scholarly journals 60 Re-ranking of estimated breeding values using different panel densities with ssGBLUP in broiler chickens

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 36-37
Author(s):  
Mayara Salvian ◽  
Gerson Barreto Mourão ◽  
Gabriel Costa Monteiro Moreira ◽  
Mônica Corrêa Ledur ◽  
Luiz Lehmann Coutinho ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Broiler Chickens ◽  
Reference Population ◽  
Single Step ◽  
P Value ◽  
Call Rate ◽  
Breeding Values ◽  
Heritability Estimates ◽  
Estimated Breeding Values ◽  
The Impact

Abstract The aim of this study was to compare the rank of estimated breeding values (EBV) for organs (heart, liver, lungs and gizzard) and carcass (breast, thigh and drumstick) traits using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) models. A total of 1,453 chickens (703 males and 750 females) from a paternal broiler (TT) reference population belonging to the Poultry Breeding Program from Embrapa Swine and Poultry were genotyped with the Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) 600K SNP panel. Samples with a call rate lower than 90% were removed. A SNP quality control was applied for removing SNP with call rate lower than 98%, MAF lower than 2% and significant deviations from HWE (p-value < 10–7) leaving 370,608 SNP for further analysis. Estimated breeding values were predicted using the blupf90 family of programs whereby a series of bi-variate animal models that included sex and hatching as fixed effects were fitted. Heritability estimates for carcass and organ traits obtained through PBLUP varied from low (0.16) for lungs to moderate (0.34 to 0.47) for heart, liver, gizzard, breast, thigh and drumstick. The genomic heritability estimates through ssGBLUP varied from low (0.14) for lungs to moderate (0.30 to .041) for all other traits. Five subsets (5, 10, 20, 40 and 80% of SNP) were randomly selected from the full SNP set to determine the impact, in terms of EBV rank, of using reduced subsets of SNP to inform relationships among individuals. Although the 5% subset of SNP consistently had the lowest correlation with the full set of SNP, all correlations were greater than 0.995. Results suggest that a relatively limited proportion of SNP could be used to reliably predict EBV via ssGBLUP in this population.

scholarly journals 61 The impact of selective phenotyping and genotyping over generations in beef cattle

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 37-39
Author(s):  
Andrea Plotzki Reis ◽  
Rodrigo Fagundes da Costa ◽  
Fabyano Fonseca e Silva ◽  
Fernando Flores Cardoso ◽  
Matthew L Spangler
Keyword(s):  
Beef Cattle ◽  
Prediction Accuracy ◽  
Cost Benefit ◽  
Single Step ◽  
Breeding Values ◽  
Additional Information ◽  
Economic Framework ◽  
Estimated Breeding Values ◽  
The Cost ◽  
The Impact

Abstract The aim of this study was to investigate selective phenotyping to maintain adequate prediction accuracy. A simulation was conducted, with 10 replicates, using QMSim to mimic the structure and size of a Braford population. A population with 50 generations, 500 animals per generation, was created with phenotyping and genotyping beginning in generation 11. The scenarios investigated were: 1) Randomly phenotype and genotype 10, 25, 50, 75, and 100% of individuals each generation and; 2) Randomly phenotype and genotype 10, 25, 50, 75, and 100% of individuals in every-other generation. Estimated breeding values (EBV) were obtained using single-step GBLUP and accuracy was determined as the correlation between true BV from simulation and those estimated from the blupf90 family of programs. For scenarios where phenotyping and genotyping occurred every generation, EBV accuracies in generation 11 and 50 ranged from 0.32 to 0.32, 0.42 to 0.43, 0.49 to 0.51, 0.53 to 0.56 and 0.57 to 0.59 when 10, 25, 50, 75, and 100% of animals were chosen, respectively. The highest accuracies were 0.40 and 0.50 in generation 38 for scenarios 10 and 25%; 0.56, 0.61 and 0.64 in generation 40 for scenarios 50, 75 and 100%, respectively. When animals were selected every-other generation, EBV accuracy in generation 11 and 50 ranged from 0.24 to 0.26, 0.36 to 0.36, 0.43 to 0.42, 0.48 to 0.44 and 0.53 to 0.48 for 10, 25, 50, 75 and 100% of selected animals, respectively. The highest accuracies were in generation 23 for scenario 10% (0.31), in generation 37 for scenarios 25 (0.43), 50 (0.50) and 75% (0.55) and in generation 39 for 100% (0.59). Although increasing the density of phenotyped and genotyped animals increased prediction accuracy, some gains were marginal. These differences in accuracy must be contemplated in an economic framework to determine the cost-benefit of additional information.

