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.

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 Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1815
Author(s):  
Enrico Mancin ◽  
Beniamino Tuliozi ◽  
Cristina Sartori ◽  
Nadia Guzzo ◽  
Roberto Mantovani
Keyword(s):  
Average Daily Gain ◽  
Cattle Breed ◽  
Single Step ◽  
Genomic Information ◽  
Dispersion Parameters ◽  
Indigenous Cattle ◽  
North East ◽  
Estimated Breeding Values ◽  
The Impact

The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.

scholarly journals Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Pattarapol Sumreddee ◽  
El Hamidi Hay ◽  
Sajjad Toghiani ◽  
Andrew Roberts ◽  
Samuel E. Aggrey ◽  
...  
Keyword(s):  
Common Ancestor ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Recent Common Ancestor ◽  
Pedigree Information ◽  
Grid Search ◽  
Genomic Information ◽  
The Impact ◽  
Daily Gain ◽  
Harmful Effects

Abstract Background Although inbreeding caused by the mating of animals related through a recent common ancestor is expected to have more harmful effects on phenotypes than ancient inbreeding (old inbreeding), estimating these effects requires a clear definition of recent (new) and ancient (old) inbreeding. Several methods have been proposed to classify inbreeding using pedigree and genomic data. Unfortunately, these methods are largely based on heuristic criteria such as the number of generations from a common ancestor or length of runs of homozygosity (ROH) segments. To mitigate these deficiencies, this study aimed to develop a method to classify pedigree and genomic inbreeding into recent and ancient classes based on a grid search algorithm driven by the assumption that new inbreeding tends to have a more pronounced detrimental effect on traits. The proposed method was tested using a cattle population characterized by a deep pedigree. Results Effects of recent and ancient inbreeding were assessed on four growth traits (birth, weaning and yearling weights and average daily gain). Thresholds to classify inbreeding into recent and ancient classes were trait-specific and varied across traits and sources of information. Using pedigree information, inbreeding generated in the last 10 to 11 generations was considered as recent. When genomic information (ROH) was used, thresholds ranged between four to seven generations, indicating, in part, the ability of ROH segments to characterize the harmful effects of inbreeding in shorter periods of time. Nevertheless, using the proposed classification method, the discrimination between new and old inbreeding was less robust when ROH segments were used compared to pedigree. Using several model comparison criteria, the proposed approach was generally better than existing methods. Recent inbreeding appeared to be more harmful across the growth traits analyzed. However, both new and old inbreeding were found to be associated with decreased yearling weight and average daily gain. Conclusions The proposed method provided a more objective quantitative approach for the classification of inbreeding. The proposed method detected a clear divergence in the effects of old and recent inbreeding using pedigree data and it was superior to existing methods for all analyzed traits. Using ROH data, the discrimination between old and recent inbreeding was less clear and the proposed method was superior to existing approaches for two out of the four analyzed traits. Deleterious effects of recent inbreeding were detected sooner (fewer generations) using genomic information than pedigree. Difference in the results using genomic and pedigree information could be due to the dissimilarity in the number of generations to a common ancestor. Additionally, the uncertainty associated with the identification of ROH segments and associated inbreeding could have an effect on the results. Potential biases in the estimation of inbreeding effects may occur when new and old inbreeding are discriminated based on arbitrary thresholds. To minimize the impact of inbreeding, mating designs should take the different inbreeding origins into consideration.

scholarly journals The Impact of Selection using Residual Average Daily Gain and Marbling EPDs on Growth Performance and Carcass Traits in Angus Steers

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
T. R. Krause ◽  
E. R. Moore ◽  
J. Duggin ◽  
J. R. Segers ◽  
T. D. Pringle
Keyword(s):  
Growth Performance ◽  
Feed Efficiency ◽  
Average Daily Gain ◽  
Backfat Thickness ◽  
Feeding Trial ◽  
Body Weights ◽  
Selection For ◽  
Carcass Merit ◽  
The Impact ◽  
Daily Gain

