基于智能饲喂开展哺乳母猪采食量基因组遗传评估研究

doi:10.11843/j.issn.0366-6964.2024.07.010

畜牧兽医学报 ›› 2024, Vol. 55 ›› Issue (7): 2890-2900.doi: 10.11843/j.issn.0366-6964.2024.07.010

基于智能饲喂开展哺乳母猪采食量基因组遗传评估研究

张瑞琪¹^,²(), 厐彦芹¹(), 李再山³, 尚秀国², 兰干球¹, 郭金彪²^,*(), 赵云翔¹^,³^,*()

1. 广西大学动物科学技术学院，南宁 530004
2. 佛山科学技术学院生命科学与工程学院，佛山 528225
3. 广西扬翔股份有限公司，贵港 537100

收稿日期:2023-11-20 出版日期:2024-07-23 发布日期:2024-07-24
通讯作者: 郭金彪,赵云翔 E-mail:582014973@qq.com;Shanxi18404985210@163.com;469190753@qq.com;yunxiangzhao@126.com
作者简介:张瑞琪(1999-)，女，云南昆明人，硕士，主要从事动物遗传育种与繁殖研究，E-mail：582014973@qq.com
厐彦芹(1998-)，女，山西朔州人，硕士，主要从事动物遗传育种与繁殖研究，E-mail: Shanxi18404985210@163.com
第一联系人：
张瑞琪和厐彦芹为同等贡献作者
基金资助:
广西科技重大专项(桂科AA23073020);国家现代农业产业技术体系(CARS-PIG-35);国家现代农业产业技术体系广西创新团队建设专项资金资助(nycytxgxcxtd-2023-15-03);广西巴马县优势特色香猪产业提质增效关键技术集成与示范(2022YFD1601307)

Research on Feeding Capacity Selection of Lactating Sows Based on Intelligent Precision Feeding

Ruiqi ZHANG¹^,²(), Yanqin PANG¹(), Zaishan LI³, Xiuguo SHANG², Ganqiu LAN¹, Jinbiao GUO²^,*(), Yunxiang ZHAO¹^,³^,*()

1. College of Animal Science and Technology, Guangxi University, Nanning 530004, China
2. School of Life Science and Engineering, Foshan University, Foshan 528225, China
3. Guangxi Yangxiang Co. Ltd, Guigang 537100, China

Received:2023-11-20 Online:2024-07-23 Published:2024-07-24
Contact: Jinbiao GUO, Yunxiang ZHAO E-mail:582014973@qq.com;Shanxi18404985210@163.com;469190753@qq.com;yunxiangzhao@126.com

摘要/Abstract

摘要：

旨在通过分析哺乳母猪采食量性状的遗传参数及全基因组关联分析(GWAS)，评估基于智能饲喂技术对哺乳期采食性状的选育效果。本研究收集2022年7月至2023年5月份分娩的922头1~4胎次大白猪的哺乳期采食量数据，分析猪只的哺乳期采食情况。利用Hiblup软件对哺乳期采食性状进行遗传参数估计，后使用Plink、GEMMA和R软件进行全基因组关联分析，筛选与采食量性状相关的SNP位点及重要候选基因，并对显著关联的SNP位点上、下游420 kb范围内注释基因进行功能富集分析。结果显示，纯种大白猪哺乳期采食性状遗传力在0.20~0.27之间，并筛选到28个SNPs位点与哺乳期采食性状显著相关。通过对相关联的SNPs位点进行注释，共筛选到15个与哺乳期采食性状相关的候选基因，这些候选基因显著富集在葡萄糖、脂肪酸、激素和辅酶等的合成代谢多个生物学过程(P < 0.05)，分析认为HAMP、NDUFB8、UPK1A、CHSY1、U6、COX7A1可作为大白猪哺乳期采食性状的关键候选基因。以上研究结果为大白猪哺乳期采食性状提供重要的遗传变异位点与后续基因，为后期利用该遗传分子标记进一步改良采食量性状的选育奠定基础，也将为大白猪哺乳母猪高繁殖力和高泌乳性能品种选育提供理论支撑。