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 Estimation of Breeding Values Using Different Densities of Snp to Inform Kinship in Broiler Chickens

2020 ◽  
Author(s):  
Mayara Salvian ◽  
Gabriel Costa Monteiro Moreira ◽  
Ândrea Plotzki Reis ◽  
Brayan Dias Dauria ◽  
Fabrício Pilonetto ◽  
...  
Keyword(s):  
Broiler Chickens ◽  
Single Step ◽  
Regression Coefficients ◽  
Whole Genome Sequence ◽  
Pedigree Information ◽  
Genomic Information ◽  
Breeding Values ◽  
Genome Wide ◽  
Estimated Breeding Values ◽  
The Cost

Abstract Background: Traditionally, breeding values are estimated based on phenotypic and pedigree information using the numerator relationship (A) matrix. With the availability of genomic information, genome-wide markers can be included in the estimation of breeding values through genomic kinship. However, the density of genomic information used can impact the cost of implementation. The aim of this study was to compare the rank, accuracy, and bias of estimated breeding values (EBV) for organs [heart (HRT), liver (LIV), gizzard (GIZ), lungs (LUN)] and carcass [breast (BRST), drumstick (DRM) and thigh (THG)] weight traits in a broiler population using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods using various densities of SNP and variants imputed from whole-genome sequence (WGS). Results: For both PBLUP and ssGBLUP, heritability estimates varied from low (LUN) to high (HRT, LIV, GIZ, BRST, DRM and THG). Regression coefficients values of EBV on genomic estimated breeding values (GEBV) were similar for both the high density (HD) and WGS sets of SNPs, ranging from 0.87 to 0.99 across scenarios. Conclusion: Results show no benefit of using WGS data compared to HD array data using an unweighted ssGBLUP. Our results suggest that 10% of the content of the HD array can yield unbiased and accurate EBV.

Practical aspects of a genetic evaluation system using parentage assigned from genetic markers

2005 ◽  
Vol 45 (8) ◽  
pp. 935 ◽  
Author(s):  
K. G. Dodds ◽  
J. A. Sise ◽  
M. L. Tate
Keyword(s):  
Genetic Markers ◽  
Evaluation System ◽  
Fixed Effects ◽  
Genetic Evaluation ◽  
Diagnostic Systems ◽  
Breeding Values ◽  
Mating Group ◽  
Animal Management ◽  
The Mean ◽  
Estimated Breeding Values

Animal breeding values can be calculated when genetic markers have been used to help determine the parentage of some of the animals, but their parentage has been incompletely determined. The pedigree sampling method is 1 computing strategy for calculating these breeding values. This paper describes and discusses methods for dealing with a number of practical issues that arise when implementing such a system for industry use. In particular, diagnostic systems for detecting inadequacies or possible errors in the genotyping systems and the recording of animal management are developed. Also, characteristics of the best assigned pedigrees are calculated according to mating group and used to check for sires missing from these groups. The correlation between breeding values estimated from a single sampled pedigree (using parentage probabilities) and those estimated as the mean from many sampled pedigrees gives a diagnostic to indicate which estimated breeding values are more influenced by uncertainties in relationships. For the analysis of survival traits, a method to enumerate and assign likely parentage to dead offspring which have not been DNA sampled and genotyped is described. When embryo transfer technology is used, the genetic dam needs to be included as a possible dam when considering parentage. If some fixed effects which depend on the parent are missing, these can be sampled similarly to parentage, and this may improve the evaluation if certain assumptions are met. A method to provide a likely list of parents, the ‘fitted pedigree’, which is based on the most likely parents, but modified to reduce the occurrence of unlikely family sets (e.g. very large litters) is also presented. The use of these methods will enhance the practical application of DNA parenting when used in conjunction with genetic evaluation.

scholarly journals Analysis of non-genetic and genetic influences underlying the growth of Kajli lambs

2020 ◽  
Vol 50 (4) ◽  
pp. 613-625
Author(s):  
A. Ali ◽  
K. Javed ◽  
I. Zahoor ◽  
K.M. Anjum
Keyword(s):  
Birth Weight ◽  
Genetic Correlation ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Breeding Value ◽  
Season Of Birth ◽  
Genetic Trend ◽  
Breeding Values ◽  
Heritability Estimates ◽  
Birth Type