ObjectivesProfitability in the beef industry has narrow margins regulated by revenue from output traits like growth and carcass merit, but profitability is also largely impacted by input expenses like feed costs. Selecting for improvements in feed efficiency during the finishing phase, one of the most feed intensive segments of the industry, can help to mitigate those input costs. This study compared growth performance, feed efficiency, body composition, and carcass characteristics in Angus steers (n = 321) from bulls divergently selected for feed efficiency and marbling.Materials and MethodsAngus sires were selected based on high (10th percentile or better) and low (85th percentile or worse) residual average daily gain (RADG) EPD as well as high (fifth percentile or better) and average (near 50th percentile) marbling (MARB) EPD. These criteria resulted in a 2 × 2 factorial design with four breeding lines: high RADG/high MARB, high RADG/average MARB, low RADG/high MARB, low RADG/average MARB. Data were analyzed using MIXED procedures of SAS with RADG and MARB as main effects. Significance was set at α = 0.05. Generation was also analyzed, where generation one (GEN1) steers were from a selected sire while generation two (GEN2) steers were from a selected sire and a selected dam.ResultsUltrasound and carcass data revealed no differences (P ≥ 0.12) in 12th rib backfat thickness from weaning through slaughter for the RADG EPD groups. Yield grade and dressing percent did not differ (P ≥ 0.56) across RADG or MARB groups. At the beginning and end of the feeding trial, the high RADG (P ≤ 0.02) group had larger ultrasound ribeye area (REA) than the low RADG group. Carcass REA tended (P = 0.08) to be larger in the high versus low RADG steers. During the feedlot trial and through slaughter, body weight was heavier (P ≤ 0.006) for the high versus low RADG steers but did not differ (P ≥ 0.44) across MARB EPD. Feed efficiency measures did not differ (P ≥ 0.32) across RADG or MARB groups apart from the tendency (P = 0.08) for residual feed intake to be lower in the high versus low RADG steers. Marbling scores differed (P ≤ 0.04) across RADG and MARB groups with the low RADG steers and the high MARB steers having improved marbling. The quality grade distribution across MARB EPD revealed that the average MARB steers graded 73% Choice and 25% Prime while the high MARB steers graded 56% and 42%, respectively. Slice shear force did not differ (P ≥ 0.32) across RADG or MARB EPD. Body weights tended (P = 0.06) to be heavier at the start of the feeding trial for GEN1 versus GEN2 steers. Total gain, average daily gain, and feed to gain (F:G) differed by generation (P ≤ 0.007) with increased rates of gain and reduced F:G in the GEN2 versus GEN1 steers. Body weights did not differ (P = 0.72) across GEN at the end of the feeding trial. Backfat thickness at the start and end of the feedlot phase was less (P ≤ 0.03) and marbling score was improved (P = 0.02) in the GEN2 versus GEN1 steers, respectively.ConclusionThese results suggest that selection using RADG EPD has negligible impacts on meat quality; and that progress in selection for efficiency can be achieved while advancing carcass quality and value. Furthermore, continued divergent selection for feed efficiency and marbling has the potential to improve feed efficiency through advancements in the rate of gain, while enhancing carcass merit through marbling.

scholarly journals 209 Genomic selection for multiple maternal and growth traits in large white pigs using Single-Step GBLUP

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 42-42
Author(s):  
Breno Fragomeni ◽  
Zulma Vitezica ◽  
Justine Liu ◽  
Yijian Huang ◽  
Kent Gray ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Growth Traits ◽  
Computing Time ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Single Step ◽  
Genomic Information ◽  
Genomic Evaluation ◽  
Large White ◽  
Daily Gain

Abstract The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was used to reduce computing time. Genetic correlation between proportion and the total number of preweaning deaths was 0.95. A strong, positive genetic correlation was also observed between weaning weight and average daily gain (r = 0.94). Conversely, the genetic correlations between mortality and growth traits were negative, with an average of -0.7. To avoid computations by expensive threshold models, preweaning mortality was transformed from a binary trait to two linear dam traits: proportion and a total number of piglets dead before weaning. Because of the high genetic correlations within groups of traits, inclusion of only one growth and one mortality trait in the model decreases computing time and allows for the inclusion of other traits. Reduction in computing time for the evaluation using APY was up to 20x, and no differences in EPD ranking were observed. The algorithm for proven and young improves the efficiency of genomic evaluation in swine without harming the quality of predictions. For this population, a binary trait of mortality can be replaced by a linear trait of the dam, resulting in a similar ranking for the selection candidates.

