Single-Step Genomic Analysis Increases the Accuracy of Within-Family Selection in a Clonally Replicated Population of Pinus taeda L.

2021 ◽  
Author(s):  
Trevor D Walker ◽  
W Patrick Cumbie ◽  
Fikret Isik
Keyword(s):  
Pinus Taeda ◽  
Predictive Ability ◽  
Single Step ◽  
Breeding Value ◽  
Family Selection ◽  
Breeding Values ◽  
Genomic Markers ◽  
Clonal Testing ◽  
Model Training ◽  
Pinus Taeda L

Abstract The use of genomic markers in forest tree breeding is expected to improve the response to selection, especially within family. To evaluate the potential improvements from genotyping, we analyzed a large Pinus taeda L. clonal population (1,831 cloned individuals) tested in multiple environments. Of the total, 723 clones from five full-sib families were genotyped using 10,337 single-nucleotide polymorphism markers. Single-step models with genomic and pedigree-based relationships produced similar heritability estimates. Breeding value predictions were greatly improved with inclusion of genomic relationships, even when clonal replication was abundant. The improvement was limited to genotyped individuals and attributable to accounting for the Mendelian sampling effect. Reducing clonal replication by omitting data indicated that genotyping improved breeding values similar to clonal replication. Genomic selection predictive ability (masking phenotypes) was greater for stem straightness (0.68) than for growth traits (0.41 to 0.44). Predictive ability for a new full-sibling family was poorer than when full-sibling relationships were present between model training and validation sets. Species that are difficult to propagate clonally can use genotyping to improve within-family selection. Clonal testing combined with genotyping can produce breeding value accuracies adequate to graft selections directly into deployment orchards without progeny testing. Study Implications Genomic markers can improve the reliability of breeding values, resulting in a more confident ranking of individuals within families. For genotyped individuals, the improvements were comparable to clonal testing. Breeding programs for species that are difficult to propagate clonally should consider genotyping to replace or supplement clonal testing as a means to improve within-family selection. For genomic prediction of breeding values without phenotypes (genomic selection), a robust genetic relationship between model training and validation sets is required. The single-step model allows genotyping a subset of the population and is a straightforward extension of well-established methods.

scholarly journals Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

2020 ◽  
Vol 44 (5) ◽  
pp. 994-1002
Author(s):  
Samet Hasan ABACI ◽  
Hasan ÖNDER
Keyword(s):  
Genomic Selection ◽  
Milk Yield ◽  
Predictive Ability ◽  
Breeding Value ◽  
Training Population ◽  
Breeding Values ◽  
Value Prediction ◽  
The Usa ◽  
Population Sizes ◽  
Estimated Breeding Values

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

scholarly journals Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-density SNP Panels

2020 ◽  
Author(s):  
Rafet Al-Tobasei ◽  
Ali R. Ali ◽  
Andre L. S. Garcia ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Predictive Ability ◽  
Study Data ◽  
Single Step ◽  
Breeding Values ◽  
Snp Chip ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Fillet Yield ◽  
Genomic Predictions

Abstract BackgroundOne of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1,568 fish genotyped for the 50K transcribed-SNP chip and ~774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). ResultsThe genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19 - 0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500 – 800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. ConclusionThese results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

scholarly journals Genomic Predictions for Muscle Yield and Fillet Firmness in Rainbow Trout using Reduced-Density SNP Panels

2020 ◽  
Author(s):  
Rafet Al-Tobasei ◽  
Ali R. Ali ◽  
Andre L. S. Garcia ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Predictive Ability ◽  
Study Data ◽  
Single Step ◽  
Breeding Values ◽  
Snp Chip ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Family Based ◽  
Genomic Predictions

