基因组和DNA甲基化组联合分析筛选猪肉质性状关键基因

doi:10.3864/j.issn.0578-1752.2024.07.014

中国农业科学 ›› 2024, Vol. 57 ›› Issue (7): 1394-1406.doi: 10.3864/j.issn.0578-1752.2024.07.014

基因组和DNA甲基化组联合分析筛选猪肉质性状关键基因

赵真坚(), 王凯, 陈栋, 申琦, 余杨, 崔晟頔, 王俊戈, 陈子旸, 禹世欣, 陈佳苗, 王翔枫, 唐国庆()

四川农业大学动物科技学院/农业农村部畜禽生物组学重点实验室/畜禽遗传资源发掘与创新利用四川省重点实验室/猪禽种业全国重点实验室，成都 611130

收稿日期:2023-10-18 接受日期:2023-12-31 出版日期:2024-04-01 发布日期:2024-04-09
通信作者:
唐国庆，E-mail：tyq003@163.com
联系方式: 赵真坚，E-mail：530773281@qq.com。
基金资助:
四川省科技厅项目(2020YFN0024); 四川省科技厅项目(2021ZDZX0008); 四川省科技厅项目(2021YFYZ0030); 四川省猪创新团队项目(sccxtd-2022-08)

Integrated Aanalysis of Genome and DNA Methylation for Screening Key Genes Related to Pork Quality Traits

ZHAO ZhenJian(), WANG Kai, CHEN Dong, SHEN Qi, YU Yang, CUI ShengDi, WANG JunGe, CHEN ZiYang, YU ShiXin, CHEN JiaMiao, WANG XiangFeng, TANG GuoQing()

College of Animal Science and Technology, Sichuan Agricultural University/Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs/Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province/State Key Laboratory of Swine and Poultry Breeding Industry, Chengdu 611130

Received:2023-10-18 Accepted:2023-12-31 Published:2024-04-01 Online:2024-04-09

摘要/Abstract

摘要：

【背景】猪的肉质性状是重要的经济性状。研究影响各类肉质性状的分子机制，发掘关键基因，从而指导猪的遗传改良，对改善猪肉品质具有重要意义。当前肉质相关的机制研究主要是基于DNA的基因组研究，而对肉质性状的DNA甲基化研究以及结合基因组和甲基化组的综合分析却鲜有报道。【目的】通过基因组和DNA甲基化组联合分析，筛选、鉴定了影响猪肉质的潜在关键基因。为猪肉品质的遗传改良研究提供借鉴。【方法】检测了140头大白猪背最长肌的28个肉质性状，通过表观基因组关联分析（EWAS）与全基因组关联分析（GWAS）筛选了各性状显著关联的CpG和SNP位点。随后以SNP为协变量对GWAS和EWAS重叠的显著关联位点进行条件关联分析，进一步筛选具有独立效应的CpG位点。然后以CpG位点的甲基化水平为因变量，以SNP为自变量进行关联分析从而鉴定甲基化数量性状位点（meQTL）。最后使用顺式甲基化数量性状位点（cis-meQTL）作为工具变量进行孟德尔随机化分析，从而推断cis-meQTL与表型之间的因果关系，同时对位点进行注释，鉴定潜在的关键基因。【结果】（1）在屠宰45 min黄度值（b_45min）、滴水损失（DL）、二十二碳六烯酸（C22:6n-3）3个肉质性状上，EWAS和GWAS在相同基因组区域鉴定到显著关联位点。（2）b_45min的7个CpG位点在条件关联分析后仍保持显著，DL有1个CpG位点在条件关联分析后仍保持显著，而C22:6n-3的3个位点在使用SNP作为协变量分析后不再显著，表明EWAS鉴定的b_45min的7个CpG位点和DL的1个CpG位点的显著关联不受附近的显著SNP影响。（3）b_45min的7个CpG位点和DL的1个CpG位点共鉴定了10个meQTL，但绝大多数是trans-meQTL，只有一个CpG位点（SSC12:44 254 675 bp）鉴定到一个cis-meQTL，表明该位点可能受到近距离SNP调控。（4）孟德尔随机化分析显示该CpG位点（SSC12:44 254 675 bp）与b_45min表型存在一定的因果关联。（5）对该位点注释发现，距离CpG位点（SSC12:44 254 675 bp）和其cis-meQTL最近的基因是NOS2，且CpG位点位于NOS2基因内。【结论】综合DNA甲基化组合基因组数据联合分析结果，可以推测NOS2基因是肉色性状关键候选基因，其DNA甲基化、SNP共同作用调控基因表达，进而影响肉色性状相关基因表达。

