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.

A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

2018 ◽  
Vol 98 (3) ◽  
pp. 565-575 ◽  
Author(s):  
Mario L. Piccoli ◽  
Luiz F. Brito ◽  
José Braccini ◽  
Fernanda V. Brito ◽  
Fernando F. Cardoso ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Production Systems ◽  
Simulated Data ◽  
Single Step ◽  
Breeding Values ◽  
True Breeding ◽  
Blending Method ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

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 38 Using pooled data for single-step genomic prediction: Impact of within-pool variance and size

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 9-9
Author(s):  
Johnna L Baller ◽  
Stephen D Kachman ◽  
Larry A Kuehn ◽  
Matthew L Spangler
Keyword(s):  
Phenotypic Variation ◽  
Equal Opportunity ◽  
Cost Effective ◽  
Single Step ◽  
Pool Size ◽  
Individual Data ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Mean Values ◽  
Genetic Evaluations

Abstract Economically relevant traits (ERT) are routinely collected within commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, which would be costly; pooling DNA and phenotypic data provides a cost-effective solution. A simulated beef cattle population consisting of 15 generations was genotyped with approximately 50k markers (841 quantitative trait loci were located across the genome) and phenotyped for a moderately heritable trait. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step GBLUP model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when no gap between genotyped parents and pooled offspring occurred. The EBV accuracies resulting from pools were greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. Pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared to individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to EBV accuracies that were statistically different than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible, although differences exist depending on pool size and pool formation strategy. The USDA is an equal opportunity employer.

scholarly journals Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 752 ◽  
Author(s):  
Jungjae Lee ◽  
Yongmin Kim ◽  
Eunseok Cho ◽  
Kyuho Cho ◽  
Soojin Sa ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genome Wide Association Study ◽  
Genomic Analysis ◽  
Snp Markers ◽  
Snp Genotyping ◽  
Muscle Area ◽  
Bayesian Approaches ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Response Variables

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

scholarly journals Reliability of breeding values between random regression and 305-day lactation models

2016 ◽  
Vol 51 (11) ◽  
pp. 1848-1856
Author(s):  
Alessandro Haiduck Padilha ◽  
◽  
Jaime Araujo Cobuci ◽  
Darlene dos Santos Daltro ◽  
José Braccini Neto
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Protein Yield ◽  
Random Regression ◽  
Holstein Cattle ◽  
Average Increase ◽  
Data Set ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

scholarly journals AlphaPart—R implementation of the method for partitioning genetic trends

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Jana Obšteter ◽  
Justin Holl ◽  
John M. Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Genetic Gain ◽  
R Package ◽  
Breeding Programme ◽  
Genetic Trend ◽  
Breeding Values ◽  
Poultry Breeding ◽  
Breeding Programmes ◽  
Estimated Breeding Values ◽  
The Relationship ◽  
Genetic Trends

Abstract Background In this paper, we present the AlphaPart R package, an open-source implementation of a method for partitioning breeding values and genetic trends to identify the contribution of selection pathways to genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from estimated breeding values averaged by year of birth. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated stylized multi-tier breeding programme mimicking a pig or poultry breeding programme. Results The package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by pre-defined selection paths, and a set of functions for handling data and results. The package is freely available from the CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by partitioning the nucleus and multiplier genetic gain of the stylized breeding programme by tier-gender paths. For traits measured and selected in the multiplier, the multiplier selection generated additional genetic gain. By using AlphaPart, we show that the additional genetic gain depends on accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. We have proven that AlphaPart is a valuable tool for understanding the sources of genetic gain in the nucleus and especially the multiplier, and the relationship between the sources and parameters that affect them. Conclusions AlphaPart implements the method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to improve genetic gain through a better understanding of the key selection points that are driving gains in each trait.

scholarly journals Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding

2004 ◽  
Vol 83 (1) ◽  
pp. 55-64 ◽  
Author(s):  
S. AVENDAÑO ◽  
J. A. WOOLLIAMS ◽  
B. VILLANUEVA
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Selective Advantage ◽  
Breeding Values ◽  
Truncation Selection ◽  
Joint Management ◽  
Breeding Schemes ◽  
Estimated Breeding Values ◽  
Available Information ◽  
Mendelian Sampling

Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.

scholarly journals Performance of steer progeny of sires differing in genetic potential for fatness and meat yield following postweaning growth at different rates. 2. Carcass traits

2009 ◽  
Vol 49 (6) ◽  
pp. 525 ◽  
Author(s):  
W. A. McKiernan ◽  
J. F. Wilkins ◽  
J. Irwin ◽  
B. Orchard ◽  
S. A. Barwick
Keyword(s):  
Production Systems ◽  
Carcass Traits ◽  
Quality Traits ◽  
Muscle Area ◽  
Eye Muscle ◽  
Meat Yield ◽  
Breeding Values ◽  
Genetic Potential ◽  
Estimated Breeding Values ◽  
Selection Of