The genetic foundation of fitness and reproduction traits in Australian pig populations. 2. Relationships between weaning to conception interval, farrowing interval, stayability, and other common reproduction and production traits.

1996 ◽  
Vol 47 (8) ◽  
pp. 1275 ◽  
Author(s):  
E Tholen ◽  
KL Bunter ◽  
S Hermesch ◽  
HU Graser
Keyword(s):  
Genetic Parameters ◽  
Performance Test ◽  
Genetic Relationships ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Data Sets ◽  
Production Traits ◽  
Breeding Programs ◽  
Reproduction Traits ◽  
Daily Gain

Data sets from 2 large Australian piggeries were used to estimate genetic parameters for the traits weaning to conception interval (WCIi-l,i) and farrowing interval (FIi-l,i), number born alive (NBAI), average piglet birthweight (BWi), 21-day litter weight (W21i), and sow stayability (STAYli) recorded for each ith parity, as well as sow average daily gain (ADG) and backfat (BF) recorded at the end of performance test. Over parities and herds, heritabilities for each trait were in the ranges: WCI/FI, 0.0-0.10; NBA, 0.09-0.16; BW, 0.11-0.35; W21, 0.12-0.23; STAYli, 0.02-0.09; ADG, 0.35-0.37; BF, 0.36-0.45. Genetic correlations between NBAl and NBA from later parities were significantly different from 1. In addition, in 1 herd negative genetic correlations (rg = -0.04 to -0.25) were found between sow stayability traits and NBA1, but not NBA recorded in later parities. Stayability was Unfavourably correlated with ADG and BF, and favourably correlated with WCI12. However, WCI12 was unfavourably correlated genetically with BF (rg = -0.24) but uncorrelated with ADG. Antagonistic relationships also existed between NBA and BW, NBA and W21, and BW and STAY. In addition to the traditional traits currently included in pig-breeding programs (e.g. ADG, BF, and NBA), traits such as WCI, BW, and STAY should also be considered as selection criteria to minimise the detrimental effects of antagonistic genetic relationships between traits.

scholarly journals PSIV-20 Impact of OutPace® Feed Additive on nursery pig growth performance: A meta-analysis

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 175-176
Author(s):  
Benjamin Bass ◽  
Stacie Crowder ◽  
Terry Weeden ◽  
Murali Raghavendra Rao ◽  
Brenda de Rodas ◽  
...  
Keyword(s):  
Feed Intake ◽  
Feed Efficiency ◽  
Meta Analysis ◽  
Block Design ◽  
Phase 1 ◽  
Average Daily Gain ◽  
Feed Additive ◽  
Significant Difference ◽  
The Impact ◽  
Daily Gain

Abstract OutPace® Feed Additive (OP), contains a carefully researched blend of activated medium chain fatty acids (MCFAs; PMI, Arden Hills, MN), formulated to help mitigate the effects of stress in nursery pigs. Several studies using OP in both late and full nursery periods resulted in improved pig performance. A meta-analysis using 9 studies (4 studies during late nursery [15 to 26.8 kg BW] and 5 studies during the full nursery [5.9 to 25.4 kg]) was done to determine the impact of OP (included at 0.25% Phase 1 and 2; 0.125% Phase 3) on average daily gain, average daily feed intake, and feed conversion. The combined data was considered a randomized complete block design. Analysis of variance was completed with mixed models using the GLIMMIX procedure of SAS (SAS 9.4, SAS Institute Inc., Cary, NC) and least squares means were compared using Fisher’s least significant difference (P < 0.05). In the analysis of 5 studies conducted in late nursery (45 pens/treatment of 6 to 20 pigs/pen), pigs provided OP had higher average daily gain (0.67 vs 0.63 kg/d; P < 0.05), increased average daily feed intake (0.99 vs 0.97 kg/d; P < 0.05), and improved feed efficiency (0.67 vs 0.65 kg gain/kg feed intake; P < 0.05) compared to pigs fed control diets. Additionally, when pigs were provided OP throughout the nursery period (20 pens/treatment of 7 to 20 pigs/pen), average daily gain was increased 6.1% (0.48 vs 0.45 kg/d; P < 0.05), average daily feed intake tended to be increased 2.2% (0.62 vs 0.61 kg/d; P < 0.1), and feed efficiency was improved 2.7% (0.76 vs 0.74 kg gain/kg feed intake; P < 0.05) compared to pigs provided control diets. In conclusion, providing OP to pigs during the nursery period improved ADG and feed efficiency.