关键词: 表观基因组关联分析, 全基因组关联分析, 肉质性状, DNA甲基化, 甲基化数量性状位点

Abstract:

【Background】 The meat quality traits of pigs are important economic traits. Studying the molecular mechanisms that affect various meat quality traits and discovering key genes can guide genetic improvement of pigs and have significant implications for improving pork quality. Currently, those researches on meat-related mechanisms were mainly based on DNA genomics, while studies on DNA methylation related to meat quality traits, as well as integrated analysis of combining genomics and methylation, are scarce. 【Objective】 The potential key genes affecting pork meat quality were screened and identified by combined analysis of genome and DNA methylome, so as to provide a reference for the genetic improvement of pork quality. 【Method】 In this study, 28 meat quality traits of the ongissimus dorsi muscle of 140 Large White pigs were examined. Epigenome-wide association analysis (EWAS) and genome-wide association analysis (GWAS) were performed to identify CpG and SNP sites significantly associated with each trait. Subsequently, the conditional association analysis was performed by using SNPs as covariates on the significantly associated sites overlapping between GWAS and EWAS to further identify CpG sites with independent effects. Association analysis was then conducted to identify methylation quantitative trait loci (meQTL) by using the methylation levels of CpG sites as the dependent variable and SNPs as the independent variable. Finally, the cis-methylation quantitative trait loci (cis-meQTL) were used as instrumental variables for Mendelian randomization analysis to infer the causal relationship between cis-meQTL and phenotypes, while potential key genes at the loci were annotated and identified. 【Result】 (1) The significant associated sites were identified in the same genomic regions for the meat quality traits, namely yellowness value at slaughter 45 minutes postmortem (b_45min), drip loss (DL), and docosahexaenoic acid (C22:6n-3), by both EWAS and GWAS. (2) After conditional association analysis, seven CpG sites for b_45min and one CpG site for DL remained significant, while the three sites for C22:6n-3 were no longer significant after using SNPs as covariates, indicating that the significant associations of the seven CpG sites for b_45min and one CpG site for DL identified by EWAS were not influenced by nearby significant SNPs. (3) A total of ten meQTL were identified for the seven CpG sites for b_45min and one CpG site for DL, but the majority were trans-meQTL, with only one CpG site (SSC12:44 254 675 bp) identifying a cis-meQTL, suggesting that this site might be regulated by nearby SNPs. (4) Mendelian randomization analysis showed a causal relationship between the CpG site (SSC12:44 254 675 bp) and the b_45min phenotype. (5) annotation of the locus revealed that the nearest gene to the CpG site (SSC12:44 254 675 bp) and its cis-meQTL was NOS2, and the CpG site was located within the NOS2 gene. 【Conclusion】 Based on the integrated analysis of DNA methylation and genomics data, this study proposed that the NOS2 gene might be a key candidate gene for meat color traits. DNA methylation and SNP jointly regulated gene expression, thereby affecting the expression of genes related to meat color traits.

Key words: epigenome-wide association study (EWAS), genome-wide association study (GWAS), meat quality traits, DNA methylation, meQTL

赵真坚, 王凯, 陈栋, 申琦, 余杨, 崔晟頔, 王俊戈, 陈子旸, 禹世欣, 陈佳苗, 王翔枫, 唐国庆. 基因组和DNA甲基化组联合分析筛选猪肉质性状关键基因[J]. 中国农业科学, 2024, 57(7): 1394-1406.

ZHAO ZhenJian, WANG Kai, CHEN Dong, SHEN Qi, YU Yang, CUI ShengDi, WANG JunGe, CHEN ZiYang, YU ShiXin, CHEN JiaMiao, WANG XiangFeng, TANG GuoQing. Integrated Aanalysis of Genome and DNA Methylation for Screening Key Genes Related to Pork Quality Traits[J]. Scientia Agricultura Sinica, 2024, 57(7): 1394-1406.

