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

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.

scholarly journals Increased Prediction Ability in Norway Spruce Trials Using a Marker X Environment Interaction and Non-Additive Genomic Selection Model

2019 ◽  
Vol 110 (7) ◽  
pp. 830-843 ◽  
Author(s):  
Zhi-Qiang Chen ◽  
John Baison ◽  
Jin Pan ◽  
Johan Westin ◽  
Maria Rosario García Gil ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Norway Spruce ◽  
Genetic Parameter ◽  
Phenotypic Selection ◽  
Mendelian Inheritance ◽  
Exome Capture ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Prediction Ability ◽  
Selection Models

Abstract A genomic selection study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in 2 Northern Swedish trials at 2 locations using exome capture as a genotyping platform. Nonadditive 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. Genomic selection models partitioned additive and nonadditive genetic variances more precisely than pedigree-based models. In addition, predictive ability 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 genomic selection per year increased up to 78.9/80.8%, 86.9/82.9%, and 91.3/88.2% compared with response to phenotypic selection per year when genomic selection was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for sites 1 and 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also when they are significant may improve the genetic gain. For tree height, Pilodyn, and modulus of elasticity (MOE), response to genomic selection per year improved up to 68.9%, 91.3%, and 92.6% compared with response to phenotypic selection per year, respectively.Subject Area: Quantitative genetics and Mendelian inheritance

scholarly journals 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

2018 ◽  
Author(s):  
Zhi-Qiang Chen ◽  
John Baison ◽  
Jin Pan ◽  
Bo Karlsson ◽  
Bengt Andersson Gull ◽  
...  
Keyword(s):  
Family Structure ◽  
Genomic Selection ◽  
Norway Spruce ◽  
Genomic Prediction ◽  
Wood Quality ◽  
Tree Height ◽  
Exome Capture ◽  
Breeding Cycle ◽  
Quality Traits ◽  
Genotyping Platform

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.

scholarly journals Genetic parameter estimates for important traits on the fifth generation in red tilapia in Vietnam

2021 ◽  
Vol 20 (1) ◽  
pp. 49-57
Author(s):  
Sang V. Nguyen
Keyword(s):  
Body Weight ◽  
Survival Rate ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Body Colour ◽  
Red Tilapia ◽  
Gxe Interaction

Genetic parameters comprising heritability, genetic correlation and genotype by environment interaction (GxE) for growth survival rate and body colour at harvest were estimated on the 5th selective generation of red tilapia grown in two environments, freshwater and brackishwater ponds. A total of 116 full-half-sib families was produced as well as 4,432 and 3,811 tagged individuals were tested in freshwater and brackishwater ponds, respectively. Genetic parameters were estimated by ASReml 4.1 software. The heritability for body weight and survival rate was high while medium heritability for body colour in freshwater was observed. The heritability for those traits of red tilapia in brackishwater. Together with the figures in earlier publication on previous generations (G1 to G4) in the same selective population, the expected medium to high response acquires if selection is done for each trait. Genetic correlations among harvest body weight, survival rate and body colour are insignificantly different and ranging from -0.25 to 0.37 (P > 0.05). These results implied that selection on one trait do not influence on responses of the other traits. GxE interaction for body weight and body colour between two tested environments is mostly negligible with genetic correlations ranging from 0.63 - 0.80 while it is important for survival trait (rg = -0.17 ± 0.40).

scholarly journals Genomic selection for lentil breeding: empirical evidence

2019 ◽  
Author(s):  
Teketel A. Haile ◽  
Taryn Heidecker ◽  
Derek Wright ◽  
Sandesh Neupane ◽  
Larissa Ramsay ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Statistical Models ◽  
Prediction Accuracy ◽  
Recombinant Inbred Lines ◽  
Genotype By Environment Interaction ◽  
Exome Capture ◽  
Environment Interaction ◽  
Multiple Trait ◽  
Similar Accuracy ◽  
Population Prediction

AbstractGenomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

Sensitivity of genotypes to feeding systems for production, weight and feed intake in dairy cattle

2000 ◽  
Vol 2000 ◽  
pp. 112-112 ◽  
Author(s):  
J.E. Pryce ◽  
R.F. Veerkamp
Keyword(s):  
Dry Matter ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Genotype By Environment Interaction ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Combine Data ◽  
Genotype By Environment ◽  
University Of Edinburgh ◽  
Feeding Systems

