Defining Multi-Trait Breeding Objectives and Selection Indexes to Develop More Efficient Breeding Programs for Superfine Wool Sheep

This study aimed to develop an optimized breeding program for the Alpine Merino sheep superfine strain by defining breeding objectives and establishing a multi-trait selection index. Using phenotypic and genetic (co)variance components of wool and growth traits, we identified six key breeding object...

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Bibliographic Details
Main Authors: Tingting Guo, Wenhui Li, Chao Yuan, Xijun Wang, Jianbin Liu, Bin Liang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Animals
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Online Access:https://www.mdpi.com/2076-2615/15/13/1873
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Summary:This study aimed to develop an optimized breeding program for the Alpine Merino sheep superfine strain by defining breeding objectives and establishing a multi-trait selection index. Using phenotypic and genetic (co)variance components of wool and growth traits, we identified six key breeding objectives: average fiber diameter (AFD), clean fleece weight (CFW), staple strength (SS), staple length (YSL), yearling weight (YWT), and weaning weight (WWT), with economic weights of 48, 48.5, 2, 12, 2.85, and 25, respectively. Through eco-bio modeling comparing three index sets with different trait combinations and information sources, we determined the optimal index comprising the AFD, CFW, fiber diameter coefficient of variation (FDcv), YSL, YWT, and WWT using data from individual phenotypes and 10 half-sibs. Recommended ram and ewe utilization periods were 3 and 5 years, respectively. Projected 10-year genetic gains showed improvements of −2 μm (AFD), 0.57 kg (CFW), 20 N/ktex (SS), 0.6 cm (YSL), 4.73 kg (YWT), and 0.38 kg (WWT). The developed program enhances both superfine wool characteristics and overall wool quality while improving the selection accuracy. Additionally, we propose alternative indices for varying measurement contexts and discuss strategies to accelerate genetic gains, including optimizing ewe reproduction rates and ram selection precision, and incorporating progeny information.
ISSN:2076-2615