代谢组学在家养动物遗传育种中的应用

doi:10.16288/j.yczz.18-226

遗传 ›› 2019, Vol. 41 ›› Issue (2): 111-124.doi: 10.16288/j.yczz.18-226

代谢组学在家养动物遗传育种中的应用

周萌^1,^2,³,景军红^1,^2,³,毛瑞涵^1,^2,³,郭静^1,^2,³,王志鹏^1,^2,³()

^1. 农业农村部鸡遗传育种重点试验室,哈尔滨 150030
^2. 黑龙江省普通高等学校动物遗传育种与繁殖重点试验室,哈尔滨 150030
^3. 东北农业大学动物科学技术学院,哈尔滨150030

收稿日期:2018-08-08 修回日期:2018-10-17 出版日期:2019-02-20 发布日期:2019-01-14
作者简介:周萌,硕士研究生,专业方向：动物分子数量遗传学。E-mail: 1341417113@qq.com
基金资助:
国家自然科学基金项目(31101709);国家留学基金委项目(201308230102);东北农业大学东农学者计划“学术骨干”项目(15XG14);农业部鸡遗传育种重点实验室开放课题(CGB-201706)

Applications of metabonomics in animal genetics and breeding

Zhou Meng^1,^2,³,Jing Junhong^1,^2,³,Mao Ruihan^1,^2,³,Guo Jing^1,^2,³,Wang Zhipeng^1,^2,³()

^1. Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China
^2. Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
^3. College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China

Received:2018-08-08 Revised:2018-10-17 Online:2019-02-20 Published:2019-01-14
Supported by:
[Supported by the Natural Science Foundation of China](31101709);[China Scholarship Council (No. 201308230102), Academic Backbone Project of Northeast Agricultural University](201308230102);[Academic Backbone Project of Northeast Agricultural University](15XG14);[the Open Projects of Key Laboratory of Chicken Genetics and Breeding Ministry of Agriculture and Rural Affairs](CGB-201706)

摘要/Abstract

摘要：

代谢组学是使用分析化学技术对生物样品(如乳液、血浆、血清等)中的大量小分子代谢物进行全面鉴定和定量分析,已经广泛应用于生物医学、营养学、作物学和畜牧学研究中。最初,代谢组学主要应用于畜牧生产中的非遗传学研究。目前,随着生理基因组学、生理遗传学研究的增多,越来越多的研究者开始利用代谢组学的技术和方法开展动物遗传育种研究。本文综述了代谢组学检测技术与平台特点及代谢组学在动物遗传学与基因组中的应用,着重总结了动物代谢分子遗传参数估计、品系(品种)间代谢图谱差异、代谢组全基因组关联分析、筛选影响重要经济性状的生物标记等领域的研究进展,讨论了代谢组学研究还需亟待解决的问题。本文通过综述代谢组学在动物育种中的研究进展,旨在为进一步利用代谢组学技术开展动物重要经济性状的遗传基础研究提供参考。

关键词: 代谢组学, 家养动物, 遗传学, 动物育种

Abstract:

Metabolomics uses advanced analytical chemistry techniques to comprehensively identify, quantify, and characterize a large number of small molecule metabolites in biological samples (e.g., milk, plasma, and serum). It is routinely used in biomedical, nutritional, crop and farm animal research. Metabolomic analyses in farm animals have been initiated in many non-genetic application fields. Recently, it is being increasingly used in animal breeding with the emergence of physiological genomics/genetics and refined phenotypic description. In this review, we describe the features of metabolomics platforms and approaches, and summarize the metabolomics applications in animal genetics and genomics with a focus on some key areas, such as the heritability estimates of metabolomic profiles, identification differences metabolites between lines or breeds, genome-wide association studies with metabotypes, biomarker discovery for economic traits. Moreover, we also discuss the potential applications based on current livestock metabolomics studies. The intent of this review is to provide a critical overview of the trends in the applications of metabolomics in animal breeding, aiming to provide a reference for further studies on the genetic background of the important traits of farm animals combined metabolomics with genomics.

Key words: metabonomics, domestic animals, genetics, animal breeding

周萌,景军红,毛瑞涵,郭静,王志鹏. 代谢组学在家养动物遗传育种中的应用[J]. 遗传, 2019, 41(2): 111-124.