图/表 5

表1

表2

表3

图1

图2

参考文献 58

[1]	孙建广, 张石蕊. 猪肉品质研究进展. 中国猪业, 2008, 3(7): 58-59.
	SUN J G, ZHANG S R. Research progress of pork quality. China Swine Industry, 2008, 3(7): 58-59. (in Chinese)
[2]	宋志芳, 解幼志, 刑荷岩, 芦春莲, 曹洪战. 影响猪肉质性状相关基因的研究进展. 中国猪业, 2017, 12(7): 48-50.
	SONG Z F, XIE Y Z, XING H Y, LU C L, CAO H Z. Research progress on genes related to pork quality traits. China Swine Industry, 2017, 12(7): 48-50. (in Chinese)
[3]	SHEN Q W, UNDERWOOD K R, MEANS W J, MCCORMICK R J, DU M. The halothane gene, energy metabolism, adenosine monophosphate-activated protein kinase, and glycolysis in postmortem pig longissimus dorsi muscle. Journal of Animal Science, 2007, 85(4): 1054-1061. doi: 10.2527/jas.2006-114 pmid: 17202397
[4]	SALAS R C D, MINGALA C N. Genetic factors affecting pork quality: halothane and rendement napole genes. Animal Biotechnology, 2017, 28(2): 148-155. doi: 10.1080/10495398.2016.1243550 pmid: 27854153
[5]	李梦云, 郑萍, 李婉涛, 陈代文. PRKAG3基因在不同品种猪不同生长阶段骨骼肌中的表达差异及其表达量与肉质的关系. 动物营养学报, 2017, 29(5): 1661-1669.
	LI M Y, ZHENG P, LI W T, CHEN D W. The expression difference of PRKAG3 gene in skeletal muscle at different growth stages for different breeds of pigs and the relationship between PRKAG3 gene expression level and meat quality. Chinese Journal of Animal Nutrition, 2017, 29(5): 1661-1669. (in Chinese)
[6]	MÜNZBERG H. Differential leptin access into the brain-A hierarchical organization of hypothalamic leptin target sites? Physiology & Behavior, 2008, 94(5): 664-669. doi: 10.1016/j.physbeh.2008.04.020
[7]	于太永. 脂肪细胞因子leptin和TNF: α对猪骨骼肌成肌细胞增殖分化的影响及其机制[D]. 杨凌: 西北农林科技大学, 2007.
	YU T Y. Effects of adipocytokines leptin and TNF-α on the proliferation and differentiation of porcine skeletal myoblasts and its mechanism[D]. Yangling: Northwest A & F University, 2007. (in Chinese)
[8]	李志娟. 猪心脏型脂肪酸结合蛋白研究进展. 养猪, 2021(2): 55-56.
	LI Z J. Research progress of heart fatty acid-binding protein. Swine Production, 2021(2): 55-56. (in Chinese)
[9]	GRZES M, SADKOWSKI S, RZEWUSKA K, SZYDLOWSKI M, SWITONSKI M. Pig fatness in relation to FASN and INSIG2 genes polymorphism and their transcript level. Molecular Biology Reports, 2016, 43(5): 381-389. doi: 10.1007/s11033-016-3969-z
[10]	ZAPPATERRA M, LUISE D, ZAMBONELLI P, MELE M, SERRA A, COSTA L N, DAVOLI R. Association study between backfat fatty acid composition and SNPs in candidate genes highlights the effect of FASN polymorphism in large white pigs. Meat Science, 2019, 156: 75-84. doi: 10.1016/j.meatsci.2019.05.013
[11]	VERNER J, HUMPOLÍČEK P, KNOLL A. Impact of MYOD family genes on pork traits in Large White and Landrace pigs. Journal of Animal Breeding and Genetics, 2007, 124(2): 81-85. pmid: 17488358
[12]	KIM J M, CHOI B D, KIM B C, PARK S S, HONG K C. Associations of the variation in the porcine myogenin gene with muscle fibre characteristics, lean meat production and meat quality traits. Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie, 2009, 126(2): 134-141.
[13]	LEE E A, KIM J M, LIM K S, RYU Y C, JEON W M, HONG K C. Effects of variation in porcine MYOD1 gene on muscle fiber characteristics, lean meat production, and meat quality traits. Meat Science, 2012, 92(1): 36-43. doi: 10.1016/j.meatsci.2012.03.018
[14]	周李生, 杨杰, 刘先先, 张志燕, 杨斌, 麻骏武. 苏太猪宰后72 h pH和肉色性状的全基因组关联分析. 中国农业科学, 2014, 47(3): 564-573. doi: 10.3864/j.issn.0578-1752.2014.03.016.