关键词: 智能饲喂, 哺乳母猪, 采食量选育, 全基因组关联分析, SNP标记

Abstract:

This study aimed to analyze the genetic parameters of feed intake traits in lactating sows and conduct a genome-wide association study (GWAS) to evaluate the impact of intelligent feeding technology on these traits during lactation. Feed intake data from 922 first to fourth parity Large White pigs, born between July 2022 and May 2023, were collected to analyze feeding conditions during lactation. The Hiblup software estimated the genetic parameters of feeding traits during lactation, while Plink, GEMMA, and R software conducted a genome-wide association study to identify significant SNP loci and candidate genes associated with feed intake traits. Functional enrichment analysis was then performed on annotated genes within the 420 kb range surrounding these SNP loci. The results indicated that the heritability of feeding traits in lactating Large White pigs ranged from 0.20 to 0.27, and 28 SNP sites were significantly correlated with these traits. Annotation of the associated SNP sites identified 15 candidate genes related to lactation feeding traits, which were significantly enriched in anabolic processes including glucose, fatty acids, hormones, and coenzymes (P < 0.05). HAMP, NDUFB8, UPK1A, CHSY1, U6, and COX7A1 were identified as key candidate genes for feeding traits in Large White pigs during lactation. The above research results provide crucial genetic variation sites and associated genes linked to feeding traits in Large White pigs during lactation, establishing a foundation for further enhancement of feed intake traits by utilizing these genetic markers in future breeding programs, and also offers theoretical support for the breeding of lactating Large White sows with enhanced fertility and lactation performance.

Key words: intelligent feeding, nursing sow, feed intake breeding, genome-wide association study, SNP marker

中图分类号:

S828.2

张瑞琪, 厐彦芹, 李再山, 尚秀国, 兰干球, 郭金彪, 赵云翔. 基于智能饲喂开展哺乳母猪采食量基因组遗传评估研究[J]. 畜牧兽医学报, 2024, 55(7): 2890-2900.

Ruiqi ZHANG, Yanqin PANG, Zaishan LI, Xiuguo SHANG, Ganqiu LAN, Jinbiao GUO, Yunxiang ZHAO. Research on Feeding Capacity Selection of Lactating Sows Based on Intelligent Precision Feeding[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(7): 2890-2900.