Zhou Meng,Jing Junhong,Mao Ruihan,Guo Jing,Wang Zhipeng. Applications of metabonomics in animal genetics and breeding[J]. Hereditas(Beijing), 2019, 41(2): 111-124.

参考文献

[1]	Nicholson JK, Lindon JC, Holmes E . ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenbiotica, 1999,29(11):1181-1189. [DOI]
[2]	Houle D, Govindaraju DR, Omholt S . Phenomics: the next challenge. Nat Rev Genet, 2010,11(12):855-866. [DOI]
[3]	Robinette SL, Holmes E, Nicholson JK, Dumas ME . Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations. Genome Med, 2012,4(4):30. [DOI]
[4]	Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, D?ring A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost HG, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T . Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol, 2012,8:615. [DOI]
[5]	Reinehr T, Wolters B, Knop C, Lass N, Hellmuth C, Harder U, Peissner W, Wahl S, Grallert H, Adamski J, Illig T, Prehn C, Yu Z, Wang-Sattler R, Koletzko B . Changes in the serum metabolite profile in obese children with weight loss. Eur J Nutr, 2015,54(2):173-181. [DOI]
[6]	Kordalewska M, Markuszewski MJ . Metabolomics in cardiovascular diseases. J Pharmaceut Biomed, 2015,113:121-136. [DOI]
[7]	Mirsaeidi M, Banoei MM, Winston BW, Schraufnagel DE . Metabolomics: applications and promise in mycobacterial disease. Ann Am Thorac Soc, 2015,12(9):1278-1287. [DOI]
[8]	Shajahan-Haq AN, Cheema MS, Clarke R . Application of metabolomics in drug resistant breast cancer research. Metabolites, 2015,5(1):100-118. [DOI]
[9]	Fontanesi L . Metabolomics and livestock genomics: insights into a phenotyping frontier and its applications in animal breeding. Animal Frontiers, 2016,6(1):73-79. [DOI]
[10]	Suhre K, Gieger C . Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet, 2012,13(11):759-769. [DOI]
[11]	Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang Y, Badran H, Grant J, Serra-Cayuela A, Liu Y, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A . HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res, 2018,46(D1):D608-D617. [DOI]
[12]	Wang L, Bo T, Meng X . The booming “metabolomics” research techniques and methods. Chem Life, 2014,34(01):75-78.
[12]	王磊, 薄涛, 蒙昔 . 正在蓬勃兴起的“代谢组学”研究技术与方法. 生命的化学, 2014,34(01):75-78. [DOI]
[13]	Goldansaz SA, Guo AC, Sajed T, Steele MA, Plastow GS, Wishart DS . Livestock metabolomics and the livestock metabolome: A systematic review. PLoS One, 2017,12(5):e0177675. [DOI]
[14]	Dharuri H , Demirkan A,van Klinken JB,Mook-Kanamori DO,van Duijn CM,'t Hoen PA,van Dijk KW.Genetics of the human metabolome, what is next? BBA-Mol Basis Dis, 2014,1842(10):1923-1931. [DOI]
[15]	Soyeurt H, Gillon A, Vanderick S, Mayeres P, Bertozzi C, Gengler N . Estimation of heritability and genetic correlations for the major fatty acids in bovine milk. J Dairy Sci, 2007,90(9):4435-4442. [DOI]
[16]	Stoop WM , Bovenhuis H, van Arendonk JA. Genetic parameters for milk urea nitrogen in relation to milk production traits. J Dairy Sci, 2007,90(4):1981-1986. [DOI]
[17]	Oikonomou G, Valergakis GE, Arsenos G, Roubies N, Banos G . Genetic profile of body energy and blood metabolic traits across lactation in primiparous Holstein cows. J Dairy Sci, 2008,91(7):2814-2822. [DOI]
[18]	Nogi T, Honda T, Mukai F, Okagaki T, Oyama K . Heritabilities and genetic correlations of fatty acid compositions in longissimus muscle lipid with carcass traits in Japanese Black cattle. J Anim Sci, 2011,89(3):615-621. [DOI]
