一种快速挖掘鸡品种特征性SNP标记集合的方法

doi:10.11843/j.issn.0366-6964.2023.08.012

摘要/Abstract

摘要： 旨在建立一种快速挖掘鸡品种/品系特征性SNP标记集合的方法，实现通过少量SNP标记将目标品种/品系与其他品种/品系区分的目标。本研究以北京油鸡、京星黄鸡、7个山东地方鸡种、5个云南地方品种、藏鸡和白羽肉鸡专门化品系等19个品种/品系全基因组重测序数据为素材，通过群体分化指数分析后，提取MEAN_FAST≥0.65的SNP标记，进一步通过连锁不平衡分析提取独立SNP来缩减特征性SNP标记集合。随机选择6个品种/品系对该方法进行检验，结果表明，通过一次群体分化指数和连锁不平衡分析后进行位点筛选后，即可分别获得白羽肉鸡品系、京星黄鸡H系和京星黄鸡D2系的114、220和226个特征性SNPs位点，将目标品系与其它共18个代表性品种分开。对于在主成分和聚类分析中聚集在同一个分支的武定鸡和瓢鸡，通过该方法可分别获得204和178个特征性SNPs标记，将之与其它代表性品种分开。综上，通过本方法，可得到目标品种114~226个SNPs标记构成的集合，进一步利用主成分分析即可将目标品种/品系与其它代表性品种进行区分。该方法流程简便，获得的特征性SNP标记相对较少，有利于控制检测成本，可应用于地方品种或商业化品系的"分子身份证"构建，辅助种质资源保护和鉴定工作。

关键词: 鸡, 主成分分析, 群体分化指数分析, 连锁不平衡, 特征性SNP标记集合

Abstract: The purpose of this study was to establish a method to rapidly mine a few SNPs markers that can distinguish the target breeds or strains from other breeds or strains. The goal is to develop a technique for quickly mining the characteristic SNP markers set of chicken breeds or strains. The study used whole genome resequencing data of 19 breeds or strains, including Beijing yellow chicken, Jingxing yellow chicken, 7 Shandong local breeds, 5 Yunnan local breeds, Tibetan chicken, and the fast-growing white feather broiler terminal. After fixation indices analysis,SNP markers with MEAN_FAST ≥ 0.65 were extracted. The SNP markers set was further reduced by extracting independent SNP through linkage disequilibrium analysis. Six breeds or strains were chosen randomly to test this method. The results showed that, after loci screening by once fixation indices analysis and linkage disequilibrium analysis, the fast growing white feather broiler, Jingxing yellow chicken selective line H and Jingxing yellow chicken selective line D2 could be separated from other 18 representative breeds by 114, 220 and 226 SNPs, respectively; Wuding chicken and Piao chicken, which were clustered in one branch by principal component analysis and Neighbour Joining tree, could be separated from other representative breeds by 204 and 178 SNPs, respectively. Obtained the set of 114-226 SNPs markers, and that the target breeds or strains could be distinguished from other representative varieties by principal component analysis. The procedure is straightforward, and the quantity of SNP markers obtained is quite low, which helps for manage detection costs. It can be applied to construct the "molecular identity cards" of local variaties or commercial strairns to assist the protection and identification of germplasm resources.

Key words: chicken, principal component analysis, fixation indices analysis, linkage disequilibrium, characteristic SNP markers set

中图分类号:

S831.2

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.

BAI Lu, WANG Mengjie, MA Xiaochun, HE Zhengxiao, TAN Xiaodong, LIU Jie, ZHAO Guiping, WEN Jie, LIU Ranran. A Method for Quickly Mining the Characteristic SNP Markers Set of Chicken[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(8): 3252-3261.

