基因组选择技术在猪肉质育种中的应用与展望

doi:10.3969/j.issn.1004-1524.20240213

浙江农业学报 ›› 2025, Vol. 37 ›› Issue (3): 726-735.DOI: 10.3969/j.issn.1004-1524.20240213

基因组选择技术在猪肉质育种中的应用与展望

王彬彬(), 齐珂珂, 门小明, 徐子伟()

浙江省农业科学学院畜牧兽医研究所,浙江杭州 310021

收稿日期:2024-03-06 出版日期:2025-03-25 发布日期:2025-04-02
作者简介:王彬彬(1990—),男,河南商丘人,博士,助理研究员,主要从事优质猪育种研究。E-mail:bbwzaas@126.com
通讯作者: * 徐子伟,E-mail:zxwfyz@126.com
基金资助:
浙江省基础公益研究计划(ZCLQ24C1702);国家自然科学基金青年科学基金(32302695);浙江省农业新品种选育重大科技专项(2021C02068);浙江省重点研发计划(2021C02007);国家现代农业产业技术体系(CARS-36)

Application and perspect of genomic selection in pork quality breeding

WANG Binbin(), QI Keke, MEN Xiaoming, XU Ziwei()

Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China

Received:2024-03-06 Online:2025-03-25 Published:2025-04-02

摘要/Abstract

摘要：

猪肉品质对消费者健康及生猪产业可持续发展至关重要。传统选育方法因肉质性状遗传解析不足存在改良瓶颈。随着高通量技术的发展,基因组选择(genomic selection, GS)凭借其高精度、低成本优势,成为突破肉质遗传改良的关键技术。本文系统梳理了GS模型的发展脉络,对各类GS模型的分类和预测准确性进行了综述。当前仍面临诸多挑战:活体精准表型采集技术尚未突破;现有模型对跨群体、跨环境数据适应性不足;多组学整合机制与表观遗传网络融入育种体系仍有欠缺等。未来需重点研发动态表型传感技术,构建可解释性深度学习框架,创建多组学联合预测模型。通过建立覆盖育种-生产-加工全产业链的遗传评估体系,实现从基因组到表型组的跨尺度解析,为我国优质猪种质创新提供理论支撑与技术路径。

关键词: 猪, 肉质性状, 基因组选择, 模型比较, 遗传改良

Abstract:

Pork quality is crucial for consumer health and the sustainable development of the swine industry. Traditional breeding methods face limitations in improving meat quality due to insufficient genetic understanding of these traits. With the development of high-throughput technologies, genomic selection(GS) technology has emerged as a key tool for breaking through the genetic improvement bottleneck in meat quality, owing to its high precision and low cost. This paper systematically reviews the development of GS models, categorizing various types of models and discussing their predictive accuracy. Several challenges remain: the technology for precise in vivo phenotypic data collection has yet to be fully realized; existing models show insufficient adaptability to cross-population and cross-environment data; and the integration of multi-omics mechanisms and epigenetic networks into breeding systems is still lacking. Future research should focus on developing dynamic phenotypic sensing technologies, constructing interpretable deep learning frameworks, and creating multi-omics-based predictive models. By establishing a genetic evaluation system covering the entire breeding-production-processing industry chain, this study aims to provide a theoretical foundation and technical pathway for the innovation of high-quality pig germplasm in China, enabling cross-scale analysis from genomics to phenomics.

Key words: pig, meat quality, genomic selection, models comparison, genetic improvement

中图分类号:

S828

王彬彬, 齐珂珂, 门小明, 徐子伟. 基因组选择技术在猪肉质育种中的应用与展望[J]. 浙江农业学报, 2025, 37(3): 726-735.

WANG Binbin, QI Keke, MEN Xiaoming, XU Ziwei. Application and perspect of genomic selection in pork quality breeding[J]. Acta Agriculturae Zhejiangensis, 2025, 37(3): 726-735.

