarios in the Section 'Simulations'.…”

Genomic prediction in plants: opportunities for machine learning-based approaches

“…For the first dataset, we selected a natural population with minimal structure, balanced LD, genotyped at roughly equal genomic spacing and mostly inbred lines: the 360 accessions in the core set of the Arabidopsis thaliana HapMap population (Baxter, Brazelton et al 2010). Genotype data of 344 out of the 360 core accessions was obtained from Farooq, van Dijk et al (2020), containing 207,981 SNPs. The phenotypes were simulated using one of the scenarios in Section 2.1.1.…”