The steer progeny of sires genetically diverse for fatness and meat yield were grown at different rates from weaning to feedlot entry and effects on growth, carcass and meat-quality traits were examined. The present paper, the second of a series, reports the effects of genetic and growth treatments on carcass traits. A total of 43 sires, within three ‘carcass class’ categories, defined as high potential for meat yield, marbling or both traits, was used. Where available, estimated breeding values for the carcass traits of retail beef yield (RBY%) and intramuscular fat (IMF%) were used in selection of the sires, which were drawn from Angus, Charolais, Limousin, Black Wagyu and Red Wagyu breeds, to provide a range of carcass sire types across the three carcass classes. Steer progeny of Hereford dams were grown at either conventional (slow: ~0.5 kg/day) or accelerated (fast: ~0.7 kg/day) rates from weaning to feedlot entry weight, with group means of ~400 kg. Accelerated and conventionally grown groups from successive calvings were managed to enter the feedlot at similar mean feedlot entry weights at the same time for the 100-day finish under identical conditions. Faster-backgrounded groups had greater fat levels in the carcass than did slower-backgrounded groups. Dressing percentages and fat colour were unaffected by growth treatment, whereas differences in ossification score and meat colour were explained by age at slaughter. There were significant effects of sire type for virtually all carcass traits measured in the progeny. Differences in hot standard carcass weight showed a clear advantage to European types, with variable outcomes for the Angus and Wagyu progeny. Sire selection by estimated breeding values (within the Angus breed) for yield and/or fat traits resulted in expected differences in the progeny for those traits. There were large differences in both meat yield and fatness among the types of greatest divergence in genetic potential for those traits, with the Black Wagyu and the Angus IMF clearly superior for IMF%, and the European types for RBY%. The Angus IMF progeny performed as well as that of the Black Wagyu for all fatness traits. Differences in RBY% among types were generally reflected by similar differences in eye muscle area. Results here provide guidelines for selecting sire types to target carcass traits for specific markets. The absence of interactions between growth and genetic treatments ensures that consistent responses can be expected across varying management and production systems.

Effect of controlling future rate of inbreeding on expected genetic gain and genetic variability in small livestock populations

2020 ◽  
Vol 60 (14) ◽  
pp. 1681
Author(s):  
S. I. Mwangi ◽  
T. K. Muasya ◽  
E. D. Ilatsia ◽  
A. K. Kahi
Keyword(s):  
Genetic Variability ◽  
Milk Yield ◽  
Genetic Gain ◽  
Genetic Relationships ◽  
Breeding Population ◽  
Breeding Values ◽  
Sahiwal Cows ◽  
Improvement Programs ◽  
Small Livestock ◽  
Estimated Breeding Values

Context In the present study we assessed the use of average relationship as a means to control future rates of inbreeding in small cattle closed nucleus and its effect on genetic gain for milk yield as a means of managing genetic variability in livestock improvement programs. Aim The aim was to strike an ideal balance between genetic gain and loss of genetic variability for Sahiwal population. Methods A total of 8452 milk yield records of Sahiwal cows from National Sahiwal Stud, Kenya, were used to estimate breeding values and 19315 records used to estimate average relatedness of all individuals. The estimated breeding values and genetic relationships were then used to optimise individual genetic contributions between the best two males and the top 210 females in 2000–2008-year group, as well as between the best four, six and eight males and top, 420, 630 and 840 females based on estimated breeding values for lactation milk yield. Weights on genetic merit and average relationship considered in this study were (1, 0), (1, −300), (1, −500), (1, −1000) and (0, −1). Key results When the best sires were selected and used for mating disregarding average relationship with their mates i.e. (0, –1), genetic gain of up to 213 kg was realised accompanied by a rate of inbreeding per generation of 4%. Restricting average relationship alone i.e. (0, –1), resulted in a future rate of inbreeding of 1.6% and average merit of 154 when top two sires were used for breeding. At the same restriction level but using eight top sires, the rate of inbreeding per generation was 0.9% accompanied by an average merit of 128.2 kg. Controlling average relationship between mates resulted in increased genetic variability i.e. lower rate of inbreeding though average merit declined. Conclusion A rate of inbreeding per generation of <1% is required for a population to maintain its long-term viability. For this level to be attained, the size of the breeding population should be increased from the current two sires vs 210 dams to eight sires vs 840 dams. Implications Practical implications for closed nucleus programs such as the Sahiwal program in Kenya should include expanding the nucleus to comprise other institutional and privately-owned herds.

EVALUATION OF DAIRY CATTLE FOR FINAL CLASS USING ANIMAL MODELS

1988 ◽  
Vol 68 (3) ◽  
pp. 639-645 ◽  
Author(s):  
J. JAMROZIK ◽  
L. R. SCHAEFFER
Keyword(s):  
Animal Models ◽  
Dairy Cattle ◽  
Fixed Effects ◽  
Genetic Relationships ◽  
Residual Effects ◽  
Breeding Values ◽  
Stage Of Lactation ◽  
Key Words Animal ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Estimated breeding values for final class of 364 868 Canadian Holstein Friesian cows and 10 186 bulls from three different animal models were compared. FIRST lactation, first classifications were described by a model with fixed effects of herd-round-classifier, linear and quadratic effects of age at calving and stage of lactation at classification, and random effects of additive genetic effects of cows, and residual effects. All additive genetic relationships among animals were included. A second model used the LATEST classification on each cow rather than the first and these observations were pre-adjusted for age and stage. The third model used ALL classifications on each cow, and these were also pre-adjusted for age and stage effects. Correlations among estimated breeding values between methods ranged from 0.92 to 0.95. Estimated breeding values from LATEST were most highly correlated to sire proofs from the currently official sire model which also used the latest classification of each cow. Correlations of estimated breeding values between sires and their sons showed that results from LATEST were more accurate than results from the other two models. A model similar to that for LATEST is proposed for official genetic evaluations for conformation in the Canadian Holstein population. Key words: Animal model, conformation, dairy cattle

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