[19]	Buitenhuis AJ, Sundekilde UK, Poulsen NA, Bertram HC, Larsen LB, S?rensen P . Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk. J Dairy Sci, 2013,96(5):3285-3295. [DOI]
[20]	Wittenburg D, Melzer N, Willmitzer L, Lisec J, Kesting U, Reinsch N, Repsilber D . Milk metabolites and their genetic variability. J Dairy Sci, 2013,96(4):2557-2569. [DOI]
[21]	Gebreyesus G, Lund MS, Janss L, Poulsen NA, Larsen LB, Bovenhuis H, Buitenhuis AJ . Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein. J Dairy Sci, 2016,99(4):2863-2866. [DOI]
[22]	Ntawubizi M, Colman E, Janssens S, Raes K , Buys N, de Smet S. Genetic parameters for intramuscular fatty acid composition and metabolism in pigs. J Anim Sci, 2010,88(4):1286-1294. [DOI]
[23]	Ibá?ez-Escriche N, Magallón E, Gonzalez E, Tejeda JF, Noguera JL . Genetic parameters and crossbreeding effects of fat deposition and fatty acid profiles in Iberian pig lines. J Anim Sci, 2016,94(1):28-37. [DOI]
[24]	Dong JQ, Zhang H, Jiang XF, Wang SZ, Du ZQ, Wang ZP, Leng L, Cao ZP, Li YM, Luan P, Li H . Comparison of serum biochemical parameters between two broiler chicken lines divergently selected for abdominal fat content. J Anim Sci, 2015,93(7):3278-3286. [DOI]
[25]	Zhang HL, Xu ZQ, Yang LL, Wang YX, Li YM, Dong JQ, Zhang XY, Jiang XY, Jiang XF, Li H, Zhang DX, Zhang H . Genetic parameters for the prediction of abdominal fat traits using blood biochemical indicators in broilers. Brit Poultry Sci, 2018,59(1):28-33. [DOI]
[26]	Karisa BK, Thomson J, Wang Z, Li C, Montanholi YR, Miller SP, Moore SS, Plastow GS . Plasma metabolites associated with residual feed intake and other productivity performance traits in beef cattle. Livest Sci, 2014,165:200-211. [DOI]
[27]	D'Alessandro A , Marrocco C, Zolla V, D’Andrea M, Zolla L . Meat quality of the longissimus lumborum muscle of Casertana and Large White pigs: metabolomics and proteomics intertwined. J Proteomics, 2011,75(2):610-627. [DOI]
[28]	He Q, Ren P, Kong X, Wu Y, Wu G, Li P, Hao F, Tang H, Blachier F, Yin Y . Comparison of serum metabolite compositions between obese and lean growing pigs using an NMR-based metabonomic approach. J Nutr Biochem, 2012,23(2):133-139. [DOI]
[29]	Straadt IK, Aaslyng MD, Bertram HC . An NMR-based metabolomics study of pork from different crossbreeds and relation to sensory perception. Meat Sci, 2014,96(2):719-728. [DOI]
[30]	Bovo S, Mazzoni G, Galimberti G, Calò DG, Fanelli F, Mezzullo M, Schiavo G, Manisi A, Trevisi P, Bosi P , Dall'Olio S, Pagotto U, Fontanesi L. Metabolomics evidences plasma and serum biomarkers differentiating two heavy pig breeds. Animal, 2016,10(10):1741-1748. [DOI]
[31]	Ji B, Middleton JL, Ernest B, Saxton AM, Lamont SJ, Campagna SR, Voy BH . Molecular and metabolic profiles suggest that increased lipid catabolism in adipose tissue contributes to leanness in domestic chickens. Physiol Genomics, 2014,46(9):315-327. [DOI]
[32]	Baéza E, Jégou M, Gondret F, Lalande-Martin J, Tea I, Le Bihan-Duval E,Berri C,Collin A,Métayer-Coustard S, Louveau I,Lagarrigue S,Duclos MJ,. Pertinent plasma indicators of the ability of chickens to synthesize and store lipids. J Anim Sci, 2015,93(1):107-116. [DOI]
[33]	Stoop WM, Schennink A, Visker MH ,VMullaart E,Vvan Arendonk JA,VBovenhuis H . Genome-wide scan for bovine milk-fat composition. I. Quantitative trait loci for short- and medium-chain fatty acids. J Dairy Sci, 2009,92(9):4664-4675. [DOI]
[34]	Bouwman AC, Bovenhuis H ,Visker MH, van Arendonk JA . Genome-wide association of milk fatty acids in Dutch dairy cattle. Bmc Genet, 2011,12:43. [DOI]