图/表 2

参考文献 54

[1]	TRAN D, THU N. Meat quality: understanding of meat tenderness and influence of fat content on meat flavor[J ]. Science and Technology for Development, 2006, 9(12): 65-70.
[2]	HU Z L, PARK C A, REECY J M. Bringing the animal QTLdb and CorrDB into the future: meeting new challenges and providing updated services[J]. Nucleic Acids Research, 2022, 50(D1): D956-D961.
[3]	王彬彬, 汪涵, 马翔, 等. 苏淮猪肌内脂肪表型测定及其与IGF2和SCD基因的多态性关联性分析[J]. 畜牧与兽医, 2018, 50(3): 20-25.
	WANG B B, WANG H, MA X, et al. Analysis of intramuscular fat content in Suhuai pigs and its correlation with polymorphisms of IGF2 and SCD genes[J]. Animal Husbandry & Veterinary Medicine, 2018, 50(3): 20-25. (in Chinese with English abstract)
[4]	谈成, 边成, 杨达, 等. 基因组选择技术在农业动物育种中的应用[J]. 遗传, 2017, 39(11): 1033-1045.
	TAN C, BIAN C, YANG D, et al. Application of genomic selection in farm animal breeding[J]. Hereditas, 2017, 39(11): 1033-1045. (in Chinese with English abstract)
[5]	HENDERSON C R. Best linear unbiased estimation and prediction under a selection model[J]. Biometrics, 1975, 31(2): 423-447.
[6]	RIBAUT J M, HOISINGTON D. Marker-assisted selection: new tools and strategies[J]. Trends in Plant Science, 1998, 3(6): 236-239.
[7]	MEUWISSEN T H, HAYES B J, GODDARD M E. Prediction of total genetic value using genome-wide dense marker maps[J]. Genetics, 2001, 157(4): 1819-1829.
[8]	GODDARD M E, HAYES B J. Genomic selection[J]. Journal of Animal Breeding and Genetics, 2007, 124(6): 323-330.
[9]	尹立林, 马云龙, 项韬, 等. 全基因组选择模型研究进展及展望[J]. 畜牧兽医学报, 2019, 50(2): 233-242.
	YIN L L, MA Y L, XIANG T, et al. The progress and prospect of genomic selection models[J]. Chinese Journal of Animal and Veterinary Sciences, 2019, 50(2): 233-242. (in Chinese with English abstract)
[10]	VANRADEN P M. Efficient methods to compute genomic predictions[J]. Journal of Dairy Science, 2008, 91(11): 4414-4423.
[11]	DE LOS CAMPOS G, NAYA H, GIANOLA D, et al. Predicting quantitative traits with regression models for dense molecular markers and pedigree[J]. Genetics, 2009, 182(1): 375-385.
[12]	AGUILAR I, MISZTAL I, JOHNSON D L, et al. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score[J]. Journal of Dairy Science, 2010, 93(2): 743-752.
[13]	HABIER D, FERNANDO R L, KIZILKAYA K, et al. Extension of the Bayesian alphabet for genomic selection[J]. BMC Bioinformatics, 2011, 12: 186.
[14]	YI N J, XU S Z. Bayesian LASSO for quantitative trait loci mapping[J]. Genetics, 2008, 179(2): 1045-1055.
[15]	HENDERSON C R. Best linear unbiased prediction of nonadditive genetic merits in noninbred populations[J]. Journal of Animal Science, 1985, 60(1): 111-117.
[16]	HAYES B J, VISSCHER P M, GODDARD M E. Increased accuracy of artificial selection by using the realized relationship matrix[J]. Genetics Research, 2009, 91(1): 47-60.
[17]	HOUSTON R D. Future directions in breeding for disease resistance in aquaculture species[J]. Revista Brasileira de Zootecnia, 2017, 46(6): 545-551.
[18]	MASUDA Y, VANRADEN P M, TSURUTA S, et al. Invited review: unknown-parent groups and metafounders in single-step genomic BLUP[J]. Journal of Dairy Science, 2022, 105(2): 923-939.
[19]	LEGARRA A, AGUILAR I, MISZTAL I. A relationship matrix including full pedigree and genomic information[J]. Journal of Dairy Science, 2009, 92(9): 4656-4663.