scholarly journals Effect of including canola meal in diets of slaughter ostriches (Struthio camelus var. domesticus)

2020 ◽  
Vol 50 (4) ◽  
pp. 537-551
Author(s):  
T.S. Brand ◽  
J. Van der Merwe ◽  
L.C. Hoffman
Keyword(s):  
Dry Matter ◽  
Average Daily Gain ◽  
Live Weight ◽  
Conversion Ratio ◽  
Feed Conversion Ratio ◽  
Dry Matter Intake ◽  
Canola Meal ◽  
Production Traits ◽  
Feed Conversion ◽  
Daily Gain

Canola meal (CM) is a locally produced protein source that may be less expensive than soybean meal (SBM). This study evaluated the effects of replacing 0%, 25%, 50%, 75%, and 100% SBM with CM in diets for slaughter ostriches. The CM was added at the expense of SBM and other concentrates, with minor changes in other ingredients. Birds (n = 15 per treatment) were reared from 77 to 337 days old on the trial diets, which were supplied ad libitum for starter, grower, and finisher phases. Bodyweights and feed intake were measured during these phases. No differences (P >0.05) were found between treatments for live weight at the end of each phase, dry matter intake (DMI), average daily gain (ADG) and feed conversion ratio (FCR) over all the growth phases. Although no differences were observed in live weight at the end of each phase, the birds reared on the diet with 50% CM were heaviest at slaughter, and birds reared with 100% CM were lightest (P <0.05). Differences (P <0.05) between diets were observed for the weight at slaughter, weights of the liver and thyroid glands and the pH of the cold carcass. However, no differences (P >0.05) were observed between diets for fat pad weight, dressing percentage, and weights of thighs and Muscularis gastrocnemius. The results indicate that CM could replace SBM in the diets of slaughter ostriches without affecting production traits and slaughter yields.Keywords: alternative protein, average daily gain, canola, dry matter intake, feed conversion ratio, growth, ostrich nutrition, production

scholarly journals Short communication: Concentrate supplementation strategies in ryegrass pasture for productive performance in lambs

2018 ◽  
Vol 16 (1) ◽  
pp. e06SC02 ◽  
Author(s):  
Sthefany K. Santos ◽  
Margarete K. Falbo ◽  
Itacir E. Sandini ◽  
Fabiano Pacentchuk ◽  
Mikael Neumann ◽  
...  
Keyword(s):  
Lolium Multiflorum ◽  
Average Daily Gain ◽  
Live Weight ◽  
Economic Losses ◽  
Control Group ◽  
Economic Return ◽  
Nutrient Quality ◽  
Concentrate Supplementation ◽  
The Impact ◽  
Daily Gain

This study evaluated the effect of two concentrate supplementation strategies on performance, metabolic profile and economic evaluation of suckling lambs and ewes in ryegrass (Lolium multiflorum) pasture. Twenty-seven ewes and 45 lambs were divided into three groups: (1) ryegrass pasture without supplementation - control (CON); (2) CON plus supplemented ewes and lambs at 1% of live weight (SEL), and (3) CON plus creep feeding supplemented lambs at 1% of live weight (CSL). Concentrate use increased (p<0.05) average daily gain (ADG) by 19.95% over CON (21.6 and 18.3% for SEL and CSL, respectively). Concentrate use contributed to minimizing forage quality fluctuation and provided greater ADG stability, mainly when ryegrass nutritional content and digestibility decreased. Blood metabolites profiles did not differ between groups, with exception of phosphorus which was higher for CON than SEL, and calcium which was higher for CSL than CON (p<0.05). Compared to CON, stoking rate values were greater to SEL (p<0.05). Compared to CSL, ewe and total stocking rate were greater (p<0.05) to SEL. Considering the control group as break even feed investment, SEL strategy had a positive economic return, while CSL showed economic losses. Concentrate use increased ADG of lambs and decrease the impact of nutrient quality changes of forage on daily gains, but must be considered that supplemental strategy used could affect negatively economic return.

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.

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