	ZHOU L S, YANG J, LIU X X, ZHANG Z Y, YANG B, MA J W. Genome-wide association analyses for musle pH 72 h value and meat color traits in sutai pigs. Scientia Agricultura Sinica, 2014, 47(3): 564-573. doi: 10.3864/j.issn.0578-1752.2014.03.016. (in Chinese)
[15]	GAO G X, GAO N, LI S C, KUANG W J, ZHU L, JIANG W, YU W W, GUO J B, LI Z L, YANG C Z, ZHAO Y X. Genome-wide association study of meat quality traits in a three-way crossbred commercial pig population. Frontiers in Genetics, 2021, 12: 614087. doi: 10.3389/fgene.2021.614087
[16]	LIU X X, ZHANG J J, XIONG X W, CHEN C Y, XING Y Y, DUAN Y Y, XIAO S J, YANG B, MA J W. An integrative analysis of transcriptome and GWAS data to identify potential candidate genes influencing meat quality traits in pigs. Frontiers in Genetics, 2021, 12: 748070. doi: 10.3389/fgene.2021.748070
[17]	YANG Y L, FAN X H, YAN J Y, CHEN M Y, ZHU M, TANG Y J, LIU S Y, TANG Z L. A comprehensive epigenome atlas reveals DNA methylation regulating skeletal muscle development. Nucleic Acids Research, 2021, 49(3): 1313-1329. doi: 10.1093/nar/gkaa1203 pmid: 33434283
[18]	HALUŠKOVÁ J, HOLEČKOVÁ B, STANIČOVÁ J. DNA methylation studies in cattle. Journal of Applied Genetics, 2021, 62(1): 121-136. doi: 10.1007/s13353-020-00604-1 pmid: 33400132
[19]	RAN J S, LI J J, YIN L Q, ZHANG D H, YU C L, DU H R, JIANG X S, YANG C W, LIU Y P. Comparative analysis of skeletal muscle DNA methylation and transcriptome of the chicken embryo at different developmental stages. Frontiers in Physiology, 2021, 12: 697121. doi: 10.3389/fphys.2021.697121
[20]	FAN Y X, LIANG Y X, DENG K P, ZHANG Z, ZHANG G M, ZHANG Y L, WANG F. Analysis of DNA methylation profiles during sheep skeletal muscle development using whole-genome bisulfite sequencing. BMC Genomics, 2020, 21(1): 327.
[21]	KANG B N, WANG J J, ZHANG H F, SHEN W, EL-MAHDY OTHMAN O, ZHAO Y, MIN L J. Genome-wide profile in DNA methylation in goat ovaries of two different litter size populations. Journal of Animal Physiology and Animal Nutrition, 2022, 106(2): 239-249. doi: 10.1111/jpn.v106.2
[22]	唐韶青, 张沅, 徐青, 孙东晓, 俞英. 不同动物部分组织基因组甲基化程度的差异分析. 农业生物技术学报, 2006, 14(4): 507-510.
	TANG S Q, ZHANG Y, XU Q, SUN D X, YU Y. Analysis of methylation level of genome in various tissues of different animal species. Journal of Agricultural Biotechnology, 2006, 14(4): 507-510. (in Chinese)
[23]	LI M Z, WU H L, LUO Z G, XIA Y D, GUAN J Q, WANG T, GU Y R, CHEN L, ZHANG K, MA J D, et al. An atlas of DNA methylomes in porcine adipose and muscle tissues. Nature Communications, 2012, 3: 850. doi: 10.1038/ncomms1854 pmid: 22617290
[24]	张小丽. 猪背部浅层和背部深层脂肪组织全基因组甲基化研究[D]. 雅安: 四川农业大学, 2013.
	ZHANG X L. Genome-wide DNA methylation changes between the superficial and deep backfat tissues of the pig[D]. Yaan: Sichuan Agricultural University, 2013. (in Chinese)
[25]	VILLICAÑA S, BELL J T. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biology, 2021, 22(1): 127.
[26]	李娜. 基于GWAS的猪肉品质性状候选基因研究[D]. 北京: 中国农业大学, 2016.
	LI N. Genome-wide association studies for pig meat traits and exploration of major genes[D]. Beijing: China Agricultural University, 2016. (in Chinese)
[27]	秦一禾. 猪肌内脂肪含量的全基因组关联研究[D]. 昆明: 云南大学, 2017.
	QIN Y H. Genome-wide association studies for intramuscular fat content in swine[D]. Kunming: Yunnan University, 2017. (in Chinese)
[28]	张俊杰. 利用多品种大样本群体精细定位影响猪背最长肌脂肪酸组成的QTL[D]. 南昌: 江西农业大学, 2018.
