scholarly journals Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations

2021 ◽  
Vol 12 ◽  
Author(s):  
Siavash Salek Ardestani ◽  
Mohsen Jafarikia ◽  
Mehdi Sargolzaei ◽  
Brian Sullivan ◽  
Younes Miar
Keyword(s):  
Genomic Prediction ◽  
Performance Test ◽  
Average Daily Gain ◽  
Accurate Method ◽  
Single Step ◽  
Prediction Methods ◽  
Genetic Progress ◽  
Breeding Values ◽  
Fat Thickness ◽  
Daily Gain

Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc (n = 6,649), Landrace (n = 5,362), and Yorkshire (n = 5,008) breeds, respectively. The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.

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

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

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

scholarly journals Estimation of genetic parameters and accuracy of genomic prediction for production traits in Duroc pigs

2019 ◽  
Vol 64 (No. 4) ◽  
pp. 160-165 ◽  
Author(s):  
Bryan Irvine Lopez ◽  
Vanessa Viterbo ◽  
Choul Won Song ◽  
Kang Seok Seo
Keyword(s):  
Genomic Prediction ◽  
Genetic Parameters ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Single Step ◽  
Production Traits ◽  
Muscle Area ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Heritability Estimates

Abstract: Genetic parameters and accuracy of genomic prediction for production traits in a Duroc population were estimated. Data were on 24 828 purebred Duroc pigs born in 2000–2016. After quality control procedures, 30 263 single nucleotide polymorphism markers and 560 animals remained that were used to predict the genomic breeding values of individuals. Accuracies of predicted breeding values for average daily gain (ADG), backfat thickness (BF), loin muscle area (LMA), lean percentage (LP) and age at 90 kg (D90) between pedigree-based and single-step methods were compared. Analyses were carried out with a multivariate animal model to estimate genetic parameters for production traits while univariate analyses were performed to predict the genomic breeding values of individuals. Heritability estimates from pedigree analysis were moderate to high. Heritability estimates and standard error for ADG, BF, LMA, LP and D90 were 0.35 ± 0.01, 0.35 ± 0.11, 0.24 ± 0.04, 0.42 ± 0.11 and 0.37 ± 0.03, respectively. Genetic correlations of ADG with BF and LP were low and negative. Genetic correlations of LMA with ADG, BF, LP and D90 were –0.37, –0.27, 0.48 and 0.31, respectively. High correlations were observed between ADG and D90 (–0.98), and also between BF and LP (–0.93). Accuracies of genomic breeding values for ADG, BF, LMA, LP and D90 were 0.30, 0.33, 0.38, 0.40 and 0.28, respectively. Corresponding accuracies using pedigree-based method were 0.29, 0.32, 0.38, 0.39 and 0.27, respectively. The results showed that the single-step method did not show significant advantage compared to the pedigree-based method.

49 Predicting Breeding Values of Purebred Pigs for Crossbred Performance Using Crossbred Phenotypes and Genotypes

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 23-23
Author(s):  
Natalia Leite ◽  
Ching-Yi Chen ◽  
Willian O Herring ◽  
Shogo Tsuruta ◽  
Daniela Lourenco
Keyword(s):  
Quality Control ◽  
Animal Model ◽  
Average Daily Gain ◽  
Snp Markers ◽  
Single Step ◽  
Low Density ◽  
Genomic Information ◽  
Breeding Values ◽  
Partial Data ◽  
Daily Gain

Abstract Phenotyping a large number of crossbred progeny for the evaluation of purebred animals can be expensive. As genotyping with low-density panels is becoming cheaper, we aimed to evaluate the tradeoff between having different percentages of genotypes and phenotypes for crossbred progeny of candidate boars. We used the linear regression (LR) method to investigate changes in accuracy, bias, and inflation of breeding values for crossbred traits in purebred boars. A total of 304,582 purebred and 147,474 crossbred animals were phenotyped for average daily gain (ADG) and backfat thickness (BF), out of which 46,691 purebred and 13,117 crossbred animals were genotyped. Genomic information consisted of imputed genotypes for 40,247 SNP markers after quality control. A four-trait animal model under single-step GBLUP was used that included phenotypes recorded in purebred and crossbred animals as correlated traits. The LR statistics were calculated based on breeding values of young purebred sires from complete and partial data. The first complete data included genotypes for purebreds and phenotypes for purebreds and crossbreds, whereas the second included also genotypes for crossbreds. The partial data included phenotypes on 50% or none of the progeny of validation sires, with or without genotypes for crossbred animals. When 50% of the progeny has phenotypes, adding genotypes for crossbred progeny marginally increased accuracy of ADG (0.77 vs 0.78) for 47 boars with more than 150 progeny with phenotypes. No increase was observed for BF. A small increase in bias and inflation by adding crossbred genotypes was observed for ADG but not for BF. When no phenotypes were available for crossbred progeny, accuracy for both traits was lower but improved with crossbred genotypes for ADG (0.61 vs 0.64) for boars with more than 150 progeny. The tradeoff between phenotypes and genotypes should be further investigated in larger datasets with more validation boars.