Getting reliable genetic parameter estimates for dry matter intake is difficult because recording it is expensive, hence it is tempting to combine data from research herds. However, there are large differences in feeding and management systems, which causes differences in means across herds. Furthermore, variances or heritabilities may differ and genetic correlations may be less than one between herds. This is one of the reasons why it is important to investigate effects of genotype by environment interaction (GxE). Another reason is that it is important to understand how high genetic merit cows perform in different feeding systems. The objective of this study was to estimate the effect of GxE for three feeding systems at two research herds belonging to ID-Lelystad (ID) and to SAC/University of Edinburgh (Langhill).

scholarly journals Multi‐trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce

2019 ◽  
Vol 13 (1) ◽  
pp. 76-94 ◽  
Author(s):  
Patrick R. N. Lenz ◽  
Simon Nadeau ◽  
Marie‐Josée Mottet ◽  
Martin Perron ◽  
Nathalie Isabel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Norway Spruce ◽  
Wood Quality ◽  
Selection For

scholarly journals Genetic improvement of sow longevity and its economic impact on commercial pork production

2020 ◽  
Vol 49 (6) ◽  
pp. 1036-1046
Author(s):  
B.E. Mote ◽  
T.V. Serenius ◽  
C Supakorn ◽  
K.J. Stalder
Keyword(s):  
Economic Impact ◽  
Genomic Selection ◽  
Genetic Parameter ◽  
Well Being ◽  
Direct Selection ◽  
Parameter Estimates ◽  
Swine Industry ◽  
Large White ◽  
Phenotypic Data ◽  
Sow Longevity

Sow longevity (sow productive lifetime) plays an important role in economically efficient piglet production. Direct selection for sow longevity is not commonly practiced in any pig-breeding program. In recent years, an increased number of peer reviewed articles addressing the economic impact, genetic parameter estimates, and genomic information (including markers and single nucleotide polymorphisms for sow longevity) have been published in the scientific literature. The studies in the literature indicate that sow longevity is a complex trait having economic value and is an animal well-being concern for commercial pork producers. Studies have concluded that sufficient genetic variation exists so that selection to improve sow longevity should be effective. Unlike the dairy industry, the primary parent animal used in the swine industry is a crossbred female, typically F1 (Landrace X Large White or Yorkshire). Sow longevity has shown to be genetically related with prolificacy and leg conformation traits. Sow longevity seems to be the ideal trait to utilize genomic selection when attempting to improve the trait. The genetic correlation between purebred and crossbred sow longevity is low. Since the crossbred sow is the breeding objective, phenotypic data from the crossbred females should ideally be used when estimating the breeding values for sow longevity that are used in the indexes to evaluate nucleus animals. Genomic selection is best suited for sex-limited traits, traits expressed later in life, and many animals do not reach some defined end-point parity, sow longevity seems ideally suited to be evaluated using the latest genome enabled selection technology. Keywords: heritability, leg conformation, selection, sow productive lifetime

Genetic analysis of wood quality traits in Norway spruce open-pollinated progenies and their parent plus trees at clonal archives and the evaluation of phenotypic selection of plus trees

2019 ◽  
Vol 49 (7) ◽  
pp. 810-818 ◽  
Author(s):  
Linghua Zhou ◽  
Zhiqiang Chen ◽  
Sven-Olof Lundqvist ◽  
Lars Olsson ◽  
Thomas Grahn ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Wood Density ◽  
Wood Quality ◽  
Microfibril Angle ◽  
Phenotypic Selection ◽  
Wood Properties ◽  
Narrow Sense Heritability ◽  
Pilodyn Penetration ◽  
Plus Tree ◽  
Selection Of

A two-generation pedigree involving 519 Norway spruce (Picea abies (L.) Karst.) plus trees (at clonal archives) and their open-pollinated (OP) progenies was studied with the aim to evaluate the potential of plus-tree selection based on phenotype data scored on the plus trees. Two wood properties (wood density and modulus of elasticity, MOE) and one fiber property (microfibril angle, MFA) were measured with a SilviScan instrument on samples from one ramet per plus tree and 12 OP progenies per plus tree (total of 6288 trees). Three ramets per plus tree and their OP progenies were also assessed for Pilodyn penetration depth and Hitman acoustic velocity, which were used to estimate MOE. The narrow-sense heritability (h2) estimates based on parent–offspring regression were marginally higher than those based on half-sib correlation when three ramets per plus tree were included. For SilviScan data, estimates of the correlation between half-sib, progeny-based breeding values (BVs) and plus-tree phenotypes, as well as repeatability estimates, were highest for wood density, followed by MOE and MFA. Considering that the repeatability estimates from the clonal archive trees were higher than any h2 estimate, selection of the best clones from clonal archives would be an effective alternative.

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