[35]	Rutten MJ, Bouwman AC ,Sprong RC,van Arendonk JA,Visker MH . Genetic variation in vitamin B-12 content of bovine milk and its association with SNP along the bovine genome. PLoS One, 2013,8(4):e62382. [DOI]
[36]	Buitenhuis B, Janss LL, Poulsen NA, Larsen LB, Larsen MK, S?rensen P . Genome-wide association and biological pathway analysis for milk-fat composition in Danish Holstein and Danish Jersey cattle. BMC Genomics, 2014,15:1112. [DOI]
[37]	Li C, Sun DX, Zhang SL, Wang S, Wu XP, Zhang Q, Liu L, Li YH, Qiao L . Genome wide association study identifies 20 novel promising genes associated with milk fatty acid traits in chinese holstein. PLoS One, 2014,9(5):e96186. [DOI]
[38]	Ha NT ,Gross JJ,van Dorland A,Tetens J,Thaller G,Schlather M,Bruckmaier R,Simianer H. Gene-based mapping and pathway analysis of metabolic traits in dairy cows. PLoS One, 2015,10(3):e0122325. [DOI]
[39]	Li X, Buitenhuis AJ, Lund MS, Li C, Sun D, Zhang Q, Poulsen NA, Su G . Joint genome-wide association study for milk fatty acid traits in Chinese and Danish Holstein populations. J Dairy Sci, 2015,98(11):8152-8163. [DOI]
[40]	Poulsen NA, Rybicka I, Larsen LB, Buitenhuis AJ, Larsen MK . Short communication: genetic variation of riboflavin content in bovine milk. J Dairy Sci, 2015,98(5):3496-3501. [DOI]
[41]	Tetens J, Heuer C, Heyer I, Klein MS, Gronwald W, Junge W, Oefner PJ, Thaller G, Krattenmacher N . Polymorphisms within the APOBR gene are highly associated with milk levels of prognostic ketosis biomarkers in dairy cows. Physiol Genomics, 2015,47(4):129-137. [DOI]
[42]	Pegolo S, Cecchinato A, Mele M, Conte G, Schiavon S, Bittante G . Effects of candidate gene polymorphisms on the detailed fatty acids profile determined by gas chromatography in bovine milk. J Dairy Sci, 2016,99(6):4558-4573. [DOI]
[43]	Duchemin SI, Bovenhuis H ,Megens HJ,van Arendonk JAM,Visker MHPW .Fine-mapping of BTA17 using imputed sequences for associations with de novo synthesized fatty acids in bovine milk. J Dairy Sci, 2017,100(11):9125-9135. [DOI]
[44]	Lopdell TJ, Tiplady K, Struchalin M, Johnson TJJ, Keehan M, Sherlock R, Couldrey C, Davis SR, Snell RG, Spelman RJ, Littlejohn MD . DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content. BMC Genomics, 2017,18(1):968. [DOI]
[45]	Ishii A, Yamaji K, Uemoto Y, Sasago N, Kobayashi E, Kobayashi N, Matsuhashi T, Maruyama S, Matsumoto H, Sasazaki S, Mannen H . Genome-wide association study for fatty acid composition in Japanese Black cattle. Anim Sci J, 2013,84(10):675-682. [DOI]
[46]	Saatchi M, Garrick DJ, Tait RG Jr, Mayes MS, Drewnoski M, Schoonmaker J, Diaz C, Beitz DC, Reecy JM . Genome-wide association and prediction of direct genomic breeding values for composition of fatty acids in Angus beef cattle. BMC Genomics, 2013,14:730. [DOI]
[47]	Buchanan JW, Reecy JM, Garrick DJ, Duan Q, Beitz DC, Koltes JE, Saatchi M, Koesterke L, Mateescu RG . Deriving gene networks from SNP associated with triacylglycerol and phospholipid fatty acid fractions from ribeyes of angus cattle. Front Genet, 2016,7:116. [DOI]
[48]	Lemos MV, Chiaia HL, Berton MP, Feitosa FL, Aboujaoud C, Camargo GM, Pereira AS, Albuquerque LG, Ferrinho AM, Mueller LF, Mazalli MR, Furlan JJ, Carvalheiro R, Gordo DM, Tonussi R, Espigolan R , Silva RM,de Oliveira HN,Duckett S,Aguilar I,Baldi F .Genome-wide associa tion between single nucleotide polymorphisms with beef fatty acid profile in Nellore cattle using the single step procedure. BMC Genomics, 2016,17:213. [DOI]