[20]	AFRAZANDEH M, ABDOLAHI-ARPANAHI R, ABBASI M A, et al. Comparison of different response variables in genomic prediction using GBLUP and ssGBLUP methods in Iranian Holstein cattle[J]. The Journal of Dairy Research, 2022: 1-7.
[21]	TEISSIER M, LARROQUE H, ROBERT-GRANIÉ C. Weighted single-step genomic BLUP improves accuracy of genomic breeding values for protein content in French dairy goats: a quantitative trait influenced by a major gene[J]. Genetics, Selection, Evolution, 2018, 50(1): 31.
[22]	ZHANG Z, LIU J F, DING X D, et al. Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix[J]. PLoS One, 2010, 5(9): e12648.
[23]	LU S, ZHU J J, DU X, et al. Genomic selection for resistance to Streptococcus agalactiae in GIFT strain of Oreochromis niloticus by GBLUP, wGBLUP, and BayesCπ[J]. Aquaculture, 2020, 523: 735212.
[24]	SU G, CHRISTENSEN O F, JANSS L, et al. Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances[J]. Journal of Dairy Science, 2014, 97(10): 6547-6559.
[25]	LIU A X, LUND M S, BOICHARD D, et al. Improvement of genomic prediction by integrating additional single nucleotide polymorphisms selected from imputed whole genome sequencing data[J]. Heredity, 2020, 124(1): 37-49.
[26]	REN D Y, AN L X, LI B J, et al. Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits[J]. Heredity, 2021, 126(2): 320-334.
[27]	GONZÁLEZ-CAMACHO J M, DE LOS CAMPOS G, PÉREZ P, et al. Genome-enabled prediction of genetic values using radial basis function neural networks[J]. Theoretical and Applied Genetics, 2012, 125(4): 759-771.
[28]	MOSER G, LEE S H, HAYES B J, et al. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model[J]. PLoS Genetics, 2015, 11(4): e1004969.
[29]	ZHU B, GUO P, WANG Z, et al. Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle[J]. Animal Genetics, 2019, 50(6): 634-643.
[30]	GAO Y, ZHANG R, HU X X, et al. Application of genomic technologies to the improvement of meat quality of farm animals[J]. Meat Science, 2007, 77(1): 36-45.
[31]	GIANOLA D. Priors in whole-genome regression: the Bayesian alphabet returns[J]. Genetics, 2013, 194(3): 573-596.
[32]	DA SILVA F A, VIANA A P, CORREA C C G, et al. Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models[J]. Scientific Reports, 2021, 11(1): 13639.
[33]	MEUWISSEN T, VAN DEN BERG I, GODDARD M. On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL[J]. Genetics, Selection, Evolution, 2021, 53(1): 19.
[34]	KJETSÅ M V, GJUVSLAND A B, NORDBØ Ø, et al. Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods[J]. Journal of Animal Breeding and Genetics, 2022, 139(6): 654-665.
[35]	张瑞峰, 黄珍, 谢水华, 等. 猪全基因组选择技术发展现状和应用前景[J]. 中国畜牧杂志, 2023, 59(10): 21-29.
	ZHANG R F, HUANG Z, XIE S H, et al. The development status and application prospect of genomic selection for pigs[J]. Chinese Journal of Animal Science, 2023, 59(10): 21-29. (in Chinese with English abstract)
[36]	XIE L, QIN J T, RAO L, et al. Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig[J]. Journal of Animal Science and Biotechnology, 2023, 14(1): 116.
[37]	LARZUL C. How to improve meat quality and welfare in entire male pigs by genetics[J]. Animals, 2021, 11(3): 699.
[38]	SALEK ARDESTANI S, JAFARIKIA M, SARGOLZAEI M, et al. Genomic prediction of average daily gain, back-fat thickness, and loin muscle depth using different genomic tools in Canadian swine populations[J]. Frontiers in Genetics, 2021, 12: 665344.
