Jiao S, Maltecca C, Gray K A, Cassady J P
Department of Animal Science, North Carolina State University, Raleigh 27695.
Department of Animal Science, North Carolina State University, Raleigh 27695
J Anim Sci. 2014 Jun;92(6):2377-86. doi: 10.2527/jas.2013-7338. Epub 2014 Mar 26.
The efficiency of producing salable products in the pork industry is largely determined by costs associated with feed and by the amount and quality of lean meat produced. The objectives of this paper were 1) to explore heritability and genetic correlations for growth, feed efficiency, and real-time ultrasound traits using both pedigree and marker information and 2) to assess accuracy of genomic prediction for those traits using Bayes A prediction models in a Duroc terminal sire population. Body weight at birth (BW at birth) and weaning (BW at weaning) and real-time ultrasound traits, including back fat thickness (BF), muscle depth (MD), and intramuscular fat content (IMF), were collected on the basis of farm protocol. Individual feed intake and serial BW records of 1,563 boars obtained from feed intake recording equipment (FIRE; Osborne Industries Inc., Osborne, KS) were edited to obtain growth, feed intake, and feed efficiency traits, including ADG, ADFI, feed conversion ratio (FCR), and residual feed intake (RFI). Correspondingly, 1,047 boars were genotyped using the Illumina PorcineSNP60 BeadChip. The remaining 516 boars, as an independent sample, were genotyped with a low-density GGP-Porcine BeadChip and imputed to 60K. Magnitudes of heritability from pedigree analysis were moderate for growth, feed intake, and ultrasound traits (ranging from 0.44 ± 0.11 for ADG to 0.58 ± 0.09 for BF); heritability estimates were 0.32 ± 0.09 for FCR but only 0.10 ± 0.05 for RFI. Comparatively, heritability estimates using marker information by Bayes A models were about half of those from pedigree analysis, suggesting "missing heritability." Moderate positive genetic correlations between growth and feed intake (0.32 ± 0.05) and back fat (0.22 ± 0.04), as well as negative genetic correlations between growth and feed efficiency traits (-0.21 ± 0.08, -0.05 ± 0.07), indicate selection solely on growth traits may lead to an undesirable increase in feed intake, back fat, and reduced feed efficiency. Genetic correlations among growth, feed intake, and FCR assessed by a multiple-trait Bayes A model resulted in increased genetic correlation between ADG and ADFI, a negative correlation between ADFI and FCR, and a positive correlation between ADG and FCR. Accuracies of genomic prediction for the traits investigated, ranging from 9.4% for RFI to 36.5% for BF, were reported that might provide new insight into pig breeding and future selection programs using genomic information.
猪肉行业中生产可销售产品的效率很大程度上取决于与饲料相关的成本以及瘦肉的产量和质量。本文的目的是:1)利用系谱和标记信息探索生长、饲料效率和实时超声性状的遗传力及遗传相关性;2)在杜洛克终端父本群体中使用贝叶斯A预测模型评估这些性状的基因组预测准确性。出生体重(出生时体重)和断奶体重(断奶时体重)以及实时超声性状,包括背膘厚度(BF)、肌肉深度(MD)和肌内脂肪含量(IMF),是根据农场规程收集的。从饲料摄入量记录设备(FIRE;奥斯本工业公司,堪萨斯州奥斯本)获得的1563头公猪的个体饲料摄入量和连续体重记录经过编辑,以获得生长、饲料摄入量和饲料效率性状,包括平均日增重(ADG)、平均日采食量(ADFI)、饲料转化率(FCR)和剩余饲料摄入量(RFI)。相应地,使用Illumina PorcineSNP60 BeadChip对1047头公猪进行了基因分型。其余516头公猪作为独立样本,使用低密度GGP - 猪BeadChip进行基因分型,并推算至60K。系谱分析得出的生长、饲料摄入量和超声性状的遗传力中等(ADG为0.44±0.11至BF为0.58±0.09);FCR的遗传力估计值为0.32±0.09,但RFI仅为0.10±0.05。相比之下,使用贝叶斯A模型的标记信息得出的遗传力估计值约为系谱分析的一半,表明存在“遗传力缺失”。生长与饲料摄入量(0.32±0.05)和背膘(0.22±0.04)之间存在中等程度的正遗传相关性,生长与饲料效率性状之间存在负遗传相关性(-0.21±0.08,-0.05±0.07),这表明仅选择生长性状可能会导致饲料摄入量、背膘不理想地增加以及饲料效率降低。通过多性状贝叶斯A模型评估的生长、饲料摄入量和FCR之间的遗传相关性导致ADG与ADFI之间的遗传相关性增加,ADFI与FCR之间呈负相关,ADG与FCR之间呈正相关。据报道,所研究性状的基因组预测准确性从RFI的9.4%到BF的36.5%不等,这可能为猪育种和未来利用基因组信息的选择计划提供新的见解。