Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies

2021 ◽  
Author(s):  
Garrett M See ◽  
Benny E Mote ◽  
Matthew L Spangler
Keyword(s):  
Variance Components ◽  
Snp Markers ◽  
Selective Genotyping ◽  
Sib Families ◽  
Component Estimation ◽  
Selection For ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
The Impact ◽  
True Values

Abstract Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performance were considered to be moderately heritable (h2=0.4). Factors evaluated were, 1) CB phenotype and genotype inclusion of 15% (n=1800) or 35% (n=4200), 2) genetic correlation between PB and CB performance (rpc=0.1, 0.5 or 0.7) and 3) selective genotyping strategy. Genotyping strategies included: a) Random: random CB selection, b) Top: highest CB phenotype and c) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

scholarly journals Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding

2021 ◽  
Author(s):  
Garrett M See ◽  
Benny E Mote ◽  
Matthew L Spangler
Keyword(s):  
Genetic Gain ◽  
Production Systems ◽  
Snp Markers ◽  
Selective Genotyping ◽  
Phenotypic Data ◽  
Breeding Values ◽  
True Breeding ◽  
Estimated Breeding Values ◽  
Highly Correlated ◽  
Genetic Evaluations

Abstract Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with three distinct purebred breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7 or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400 or 800 CB animals to genotype per generation. Selection strategies included: 1) Random: random selection, 2) Top: highest phenotype, 3) Bottom: lowest phenotype, 4) Extreme: half highest and half lowest phenotypes, and 5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P<0.05) rates of genetic gain in CB performance (ΔG) when rpc<0.9. For highly correlated traits (rpc=0.9) selective genotyping did not impact (P>0.05) ΔG. No differences (P>0.05) were observed in ΔG between Top, Bottom or Middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc<0.9. In general, family sampling method did not impact ΔG or the correlation between TBV and EBV. Overall, the Extreme genotyping strategy produced the greatest genetic gain and the highest correlations between TBV and EBV, suggesting that two tailed sampling of CB animals is the most informative when CB performance is the selection goal.

scholarly journals Genetic parameters for wool traits in Finnsheep lambs

2008 ◽  
Vol 16 (2) ◽  
pp. 124 ◽  
Author(s):  
M-L. PUNTILA ◽  
K. MÄKI ◽  
A. NYLANDER
Keyword(s):  
Genetic Correlation ◽  
Variance Components ◽  
Genetic Parameters ◽  
Fibre Diameter ◽  
Live Weight ◽  
Bivariate Analysis ◽  
Component Estimation ◽  
Selection For ◽  
Fleece Weight ◽  
Staple Length

Genetic parameters were estimated for wool characteristics of white and coloured Finnsheep. The data consisted of 5 309 lambs from ordinary production flocks, the Finnsheep nucleus flock and a breeding flock. The variance component estimation was done applying REML analyses. Wool traits included fleece uniformity, density, staple formation, lustre, crimp frequency, fineness grade and staple length. There was a smaller dataset that contained also lamb live weight, greasy fleece weight and additional fleece characteristics including fibre diameter measured with the OFDA method. The variance components for direct and maternal effects were estimated using bivariate analysis for 42-day, 120-day weight and greasy fleece weight. Heritability for visually assessed wool characteristics varied from 0.23 to 0.43 and for measured traits from 0.45 to 0.62. Staple length had a high negative genetic correlation with crimp frequency and fineness grade. Heritability of greasy fleece weight was high (0.55) and that of fibre diameter 0.62. The genetic correlation between crimp frequency and fibre diameter was negative (- 0.56). The results imply that the assessed traits are useful indicators for fleece quality and those of major importance can be introduced into the breeding programme. The results suggest that there is no antagonism in selection for both growth capacity and wool quantity.;

scholarly journals Impact of inclusion rates of crossbred phenotypes and genotypes in nucleus selection programs

2020 ◽  
Vol 98 (12) ◽  
Author(s):  
Garrett M See ◽  
Benny E Mote ◽  
Matthew L Spangler
Keyword(s):  
Data Collection ◽  
Genetic Correlation ◽  
Snp Markers ◽  
Breeding Value ◽  
Single Nucleotide ◽  
Data Collection Strategy ◽  
Inclusion Rate ◽  
Genetic Evaluations ◽  
The Impact ◽  
Selection Of