[39]	MEHER P K, RUSTGI S, KUMAR A. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results[J]. Heredity, 2022, 128(6): 519-530.
[40]	WON S, JUNG J, PARK E, et al. Identification of genes related to intramuscular fat content of pigs using genome-wide association study[J]. Asian-Australasian Journal of Animal Sciences, 2018, 31(2): 157-162.
[41]	HERNÁNDEZ-SÁNCHEZ J, AMILLS M, PENA R N, et al. Genomic architecture of heritability and genetic correlations for intramuscular and back fat contents in Duroc pigs[J]. Journal of Animal Science, 2013, 91(2): 623-632.
[42]	SUZUKI K, INOMATA K, KATOH K, et al. Genetic correlations among carcass cross-sectional fat area ratios, production traits, intramuscular fat, and serum leptin concentration in Duroc pigs[J]. Journal of Animal Science, 2009, 87(7): 2209-2215.
[43]	WANG B B, LI P H, HOU L M, et al. Genome-wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole-genome sequencing data[J]. Evolutionary Applications, 2022, 15(12): 2054-2066.
[44]	LIU Q, LONG Y, ZHANG Y F, et al. Phenotypic and genetic correlations of pork myoglobin content with meat colour and other traits in an eight breed-crossed heterogeneous population[J]. Animal, 2021, 15(11): 100364.
[45]	YAN M, LI L Y, HUANG Y Z, et al. Investigation on muscle fiber types and meat quality and estimation of their heritability and correlation coefficients with each other in four pig populations[J]. Animal Science Journal, 2024, 95(1): e13915.
[46]	ZHUANG Z W, WU J, XU C N, et al. The genetic architecture of meat quality traits in a crossbred commercial pig population[J]. Foods, 2022, 11(19): 3143.
[47]	ZHUANG Z W, WU J, QIU Y B, et al. Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs[J]. Journal of Animal Science and Biotechnology, 2023, 14(1): 67.
[48]	刘航. 肉色候选基因的鉴定及其在基因组选择中的应用[D]. 南京: 南京农业大学, 2021.
	LIU H. Identification of candidate genes associated with meat color traits in suhuai pigs and its application in genome selection[D]. Nanjing: Nanjing Agricultural University, 2021. (in Chinese with English abstract)
[49]	LOPEZ B I, SANTIAGO K G, LEE D H, et al. Single-step genomic evaluation for meat quality traits, sensory characteristics, and fatty-acid composition in duroc pigs[J]. Genes, 2020, 11(9): 1062.
[50]	BARAJAS B K A, CANTET R J C, STEIBEL J P, et al. Heritability estimates and predictive ability for pig meat quality traits using identity-by-state and identity-by-descent relationships in an F₂ population[J]. Journal of Animal Breeding and Genetics, 2023, 140(1): 13-27.
[51]	DUARTE D A S, SCHROYEN M, MOTA R R, et al. Recent genetic advances on boar taint reduction as an alternative to castration: a review[J]. Journal of Applied Genetics, 2021, 62(1): 137-150.
[52]	DUGUÉ C, PRUNIER A, MERCAT M J, et al. Genetic determinism of boar taint and relationship with growth traits, meat quality and lesions[J]. Animal, 2020, 14(7): 1333-1341.
[53]	DE CAMPOS C F, LOPES M S, E SILVA F F, et al. Genomic selection for boar taint compounds and carcass traits in a commercial pig population[J]. Livestock Science, 2015, 174: 10-17.
[54]	FAGGION S, BOSCHI E, VERONEZE R, et al. Genomic prediction and genome-wide association study for boar taint compounds[J]. Animals, 2023, 13(15): 2450.

