Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population1

2016 ◽  
Vol 94 (9) ◽  
pp. 3613-3623 ◽  
Author(s):  
R. M. O. Silva ◽  
B. O. Fragomeni ◽  
D. A. L. Lourenco ◽  
A. F. B. Magalhães ◽  
N. Irano ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Feed Efficiency ◽  
Validation Methods ◽  
Nelore Cattle

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 Identification of genomic regions associated with feed efficiency in Nelore cattle

BMC Genetics ◽  
2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Priscila SN de Oliveira ◽  
Aline SM Cesar ◽  
Michele L do Nascimento ◽  
Amália S Chaves ◽  
Polyana C Tizioto ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Genomic Regions ◽  
Nelore Cattle

scholarly journals PSXII-22 Genomic prediction accuracy for feed efficiency related traits using different pseudo-phenotypes, prediction and validation methods in Nellore cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 245-246
Author(s):  
Cláudio U Magnabosco ◽  
Fernando Lopes ◽  
Valentina Magnabosco ◽  
Raysildo Lobo ◽  
Leticia Pereira ◽  
...  
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Body Weight Gain ◽  
Prediction Methods ◽  
Genomic Breeding ◽  
Validation Population ◽  
Nellore Cattle

Abstract The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

SNP annotation-based whole genomic prediction and selection: An application to feed efficiency and its component traits in pigs1

2015 ◽  
Vol 93 (5) ◽  
pp. 2056-2063 ◽  
Author(s):  
Duy Ngoc Do ◽  
Luc L. G. Janss ◽  
Just Jensen ◽  
Haja N. Kadarmideen
Keyword(s):  
Genomic Prediction ◽  
Feed Efficiency ◽  
Component Traits

scholarly journals Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173620 ◽  
Author(s):  
Tianfei Liu ◽  
Chenglong Luo ◽  
Jie Wang ◽  
Jie Ma ◽  
Dingming Shu ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Meat Type

scholarly journals The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Amir Aliakbari ◽  
Emilie Delpuech ◽  
Yann Labrune ◽  
Juliette Riquet ◽  
Hélène Gilbert
Keyword(s):  
Feed Intake ◽  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Target Line ◽  
The Other ◽  
Training Set ◽  
The Impact ◽  
Genomic Predictions ◽  
Training Sets

Abstract Background Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. Results Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. Conclusions Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

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