scholarly journals PSIV-B-28 Late-Breaking: Performance and carcass quality of Nelore bull’s progenies with contrasting traits for precocity, growth and muscularity

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 319-321
Author(s):  
Taiane S Martins ◽  
Juliana Silva ◽  
Lenise Mueller ◽  
Tamyres Amorim ◽  
Annelise Aila G Lobo ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Average Daily Gain ◽  
Backfat Thickness ◽  
Longissimus Muscle ◽  
Muscle Area ◽  
Final Body Weight ◽  
Fat Thickness ◽  
Nelore Cattle ◽  
Daily Gain

Abstract The goal of this study was to evaluate the performance and the carcass traits of Nelore cattle progenies from bulls selected by contrasting traits for precocity, growth and muscularity, through the Expected Progeny Difference (EPD). One hundred and five Nelore bulls (initial weight of 350kg±15kg) and 20 months of age were confined and fed with same diet (73% of concentrate). Thirty-two animals were selected to create the contrasting groups for precocity, growth and muscularity (16 animals assigned as a low EPD group - LEPD and 16 animals assigned as a high EPD group - HEPD), based on the EPD of their parents. The ribeye area and backfat thickness were performed by ultrasonography of 12–13th rib fat thickness and longissimus muscle area (LMA), as well as rump fat thickness (RF) measurements. Animals were harvested after 100 days and during the deboning, meat cuts were weight for cutting yield. The animals selected for the HEPD group had greater average daily gain (P = 0.006), which can be explained by the higher feed intake (P = 0.006). However, there are no difference between groups for the final body weight (P = 0.254) and feed efficiency (P = 0.715). The LEPD group presented higher dressing percentage (P = 0.028). Although the groups evaluated did not presented difference in LMA (P = 0.329) and weight of longissimus muscle (P = 0.480), the weight of rump displayed heaviest in the HEPD (P = 0.037). There was no difference between groups for RF (P = 0.086). Nevertheless, backfat thickness was higher in HEPD group (P = 0.006). The present study indicates that Nelore cattle progenies, with parents displaying higher potential for precocity, growth, and muscularity, show greater backfat thickness and weightiest of rump than the other genetic backgrounds. Thanks to FAPESP for the scholarship (Grant # 2017/02349–1).

scholarly journals Genomic prediction using models with dominance and imprinting effects for backfat thickness and average daily gain in Danish Duroc pigs

2016 ◽  
Vol 48 (1) ◽  
Author(s):  
Xiangyu Guo ◽  
Ole Fredslund Christensen ◽  
Tage Ostersen ◽  
Yachun Wang ◽  
Mogens Sandø Lund ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Average Daily Gain ◽  
Backfat Thickness ◽  
Daily Gain

283 Precision versus Group Supplementation at Two Levels on Growth and Development of Replacement Heifers

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 144-145
Author(s):  
Susan K Duckett ◽  
Jacob Cathcart ◽  
Austin Cathcart ◽  
Zach Dantzler ◽  
Hunter Dove ◽  
...  
Keyword(s):  
Average Daily Gain ◽  
Versus Group ◽  
Use Of Technology ◽  
Grazing System ◽  
Group Feeding ◽  
Fat Thickness ◽  
Angus Heifers ◽  
Daily Gain ◽  
Supplement Intake ◽  
Feeding Systems

Abstract Two experiments were conducted to evaluate use of the Super SmartFeeder (SSF; C-Lock, Inc.) for individual versus group supplementation of heifers grazing stockpiled, novel tall fescue. In experiment 1, Angus heifers (n = 64; 267 + 31.7 kg) had access to the SSF and were allowed 3.6 kg/d of grower supplement. Individual intake was recorded daily and analyzed to determine adoption. Twenty-four percent of the heifers did not adopt to individual SSF supplementation (P < 0.05). In experiment 2, heifers (n = 64; BW= 275 + 31.3 kg) were allotted, based on adoption to SSF, to supplementation system of group feeding (n = 2 reps/level; GRP) or SSF precision feeding (n =16/level; PRE) at two levels (0.5% or 1% of BW) for 127-d in a 2 x 2 factorial. Data were analyzed using Mixed procedure. The interaction between supplementation level and feeding system tended to be significant (P = 0.10) for overall ADG. Average daily gain was greater for 1% BW than 0.5% BW (0.81 vs. 0.47 kg/d) for PRE but did not differ for GRP (0.69 kg/d). Daily supplement dry matter intake differed (P < 0.05) by supplementation level and total BW gain was greater (P < 0.05) by 24.3 kg for 1% versus 0.5% supplementation level. Ultrasound ribeye area and fat thickness measures were greater (P < 0.05) for 1% BW supplement compared to 0.5% BW at the end of the 127d study. When PRE was analyzed independently, heifer BW differed (P < 0.05) on d 91, 117 and 127 between supplement levels. The correlation between individual heifer daily supplement intake and overall ADG for PRE was 0.68 (P < 0.0001). The use of technology to precisely control intake in a grazing system created greater divergence in growth by supplementation level compared to group feeding systems.

