Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP

Cappa, Eduardo P.; Lima, Bruno Marco de; Silva‐Junior, Orzenil B.; Garcia, Carla Costa; Mansfield, Shawn D.; Grattapaglia, Dário

doi:10.1016/j.plantsci.2019.03.017

Cited by 48 publications

(51 citation statements)

References 38 publications

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“…In this study, we used single-step GBLUP (ssGBLUP), GBLUP and ABLUP models to estimate genetic parameters and to test the prediction accuracies of different traits in an E. pellita breeding population. Most of the previous studies in forest trees which used ssGBLUP have included only the additive effects in the model ( Ratcliffe et al 2017 ; Cappa et al 2017 , 2019 ; Klápšte et al 2018 ). In contrast, we used additive and dominance effects in ssGBLUP model to study the genetic parameters and to test prediction accuracies of different traits.…”

Section: Discussionmentioning

confidence: 99%

“…While, there are many studies using the ssGBLUP in animal breeding there are only a few such studies in forest trees ( Ratcliffe et al 2017 ; Cappa et al 2017 , 2019 ; Klápšte et al 2018 ). In one of the first studies to implement this method in tree breeding, Cappa et al (2017) found increased accuracies of breeding values with the ssGBLUP compared to pedigree-based ABLUP method.…”

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confidence: 99%

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

Thavamanikumar¹,

Arnold

Luo

et al. 2020

G3 Genes|Genomes|Genetics

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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.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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

Thavamanikumar¹,

Arnold

Luo

et al. 2020

G3 Genes|Genomes|Genetics

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“…Nevertheless, for the Eucalyptus genus, the use of RADseq-derived methods is scarce. To date, the easy access to the commercial SNP array (EUChip60K) has led researchers to use it in the analysis of the genus [41][42][43][44], rather than RADseq-derived methods. Some species are poorly or less frequently represented in this chip than E. grandis (which is represented for its economic importance), and for this reason, the population allele frequencies and genetic relationships between individuals can be affected [45][46][47].…”

Section: Discussionmentioning

confidence: 99%

Optimizing ddRADseq in Non-Model Species: A Case Study in Eucalyptus dunnii Maiden

et al. 2019

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Restriction site-associated DNA sequencing (RADseq) and its derived protocols, such as double digest RADseq (ddRADseq), offer a flexible and highly cost-effective strategy for efficient plant genome sampling. This has become one of the most popular genotyping approaches for breeding, conservation, and evolution studies in model and non-model plant species. However, universal protocols do not always adapt well to non-model species. Herein, this study reports the development of an optimized and detailed ddRADseq protocol in Eucalyptus dunnii, a non-model species, which combines different aspects of published methodologies. The initial protocol was established using only two samples by selecting the best combination of enzymes and through optimal size selection and simplifying lab procedures. Both single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) were determined with high accuracy after applying stringent bioinformatics settings and quality filters, with and without a reference genome. To scale it up to 24 samples, we added barcoded adapters. We also applied automatic size selection, and therefore obtained an optimal number of loci, the expected SNP locus density, and genome-wide distribution. Reliability and cross-sequencing platform compatibility were verified through dissimilarity coefficients of 0.05 between replicates. To our knowledge, this optimized ddRADseq protocol will allow users to go from the DNA sample to genotyping data in a highly accessible and reproducible way.

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“…The usage of molecular marker data for implementing predictive breeding methods to support the development of new and improved varieties has strongly increased during recent years (Haikka et al., 2020; Juliana et al., 2019; Schrag, Schipprack, & Melchinger, 2019), as such a strategy can result in higher selection gains than those possible by relying merely on observed phenotypic performance of quantitatively inherited traits like yield (Rife, Graybosch, & Poland, 2018; Sleper et al., 2020). Aside from introducing genomic relationships into various prediction models, the possibility to utilize relationships based on pedigree records has been the subject of past (Bauer, Reetz, & Léon, 2006) and more recent investigations (Cappa et al., 2019). The efficiency of both pedigree and genomic selection is, amongst others, dependant on the heritability of the trait of interest as well as the availability of correlated traits that can be exploited in a pedigree‐assisted or genomic‐assisted prediction in order to increase the accuracy of estimated breeding values (Schulthess, Zhao, Longin, & Reif, 2018; Velazco, Jordan, et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Mitigating the impact of selective phenotyping in training populations on the prediction ability by multi‐trait pedigree and genomic selection models

Michel

Löschenberger²,

Sparry³

et al. 2020

Plant Breeding

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Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP

Cited by 48 publications

References 38 publications

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

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

Optimizing ddRADseq in Non-Model Species: A Case Study in Eucalyptus dunnii Maiden

Mitigating the impact of selective phenotyping in training populations on the prediction ability by multi‐trait pedigree and genomic selection models

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