Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

Abstract

Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyse the genetic architecture of growth and wood traits in an open-pollinated (OP) population of Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits (diameter at breast height - DBH, total height - Ht and tree volume - Vol) and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP - ssGBLUP and genomic BLUP - GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes.

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill

“…Leaves were sampled from 164 parents and 74 others in the EG1 program and from 93 parents and 51 others in the EG2 program, where “others” refers to phenotypic selections that have not been progeny tested. DNA extraction and genotyping were conducted at Gondwana Genomics Pty Ltd, Canberra ( Thavamanikumar et al 2020 ). The E. globulus marker panel consisted of 2579 SNP and small biallelic insertion/deletion (INDEL) markers within candidate genes associated with diameter, density, and cellulose yield that were identified using association analyses in previous studies ( Southerton et al 2011 ).…”

“…Relationships determined by genotyping have been used to recover nonadditive genetic variances from OP tree families ( Klápště et al 2014 ; Gamal El-Dien et al 2016 ) and remove inflation in half-sib-based additive genetic variance estimates ( Klápště et al 2014 ; Ratcliffe et al 2017 ). Large-scale genotyping in OP families has also produced substantial improvements to breeding value accuracy ( Gamal El-Dien et al 2016 ; Thavamanikumar et al 2020 ) and accounted for inbreeding depression ( Klápště et al 2017 , 2018 ). In hybrid tree populations, genotypic relationships have revealed the magnitude of epistatic variation and partitioned genetic variances more precisely ( Bouvet et al 2016 ; Tan et al 2018 ).…”

“…HBLUP has been applied to the analysis of growth and wood quality traits for OP populations of Picea glauca ( Ratcliffe et al 2017 ), Eucalyptus grandis ( Cappa et al 2017 ; 2018 ), Eucalyptus nitens ( Klápště et al 2018 ), and Eucalyptus pellita ( Thavamanikumar et al 2020 ) and for OP and CP families of Pinus contorta ( Ukrainetz and Mansfield 2020 ). These studies involved genotyping of progeny and unanimously revealed the presence of relationships amongst parents that had not been previously known.…”

“…Assumed relationships between parents and progeny (expected 0.5) and between half-sib progeny (expected 0.25) are more precisely estimated when blended with genomic similarities in G to create the H matrix ( Cappa et al 2017 ). A greater density of relationship information in H compared with the sparse relationships in A led to improved theoretical accuracy of estimated breeding values (EBVs), in some cases only for genotyped individuals ( Ratcliffe et al 2017 ) and in other cases for ungenotyped individuals and parents as well ( Cappa et al 2017 ; Thavamanikumar et al 2020 ). Phenotypic information from trees that have not been genotyped was also shown to improve genomic predictions ( Cappa et al 2019 ).…”

“…In forest tree populations, HBLUP efficiently combines genomic, pedigree, and phenotypic information and offers substantial advantages over conventional pedigree-based analysis. On the other hand, the published studies applying HBLUP to forest tree populations have all been limited to small experimental populations, ranging upwards to 5742 genotyped and ungenotyped progeny ( Thavamanikumar et al 2020 ). This is one to two orders of magnitude smaller than required for long-running commercial tree improvement programs.…”