参考文献 44

[1]	张小波.地方鸡品种特性及规模化高效养殖技术[J].中国畜牧业, 2022(20):62-63.ZHANG X B.Local chicken breed characteristics and scale up efficient breeding technology[J].China Animal Industry, 2022(20):62-63.(in Chinese)
[2]	陈力力, 张爱琼, 王可人.乌蒙凤鸡的品种特性与饲养管理概述[J].吉林畜牧兽医, 2022, 43(7):61-62.CHEN L L, ZHANG A Q, WANG K R.Overview of breed characteristics and feeding management of Wumeng crested chicken[J].Jilin Animal Husbandry and Veterinary Medicine, 2022, 43(7):61-62. (in Chinese)
[3]	霍献强, 杨祝良, 邹乐勤, 等.广西6个地方鸡肉品质测定及差异性分析[J].家畜生态学报, 2022, 43(5):47-51.HUO X Q, YANG Z L, ZOU L Q, et al.Meat quality determination and difference analysis of six local chicken breeds in Guangxi[J].Acta Ecologae Animalis Domastici, 2022, 43(5):47-51.(in Chinese)
[4]	唐一通, 肖娜, 李智山, 等.单核苷酸多态性检测方法研究进展[J].现代生物医学进展, 2013, 13(27):5393-5397.TANG Y T, XIAO N, LI Z S, et al.Progress in detection methods of single nucleotide polymorphisms[J].Progress in Modern Biomedicine, 2013, 13(27):5393-5397.(in Chinese)
[5]	平措占堆.奶牛精液品质性状的分子标记技术研究进展[J].中国奶牛, 2018(1):15-19.PINGCUO Z D.The research progress of molecular markers for dairy semen quality[J].China Dairy Cattle, 2018(1):15-19.(in Chinese)
[6]	ZHANG W, LI X J, JIANG Y, et al.Genetic architecture and selection of Anhui autochthonous pig population revealed by whole genome resequencing[J].Front Genet, 2022, 13:1022261.
[7]	DESTA Z A, ORTIZ R.Genomic selection:genome-wide prediction in plant improvement[J].Trends Plant Sci, 2014, 19(9):592-601.
[8]	宋志芳, 芦春莲, 曹洪战.全基因组重测序及其在动物育种的研究进展[J].畜牧与兽医, 2017, 49(11):145-148.SONG Z F, LU C L, CAO H Z.Research progress on Whole genome resequencing in animal breeding[J].Animal Husbandry & Veterinary Medicine, 2017, 49(11):145-148.(in Chinese)
[9]	YANG Z L, ZOU L Q, SUN T T, et al.Genome-wide association study using whole-genome sequencing identifies a genomic region on chromosome 6 associated with comb traits in Nandan-Yao chicken[J].Front Genet, 2021, 12:682501.
[10]	ZHANG Z R, QIU M H, DU H R, et al.Whole genome re-sequencing identifies unique adaption of single nucleotide polymorphism, insertion/deletion and structure variation related to hypoxia in Tibetan chickens[J].Gene Expr Patterns, 2021, 40:119181.
[11]	KWOK P Y, CHEN X N.Detection of single nucleotide polymorphisms[J].Curr Issues Mol Biol, 2003, 5(2):43-60.
[12]	汪琳, 赖平安, 张鹤晓, 等.液相芯片——高通量检测技术的新亮点[J].检验检疫科学, 2005, 15(6):55-56.WANG L, LAI P A, ZHANG H X, et al.Liquichip-a new highlight of high throughput detection technology[J]. Quality Safety Inspection and Testing, 2005, 15(6):55-56.(in Chinese)
[13]	岳龙涛, 高雪芹, 樊景凤.固相生物芯片表面处理及化学修饰的研究进展[J].中国生物制品学杂志, 2008, 21(8):727-731.YUE L T, GAO X Q, FAN J F.Advances in surface treatment and chemical modification of solid-phase biochips[J].Chinese Journal of Biologicals, 2008, 21(8):727-731.(in Chinese)
[14]	朱兰, 王鹏, 欧阳依娜, 等.基于SNP分子标记的云上黑山羊3个核心群遗传多样性分析[J].中国畜牧杂志, 2022, 58(10):136-143.ZHU L, WANG P, OYANG Y N, et al.Genetic diversity analysis of 3 Core Yunshang black goat populations based on SNP markers[J].Chinese Journal of Animal Science, 2022, 58(10):136-143.(in Chinese)
[15]	LIU R R, XING S Y, WANG J, et al.A new chicken 55K SNP genotyping array[J].BMC Genomics, 2019, 20(1):410.
[16]	LIU Z, SUN C J, YAN Y Y, et al.Design and evaluation of a custom 50K Infinium SNP array for egg-type chickens[J]. Poult Sci, 2021, 100(5):101044.
[17]	LI W, ZHENG M Q, ZHAO G P, et al.Identification of QTL regions and candidate genes for growth and feed efficiency in broilers[J].Genet Sel Evol, 2021, 53(1):13.
[18]	杨欣婷, 郑麦青, 谭晓冬, 等.快大型黄羽肉鸡肉品质性状的遗传参数估计和关键基因挖掘[J].畜牧兽医学报, 2021, 52(9):2416-2428.YANG X T, ZHENG M Q, TAN X D, et al.Genetic parameters estimation and key genes identification for meat quality traits of fast-growing yellow-feather meat-type chickens[J].Acta Veterinaria et Zootechnica Sinica, 2021, 52(9):2416-2428.(in Chinese)