Data on 2931 Kajli lambs, born from 2007 to 2018, were used to quantify environmental and genetic effects on growth performance of Kajli sheep. Traits considered for evaluation were birth weight (BWT), 120-day adjusted weight (120DWT), 180-day adjusted weight (180DWT), 270-day adjusted weight (270DWT), and 365-day adjusted weight (365DWT). Fixed effects of year of birth, season of birth, sex, birth type, and dam age on these traits were evaluated using linear procedures of SAS, 9.1. Similarly, BWT, 120DWT, 180DWT, and 270DWT were used as fixed effects mixed model analyses. Variance components, heritability and breeding values were estimated by restricted maximum likelihood. The genetic trend for each trait was obtained by regression of the estimated breeding values (EBV) on year of birth. Analyses revealed substantial influence of birth year on all traits. Sex and birth type were the significant sources of variation for BWT and 120DWT. Season of birth did not influence birth weight meaningfully, but had a significant role in the expression of 120DWT, 180DWT, and 270DWT. Heritability estimates were generally low (0.003 ± 0.018 to 0.099 ± 0.067) for all traits. With the exception of the genetic correlation of 180DWT and 365DWT, the genetic correlations between trait were strong and positive. Only 365DWT had a positive genetic trend. Although the heritability estimates for almost all weight traits were low, high and positive genetic correlations between BWT and other weight traits suggest that selection based on BWT would result in the improvement of other weight traits as a correlated response.Keywords: bodyweight, breeding value, genetic correlation, sheep

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 Level-biases in estimated breeding values due to the use of different SNP panels over time in ssGBLUP

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Øyvind Nordbø ◽  
Arne B. Gjuvsland ◽  
Leiv Sigbjørn Eikje ◽  
Theo Meuwissen
Keyword(s):  
Value Added ◽  
Single Step ◽  
Fine Tuning ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Optimal Selection ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Genomic Predictions

Abstract Background The main aim of single-step genomic predictions was to facilitate optimal selection in populations consisting of both genotyped and non-genotyped individuals. However, in spite of intensive research, biases still occur, which make it difficult to perform optimal selection across groups of animals. The objective of this study was to investigate whether incomplete genotype datasets with errors could be a potential source of level-bias between genotyped and non-genotyped animals and between animals genotyped on different single nucleotide polymorphism (SNP) panels in single-step genomic predictions. Results Incomplete and erroneous genotypes of young animals caused biases in breeding values between groups of animals. Systematic noise or missing data for less than 1% of the SNPs in the genotype data had substantial effects on the differences in breeding values between genotyped and non-genotyped animals, and between animals genotyped on different chips. The breeding values of young genotyped individuals were biased upward, and the magnitude was up to 0.8 genetic standard deviations, compared with breeding values of non-genotyped individuals. Similarly, the magnitude of a small value added to the diagonal of the genomic relationship matrix affected the level of average breeding values between groups of genotyped and non-genotyped animals. Cross-validation accuracies and regression coefficients were not sensitive to these factors. Conclusions Because, historically, different SNP chips have been used for genotyping different parts of a population, fine-tuning of imputation within and across SNP chips and handling of missing genotypes are crucial for reducing bias. Although all the SNPs used for estimating breeding values are present on the chip used for genotyping young animals, incompleteness and some genotype errors might lead to level-biases in breeding values.

scholarly journals 213 Genomic selection of carcass quality traits in crossbred pigs using a reference population

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 41-41
Author(s):  
Matteo Bergamaschi ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Justin Fix ◽  
Francesco Tiezzi
Keyword(s):  
Fixed Effects ◽  
Carcass Traits ◽  
Predictive Ability ◽  
Reference Population ◽  
Single Step ◽  
Carcass Quality ◽  
Quality Traits ◽  
Single Gender ◽  
Training Set ◽  
Crossbred Pigs