	ZHANG J J. Mapping and fine-mapping QTL affecting longissimus muscle fatty acid composition in large scale multiple pig populations[D]. Nanchang: Jiangxi Agricultural University, 2018. (in Chinese)
[29]	BOLGER A M, LOHSE M, USADEL B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 2014, 30(15): 2114-2120. doi: 10.1093/bioinformatics/btu170 pmid: 24695404
[30]	KRUEGER F, ANDREWS S R. Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 2011, 27(11): 1571-1572. doi: 10.1093/bioinformatics/btr167 pmid: 21493656
[31]	LANGMEAD B, SALZBERG S L. Fast gapped-read alignment with Bowtie 2. Nature Methods, 2012, 9: 357-359. doi: 10.1038/nmeth.1923 pmid: 22388286
[32]	LI H, DURBIN R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009, 25(14): 1754-1760. doi: 10.1093/bioinformatics/btp324 pmid: 19451168
[33]	LI H, HANDSAKER B, WYSOKER A, FENNELL T, RUAN J, HOMER N, MARTH G, ABECASIS G, DURBIN R. 1 0 0 0 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 2009, 25(16): 2078-2079. doi: 10.1093/bioinformatics/btp352 pmid: 19505943
[34]	DEPRISTO M A, BANKS E, POPLIN R, GARIMELLA K V, MAGUIRE J R, HARTL C, PHILIPPAKIS A A, DEL ANGEL G, RIVAS M A, HANNA M, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 2011, 43: 491-498. doi: 10.1038/ng.806 pmid: 21478889
[35]	CHANG C C, CHOW C C, TELLIER L C, VATTIKUTI S, PURCELL S M, LEE J J. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 2015, 4: 7. doi: 10.1186/s13742-015-0047-8 pmid: 25722852
[36]	ZHOU X, STEPHENS M. Genome-wide efficient mixed-model analysis for association studies. Nature Genetics, 2012, 44: 821-824. doi: 10.1038/ng.2310 pmid: 22706312
[37]	AULCHENKO Y S, RIPKE S, ISAACS A, VAN DUIJN C M. GenABEL: An R library for genome-wide association analysis. Bioinformatics, 2007, 23(10): 1294-1296. doi: 10.1093/bioinformatics/btm108 pmid: 17384015
[38]	SHEN Q, WANG K, WANG S J, ZHAO Z J, JI X, CHEN D, YU Y, CUI S D, WANG J G, CHEN Z Y, GU Y R, TANG G Q. Identifying key DNA methylation sites and their cis-methylation quantitative loci for intramuscular fatty acid traits using genome and methylome data in Yorkshire pigs. Italian Journal of Animal Science, 2023, 22(1): 829-840. doi: 10.1080/1828051X.2023.2247003
[39]	AHSAN M, EK W E, RASK-ANDERSEN M, KARLSSON T, LIND-THOMSEN A, ENROTH S, GYLLENSTEN U, JOHANSSON Å. The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases. PLoS Genetics, 2017, 13(9): e1007005.
[40]	YU F T, QIU C, XU C, TIAN Q, ZHAO L J, WU L, DENG H W, SHEN H. Mendelian randomization identifies CpG methylation sites with mediation effects for genetic influences on BMD in peripheral blood monocytes. Frontiers in Genetics, 2020, 11: 60. doi: 10.3389/fgene.2020.00060 pmid: 32180791
[41]	BURGESS S, THOMPSON S G. Use of allele scores as instrumental variables for Mendelian randomization. International Journal of Epidemiology, 2013, 42(4): 1134-1144. doi: 10.1093/ije/dyt093 pmid: 24062299
[42]	ZHI D G, ASLIBEKYAN S, IRVIN M R, CLAAS S A, BORECKI I B, ORDOVAS J M, ABSHER D M, ARNETT D K. SNPs located at CpG sites modulate genome-epigenome interaction. Epigenetics, 2013, 8(8): 802-806. doi: 10.4161/epi.25501 pmid: 23811543
[43]	BIRNEY E, SMITH G D, GREALLY J M. Epigenome-wide association studies and the interpretation of disease-omics. PLoS Genetics, 2016, 12(6): e1006105.
