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本文引用的文献

1
Genetic parameter estimation in seedstock Swine population for growth performances.种猪群体生长性能的遗传参数估计
Asian-Australas J Anim Sci. 2013 Apr;26(4):470-5. doi: 10.5713/ajas.2012.12454.
2
Longitudinal changes in ultrasonic measurements of body composition during growth in Suffolk ram lambs and evaluation of alternative adjustment strategies for ultrasonic scan data.在萨福克公羊羔羊生长过程中对身体成分的超声测量的纵向变化,以及对超声扫描数据的替代调整策略的评估。
J Anim Sci. 2010 Apr;88(4):1341-8. doi: 10.2527/jas.2009-2378. Epub 2009 Dec 18.
3
Effectiveness of adjusting for heterogeneity of variance in genetic evaluation of Japanese Black cattle.日本黑牛遗传评估中调整方差异质性的有效性。
J Anim Sci. 2007 Oct;85(10):2429-36. doi: 10.2527/jas.2007-0063. Epub 2007 Jun 12.
4
Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs.杜洛克猪脂肪组织不同部位脂肪酸组成、产肉性能和肉品质性状之间的遗传相关性。
J Anim Sci. 2006 Aug;84(8):2026-34. doi: 10.2527/jas.2005-660.
5
Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows.大白母猪产仔死亡率、产仔数及生产性能遗传参数的阈值线性估计
J Anim Sci. 2005 Mar;83(3):499-506. doi: 10.2527/2005.833499x.
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Multivariate analysis of litter size for multiple parities with production traits in pigs: I. Bayesian variance component estimation.猪多胎产仔数与生产性状的多变量分析:I. 贝叶斯方差成分估计
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7
Genetic parameters and trends for lean growth rate and its comvonents in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs.美国约克夏猪、杜洛克猪、汉普夏猪和长白猪瘦肉生长率及其组成部分的遗传参数和趋势
J Anim Sci. 2002 Aug;80(8):2062-70. doi: 10.2527/2002.8082062x.
8
Genetic parameters for growth rate and backfat in Canadian Yorkshire, Landrace, Duroc, and Hampshire pigs.加拿大约克夏猪、长白猪、杜洛克猪和汉普夏猪生长速度和背膘厚度的遗传参数
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10
Scrotal circumference in yearling Hereford bulls: adjustment factors, heritabilities and genetic, environmental and phenotypic relationships with growth traits.一岁赫里福德公牛的阴囊周长:校正因子、遗传力以及与生长性状的遗传、环境和表型关系
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韩国三个种猪品种群体中预调整体重增长的遗传参数及身体组织发育的超声测量

Genetic Parameters of Pre-adjusted Body Weight Growth and Ultrasound Measures of Body Tissue Development in Three Seedstock Pig Breed Populations in Korea.

作者信息

Choy Yun Ho, Mahboob Alam, Cho Chung Il, Choi Jae Gwan, Choi Im Soo, Choi Tae Jeong, Cho Kwang Hyun, Park Byoung Ho

机构信息

Korea Animal Improvement Association, Seoul 137-811, Korea .

出版信息

Asian-Australas J Anim Sci. 2015 Dec;28(12):1696-702. doi: 10.5713/ajas.14.0971.

DOI:10.5713/ajas.14.0971
PMID:26580436
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4647077/
Abstract