scholarly journals PSII-24 Performance, carcass and meat quality of castrated and intact Dorper lambs slaughtered at different body condition scores

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 170-171
Author(s):  
Edson Luis de A Ribeiro ◽  
Francisco Fernandes Jr ◽  
Camila Constantino ◽  
Fernando Augusto Grandis ◽  
Natália Albieri Koritiaki ◽  
...  
Keyword(s):  
Body Weight ◽  
Body Condition ◽  
Body Condition Score ◽  
Average Daily Gain ◽  
Final Body Weight ◽  
Condition Score ◽  
Fat Thickness ◽  
Daily Gain ◽  
Body Condition Scores

Abstract This study was conducted with the objective of evaluating the performance, carcass and meat characteristics of castrated and intact Dorper male lambs slaughtered at three different body condition scores. Thirty-five lambs, approximately 3 months old, 21.6 ± 4.0 kg of body weight and 3.1 ± 0.5 of body condition score (BCS), were used. The BCS scale used varied from 1.0 (very thin) to 5.0 (very fat). Four weeks prior to the experiment, 17 lambs were castrated. Lambs were further divided into three groups, according to the criteria for slaughter: BCS of 3.5, 4.0 and 4.5. All animals received the same diet. BCS were achieved after 27, 84 and 130 days in feedlot, respectively. There were no interactions (P > 0.05) between sexual conditions and slaughter criteria. Average daily gain of weight (ADG) was different (P < 0.05) for the three BCS (average of 0.263). However, final body weight (28.6 ± 1.7; 42.7 ± 1.8 and 56.8 ± 1.7 kg) and back fat thickness (0.5 ± 0.5; 4.2 ± 0.5 and 7.3 ± 0.5 mm) were different (P < 0.05) among BCS criteria, panelists found that meats from lambs slaughtered with greater fat content were less (P < 0.05) desirable. We can conclude by the performance and meat results that it is better to slaughter Dorper lambs with BCS with no more than 4.0, or 4.2 mm of back fat cover, and for the best acceptability of the meat the lambs should be castrated.

scholarly journals Productive performance of Nellore steers on rotational grazing on Marandu grass and Convert HD 364 grass

2018 ◽  
Vol 39 (4) ◽  
pp. 1627
Author(s):  
André Felipe Borges Krinchev ◽  
Valter Harry Bumbieris Junior ◽  
José Renato Silva Gonçalves ◽  
Laísse Garcia Lima ◽  
Ana Maria Bridi ◽  
...  
Keyword(s):  
Body Weight ◽  
Leaf Blade ◽  
Dry Matter ◽  
Average Daily Gain ◽  
Subcutaneous Fat ◽  
Rotational Grazing ◽  
Grazing System ◽  
Subcutaneous Fat Thickness ◽  
Fat Thickness ◽  
Daily Gain

The objective of this study was to evaluate the effect of Brachiaria spp. Cv. Mulato II (Convert) on performance, meat quality and carcass characteristics of castrated Nellore steers in the growing and finishing phases, in rotational grazing system, compared to Brachiaria brizantha cv. Marandu, under the same conditions. The experimental area was divided into two treatments: Marandu and Convert with 20 hectares each, splited in four replications per treatment, composed of five paddocks of one hectare each. Animals were managed based on the availability of leaf blade dry matter (6.19% body weight) in rotational grazing with 7 days of occupation and 28 days of rest. Ten animals were used in each replicate (testers), as well as regulatory animals whenever necessary. Animals were managed based on the availability of leaf blade dry matter (6.19% body weight) in rotational grazing with 7 days of occupation and 28 days of rest. Ten animals were used in each replicate (testers), as well as regulatory animals whenever necessary. For performance analysis, 10 animals of each replicate were used, while for carcass and meat analyses, only six of each replicate. Production and chemial characteristics of the two grasses were evaluated. The experimental design was completely randomized with two treatments and four replicates; data were tested by analysis of variance using the R software. Animals fed on Convert grass were superior than those fed on Marandu grass, with higher average daily gain (0.682 kg vs. 0.605 kg), slaughter weight (470.45 kg vs. 451.43 kg), hot carcass weight (239.93 kg vs. 232.36 kg). The study also showed the possibility of finishing castrated young steers (up to 30 months) and with subcutaneous fat thickness required by industry (3 mm) in both pastures.

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.

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