Abstract The objective of this work was to evaluate the predictive ability of different models applied to carcass traits in crossbred pigs. The pigs were divided in 2 finishing flows: A=36,110 and B=95,041 animals, and were progeny of 386 sires (almost entirely genotyped with the 60k SNP chip). In flow A, individuals were housed into single-sire single-gender pens, and split-marketing on a pen basis was applied. In flow B, individuals were kept in standard commercial conditions and split-marketing on an individuals basis was applied. A dataset containing individual records of three carcass traits: back-fat (BF), loin depth (LD), and carcass daily gain (CACG) was used. Data from flow A were divided into training and validation sets on the basis of contemporary groups (8 in training and 1 in testing). Variance components and solutions were obtained using the BLUPF90 suite of programs. Models included fixed effects (dam line, sow parity, sex, cross fostering, and contemporary group) and random effects (additive genetic, batch, litter, and residual). Models tested were univariate vs multivariate and pedigree vs single-step. The addition of flow B records to the training set was evaluated, by including or excluding these records. Heritabilities were 0.68±0.023 for BF, 0.47±0.018 for LD, and 0.55±0.023 for CACG. CACG gain was correlated with BF (0.43±0.029) and LD (0.39±0.03). Low genetic correlation was found between BF and LD (0.17±0.034). Prediction accuracies were 0.39±0.05, 0.17±0.06, and 0.13±0.03 for BF, LD, and CACG respectively. The mean accuracy of BF, LD, and CG increased (~6%) when records from flow B were included in the training set, whereas the increase of accuracy between models (univariate vs multivariate) was not significant. The inclusion of sire genotypes did not improve prediction accuracy significantly. Based on these results, the prediction of carcass quality traits in crossbred pigs is possible.

Safety profile of ripretinib, including impact of alopecia, and Palmar-Plantar Erythrodysesthesia Syndrome (PPES) on patient-reported outcomes (PROs), in ≥ fourth-line advanced gastrointestinal stromal tumors (GIST): Analyses from INVICTUS.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 11539-11539
Author(s):  
Suzanne George ◽  
Michael C. Heinrich ◽  
John Raymond Zalcberg ◽  
Sebastian Bauer ◽  
Hans Gelderblom ◽  
...  
Keyword(s):  
Safety Profile ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Statistical Significance ◽  
P Value ◽  
Tolerability Profile ◽  
Patient Reported ◽  
The Impact ◽  
Advanced Gist ◽  
Over Time

11539 Background: Ripretinib is a novel switch-control TKI that broadly inhibits KIT and PDGFRA kinase signaling. In INVICTUS (NCT03353753), a randomized, double-blind, placebo (PBO)-controlled trial of ripretinib in ≥4th-line advanced GIST, ripretinib reduced the risk of disease progression or death by 85% vs PBO with a favorable overall safety profile. Common ( > 20%) adverse events (AEs) included, but were not limited to, alopecia and PPES. Exploratory analyses evaluated the impact of alopecia and PPES on quality of life (QoL). Methods: Patients (pts) with advanced GIST previously treated with at least imatinib, sunitinib, and regorafenib were randomized (2:1) to ripretinib 150 mg QD or PBO. AEs were graded using CTCAE v4 and PROs collected using EQ-5D-5L (EQ5D) and EORTC QLQ-C30 (C30). Repeated measures (RM) models assessed the impact of alopecia and PPES on 5 PROs (EQ5D visual analogue scale; and C30 physical functioning, role functioning, and the overall health and overall QoL questions) within the ripretinib arm. Fixed effects were sex, alopecia/PPES, and ECOG scores at baseline. Results: 128/129 randomized pts received treatment (85 ripretinib 150 mg QD; 43 PBO). Alopecia, regardless of causality, occurred in 44 (51.8%) on ripretinib (34 [40.0%] grade 1; 10 [11.8%] grade 2) and 2 (4.7%) on PBO (both grade 1). PPES occurred in 18 (21.2%) on ripretinib (11 [12.9%] grade 1; 7 [8.2%] grade 2); none on PBO. The median times in days to first occurrence and worst severity grade with ripretinib were 57.0 and 62.5 for alopecia; 56.5 and 57.0 for PPES. The RM models showed a slight trend towards improvement in PRO score over time for pts with alopecia; the only association reaching a P-value of < 0.05 was between alopecia and increased overall QoL. None of the associations between PPES and PRO scores reach P < 0.05. All PRO p-values are nominal, and no statistical significance is being claimed. Conclusions: Ripretinib had a favorable overall safety and tolerability profile. When stratified by alopecia and PPES, patient-reported assessments of function, overall health, and overall QoL were maintained over time. For both alopecia and PPES, onset and maximum severity occurred almost simultaneously, indicating that these events generally did not progressively worsen. These results suggest that alopecia and PPES are manageable and do not have a negative effect on function, overall health, and QoL. Clinical trial information: NCT03353753 .

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