[44]	LAPPALAINEN T, GREALLY J M. Associating cellular epigenetic models with human phenotypes. Nature Reviews Genetics, 2017, 18: 441-451. doi: 10.1038/nrg.2017.32 pmid: 28555657
[45]	DAVEY SMITH G, HEMANI G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 2014, 23(R1): R89-R98.
[46]	RELTON C L, DAVEY SMITH G. Mendelian randomization: applications and limitations in epigenetic studies. Epigenomics, 2015, 7(8): 1239-1243. doi: 10.2217/epi.15.88 pmid: 26639554
[47]	BATTRAM T, RICHMOND R C, BAGLIETTO L, HAYCOCK P C, PERDUCA V, BOJESEN S E, GAUNT T R, HEMANI G, GUIDA F, CARRERAS-TORRES R, et al. Appraising the causal relevance of DNA methylation for risk of lung cancer. International Journal of Epidemiology, 2019, 48(5): 1493-1504. doi: 10.1093/ije/dyz190 pmid: 31549173
[48]	WAHL S, DRONG A, LEHNE B, LOH M, SCOTT W R, KUNZE S, TSAI P C, RIED J S, ZHANG W H, YANG Y W, et al. Epigenome- wide association study of body mass index, and the adverse outcomes of adiposity. Nature, 2017, 541: 81-86. doi: 10.1038/nature20784
[49]	TSAPROUNI L G, YANG T P, BELL J, DICK K J, KANONI S, NISBET J, VIÑUELA A, GRUNDBERG E, NELSON C P, MEDURI E, et al. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics, 2014, 9(10): 1382-1396. doi: 10.4161/15592294.2014.969637 pmid: 25424692
[50]	ZWAMBORN R A J, SLIEKER R C, MULDER P C, ZOETEMELK I, VERSCHUREN L, SUCHIMAN H E D, TOET K H, DROOG S, SLAGBOOM P E, KOOISTRA T, et al. Prolonged high-fat diet induces gradual and fat depot-specific DNA methylation changes in adult mice. Scientific Reports, 2017, 7: 43261. doi: 10.1038/srep43261 pmid: 28256596
[51]	CHOI I, STEIBEL J P, BATES R O, RANEY N E, RUMPH J M, ERNST C W. Identification of carcass and meat quality QTL in an F(2) duroc × pietrain pig resource population using different least-squares analysis models. Frontiers in Genetics, 2011, 2: 18.
[52]	KIM J J, ZHAO H H, THOMSEN H, ROTHSCHILD M F, DEKKERS J C M. Combined line-cross and half-sib QTL analysis of crosses between outbred lines. Genetical Research, 2005, 85(3): 235-248. doi: 10.1017/S0016672305007597
[53]	SESSA W C. The nitric oxide synthase family of proteins. Journal of Vascular Research, 1994, 31(3): 131-143. pmid: 7511942
[54]	HATTORI Y, HATTORI S, KASAI K. Lipopolysaccharide activates Akt in vascular smooth muscle cells resulting in induction of inducible nitric oxide synthase through nuclear factor-kappa B activation. European Journal of Pharmacology, 2003, 481(2/3): 153-158. doi: 10.1016/j.ejphar.2003.09.034
[55]	SILVA G B, GARVIN J L. Akt1 mediates purinergic-dependent NOS3 activation in thick ascending limbs. American Journal of Physiology Renal Physiology, 2009, 297(3): F646-F652.
[56]	KOEBKE K J, PAULY D J, LERNER L, LIU X E, PACHECO A A. Does the oxidation of nitric oxide by oxyMyoglobin share an intermediate with the metMyoglobin-catalyzed isomerization of peroxynitrite? Inorganic Chemistry, 2013, 52(13): 7623-7632. doi: 10.1021/ic400697a pmid: 23768169
[57]	LOKE K E, LAYCOCK S K, MITAL S, WOLIN M S, BERNSTEIN R, OZ M, ADDONIZIO L, KALEY G, HINTZE T H. Nitric oxide modulates mitochondrial respiration in failing human heart. Circulation, 1999, 100(12): 1291-1297. pmid: 10491373
[58]	RAYNER B S, HUA S S, SABARETNAM T, WITTING P K. Nitric oxide stimulates myoglobin gene and protein expression in vascular smooth muscle. The Biochemical Journal, 2009, 423(2): 169-177. doi: 10.1042/BJ20090716