Abstract Numerous methods have been suggested to incorporate crossbred (CB) phenotypes and genotypes into swine selection programs, yet little research has focused on the implicit trade-off decisions between generating data at the nucleus or commercial level. The aim of this study was to investigate the impact of altering the proportion of purebred (PB) and CB phenotypes and genotypes in genetic evaluations on the response to selection of CB performance. Assuming CB and PB performance with moderate heritabilities (h2=0.4), a three-breed swine crossbreeding scheme was simulated and selection was practiced for six generations, where the goal was to increase CB performance. Phenotypes, genotypes, and pedigrees for three PB breeds (25 and 175 mating males and females for each breed, respectively), F1 crosses (400 mating females), and terminal cross progeny (2,500) were simulated. The genome consisted of 18 chromosomes with 1,800 quantitative trait loci and 72k single nucleotide polymorphism (SNP) markers. Selection was performed in PB breeds using estimated breeding value for each phenotyping/genotyping strategy. Strategies investigated were: 1) increasing the proportion of CB with genotypes, phenotypes, and sire pedigree relationships, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB performance (rpc). Each unique rpc scenario and data collection strategy was replicated 10 times. Results showed that including CB data improved the CB performance regardless of  rpc or data collection strategy compared with when no CB data were included. Compared with using only PB information, including 10% of CB progeny per generation with sire pedigrees and phenotypes increased the response in CB phenotype by 134%, 55%, 33%, 23%, and 21% when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. When the same 10% of CB progeny were also genotyped, CB performance increased by 243%, 54%, 38%, 23%, and 20% when the rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, compared with when no CB data were utilized. Minimal change was observed in the average CB phenotype when PB phenotypes were included or proportionally removed when CB were genotyped. Removal of both PB phenotypes and genotypes when CB were genotyped greatly reduced the response in CB performance. In practice, the optimal inclusion rate of CB and PB data depends upon the genetic correlation between CB and PB animals and the expense of additional CB data collection compared with the economic benefit associated with increased CB performance.

scholarly journals Potential of genomic selection for improvement of resistance to Ostreid Herpes virus in Pacific oyster (Crassostrea gigas)

2019 ◽  
Author(s):  
Alejandro P. Gutierrez ◽  
Jane Symonds ◽  
Nick King ◽  
Konstanze Steiner ◽  
Tim P. Bean ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Pacific Oyster ◽  
Snp Array ◽  
Cost Effective ◽  
Snp Markers ◽  
Density Reduction ◽  
Pacific Oysters ◽  
Summer Mortality ◽  
Selection For ◽  
Estimated Breeding Values

AbstractIn genomic selection (GS), genome-wide SNP markers are used to generate genomic estimated breeding values (gEBVs) for selection candidates. The application of GS in shellfish looks promising and has the potential to help in dealing with one of the main issues currently affecting Pacific oyster production worldwide, which is the “summer mortality syndrome”. This causes periodic mass mortality in farms worldwide and has mainly been attributed to a specific variant of the Ostreid herpesvirus (OsHV-1-μvar). In the current study, we evaluated the potential of genomic selection for host resistance OsHV in Pacific oysters, and compared it to pedigree-based approaches. An OsHV-1 disease challenge was performed using an immersion-based virus exposure treatment for oysters for seven days. 768 samples were genotyped using the medium density SNP array for oysters. GWAS was performed for the survival trait using a GBLUP approach in BLUPF90 software. Heritability ranged from 0.25±0.05 to 0.37±0.05 (mean±s.e) based on pedigree and genomic information, respectively. Genomic prediction was more accurate than pedigree prediction, and SNP density reduction had little impact on prediction accuracy until marker densities dropped below ∼500 SNPs. This demonstrates the potential for GS in Pacific oyster breeding programs and importantly, demonstrates that a low number of SNPs might suffice to obtain accurate gEBVs, thus potentially making the implementation of GS more cost effective.

scholarly journals Genetic parameters for longevity measures in Brazilian Holstein cattle using linear and threshold models

2014 ◽  
Vol 57 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Elisandra Lurdes Kern ◽  
Jaime Araujo Cobuci ◽  
Cláudio Napolis Costa ◽  
José Braccini Neto ◽  
Gabriel Soares Campos ◽  
...  
Keyword(s):  
Variance Components ◽  
Genetic Parameters ◽  
Threshold Model ◽  
Holstein Cows ◽  
Holstein Cattle ◽  
Threshold Models ◽  
Productive Life ◽  
Selection For ◽  
Estimate Variance ◽  
Genetic Evaluations

Abstract. The aim in this study was to estimate variance components and heritability of different longevity measures related to productive life and survival at a specified age, using linear and threshold models, to specify the more appropriate measure to express longevity in Brazilian Holstein cows. Production and reproduction records of Holstein cows were collected from 1991 to 2010, for cows born between 1987 and 2006. Variance components were obtained by restricted maximum likelihood (REML) for measures of productive life and a Bayesian analysis for survival measures. The heritability estimates for longevity measures ranged from 0.06 to 0.09, using the linear model and from 0.05 to 0.18 for traits using the threshold model. This suggests an inexpressive genetic gain using selection for these traits, whereas improvements in environmental factors which affect these animals may lead to greater phenotypic gains. Survival up to 48 months from first calving was the measureing point defined as the most appropriate to be included in future official genetic evaluations of Holstein cattle in Brazil.

