Validation of selection accuracy for the total number of piglets born in Landrace pigs using genomic selection
This study was to determine the relationship between estimated breeding value and phenotype information after farrowing when juvenile selection was made in candidate pigs without phenotype information. After collecting phenotypic and genomic information for the total number of piglets born by Landra...
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Published in | Animal bioscience Vol. 30; no. 2; pp. 149 - 153 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Korea (South)
Asian - Australasian Association of Animal Production Societies
01.02.2017
Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST) Asian-Australasian Association of Animal Production Societies 아세아·태평양축산학회 |
Subjects | |
Online Access | Get full text |
ISSN | 1011-2367 2765-0189 1976-5517 2765-0235 |
DOI | 10.5713/ajas.16.0394 |
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Summary: | This study was to determine the relationship between estimated breeding value and phenotype information after farrowing when juvenile selection was made in candidate pigs without phenotype information.
After collecting phenotypic and genomic information for the total number of piglets born by Landrace pigs, selection accuracy between genomic breeding value estimates using genomic information and breeding value estimates of best linear unbiased prediction (BLUP) using conventional pedigree information were compared.
Genetic standard deviation (
) for the total number of piglets born was 0.91. Since the total number of piglets born for candidate pigs was unknown, the accuracy of the breeding value estimated from pedigree information was 0.080. When genomic information was used, the accuracy of the breeding value was 0.216. Assuming that the replacement rate of sows per year is 100% and generation interval is 1 year, genetic gain per year is 0.346 head when genomic information is used. It is 0.128 when BLUP is used.
Genetic gain estimated from single step best linear unbiased prediction (ssBLUP) method is by 2.7 times higher than that the one estimated from BLUP method, i.e., 270% more improvement in efficiency. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work. G704-001112.2017.30.2.015 |
ISSN: | 1011-2367 2765-0189 1976-5517 2765-0235 |
DOI: | 10.5713/ajas.16.0394 |