性状 Trait	GWAS关联 GWAS associations		EWAS关联 EWAS associations
性状 Trait	染色体SSC	位点或区域 Position or Region (bp)	SNP数量 Number of SNP	CpG位点 CpG site (bp)
b_45min	1	4049232	1	SSC1:4667485
b_45min	6	5191168-5483926	10	SSC6:4737404
b_45min	7	9770008-10877899	20	SSC7:10489621
b_45min	10	35692369	1	SSC10:36329538
b_45min	12	44218560	1	SSC12:44254675
b_45min	14	17744509-18820911	19	SSC14:17859513
b_45min	18	49231979-50010893	7	SSC18:48429425
b_45min	18	49231979-50010893	7	SSC18:50979305
DL	7	121791317	1	SSC7:120954182
C22:6n-3	4	101860069-103765886	146	SSC4:102796889
C22:6n-3	4	101860069-103765886	146	SSC4:103218200
C22:6n-3	4	101860069-103765886	146	SSC4:103552805

性状Trait	CpG位点 CpG site (bp)	染色体SSC	位置 Position (bp)	P value (EWAS)	P value (after correct)
b_45min	SSC1: 4667485	1	4667485	3.95E-9	3.95E-9
	SSC6: 4737404	6	4737404	7.59E-9	0.35
	SSC7: 10489621	7	10489621	1.53E-9	1.53E-9
	SSC10: 36329538	10	36329538	2.76E-13	2.76E-13
	SSC12: 44254675	12	44254675	5.45E-9	5.45E-9
	SSC14: 17859513	14	17859513	1.26E-9	1.26E-9
	SSC18: 48429425	18	48429425	1.06E-10	1.06E-10
	SSC18: 50979305	18	50979305	1.18E-9	1.18E-9
DL	SSC7: 120954182	7	120954182	7.79E-9	7.79E-9
C22:6n-3	SSC4: 102796889	4	102796889	1.32E-12	0.99
	SSC4: 103218200	4	103218200	3.32E-9	0.13
	SSC4: 103552805	4	103552805	1.32E-12	0.99

CpG位点 CpG site (bp)	甲基化数量性状位点 meQTL	顺式meQTL cis-meQTL	反式meQTL trans-meQTL	SNP
SSC1:4667485	0	0	0	0
SSC7:10489621	2	0	2	9
SSC10:36329538	0	0	0	0
SSC12:44254675	2	1	1	11
SSC14:17859513	2	0	2	46
SSC18:48429425	0	0	0	0
SSC18:50979305	3	0	3	345
SSC7:120954182	1	0	1	4

基因组和DNA甲基化组联合分析筛选猪肉质性状关键基因

Integrated Aanalysis of Genome and DNA Methylation for Screening Key Genes Related to Pork Quality Traits