scholarly journals Genetic parameters for weight, morphometry and oviposition of Africanized honeybee queens using simulated data

2020 ◽  
Vol 72 (5) ◽  
pp. 1959-1964
Author(s):  
E.H. Martins ◽  
G. Tarôco ◽  
G.A. Rovadoscki ◽  
M.H.V. Oliveira ◽  
G.B. Mourão ◽  
...  
Keyword(s):  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Simulated Data ◽  
Real Data ◽  
Biological Data ◽  
Likelihood Method ◽  
Phenotypic Correlations ◽  
Genetic Evaluations ◽  
The Impact

ABSTRACT This study aimed to estimate genetic parameters for simulated data of body weight (BW), abdominal width (AW), abdominal length (AL), and oviposition. Simulation was performed based on real data collected at apiaries in the region of Campo das Vertentes, Minas Gerais, Brazil. Genetic evaluations were performed using single- and two-trait models and (co)variance components were estimated by the restricted maximum likelihood method. The heritability for BW, AW, AL and oviposition were 0.54, 0.47, 0.31 and 0.66, respectively. Positive genetic correlations of high magnitude were obtained between BW and AW (0.80), BW and oviposition (0.69), AW and oviposition (0.82), and AL and oviposition (0.96). The genetic correlations between BW and AL (0.11) and between AW and AL (0.26) were considered moderate and low. In contrast, the phenotypic correlations were positive and high between BW and AW (0.97), BW and AL (0.96), and AW and AL (0.98). Phenotypic correlations of low magnitude and close to zero were obtained for oviposition with AL (0.02), AW (-0.02), and BW (-0.03). New studies involving these characteristics should be conducted on populations with biological data in order to evaluate the impact of selection on traits of economic interest.

scholarly journals Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP - using real data from pigs

2021 ◽  
Author(s):  
Ibrahim Jibrila ◽  
Jeremie Vandenplas ◽  
Jan ten Napel ◽  
Rob Bergsma ◽  
Roel F Veerkamp ◽  
...  
Keyword(s):  
Average Daily Gain ◽  
Performance Testing ◽  
Real Data ◽  
Single Step ◽  
Reference Scenario ◽  
Production Traits ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
The Impact ◽  
Daily Gain

Background: Empirically assessing the impact of preselection on subsequent genetic evaluations of preselected animals requires comparison of scenarios with and without preselection. However, preselection almost always takes place in animal breeding programs, so it is difficult, if not impossible, to have a dataset without preselection. Hence most studies on preselection used simulated datasets, concluding that subsequent genomic estimated breeding values (GEBV) from single-step genomic best linear unbiased prediction (ssGBLUP) are unbiased. The aim of this study was to investigate the impact of genomic preselection, using real data, on accuracy and bias of GEBV of validation animals. Methods: We used data on four pig production traits from one sire-line and one dam-line, with more intense original preselection in the dam-line than in the sire-line. The traits are average daily gain during performance testing, average daily gain throughout life, backfat, and loin depth. Per line, we ran ssGBLUP with the entire data until validation generation and considered this scenario as the reference scenario. We then implemented two scenarios with additional layers of genomic preselection by removing all animals without progeny either i) only in the validation generation, or ii) in all generations. In computing accuracy and bias, we compared GEBV against progeny yield deviation of validation animals. Results: Results showed only a limited loss in accuracy due to the additional layers of genomic preselection. This is true in both lines, for all traits, and regardless of whether validation animals had records or not. Bias too was largely absent, and did not differ greatly among corresponding scenarios with or without additional layers of genomic preselection. Conclusion: We concluded that impact of recent and/or historical genomic preselection is minimal on subsequent genetic evaluations of selection candidates, if these subsequent genetic evaluations are done using ssGBLUP.

scholarly journals Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP - using real data from pigs

2021 ◽  
Author(s):  
Ibrahim Jibrila ◽  
Jeremie Vandenplas ◽  
Jan ten Napel ◽  
Rob Bergsma ◽  
Roel F Veerkamp ◽  
...  
Keyword(s):  
Average Daily Gain ◽  
Performance Testing ◽  
Real Data ◽  
Single Step ◽  
Reference Scenario ◽  
Production Traits ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
The Impact ◽  
Daily Gain