图/表 6

表 1

表 2

图 1

图 2

表 3

图 3

参考文献 42

1	TUMMARUK P , DE RENSIS F , KIRKWOOD R N . Managing prolific sows in tropical environments[J]. Mol Reprod Dev, 2023, 90 (7): 533- 545. doi: 10.1002/mrd.23661
2	YANG Y Y , HU C J , ZHAO X C , et al. Dietary energy sources during late gestation and lactation of sows: effects on performance, glucolipid metabolism, oxidative status of sows, and their offspring[J]. J Anim Sci, 2019, 97 (11): 4608- 4618. doi: 10.1093/jas/skz297
3	RODRÍGUEZ M , DÍAZ-AMOR G , MORALES J , et al. Feed intake patterns of modern genetics lactating sows: characterization and effect of the reproductive parameters[J]. Porcine Health Manag, 2023, 9 (1): 6. doi: 10.1186/s40813-022-00300-y
4	BERGSMA R , KANIS E , VERSTEGEN M W A , et al. Genetic parameters and predicted selection results for maternal traits related to lactation efficiency in sows[J]. J Anim Sci, 2008, 86 (5): 1067- 1080. doi: 10.2527/jas.2007-0165
5	MANZANILLA-PECH C I V , STEPHANSEN R B , LASSEN J . Genetic parameters for feed intake and body weight in dairy cattle using high-throughput 3-dimensional cameras in Danish commercial farms[J]. J Dairy Sci, 2023, 106 (12): 9006- 9015. doi: 10.3168/jds.2023-23405
6	KIM S W , EASTER R A . Nutrient mobilization from body tissues as influenced by litter size in lactating sows[J]. J Anim Sci, 2001, 79 (8): 2179- 2186. doi: 10.2527/2001.7982179x
7	GREINER L , NEILL C , ALLEE G L , et al. The feeding of dried distillers' grains with solubles to lactating sows[J]. J Anim Sci, 2015, 93 (12): 5718- 5724. doi: 10.2527/jas.2015-9545
8	米蕾英, 王勇. 浅论日粮中添加苜蓿草粉对母猪繁殖性能的影响[J]. 吉林畜牧兽医, 2021, 42 (10): 29- 30.
	MI L Y , WANG Y . Effects of alfalfa meal supplementation on reproductive performance of sows were discussed[J]. Jilin Animal Husbandry and Veterinary Medicine, 2021, 42 (10): 29- 30.
9	李颖, 任丽萍, 远永来. 苜蓿草粉对仔猪生长性能、肠道抗氧化指标和肠道形态结构的影响[J]. 中国饲料, 2024, (6): 18- 21.
	LI Y , REN L P , YUAN Y L . The effects of alfalfa meal on growth performance, intestinal antioxidant indicators, and intestinal morphology of piglets[J]. China Feed, 2024, (6): 18- 21.
10	GILBERT H , BIDANEL J P , BILLON Y , et al. Correlated responses in sow appetite, residual feed intake, body composition, and reproduction after divergent selection for residual feed intake in the growing pig[J]. J Anim Sci, 2012, 90 (4): 1097- 1108. doi: 10.2527/jas.2011-4515
11	BERGSMA R , MATHUR P K , KANIS E , et al. Genetic correlations between lactation performance and growing-finishing traits in pigs[J]. J Anim Sci, 2013, 91 (8): 3601- 3611. doi: 10.2527/jas.2012-6200
12	LUNDGREN H , FIKSE W F , GRANDINSON K , et al. Genetic parameters for feed intake, litter weight, body condition and rebreeding success in primiparous Norwegian Landrace sows[J]. Animal, 2014, 8 (2): 175- 183. doi: 10.1017/S1751731113002000
13	THEKKOOT D M , YOUNG J M , ROTHSCHILD M F , et al. Genomewide association analysis of sow lactation performance traits in lines of Yorkshire pigs divergently selected for residual feed intake during grow-finish phase[J]. J Anim Sci, 2016, 94 (6): 2317- 2331. doi: 10.2527/jas.2015-0258
14	赵云翔, 邝伟键, 高宁, 等. 杜洛克公猪背膘厚度、日增重、日采食量和饲料效率相关性状的遗传参数估计[J]. 家畜生态学报, 2019, 40 (11): 18- 21.
	ZHAO Y X , KUANG W J , GAO N , et al. Estimation of genetic parameters of growth and feed efficiency related traits in YX China-line Duroc specialized strain[J]. Acta Ecologae Animalis Domastici, 2019, 40 (11): 18- 21.
15	WANG S L , JIANG H H , QIAO Y L , et al. The research progress of vision-based artificial intelligence in smart pig farming[J]. Sensors, 2022, 22 (17): 6541. doi: 10.3390/s22176541
16	PURCELL S , NEALE B , TODD-BROWN K , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses[J]. Am J Hum Genet, 2007, 81 (3): 559- 575. doi: 10.1086/519795
17	YIN L L , ZHANG H H , TANG Z S , et al. HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data[J]. Nucleic Acids Res, 2023, 51 (8): 3501- 3512. doi: 10.1093/nar/gkad074
18	ZHOU X , STEPHENS M . Genome-wide efficient mixed-model analysis for association studies[J]. Nat Genet, 2012, 44 (7): 821- 824. doi: 10.1038/ng.2310
19	XIE C , MAO X Z , HUANG J J , et al. KOBAS 2.0:a web server for annotation and identification of enriched pathways and diseases[J]. Nucleic Acids Res, 2011, 39 (Web Server issue): W316- W322.
20	STRATHE A V , STRATHE A B , THEIL P K , et al. Determination of protein and amino acid requirements of lactating sows using a population-based factorial approach[J]. Animal, 2015, 9 (8): 1319- 1328. doi: 10.1017/S1751731115000488
21	SOEDE N M, KEMP B. Best practices in the lactating and weaned sow to optimize reproductive physiology and performance[M]//FARMER C. The Gestating and Lactating Sow. Wageningen: Wageningen Academic Publishers, 2015: 377-408.