RichHTML

PDF

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 58

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	叶美金, 吴雷, Lohani Md Nahibuzzaman, 尹丽, 胡欣荣, 刘亚西, 蒋云峰, 陈国跃, 蒲至恩, 李阳, 李婷, 邹亚亚, 吴佳怡, 马建. 基于GWAS的中国地方小麦成熟胚大小位点的鉴定及其遗传效应解析[J]. 中国农业科学, 2026, 59(6): 1157-1171.
[2]	杨丽娟, 陈丝雨, 赵薇, 朱玲, 郭磊, 马丽娜, 马瑞敏, 张娟. 全基因组重测序揭示静原鸡羽色的遗传机制[J]. 中国农业科学, 2026, 59(6): 1348-1360.
[3]	王勇胜, 牛丽, 王长杰, 马立花, 廉潇潇, 孟亚雄, 马小乐, 姚立蓉, 张宏, 杨轲, 李葆春, 王化俊, 司二静, 汪军成. 冬小麦千粒重的全基因组关联分析及候选基因预测[J]. 中国农业科学, 2026, 59(3): 499-514.
[4]	李云丽, 刁邓超, 刘雅睿, 孙玉晨, 孟祥宇, 邬陈芳, 汪妤, 吴建辉, 李春莲, 曾庆东, 韩德俊, 郑炜君. 小麦苗期耐热性全基因组关联分析[J]. 中国农业科学, 2025, 58(9): 1663-1683.
[5]	周广飞, 马亮, 马璐, 张舒钰, 章慧敏, 宋旭东, 张振良, 陆虎华, 郝德荣, 冒宇翔, 薛林, 陈国清. 玉米苞叶性状全基因组关联分析[J]. 中国农业科学, 2025, 58(3): 431-442.
[6]	武书羽, 衡燕芳, 于太飞, 王世佳, 于思佳, 李园, 胡正, 张辉, 孙现军, 黎亮, 姜奇彦. 玉米自然群体苗期耐盐性鉴定及耐盐相关基因分析[J]. 中国农业科学, 2025, 58(20): 4085-4099.
[7]	向爱慧, 白荣基, 郝宇琼, 赵佳佳, 武棒棒, 李晓华, 郑兴卫, 关攀锋, 郑军. 山西小麦矮秆基因的鉴定及株高遗传位点挖掘[J]. 中国农业科学, 2025, 58(17): 3372-3388.
[8]	郑敏华, 陈洛, 邢甲乐, 谢月兰, 姜先芽, 聂帅, 蔡甫格, 巫浩翔, 陆展华, 孙伟, 霍兴, 白嵩, 赵均良, 杨武. 华南籼稻稻瘟病抗性QTL鉴定与候选基因挖掘[J]. 中国农业科学, 2025, 58(14): 2707-2719.
[9]	李宁, 高丽锋, 黄鑫, 史华伟, 杨进文, 史雨刚, 陈明, 贾继增, 孙黛珍. 耐低氮小麦品种的筛选及耐低氮指数的全基因组关联分析[J]. 中国农业科学, 2025, 58(13): 2487-2503.
[10]	史顺宇, 杨涛, 庞博, 李静, 林轶峰, 王正瑞, 傅林成, 扎尔加玛丽·阿不都别克, 高文伟, 吴鹏昊. 海岛棉叶绿素含量的全基因组关联分析及候选基因预测[J]. 中国农业科学, 2025, 58(10): 1878-1895.
[11]	张颖, 石婷瑞, 曹瑞, 潘文秋, 宋卫宁, 王利, 聂小军. ICARDA引进-小麦苗期抗旱性的全基因组关联分析[J]. 中国农业科学, 2024, 57(9): 1658-1673.
[12]	高晨曦, 郝陆洋, 胡悦, 李永祥, 张登峰, 李春辉, 宋燕春, 石云素, 王天宇, 黎裕, 刘旭洋. 干旱条件下玉米转座子插入关联的表观调控分析[J]. 中国农业科学, 2024, 57(6): 1034-1048.
[13]	苏小雨, 谭政委, 李春明, 李磊, 鲁丹丹, 余永亮, 董薇, 安素妨, 杨青, 孙瑶, 许兰杰, 杨红旗, 梁慧珍. 高温胁迫下芝麻全基因组甲基化差异及关联基因表达分析[J]. 中国农业科学, 2024, 57(24): 4825-4838.
[14]	郭军, 邵丹, 窦套存, 马猛, 卢建, 胡玉萍, 王星果, 王强, 李永峰, 郭伟, 童海兵, 曲亮. 鸡产蛋期剩余采食量的随机回归分析及遗传标记筛选[J]. 中国农业科学, 2024, 57(22): 4568-4577.
[15]	郭军, 曲亮, 邵丹, 马猛, 窦套存, 卢建, 胡玉萍, 王星果, 王强, 李永峰, 郭伟, 童海兵. 基于一步法全基因组关联分析解析蛋黄比率遗传结构[J]. 中国农业科学, 2024, 57(21): 4356-4366.