scholarly journals Prediction Accuracy of Pedigree and Genomic Estimated Breeding Values over Generations in Layer Chickens

Author(s):  
Ziqing Weng ◽  
Anna Wolc ◽  
Rohan L Fernando ◽  
Jack C. M. Dekkers ◽  
Dorian J. Garrick
2011 ◽  
Vol 43 (1) ◽  
Author(s):  
Anna Wolc ◽  
Jesus Arango ◽  
Petek Settar ◽  
Janet E Fulton ◽  
Neil P O'Sullivan ◽  
...  

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

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.


2017 ◽  
Vol 10 (1) ◽  
Author(s):  
B.S. Vivek ◽  
Girish Kumar Krishna ◽  
V. Vengadessan ◽  
R. Babu ◽  
P.H. Zaidi ◽  
...  

2013 ◽  
Vol 3 (5) ◽  
pp. 909-916 ◽  
Author(s):  
Jaime Zapata-Valenzuela ◽  
Ross W. Whetten ◽  
David Neale ◽  
Steve McKeand ◽  
Fikret Isik

2016 ◽  
Vol 94 (3) ◽  
pp. 902-908 ◽  
Author(s):  
B. J. Hayes ◽  
K. A. Donoghue ◽  
C. M. Reich ◽  
B. A. Mason ◽  
T. Bird-Gardiner ◽  
...  

animal ◽  
2012 ◽  
Vol 6 (5) ◽  
pp. 789-796 ◽  
Author(s):  
J.R. Thomasen ◽  
B. Guldbrandtsen ◽  
G. Su ◽  
R.F. Brøndum ◽  
M.S. Lund

Sign in / Sign up

Export Citation Format

Share Document