22	KOKETSU Y , TANI S , ⅡDA R . Factors for improving reproductive performance of sows and herd productivity in commercial breeding herds[J]. Porcine Health Manag, 2017, 3, 1- 10. doi: 10.1186/s40813-016-0049-7
23	STRATHE A V , BRUUN T S , HANSEN C F . Sows with high milk production had both a high feed intake and high body mobilization[J]. Animal, 2017, 11 (11): 1913- 1921. doi: 10.1017/S1751731117000155
24	COSTERMANS N G J , TEERDS K J , KEMP B , et al. Physiological and metabolic aspects of follicular developmental competence as affected by lactational body condition loss[J]. Mol Reprod Dev, 2023, 90 (7): 491- 502. doi: 10.1002/mrd.23628
25	COSTERMANS N G J , SOEDE N M , MIDDELKOOP A , et al. Influence of the metabolic state during lactation on milk production in modern sows[J]. Animal, 2020, 14 (12): 2543- 2553. doi: 10.1017/S1751731120001536
26	KOKETSU Y . Influence of cumulative feed intake during early and mid-lactation on luteinizing hormone secretion and weaning-to-estrus interval in primiparous sows[J]. J Vet Med Sci, 1999, 61 (4): 325- 329. doi: 10.1292/jvms.61.325
27	KUHLA B , METGES C C , HAMMON H M . Endogenous and dietary lipids influencing feed intake and energy metabolism of periparturient dairy cows[J]. Domest Anim Endocrinol, 2016, 56 (Suppl): S2- S10.
28	CHALKIAS H , JONAS E , ANDERSSON L S , et al. Identification of novel candidate genes for the inverted teat defect in sows using a genome-wide marker panel[J]. J Appl Genet, 2017, 58 (2): 249- 259. doi: 10.1007/s13353-016-0382-1
29	JIANG L , WANG J M , WANG K , et al. RNF217 regulates iron homeostasis through its E3 ubiquitin ligase activity by modulating ferroportin degradation[J]. Blood, 2021, 138 (8): 689- 705. doi: 10.1182/blood.2020008986
30	WILMAN H R , PARISINOS C A , ATABAKI-PASDAR N , et al. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration[J]. J Hepatol, 2019, 71 (3): 594- 602. doi: 10.1016/j.jhep.2019.05.032
31	JULIÁN-SERRANO S , YUAN F C , WHEELER W , et al. Hepcidin-regulating iron metabolism genes and pancreatic ductal adenocarcinoma: a pathway analysis of genome-wide association studies[J]. Am J Clin Nutr, 2021, 114 (4): 1408- 1417. doi: 10.1093/ajcn/nqab217
32	LIU N , XIAO B , REN H Y , et al. Systematic identification and characterization of porcine snoRNAs: structural, functional and developmental insights[J]. Anim Genet, 2013, 44 (1): 24- 33. doi: 10.1111/j.1365-2052.2012.02363.x
33	TALBOT N C , SHANNON A E , GARRETT W M . Pancreatic duct-like cell line derived from pig embryonic stem cells: expression of uroplakin genes in pig pancreatic tissue[J]. In Vitro Cell Dev Biol Anim, 2019, 55 (4): 285- 301. doi: 10.1007/s11626-019-00336-5
34	DRÖGEMÜLLER C , KUIPER H , VOß-NEMITZ R , et al. Molecular characterization and chromosome assignment of the porcine gene COX7A1 coding for the muscle specific cytochrome c oxidase subunit VⅡa-M[J]. Cytogenet Cell Genet, 2001, 94 (3-4): 190- 193. doi: 10.1159/000048814
35	FENG Y T , XU J Y , SHI M J , et al. COX7A1 enhances the sensitivity of human NSCLC cells to cystine deprivation-induced ferroptosis via regulating mitochondrial metabolism[J]. Cell Death Dis, 2022, 13 (11): 988. doi: 10.1038/s41419-022-05430-3
36	GUO M , LIU Z , WILLEN J , et al. Epigenetic profiling of growth plate chondrocytes sheds insight into regulatory genetic variation influencing height[J]. eLife, 2017, 6, e29329. doi: 10.7554/eLife.29329
37	BERLAND C , CASTEL J , TERRASI R , et al. Identification of an endocannabinoid gut-brain vagal mechanism controlling food reward and energy homeostasis[J]. Mol Psychiatry, 2022, 27 (4): 2340- 2354.
38	KUHLA B . Review: pro-inflammatory cytokines and hypothalamic inflammation: implications for insufficient feed intake of transition dairy cows[J]. Animal, 2020, 14 Suppl 1, s65- s77.
39	GAITÁN A V , WOOD J T , LIU Y P , et al. Maternal dietary fatty acids and their relationship to derived endocannabinoids in human milk[J]. J Hum Lact, 2021, 37 (4): 813- 820.
40	XIE J H , SHI S Y , LIU Y C , et al. Fructose metabolism and its role in pig production: a mini-review[J]. Front Nutr, 2022, 9, 922051.
41	DIMITRIADIS G D , MARATOU E , KOUNTOURI A , et al. Regulation of postabsorptive and postprandial glucose metabolism by insulin-dependent and insulin-independent mechanisms: an integrative approach[J]. Nutrients, 2021, 13 (1): 159.
42	杨俊, 赵小刚, 张桂红, 等. 哺乳母猪饲粮中添加鱼油对母猪泌乳性能的影响[J]. 饲料研究, 2024, 47 (5): 26- 31.
	YANG J , ZHAO X G , ZHANG G H , et al. Effect of fish oil supplementation in lactating sow diet on lactation performance[J]. Feed Research, 2024, 47 (5): 26- 31.