Abstract Background One of the most important goals for the rainbow trout aquaculture industry is to improve muscle yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with muscle yield and fillet firmness. In this study, data from 1,568 fish genotyped for the 50K transcribed-SNP chip and ~774 fish phenotyped for muscle yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for muscle yield and 42% for fillet firmness. The predictive ability for muscle yield and fillet firmness was 0.19 - 0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500 – 800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. Conclusion These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

scholarly journals The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 569
Author(s):  
Chen Wei ◽  
Hanpeng Luo ◽  
Bingru Zhao ◽  
Kechuan Tian ◽  
Xixia Huang ◽  
...  
Keyword(s):  
Rank Correlation ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Value ◽  
Genomic Information ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Merino Sheep ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

scholarly journals Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rafet Al-Tobasei ◽  
Ali Ali ◽  
Andre L. S. Garcia ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Predictive Ability ◽  
Study Data ◽  
Single Step ◽  
Breeding Values ◽  
Snp Chip ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Fillet Yield ◽  
Genomic Predictions

Abstract Background One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. Conclusion These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

Correspondence between Breeding Values of the Same Pinus taeda L. Genotypes from Clonal Trials and Half-Sib Seedling Progeny Trials

2020 ◽  
Vol 66 (5) ◽  
pp. 600-611
Author(s):  
Mohammad Nasir Shalizi ◽  
Salvador Alejandro Gezan ◽  
Steven E McKeand ◽  
Joshua R Sherrill ◽  
W Patrick Cumbie ◽  
...  
Keyword(s):  
Pinus Taeda ◽  
Loblolly Pine ◽  
Additive Genetic Variance ◽  
Tree Height ◽  
Forest Tree ◽  
Stem Volume ◽  
Breeding Values ◽  
Progeny Tests ◽  
Pinus Taeda L ◽  
Analytical Approaches

Abstract The correspondence between breeding values of 65 loblolly pine (Pinus taeda L.) genotypes from clonal genetic tests and half-sib seedling progeny tests was studied in the southern United States. The two experiments were established separately, 10 years apart. Additive genetic variance estimates from clonal tests were larger compared with the estimates from the half-sib progeny tests, regardless of the covariance structure used in the statistical models and the traits. However, clone-mean and half-sib family-mean heritability estimates were comparable for all traits, ranging between 0.88 and 0.99. Based on the independent analysis, the correlation between the breeding values of the same genotypes from two propagule types was moderate (0.59) for tree height and stem volume. The combined analysis resulted in a strong genetic correlation (>0.93) between the breeding values of two propagule types. Herein the large discrepancy is mainly the outcome of different data analytical approaches. Conclusively, selecting genotypes for deployment based on clonal testing may not be optimal, but forest tree breeders can use the results from clonal tests to make some informed decisions.

PSXII-9 Modeling genetic differences of combined broiler chicken populations in single-step GBLUP

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 254-254
Author(s):  
Matias Bermann ◽  
Daniela Lourenco ◽  
Vivian Breen ◽  
Rachel Hawken ◽  
Fernando Brito Lopes ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Broiler Chicken ◽  
Predictive Ability ◽  
Single Step ◽  
Fixed Effect ◽  
Model Specification ◽  
Breeding Values ◽  
Chicken Population ◽  
Estimated Breeding Values ◽  
Genomic Predictions

Abstract The objectives of this study were to model the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations and evaluating the behavior of two accuracy estimators under different model specifications. The pedigree was composed by 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the origin of parents and to use UPG or metafounders. Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP (ssGBLUP) using the Algorithm for Proven and Young (APY). Bias, dispersion, and accuracy of GEBV for the validation birds, i.e., from the most recent generation, were computed. The bias and dispersion were estimated with the LR-method, whereas accuracy was estimated by the LR-method and predictive ability. Models with fixed UPG and estimated inbreeding or random UPG resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation, while models without such an effect were significantly biased. Genomic predictions with metafounders were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy and smallest bias can be obtained by adding an extra fixed effect to account for the origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR-method is more consistent among models, whereas predictive ability greatly depends on the model specification, that is, on the fixed effects included in the model. When changing model specification, the largest variation for the LR-method was 20%, while for predictive ability was 110%.

scholarly journals 9 A comparison of different genomic prediction methods in the Canadian Landrace swine population

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 10-10
Author(s):  
Siavash Salek Ardestani ◽  
Mohsen Jafarikia ◽  
Brian Sullivan ◽  
Mehdi Sargolzaei ◽  
Younes Miar
Keyword(s):  
Genomic Prediction ◽  
Performance Test ◽  
Average Daily Gain ◽  
Single Step ◽  
Breeding Value ◽  
Prediction Methods ◽  
Nucleotide Polymorphisms ◽  
Economic Traits ◽  
Genetic Progress ◽  
Breeding Values

