logo
ResearchBunny Logo
Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Veterinary Science

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

X. Ma, O. F. Christensen, et al.

Discover groundbreaking insights from Xiang Ma and colleagues on how genomic information and correlated traits can elevate the accuracy of genetic evaluations for group-recorded traits in pigs. This innovative study reveals the significance of group records for challenging traits, showcasing the interplay between group sizes and individual relationships for optimal breeding value accuracy.

00:00
00:00
Playback language: English
Abstract
This study investigated the use of genomic information and a correlated trait with individual records to improve the accuracy of genetic evaluation for group-recorded traits. Using simulated pig data and three group structure scenarios, the results showed that both genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs). Smaller group sizes and closer relationships between individuals within groups led to higher accuracy. The study suggests that group records are valuable for traits difficult to measure individually, and that genomic information significantly improves accuracy, especially when combined with correlated individual traits.
Publisher
Heredity
Published On
Jul 14, 2020
Authors
Xiang Ma, Ole F. Christensen, Hongding Gao, Ruihua Huang, Bjarne Nielsen, Per Madsen, Just Jensen, Tage Ostersen, Pinghua Li, Mahmoud Shirali, Guosheng Su
Tags
genomic information
genetic evaluation
estimated breeding values
traits
pig data
group structure
correlated trait
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny