文献类型： 外文期刊

作者： Qin, Q. ¹ ; Zhang, C. Y. ¹ ; Liu, Z. C. ¹ ; Wang, Y. C. ¹ ; Kong, D. Q. ¹ ; Zhao, D. ¹ ; Zhang, J. W. ¹ ; Lan, M. X. ¹ ; Wang, Z. X. ¹ ; Alatan, S. H. ⁵ ; Batu, I. ⁵ ; Qi, X. D. ⁶ ; Zhao, R. Q. ⁶ ; Li, J. Q. ¹ ; Wang, B. Y. ² ; Liu, Z. H. ¹ ;

作者机构： 1.Inner Mongolia Agr Univ, Inner Mongolia Agr Univ Anim Sci Dept, Zhaowuda Rd,8 Teaching & Res Bldg, Hohhot 010018, Inner Mongolia, Peoples R China

2.Inner Mongolia Agr Univ, Inner Mongolia Agr Univ Coll Comp & Informat Engn, Zhaowuda Rd,8 Teaching & Res Bldg, Hohhot 010018, Inner Mongolia, Peoples R China

3.Key Lab Anim Genet Breeding & Reprod Inner Mongoli, Zhaowuda Rd,8 Teaching & Res Bldg, Hohhot 010018, Inner Mongolia, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab Mutton Sheep & Goat Genet & Breeding, Zhaowuda Rd,8 Teaching & Res Bldg, Hohhot 010018, Inner Mongolia, Peoples R China

5.East Ujumqin Sheep Original Breeding Farm, East Ujumqin Banner, Peoples R China

6.Inner Mongolia Huawen Technol & Informat Co Ltd, Alatan St, Hohhot 010018, Inner Mongolia, Peoples R China

7.Inst Grassland Res CAAS, 120 Ulanqab East St, Hohhot 010018, Inner Mongolia, Peoples R China

8.Xinjiang Acad Anim Sci, Key Lab Anim Biotechnol Xinjiang, Urumqi 830000, Peoples R China

关键词： Body sizes; Correlation; Heritability; Precision animal husbandry; Random regression model

期刊名称：ANIMAL （ 影响因子：4.0； 五年影响因子：3.9 ）

ISSN： 1751-7311

年卷期： 2024 年 18 卷 7 期

页码：

收录情况： SCI

摘要： In the realm of animal phenotyping, manual measurements are frequently utilised. While machinegenerated data show potential for enhancing high -throughput breeding, additional research and validation are imperative before incorporating them into genetic evaluation processes. This research presents a method for managing meat sheep and collecting data, utilising the Sheep Data Recorder system for data input and the Sheep Body Size Collector system for image capture. The study aimed to investigate the genetic parameter changes of growth traits in Ujumqin sheep by comparing machine -generated measurements with manual measurements. The dataset consisted of 552 data points from the offspring of 75 breeding rams and 399 breeding ewes. Six distinct random regression models were assessed to pinpoint the most suitable model for estimating genetic parameters linked to growth traits. These models were distinguished based on the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and covariance between maternal and direct genetic effects. Fixed factors such as individual age, individual sex, and ewe age were taken into account in the analysis. The genetic parameters for the yearling growth traits of Ujumqin sheep were calculated using ASReml software. The Akaike information criterion, the Bayesian information criterion, and fivefold cross -validation were employed to identify the optimal model. Research findings indicate that the most accurate models for manually measured data revealed heritability estimates of 0.12 +/- 0.15 for BW, 0.05 +/- 0.07 for body slanting length, 0.03 +/- 0.07 for withers height, 0.15 +/- 0.12 for hip height, 0.11 +/- 0.11 for chest depth, 0.13 +/- 0.13 for shoulder width, and 0.53 +/- 0.15 for chest circumference. The optimal models for machine -predicted data showed heritability estimates of 0.1 +/- 0.09 for body slanting length, 0.14 +/- 0.12 for withers height, 0.55 +/- 0.15 for hip height, 0.34 +/- 0.15 for chest depth, 0.26 +/- 0.15 for shoulder width, and 0.47 +/- 0.16 for chest circumference. In manually measured data, genetic correlations ranged from 0.35 to 0.99, while phenotypic correlations ranged from 0.07 to 0.90. In machine data, genetic correlations ranged from -0.05 to 0.99, while phenotypic correlations ranged from 0.03 to 0.84. The results suggest that machine -based estimations may lead to an overestimation of heritability, but this discrepancy does not impact the selection of breeding models. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).