性状Trait	头数Number	最大值/(g·d^-1) Max	最小值/(g·d^-1) Min	平均值±标准差/(g·d^-1) Mean±Standard deviation	变异系数/% Coefficient of variation	h²
哺乳期日均采食量Average daily feed intake during lactation	902	8 407	3 508	5 695±777	13.64	0.27
哺乳期第一周日均采食量Average daily feed intake during the first week of lactation	868	6 194	947	4 065±824	20.27	0.23
哺乳期第二周日均采食量Average daily feed intake during the second week of lactation	878	8 844	2 963	6 031±956	15.85	0.26
哺乳期第三周日均采食量Average daily feed intake during the third week of lactation	742	10 461	3 438	6 543±1 064	16.26	0.20

性状Trait	哺乳期日均采食量Average daily feed intake during lactation	哺乳期第一周日均采食量Average daily feed intake during the first week of lactation	哺乳期第二周日均采食量Average daily feed intake during the second week of lactation	哺乳期第三周日均采食量Average daily feed intake during the third week of lactation	断配间隔Dismatching interval	下一胎次总产仔数Total litter size at next birth
哺乳期日均采食量Average daily feed intake during lactation	1
哺乳期第一周日均采食量Average daily feed intake during the first week of lactation	0.75^***	1
哺乳期第二周日均采食量Average daily feed intake during the second week of lactation	0.94^***	0.73^***	1
哺乳期第三周日均采食量Average daily feed intake during the third week of lactation	0.85^***	0.29^***	0.78^***	1
断配间隔Dismatching interval	-0.07	0.21	0.04	-0.27^**	1
下一胎次总产仔数Total litter size at next birth	0.18	0.14	-0.02	0.16^**	-0.87	1