The objective of this study was to compare the effects of body weight growth adjustment methods on genetic parameters of body growth and tissue among three pig breeds. Data collected on 101,820 Landrace, 281,411 Yorkshire, and 78,068 Duroc pigs, born in Korean swine breeder farms since 2000, were analyzed. Records included body weights on test day and amplitude (A)-mode ultrasound carcass measures of backfat thickness (BF), eye muscle area (EMA), and retail cut percentage (RCP). Days to 90 kg body weight (DAYS90), through an adjustment of the age based on the body weight at the test day, were obtained. Ultrasound measures were also pre-adjusted (ABF, EMA, AEMA, ARCP) based on their test day measures. The (co)variance components were obtained with 3 multi-trait animal models using the REMLF90 software package. Model I included DAYS90 and ultrasound traits, whereas model II and III accounted DAYS90 and pre-adjusted ultrasound traits. Fixed factors were sex (sex) and contemporary groups (herd-year-month of birth) for all traits among the models. Additionally, model I and II considered a linear covariate of final weight on the ultrasound measure traits. Heritability (h(2)) estimates for DAYS90, BF, EMA, and RCP ranged from 0.36 to 0.42, 0.34 to 0.43, 0.20 to 0.22, and 0.39 to 0.45, respectively, among the models. The h(2) estimates of DAYS90 from model II and III were also somewhat similar. The h(2) for ABF, AEMA, and ARCP were 0.35 to 0.44, 0.20 to 0.25, and 0.41 to 0.46, respectively. Our heritability estimates varied mostly among the breeds. The genetic correlations (rG) were moderately negative between DAYS90 and BF (-0.29 to -0.38), and between DAYS90 and EMA (-0.16 to -0.26). BF had strong rG with RCP (-0.87 to -0.93). Moderately positive rG existed between DAYS90 and RCP (0.20 to 0.28) and between EMA and RCP (0.35 to 0.44) among the breeds. For DAYS90, model II and III, its correlations with ABF, AEMA, and ARCP were mostly low or negligible except the rG between DAYS90 and AEMA from model III (0.27 to 0.30). The rG between AEMA and ABF and between AEMA and ARCP were moderate but with negative and positive signs, respectively; also reflected influence of pre-adjustments. However, the rG between BF and RCP remained non-influential to trait pre-adjustments or covariable fits. Therefore, we conclude that ultrasound measures taken at a body weight of about 90 kg as the test final should be adjusted for body weight growth. Our adjustment formulas, particularly those for BF and EMA, should be revised further to accommodate the added variation due to different performance testing endpoints with regard to differential growth in body composition.

摘要

本研究的目的是比较体重增长调整方法对三个猪品种的身体生长和组织遗传参数的影响。分析了自2000年以来在韩国养猪场出生的101,820头长白猪、281,411头约克夏猪和78,068头杜洛克猪的数据。记录包括测试日的体重以及背膘厚度(BF)、眼肌面积(EMA)和零售切块百分比(RCP)的A模式超声胴体测量值。通过根据测试日的体重调整年龄,得出达到90千克体重的天数(DAYS90)。超声测量值也根据其测试日测量值进行了预调整(ABF、EMA、AEMA、ARCP)。使用REMLF90软件包通过3种多性状动物模型获得(协)方差分量。模型I包括DAYS90和超声性状,而模型II和III考虑了DAYS90和预调整的超声性状。模型中所有性状的固定因素为性别(sex)和同期组(出生的猪场-年份-月份)。此外,模型I和II考虑了最终体重对超声测量性状的线性协变量。模型中DAYS90、BF、EMA和RCP的遗传力(h²)估计值分别为0.36至0.42、0.34至0.43、0.20至0.22和0.39至0.45。模型II和III中DAYS90的h²估计值也有些相似。ABF、AEMA和ARCP的h²分别为0.35至0.44、0.20至0.25和0.41至0.46。我们的遗传力估计值在品种间大多有所不同。DAYS90与BF之间(-0.29至-0.38)以及DAYS90与EMA之间(-0.16至-0.26)的遗传相关性(rG)为中度负相关。BF与RCP之间具有较强的rG(-0.87至-0.93)。品种间DAYS90与RCP之间(0.20至0.28)以及EMA与RCP之间(0.35至0.44)存在中度正相关。对于DAYS90,模型II和III中,其与ABF、AEMA和ARCP的相关性大多较低或可忽略不计,除了模型III中DAYS90与AEMA之间的rG(0.27至0.30)。AEMA与ABF之间以及AEMA与ARCP之间的rG为中度,但分别为负号和正号;这也反映了预调整的影响。然而,BF与RCP之间的rG对性状预调整或协变量拟合仍无影响。因此,我们得出结论,应以约90千克体重时的超声测量值作为测试终值,并对体重增长进行调整。我们的调整公式,特别是BF和EMA的公式,应进一步修订,以适应由于身体组成差异生长导致的不同性能测试终点所增加的数据变化。