[49]	Sasago N, Abe T, Sakuma H, Kojima T, Uemoto Y . Genome-wide association study for carcass traits, fatty acid composition, chemical composition, sugar, and the effects of related candidate genes in Japanese Black cattle. Anim Sci J, 2017,88(1):33-44. [DOI]
[50]	Uemoto Y, Ohtake T, Sasago N, Takeda M, Abe T, Sakuma H, Kojima T, Sasaki S . Effect of two non- synonymous ecto-5'-nucleotidase variants on the genetic architecture of inosine 5'-monophosphate (IMP) and its degradation products in Japanese Black beef. BMC Genomics, 2017,18(1):874. [DOI]
[51]	Zhu B, Niu H, Zhang W, Wang Z, Liang Y, Guan L, Guo P, Chen Y, Zhang L, Guo Y, Ni H, Gao X, Gao H, Xu L, Li J . Genome wide association study and genomic prediction for fatty acid composition in Chinese Simmental beef cattle using high density SNP array. BMC Genomics, 2017,18(1):464. [DOI]
[52]	Kawaguchi F, Kigoshi H, Nakajima A, Matsumoto Y, Uemoto Y, Fukushima M, Yoshida E, Iwamoto E, Akiyama T, Kohama N, Kobayashi E, Honda T, Oyama K, Mannen H, Sasazaki S . Pool-based genome-wide association study identified novel candidate regions on BTA9 and 14 for oleic acid percentage in Japanese Black cattle. Anim Sci J, 2018,89(8):1060-1066. [DOI]
[53]	Sasago N, Takeda M, Ohtake T, Abe T, Sakuma H, Kojima T, Sasaki S, Uemoto Y . Genome-wide association studies identified variants for taurine concentration in Japanese Black beef. Anim Sci J, 2018,89(8):1051-1059. [DOI]
[54]	Chen L, Ekine-Dzivenu C, Vinsky M, Basarab J, Aalhus J, Dugan ME, Fitzsimmons C, Stothard P, Li C . Genome- wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle. BMC Genet, 2015,16:135. [DOI]
[55]	Uemoto Y, Abe T, Tameoka N, Hasebe H, Inoue K, Nakajima H, Shoji N, Kobayashi M, Kobayashi E . Whole- genome association study for fatty acid composition of oleic acid in Japanese Black cattle. Anim Genet, 2011,42(2):141-148. [DOI]
[56]	Cesar AS, Regitano LC, Mour?o GB, Tullio RR, Lanna DP, Nassu RT, Mudado MA ,Oliveira PS,do Nascimento ML,Chaves AS,Alencar MM,Sonstegard TS,Garrick DJ,Reecy JM,Coutinho LL. Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genet, 2014,15:39. [DOI]
[57]	Bhuiyan MSA, Kim YK, Kim HJ, Lee DH, Lee SH, Yoon HB, Lee SH . Genome-wide association study and prediction of genomic breeding values for fatty-acid composition in Korean Hanwoo cattle using a high-density single- nucleotide polymorphism array. J Anim Sci, 2018,96(10):4063-4075. [DOI]
[58]	Widmann P, Reverter A, Fortes MR, Weikard R, Suhre K, Hammon H, Albrecht E, Kuehn C . A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle. BMC Genomics, 2013,14(1):798. [DOI]
[59]	Ramayo-Caldas Y, Mercadé A, Castelló A, Yang B, Rodríguez C, Alves E, Díaz I, Ibá?ez-Escriche N, Noguera JL, Pérez-Enciso M, Fernández AI, Folch JM . Genome- wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross. J Anim Sci, 2012,90(9):2883-2893. [DOI]
[60]	Mu?oz M, Rodríguez MC, Alves E, Folch JM, Iba?ez- Escriche N, Silió L, Fernández AI . Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics, 2013,14:845. [DOI]
[61]	Welzenbach J, Neuhoff C, Heidt H, Cinar MU, Looft C, Schellander K, Tholen E, Gro?e-Brinkhaus C . Integrative analysis of metabolomic, proteomic and genomic data to reveal functional pathways and candidate genes for drip loss in pigs. Int J Mol Sci, 2016,17(9):1426. [DOI]
[62]	Zhang W, Yang B, Zhang J, Cui L, Ma J, Chen C, Ai H, Xiao S, Ren J, Huang L . Genome-wide association studies for fatty acid metabolic traits in five divergent pig populations. Sci Rep, 2016,6:24718. [DOI]