Abstract Increasing the accuracy of breeding value prediction can lead to more profitability through accelerating genetic progress for economic traits. The objective of this study was to assess the predictive abilities and unbiasedness of best linear unbiased prediction (BLUP) and popular genomic prediction methods of BayesC, BayesC(π = 0.99), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP). Genotypic information (50K and 60K) of 4,890 performance tested Landrace pigs before February 2019 and 471 validation Landrace pigs that both had phenotypic information on backfat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) from two Canadian pig breeding companies (AlphaGene and Alliance Genetics Canada) were used. The de-regressed breeding values (DEBV) were employed in GBLUP and Bayesian methods. A total number of 48,580 single nucleotide polymorphisms remained after quality control and imputation steps. The prediction accuracies were calculated using the correlation between predicted breeding values before performance test and DEBVs after performance test. All employed genomic prediction methods showed higher prediction accuracies for BFT (50.80–52.68%), ADG (26.61–34.47%), and LMD (18.25–25.08%) compared to BLUP method (BFT = 28.54%, ADG = 16.41%, LMD = 17.15%). The highest prediction accuracies for BFT and ADG were obtained using ssGBLUP method, and for LMD it was obtained using BayesC(π = 0.99). The BayesC(π = 0.99) showed also the lowest prediction biases across the studied traits (+0.05 for BFT, 0.00 for AGD, and -0.10 for LMD). In conclusion, our results revealed the superiority of ssGBLUP (for BFT and ADG) and BayesC(π = 0.99) (for LMD) over other tested methods in this study. However, the prediction accuracies from the tested genomic prediction methods were not significantly different from each other. Thus, employing these methods can be helpful for accelerating the genetic improvement of BFT, ADG, and LMD in the moderate population size of Canadian Landrace.

scholarly journals 17 Investigating pig survival in different productive stages using genomic models

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 14-15
Author(s):  
Natalia Leite ◽  
Daniela Lourenco ◽  
Egbert Knol ◽  
Marcos Lopes ◽  
Fabyano Silva
Keyword(s):  
Maternal Effects ◽  
Variance Components ◽  
Single Step ◽  
Breeding Value ◽  
Genomic Information ◽  
Gene Effects ◽  
Breeding Values ◽  
Variance Components Estimation ◽  
Individual Survival ◽  
The Individual

Abstract Pig survival is an economically important trait with animal welfare implications. Although survival is highly affected by the environment, previous studies reported genetic variability of this trait, indicating the opportunity for improvement through selection. Genomic information is currently included in the major commercial pig breeding programs, which can be incorporated by single-step genomic BLUP (ssGBLUP). The objectives of this study were to: 1) estimate (co)variance components for farrowing, lactation, nursery, and finishing survival; and 2) compare the individual breeding value accuracies obtained using traditional pedigree-based BLUP (BLUP) and ssGBLUP methods. Individual survival records were collected for a crossbred pig population, and two-trait threshold models, which included maternal effects, were used for (co)variance components estimation. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods, and individual accuracies were obtained based on posterior standard deviation. Heritabilities for the four survival phases were low, ranging from 0.04 to 0.12. Pre-weaning survival was controlled by dam and piglet additive gene effects. The additive direct and maternal components were equally important at farrowing, whereas the piglet’s own genetic merit was the most expressive during lactation. Common environment estimates were higher than maternal genetic effects, indicating early life experiences related to the sow, but independent of the maternal genetic component. Nursery and finishing survival showed the same or higher heritabilities compared to pre-weaning stages. The genetic correlation between the pre-weaning phases was high (0.68), whereas the correlation between the post-weaning measurements was moderate (0.42). The incorporation of genomic information through ssGBLUP increased the individual accuracy, on average, from 0.36 to 0.41 for direct and from 0.29 to 0.37 for maternal effects compared to BLUP. Although the heritabilities for survival in different productive stages are low, genetic gains can be obtained, given that breeding values benefit from the inclusion of genomic information.

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