性状Trait	SNP位点SNP loci	染色体Chromosome	位置Position	最小等位基因频率Minimum allele frequency	P	最近基因Nearest gene	最近距离Nearest distance
第一周日均采食量Average daily feed intake during the first week of lactation	rs321811272	6	44908270	0.147	2.83×10^-6	HAMP	120 965
	rs341818044	6	45092427	0.161	1.90×10^-6	UPK1A	-11 594
	rs330188241	6	45142409	0.161	1.90×10^-6	UPK1A	28 287
	rs3471691301	6	45223393	0.161	1.90×10^-6	U6	-99 895
	rs322027839	6	45244146	0.161	1.90×10^-6	U6	-79 142
	rs325439878	6	45811589	0.445	1.77×10^-5	COX7A1	290 876
	rs344506073	7	63617372	0.153	2.33×10^-5
	rs3473795189	7	63630576	0.155	2.84×10^-5
	rs321926366	9	123440360	0.363	7.89×10^-5
	rs81232569	17	44965892	0.188	9.31×10^-5
	rs342256202	17	44988096	0.200	6.02×10^-5
第二周日均采食量Average daily Feed intake during the second week of lactation	rs80782279	4	8954952	0.399	8.31×10^-5
	rs341143158	4	9059801	0.242	4.79×10^-5
	rs80835898	4	9059829	0.397	7.58×10^-5
	rs337240073	14	5402905	0.312	9.02×10^-5
	rs337081410	14	5402971	0.312	9.02×10^-5
	rs319708616	14	5402978	0.312	9.02×10^-5
	rs331157866	14	5736105	0.300	4.88×10^-5
	rs339438831	14	5736112	0.300	4.88×10^-5
	rs322722609	14	5736160	0.299	5.33×10^-5
第三周日均采食量Averag daily feed intake during the third week of lactation	rs327338831	1	138852678	0.454	6.14×10^-5	U6	250 527
	rs322009388	1	138949706	0.454	5.94×10^-5	U6	347 555
	rs333310113	1	139207648	0.454	5.92×10^-5
	rs338115020	1	139240890	0.454	7.03×10^-5
	rs329224371	1	139495954	0.454	7.76×10^-5	CHSY1	-234 767
	rs322312894	6	66166982	0.379	1.02×10^-4
	rs320830362	14	111845810	0.155	5.98×10^-5	NDUFB8	212 246
	rs80922182	14	116346626	0.135	9.57×10^-5	U1	-56 152