基因组选择技术在猪肉质育种中的应用与展望

Application and perspect of genomic selection in pork quality breeding

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 2

参考文献 54

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张楚妮, 徐计东, 高钦, 单颖, 方维焕, 李肖梁. 丁酸抑制猪流行性腹泻病毒体外复制的分子机制[J]. 浙江农业学报, 2025, 37(3): 579-590.
[2]	赵嘉豪, 徐杏, 周卫东, 杨华, 赵喜红, 汪雯. 猪场废水与周边水环境中细菌和耐药基因的特征[J]. 浙江农业学报, 2025, 37(3): 621-632.
[3]	诸绿寒, 方堃, 吴晓丽, 姚青, 谢淑丽, 郑雷, 敖月, 陈黎洪, 赵珂, 魏俊, 张晋. 微波辅助过热蒸汽加热对猪肉糜理化特性和感官品质的影响[J]. 浙江农业学报, 2025, 37(3): 679-688.
[4]	向进, 王春源, 吴燕, 谭元成, 杨酸, 张依裕. 柯乐猪CRISP3基因SNP鉴定及其对繁殖性状的影响[J]. 浙江农业学报, 2024, 36(6): 1270-1278.
[5]	秦凯鹏, 门小明, 徐子伟. 猪肉鲜味物质及其形成机理研究进展[J]. 浙江农业学报, 2024, 36(3): 719-728.
[6]	吴兴凤, 章啸君, 朱江, 余敏洁, 徐娥, 马灵燕, 肖英平. 腹泻仔猪肠道菌群结构特征及肠道炎症因子、腺苷酸环化酶和鸟苷酸环化酶mRNA表达变化研究[J]. 浙江农业学报, 2024, 36(11): 2465-2475.
[7]	刘伟东, 周素茵, 徐爱俊, 叶俊华. 生猪虹膜快速定位算法研究[J]. 浙江农业学报, 2024, 36(11): 2617-2626.
[8]	华涛, 常晨, 李倩文, 张道华, 唐波. 猪细小病毒1~7型全基因组遗传变异[J]. 浙江农业学报, 2024, 36(10): 2193-2203.
[9]	潘梓博, 周昕, 徐杏, 刘凯歌, 吉洪湖, 路伏增, 叶春林, 周卫东. 基于激光-视觉融合的配怀猪舍内导航建图技术研究[J]. 浙江农业学报, 2024, 36(10): 2358-2367.
[10]	李颀, 李煜哲. 弱光条件下猪舍清洗目标检测[J]. 浙江农业学报, 2023, 35(9): 2240-2249.
[11]	潘亚杰, 常会庆, 宋盼盼. 规模化猪场粪便养分特征与堆肥过程中填料添加的影响[J]. 浙江农业学报, 2023, 35(7): 1690-1698.
[12]	孙仁杰, 单颖, 安慧婷, 王雅婷, 谢荣辉, 张传亮, 赵灵燕, 方维焕, 李肖梁. 猪圆环病毒3型与宿主相互作用机制研究进展[J]. 浙江农业学报, 2023, 35(7): 1755-1762.
[13]	刘苇, 陶建平. 畜禽禁养区政策何以影响中国生猪市场稳定?[J]. 浙江农业学报, 2023, 35(5): 1195-1210.
[14]	杨酸, 郭小江, 杨红文, 熊力, 李晨, 谭元成, 王春源, 张依裕. 柯乐猪PRLR基因多态性与繁殖性状的关联性[J]. 浙江农业学报, 2023, 35(3): 556-564.
[15]	杨悦, 邵靓, 张鹏飞, 陈弟诗, 陈婉婷, 王印, 罗燕, 杨泽晓, 姚学萍, 任梅渗. 基于PMA-qPCR方法检测兽用消毒剂对非洲猪瘟病毒的灭活效率[J]. 浙江农业学报, 2023, 35(2): 301-307.