Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as genotyping platform in Norway spruce

AbstractBackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as a genotyping platform. We used 116,765 high quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G×E), genetic composition, size of the training and validation set, relatedness, and the number of SNPs on the accuracy and predictive ability (PA) of GS.ResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11–14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased more than 30% using one site as training for GS prediction to the second site, indicating that G×E for tree height should be accommodated in model fitting. Using half-sib family structure instead of full-sib led a significant reduction in GS accuracy and PA. The full-sib family structure only needed 750 makers to reach similar accuracy and PA as 100,000 markers required for half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000–8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all makers.ConclusionsThe study indicates GS would be efficient in reducing generation time of a breeding cycle in conifer tree breeding program that requires a long-term progeny testing. Sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G×E is detected.

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Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce

BMC Genomics ◽

10.1186/s12864-018-5256-y ◽

2018 ◽

Vol 19 (1) ◽

Cited By ~ 22

Author(s):

Zhi-Qiang Chen ◽

John Baison ◽

Jin Pan ◽

Bo Karlsson ◽

Bengt Andersson ◽

...

Keyword(s):

Genomic Selection ◽

Norway Spruce ◽

Wood Quality ◽

Exome Capture ◽

Quality Traits ◽

Genotyping Platform ◽

Selection For

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Increased prediction ability in Norway spruce trials using a marker x environment interaction and non-additive genomic selection model

10.1101/478404 ◽

2018 ◽

Author(s):

Zhi-Qiang Chen ◽

John Baison ◽

Jin Pan ◽

Johan Westin ◽

María Rosario García Gil ◽

...

Keyword(s):

Genomic Selection ◽

Norway Spruce ◽

Wood Quality ◽

Genetic Parameter ◽

Tree Height ◽

Phenotypic Selection ◽

Exome Capture ◽

Parameter Estimates ◽

Environment Interaction ◽

Prediction Ability

AbstractA genomic selection (GS) study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in two Northern Swedish trials at two locations using exome capture as a genotyping platform. Non-additive effects including dominance and first-order epistatic interactions (including additive by additive, dominance by dominance, and additive by dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. GS models partitioned additive and non-additive genetic variances more precisely compared with pedigree-based models. In addition, predictive ability (PA) in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to GS (RGS) per year increased 91.3/43.7%, 86.9/82.9%, and 78.9/80.8% compared with response to phenotypic selection (RPS) per year when GS was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for site 1/site 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also may improve the genetic gain when they are significant. For tree height, Pilodyn, and modulus of elasticity (MOE), RGS per year improved up to 84.2%, 91.3%, and 92.6% compared with RPS per year, respectively.

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Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits

Evolutionary Applications ◽

10.1111/eva.13076 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2704-2722 ◽

Cited By ~ 1

Author(s):

Jean Beaulieu ◽

Simon Nadeau ◽

Chen Ding ◽

Jose M. Celedon ◽

Aïda Azaiez ◽

...

Keyword(s):

Genomic Selection ◽

Wood Quality ◽

Spruce Budworm ◽

White Spruce ◽

Quality Traits ◽

Selection For

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Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

Plants ◽

10.3390/plants9060719 ◽

2020 ◽

Vol 9 (6) ◽

pp. 719

Author(s):

Mulusew Fikere ◽

Denise M. Barbulescu ◽

M. Michelle Malmberg ◽

Pankaj Maharjan ◽

Phillip A. Salisbury ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Seed Quality ◽

Agronomic Traits ◽

Genetic Correlations ◽

Quality Traits ◽

Blackleg Disease ◽

Genetic Progress ◽

Seed Quality Traits

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

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How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

Frontiers in Plant Science ◽

10.3389/fpls.2020.592977 ◽

2020 ◽

Vol 11 ◽

Author(s):

Christian R. Werner ◽

R. Chris Gaynor ◽

Gregor Gorjanc ◽

John M. Hickey ◽

Tobias Kox ◽

...

Keyword(s):

Family Structure ◽

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Cross Validation ◽

Careful Analysis ◽

Critical Approach ◽

Crop Species ◽

Breeding Programs ◽

Mendelian Sampling

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

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Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform

BMC Genomics ◽

10.1186/s12864-017-4258-5 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 28

Author(s):

Frances R. Thistlethwaite ◽

Blaise Ratcliffe ◽

Jaroslav Klápště ◽

Ilga Porth ◽

Charles Chen ◽

...

Keyword(s):

Wood Density ◽

Genomic Prediction ◽

Douglas Fir ◽

Exome Capture ◽

Space And Time ◽

Genotyping Platform

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Genomic prediction accuracies and abilities for growth and wood quality traits of Scots pine, using genotyping-by-sequencing (GBS) data

10.1101/607648 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ainhoa Calleja-Rodriguez ◽

Jin Pan ◽

Tomas Funda ◽

Zhi-Qiang Chen ◽

John Baison ◽

...

Keyword(s):

Scots Pine ◽

Genomic Prediction ◽

Prediction Models ◽

Wood Quality ◽

Genotyping By Sequencing ◽

Tree Breeding ◽

Snp Markers ◽

Progeny Testing ◽

Quality Traits ◽

The Impact

ABSTRACTHigher genetic gains can be achieved through genomic selection (GS) by shortening time of progeny testing in tree breeding programs. Genotyping-by-sequencing (GBS), combined with two imputation methods, allowed us to perform the current genomic prediction study in Scots pine (Pinus sylvestrisL.). 694 individuals representing 183 full-sib families were genotyped and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic prediction models. In addition, the impact on the predictive ability (PA) and prediction accuracy to estimate genomic breeding values was evaluated by assigning different ratios of training and validation sets, as well as different subsets of SNP markers. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed higher PAs and prediction accuracies than Bayesian LASSO (BL). A subset of approximately 4000 markers was sufficient to provide the same PAs and accuracies as the full set of 8719 markers. Furthermore, PAs were similar for both pedigree- and genomic-based estimations, whereas accuracies and heritabilities were slightly higher for pedigree-based estimations. However, prediction accuracies of genomic models were sufficient to achieve a higher selection efficiency per year, varying between 50-87% compared to the traditional pedigree-based selection.

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Genomic Prediction and Selection for Fruit Traits in Winter Squash

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401215 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3601-3610

Author(s):

Christopher O. Hernandez ◽

Lindsay E. Wyatt ◽

Michael R. Mazourek

Keyword(s):

Genomic Selection ◽

Fruit Quality ◽

Quality Index ◽

Recurrent Selection ◽

Selection Model ◽

Breeding Cycle ◽

Quality Traits ◽

Genomic Selection Model ◽

Selection For ◽

Winter Squash

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.

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