[1]	崔晟頔, 王凯, 赵真坚, 陈栋, 申琦, 余杨, 王俊戈, 陈子旸, 禹世欣, 陈佳苗, 王翔枫, 唐国庆. 利用GWAS和DNA甲基化共定位鉴定猪肉质性状的候选基因[J]. 畜牧兽医学报, 2024, 55(5): 1945-1957.
[2]	钟欣, 张晖, 张充, 刘小红. 母猪繁殖力基因遗传育种研究进展[J]. 畜牧兽医学报, 2024, 55(2): 438-450.
[3]	唐鑫鑫, 郑炬梅, 骆娜, 营凡, 朱丹, 李森, 刘大伟, 安炳星, 文杰, 赵桂苹, 李和刚. 基于全基因组关联分析揭示肉鸡腿病发生的遗传机制[J]. 畜牧兽医学报, 2024, 55(1): 99-109.
[4]	李柯安宁, 杜丽丽, 安炳星, 邓天宇, 梁忙, 曹晟, 杜悦莹, 徐凌洋, 高雪, 张路培, 李俊雅, 高会江. 华西牛胴体及原始分割肉块重量性状遗传参数估计与全基因组关联分析[J]. 畜牧兽医学报, 2023, 54(9): 3664-3676.
[5]	白露, 王梦杰, 马小春, 何政肖, 谭晓冬, 刘杰, 赵桂苹, 文杰, 刘冉冉. 一种快速挖掘鸡品种特征性SNP标记集合的方法[J]. 畜牧兽医学报, 2023, 54(8): 3252-3261.
[6]	张笑科, 廖伟莉, 陈信佑, 李婷婷, 袁晓龙, 李加琪, 黄翔, 张豪. 杜洛克猪生长性状全基因组关联分析及候选基因鉴定[J]. 畜牧兽医学报, 2023, 54(5): 1868-1876.
[7]	吴骏, 蔡晓钿, 林清, 钟展明, 叶浩强, 魏趁, 徐志婷, 吴细波, 司景磊, 张哲, 李加琪. 大白猪眼肌面积、估计瘦肉率和背膘厚的加权一步法全基因组关联分析[J]. 畜牧兽医学报, 2023, 54(4): 1403-1414.
[8]	张高猛, 丁纪强, 刘昱宏, 郑麦青, 文杰, 赵桂苹, 李庆贺. 全基因组关联分析揭示白羽肉鸡孵化性状的遗传基础[J]. 畜牧兽医学报, 2023, 54(2): 534-544.
[9]	高超群, 曹然然, 杜文苹, 胡晓玉, 雷艳茹, 李文婷, 康相涛. 基于全基因组SNP标记分析中国地方鸡品种的遗传多样性和种群结构[J]. 畜牧兽医学报, 2023, 54(2): 554-562.
[10]	范晨宇, 单艳菊, 章明, 姬改革, 巨晓军, 屠云洁, 贺喜, 束婧婷, 刘一帆, 张海涵. 立华麻黄鸡体重和肉品质性状全基因组关联分析[J]. 畜牧兽医学报, 2023, 54(12): 4982-4992.
[11]	赵雪洋, 李丹妮, 王钰晨, 郭磊, 王丽, 黄杰, 焦仪强, 安小鹏, 张希云, 张磊, 宋宇轩. 东湖杂交羊产羔性状的GWAS分析及候选基因GRID2的验证[J]. 畜牧兽医学报, 2023, 54(11): 4625-4635.
[12]	张昌政, 李德森, 黄敏, 方晓敏, 赵为民, 任守文, 董焕声, 任军, 周李生. 基于全基因组填充重测序关联分析鉴别影响苏山猪初生体尺与乳头数性状的遗传位点[J]. 畜牧兽医学报, 2023, 54(1): 88-102.
[13]	吴平先, 陈力, 龙熙, 柴捷, 张廷焕, 徐顺来, 郭宗义, 王金勇. 荣昌猪初产繁殖性状的全基因组关联研究[J]. 畜牧兽医学报, 2023, 54(1): 103-112.
[14]	李玲, 李业芳, 梁奔梦, 孙玉江, 马月辉, 马青, 蒋琳, 刘书琴. 基于SNP标记的滩羊亲子鉴定研究[J]. 畜牧兽医学报, 2022, 53(9): 2912-2919.
[15]	马丽霞, 曹国伟, 朱红芳, 邓占钊, 蔡正云, 周成浩, 韩威, 顾亚玲, 张娟. 基于RAD-seq静原鸡保种群体的遗传变异分析[J]. 畜牧兽医学报, 2022, 53(7): 2104-2117.

基于智能饲喂开展哺乳母猪采食量基因组遗传评估研究

Research on Feeding Capacity Selection of Lactating Sows Based on Intelligent Precision Feeding

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 42

相关文章 15

编辑推荐

Metrics

本文评价