Abstract Background Empirically assessing the impact of preselection on subsequent genetic evaluations of preselected animals requires comparison of scenarios with and without preselection. However, preselection almost always takes place in animal breeding programs, so it is difficult, if not impossible, to have a dataset without preselection. Hence most studies on preselection used simulated datasets, concluding that subsequent genomic estimated breeding values (GEBV) from single-step genomic best linear unbiased prediction (ssGBLUP) are unbiased. The aim of this study was to investigate the impact of genomic preselection, using real data, on accuracy and bias of GEBV of validation animals. Methods We used data on four pig production traits from one sire-line and one dam-line, with more intense original preselection in the dam-line than in the sire-line. The traits are average daily gain during performance testing, average daily gain throughout life, backfat, and loin depth. Per line, we ran ssGBLUP with the entire data until validation generation and considered this scenario as the reference scenario. We then implemented two scenarios with additional layers of genomic preselection by removing all animals without progeny either i) only in the validation generation, or ii) in all generations. In computing accuracy and bias, we compared GEBV against progeny yield deviation of validation animals. Results Results showed only a limited loss in accuracy due to the additional layers of genomic preselection. This is true in both lines, for all traits, and regardless of whether validation animals had records or not. Bias too was largely absent, and did not differ greatly among corresponding scenarios with or without additional layers of genomic preselection. Conclusion We concluded that impact of recent and/or historical genomic preselection is minimal on subsequent genetic evaluations of selection candidates, if these subsequent genetic evaluations are done using ssGBLUP.

Can Values of Honesty, Hard Work, Loyalty and Discipline Predict Entrepreneurial Orientation of Muslim Owner Managers?

2015 ◽  
Vol 3 (1) ◽  
pp. 31 ◽  
Author(s):  
Rohani Mohd ◽  
Badrul Hisham Kamaruddin ◽  
Khulida Kirana Yahya ◽  
Elias Sanidas
Keyword(s):  
Entrepreneurial Orientation ◽  
Two Dimensions ◽  
Hard Work ◽  
Small Scale ◽  
Sampling Frame ◽  
Policy Makers ◽  
Learning Institutions ◽  
Innovative Orientation ◽  
The Impact ◽  
True Values

The purpose of the present study is twofold: first, to investigate the true values of Muslim owner managers; second, to examine the impact of these values on entrepreneurial orientations of Muslim small-scale entrepreneurs. 850 Muslim owner managers were selected randomly using the sampling frame provided by MajlisAmanah Rakyat Malaysia (MARA). 162 completed questionnaires were collected and analyzed. For this paper only two dimensions of entrepreneurial orientations were analyzed: proactive orientation and innovative orientation. Interestingly, the findings revealed that Muslim businessmen/women are honest, loyal, disciplined and hard working. Loyalty and honesty are positively related to proactive orientation, while discipline and hard-work are positively related to innovative orientation. The findings provide implications for existing relevant theories, policy makers, practitioners and learning institutions. 

scholarly journals Identifying Critical Binder Attributes to Facilitate Binder Selection for Efficient Formulation Development in a Continuous Twin Screw Wet Granulation Process

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 210
Author(s):  
Lise Vandevivere ◽  
Maxine Vangampelaere ◽  
Christoph Portier ◽  
Cedrine de Backere ◽  
Olaf Häusler ◽  
...  
Keyword(s):  
Dissolution Kinetics ◽  
Wet Granulation ◽  
Continuous Manufacturing ◽  
Formulation Development ◽  
Granulation Process ◽  
Low Viscosity ◽  
Twin Screw ◽  
Poorly Soluble ◽  
Selection For ◽  
The Impact

The suitability of pharmaceutical binders for continuous twin-screw wet granulation was investigated as the pharmaceutical industry is undergoing a switch from batch to continuous manufacturing. Binder selection for twin-screw wet granulation should rely on a scientific approach to enable efficient formulation development. Therefore, the current study identified binder attributes affecting the binder effectiveness in a wet granulation process of a highly soluble model excipient (mannitol). For this formulation, higher binder effectiveness was linked to fast activation of the binder properties (i.e., fast binder dissolution kinetics combined with low viscosity attributes and good wetting properties by the binder). As the impact of binder attributes on the granulation process of a poorly soluble formulation (dicalcium phosphate) was previously investigated, this enabled a comprehensive comparison between both formulations in current research focusing on binder selection. This comparison revealed that binder attributes that are important to guide binder selection differ in function of the solubility of the formulation. The identification of critical binder attributes in the current study enables rational and efficient binder selection for twin-screw granulation of well soluble and poorly soluble formulations. Binder addition proved especially valuable for a poorly soluble formulation.

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