[63]	Sato S, Uemoto Y, Kikuchi T, Egawa S, Kohira K, Saito T, Sakuma H, Miyashita S, Arata S, Suzuki K . Genome-wide association studies reveal additional related loci for fatty acid composition in a Duroc pig multigenerational population. Anim Sci J, 2017,88(10):1482-1490. [DOI]
[64]	Zhang J, Zhang Y, Gong H, Cui L, Huang T ,Ai H1,Ren J,Huang L,Yang B.Genetic mapping using 1.4M SNP array refined loci for fatty acid composition traits in Chinese Erhualian and Bamaxiang pigs. J Anim Breed Genet, 2017,134(6):472-483. [DOI]
[65]	Viterbo VS, Lopez BIM, Kang H, Kim H, Song CW, Seo KS . Genome wide association study of fatty acid composition in Duroc swine. Asian Austral J Anim, 2018,31(8):1127-1133. [DOI]
[66]	Corominas J, Ramayo-Caldas Y, Puig-Oliveras A, Pérez-Montarelo D, Noguera JL, Folch JM, Ballester M . Polymorphism in the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs. PLoS One, 2013,8(1):e53687. [DOI]
[67]	Yang B, Zhang W, Zhang Z, Fan Y, Xie X, Ai H, Ma J, Xiao S, Huang L, Ren J . Genome-wide association analyses for fatty acid composition in porcine muscle and abdominal fat tissues. PLoS One, 2013,8(6):e65554. [DOI]
[68]	van Son M, Enger EG, Grove H, Ros-Freixedes R, Kent MP, Lien S, Grindflek E . Genome-wide association study confirm major QTL for backfat fatty acid composition on SSC14 in Duroc pigs. BMC Genomics, 2017,18(1):369. [DOI]
[69]	Zappaterra M, Ros-Freixedes R, Estany J, Davoli R . Association study highlights the influence of ELOVL fatty acid elongase 6 gene region on backfat fatty acid composition in Large White pig breed. Animal, 2018,12(12):2443-2452. [DOI]
[70]	Duijvesteijn N, Knol EF, Bijma P . Boar taint in entire male pigs: a genomewide association study for direct and indirect genetic effects on androstenone. J Anim Sci, 2014,92(10):4319-4328. [DOI]
[71]	van Goor A, Ashwell CM, Persia ME, Rothschild MF, Schmidt CJ, Lamont SJ . Quantitative trait loci identified for blood chemistry components of an advanced intercross line of chickens under heat stress. BMC Genomics, 2016,17:287. [DOI]
[72]	Javanrouh-Aliabad A, Vaez Torshizi R, Masoudi AA, Ehsani A . Identification of candidate genes for blood metabolites in Iranian chickens using a genome-wide association study. Brit Poultry Sci, 2018,59(4):381-388. [DOI]
[73]	Geishauser T, Leslie K, Tenhag J, Bashir A . Evaluation of eight cow-side ketone tests in milk for detection of subclinical ketosis in dairy cows. J Dairy Sci, 2000,83(2):296-299. [DOI]
[74]	Lundén A, Marklund S, Gustafsson V, Andersson L . A nonsense mutation in the FMO3 gene underlies fishy off-flavor in cow’s milk. Genome Res, 2002,12(12):1885-1888. [DOI]
[75]	Weikard R, Altmaier E, Suhre K, Weinberger K, Hammon H, Albrecht E, Setoguchi K, Takasuga A, Küehn C . Metabolomic profiles indicate distinct physiological pathways affected by two loci with major divergent effect on Bos taurus growth and lipid deposition. Physiol Genomics, 2010,42A(2):79-88. [DOI]
[76]	Melzer N, Wittenburg D, Hartwig S, Jakubowski S, Kesting U, Willmitzer L, Lisec J, Reinsch N, Repsilber D . Investigating associations between milk metabolite profiles and milk traits of Holstein cows. J Dairy Sci, 2013,96(3):1521-1534. [DOI]
[77]	Klein MS, Buttchereit N, Miemczyk SP, Immervoll AK, Louis C, Wiedemann S, Junge W, Thaller G, Oefner PJ, Gronwald W . NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis. J Proteome Res, 2012,11(2):1373-1381. [DOI]
[78]	Sun HZ, Wang DM, Wang B, Wang JK, Liu HY, Guan LL, Liu JX . Metabolomics of four biofluids from dairy cows: potential biomarkers for milk production and quality. J Proteome Res, 2015,14(2):1287-1298. [DOI]
[79]	Humer E, Khol-Parisini A, Metzler-Zebeli BU, Gruber L, Zebeli Q . Alterations of the lipid metabolome in dairy cows experiencing excessive lipolysis early postpartum. PLoS One, 2016,11(7):e0158633. [DOI]
[80]	Meale SJ, Morgavi DP, Cassar-Malek I, Andueza D, Ortigues-Marty I, Robins RJ, Schiphorst AM, Laverroux S, Graulet B, Boudra H, Cantalapiedra-Hijar G . Exploration of biological markers of feed efficiency in young bulls. J Agr Food Chem, 2017,65(45):9817-9827. [DOI]
[81]	Dervishi E, Zhang G, Mandal R, Wishart DS, Ametaj BN . Targeted metabolomics: new insights into pathobiology of retained placenta in dairy cows and potential risk biomarkers. Animal, 2018,12(5):1050-1059. [DOI]
[82]	Velho ALC, Menezes E, Dinh T, Kaya A, Topper E, Moura AA, Memili E . Metabolomic markers of fertility in bull seminal plasma. PLoS One, 2018,13(4):e0195279. [DOI]
[83]	Rohart F, Paris A, Laurent B, Canlet C, Molina J, Mercat MJ, Tribout T, Muller N, Iannuccelli N, Villa-Vialaneix N, Liaubet L, Milan D, San Cristobal M . Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds. J Anim Sci, 2012,90(13):4729-4740. [DOI]
[84]	Nishita T, Yatsu J, Watanabe K, Ochiai H, Ichihara N, Orito K, Arishima K . Urinary carbonic anhydrase VI as a biomarker for kidney disease in pigs. Vet J, 2014,202(2):378-380. [DOI]
[85]	BoltonW, CarterTC, Morley JR . The hen’s egg: genetics of taints in eggs from hens fed on rapeseed meal. Br Poult Sci, 1976,17(3):313-320. [DOI]
[86]	Shen Y, Shi S, Tong H, Guo Y, Zou J . Metabolomics analysis reveals that bile acids and phospholipids contribute to variable responses to low-temperature-induced ascites syndrome. Mol Biosyst, 2014,10(6):1557-1567. [DOI]
[87]	Abasht B, Mutryn MF, Michalek RD, Lee WR . Oxidative stress and metabolic perturbations in wooden breast disorder in chickens. PLoS One, 2016,11(4):e0153750. [DOI]
[88]	Shi S, Shen Y, Zhang S, Zhao Z, Hou Z, Zhou H, Zou J, Guo Y . Combinatory evaluation of transcriptome and metabolome profiles of low temperature-induced resistant ascites syndrome in Broiler Chickens. Sci Rep, 2017,7(1):2389. [DOI]
[89]	Sundekilde UK, Rasmussen MK, Young JF, Bertram HC . High resolution magic angle spinning NMR spectroscopy reveals that pectoralis muscle dystrophy in chicken is associated with reduced muscle content of anserine and carnosine. Food Chem, 2017,217:151-154. [DOI]
[90]	Beauclercq S, Nadal-Desbarats L, Hennequet-Antier C, Gabriel I, Tesseraud S, Calenge F , Le Bihan-Duval E, Mignon-Grasteau S. Relationships between digestive efficiency and metabolomic profiles of serum and intestinal contents in chickens. Sci Rep, 2018,8:6678. [DOI]
[91]	Peng ML, Li SN, He QQ, Zhao JL, Li LL, Ma HT . Based serum metabolomics analysis reveals simultaneous interconnecting changes during chicken embryonic development. J Anim Physiol Anim Nutr(Berl)), 2018,102(5):1210-1219. [DOI]
[92]	Chu Q, Zhang J, Zhu S, Zhang Y, Wang H, Geng A, Liu H . The detection and elimination of flavin-containing monooxygenase 3 gene T329S mutation in the Beijing You chicken. Poul Science, 2013,92(12):3109-3112. [DOI]
[93]	Li H, Jiang Y, He FC . Recent development of metabonomics and its applications in clinical research. Hereditas (Beijing), 2008,30(4):389-399.
[93]	李灏, 姜颖, 贺福初 . 代谢组学技术及其在临床研究中的应用. 遗传, 2008,30(4):389-399. [DOI]

编辑推荐

Metrics

www.chinagene.cn
备案号：京ICP备09063187号
总访问:,今日访问:,当前在线:

代谢组学在家养动物遗传育种中的应用

Applications of metabonomics in animal genetics and breeding

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张凤霞,王国栋. 现代代谢组学平台建设及相关技术应用[J]. 遗传, 2019, 41(9): 883-892.
[2]	张競文,续倩,李国亮. 癌症发生发展中的表观遗传学研究[J]. 遗传, 2019, 41(7): 567-581.
[3]	邢万金. 乳糖操纵子模型的建立与教学中若干问题的解析[J]. 遗传, 2019, 41(6): 548-563.
[4]	马志鹏, 陈军. 无义突变与“遗传补偿效应”[J]. 遗传, 2019, 41(5): 359-364.
[5]	吴燕华, 范慧慧, 钱榕, 曾勇, 姚瑶, 林娟, 卢大儒, 丁妍, 乔守怡. 一致性建构原则下遗传学混合式教学设计与实践[J]. 遗传, 2019, 41(5): 439-446.
[6]	孙兆庆, 闫波. 转录因子GATA6在心血管疾病中的作用及其调控机制[J]. 遗传, 2019, 41(5): 375-383.
[7]	吴凯,罗朝晖. 昆虫学案例在遗传学教学中的应用[J]. 遗传, 2019, 41(4): 349-358.
[8]	陈建民. 植物遗传学中的世代及符号应用的建议[J]. 遗传, 2018, 40(6): 508-514.
[9]	周瑞,王以鑫,龙科任,蒋岸岸,金龙. LncRNA调控骨骼肌发育的分子机制及其在家养动物中的研究进展[J]. 遗传, 2018, 40(4): 292-304.
[10]	张丽, 徐海冬, 冷奇颖, 刘艳芬, 赵志辉, 效梅, 吴江, 张权, 安立龙. 鸡快慢羽性状遗传学基础分析综合实验探索与实践[J]. 遗传, 2018, 40(3): 250-256.
[11]	马磊, 张婷婷. 应用嵌合基因实例拓展遗传学染色体畸变的教学[J]. 遗传, 2018, 40(12): 1129-1135.
[12]	文莹, 张立新. 阿维菌素的中国“智”造[J]. 遗传, 2018, 40(10): 888-899.
[13]	陈德富,卢大儒,张飞雄,张根发. 中国遗传学教学40年发展及展望[J]. 遗传, 2018, 40(10): 916-923.
[14]	贺竹梅,别林赛,李蔚. 医学病例在高校普通遗传学教学中的运用[J]. 遗传, 2018, 40(1): 75-85.
[15]	黄雪盈,范凯,叶炎芳,汪斌,吴为人,兰涛. 基于SSLP分子标记验证遗传学三大定律的教学实践探索与体会[J]. 遗传, 2017, 39(9): 856-862.