[19]	LIU L, CUI H X, XING S Y, et al.Effect of divergent selection for intramuscular fat content on muscle lipid metabolism in chickens[J].Animals (Basel), 2020, 10(1):4.
[20]	HE Z X, LI S, LI W, et al.Comparison of genomic prediction methods for residual feed intake in broilers[J]. Anim Genet, 2022, 53(3):466-469.
[21]	HUANG S W, HE Y T, YE S P, et al.Genome-wide association study on chicken carcass traits using sequence data imputed from SNP array[J].J Appl Genet, 2018, 59(3):335-344.
[22]	KAWAGUCHI F, NAKAMURA M, KOBAYASHI E, et al.Comprehensive assessment of genetic diversity, structure, and relationship in four Japanese cattle breeds by Illumina 50 K SNP array analysis[J].Anim Sci J, 2022, 93(1):e13770.
[23]	曾滔, 赵福平, 王光凯, 等.基于群体分化指数Fst的绵羊全基因组选择信号检测[J].畜牧兽医学报, 2013, 44(12):1891-1899.ZENG T, ZHAO F P, WANG G K, et al.Genome-wide detection of selection signatures in sheep populations with use of population differentiation index Fst[J].Acta Veterinaria et Zootechnica Sinica, 2013, 44(12):1891-1899.(in Chinese)
[24]	WANG Q, LI D, GUO A, et al.Whole-genome resequencing of Dulong Chicken reveal signatures of selection[J].Br Poult Sci, 2020, 61(6):624-631.
[25]	YANG W J, LIU Z, ZHAO Q Q, et al.Population genetic structure and selection signature analysis of beijing black pig[J].Front Genet, 2022, 13:860669.
[26]	XIA X T, ZHANG S J, ZHANG H J, et al.Assessing genomic diversity and signatures of selection in Jiaxian Red cattle using whole-genome sequencing data[J].BMC Genomics, 2021, 22(1):43.
[27]	尼桂琰, 张哲, 姜力, 等.利用全基因组连锁不平衡估计中国荷斯坦牛有效群体大小[J].遗传, 2012, 34(1):50-58.NI G Y, ZHANG Z, JIANG L, et al.Chinese Holstein Cattle effective population size estimated from whole genome linkage disequilibrium[J].Hereditas (Beijing), 2012, 34(1):50-58.(in Chinese)
[28]	张鹏, 李金泉.利用连锁不平衡法发掘作物种质资源中优异基因的研究进展[J].中国农学通报, 2009, 25(8):34-37.ZHANG P, LI J Q.Reviews of the advancement on mining exotic genes in crop germplasm resources by linkage disequilibrium method[J].Chinese Agricultural Science Bulletin, 2009, 25(8):34-37.(in Chinese)
[29]	谢水华, 李加琪, 张豪, 等.利用连锁不平衡进行畜禽QTL定位的研究进展[J].中国畜牧兽医, 2007, 34(6):36-39.XIE S H, LI J Q, ZHANG H, et al.Research progress of livestock QTL localization by linkage disequilibrium[J]. China Animal Husbandry & Veterinary Medicine, 2007, 34(6):36-39.(in Chinese)
[30]	TAN X D, LIU R R, LI W, et al.Assessment the effect of genomic selection and detection of selective signature in broilers[J].Poult Sci, 2022, 101(6):101856.
[31]	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.
[32]	YANG J, LEE S H, GODDARD M E, et al.Genome-wide complex trait analysis (GCTA):methods, data analyses, and interpretations[M]//GONDRO C, VAN DER WERF J, HAYES B.Genome-Wide Association Studies and Genomic Prediction.Totowa:Humana Press, 2013:215-236.
[33]	KUMAR S, STECHER G, LI M, et al.MEGA X:molecular evolutionary genetics analysis across computing platforms[J].Mol Biol Evol, 2018, 35(6):1547-1549.
[34]	DANECEK P, AUTON A, ABECASIS G, et al.The variant call format and VCFtools[J].Bioinformatics, 2011, 27(15):2156-2158.
[35]	ZHANG K, DENG M H, CHEN T, et al.A dynamic programming algorithm for haplotype block partitioning[J]. Proc Natl Acad Sci U S A, 2002, 99(11):7335-7339.
[36]	BADKE Y M, BATES R O, ERNST C W, et al.Methods of tagSNP selection and other variables affecting imputation accuracy in swine[J].BMC Genet, 2013, 14:8.
[37]	LIU Y X, CHEN H J, HEINE J, et al.A genome-wide association study of mammographic texture variation[J]. Breast Cancer Res, 2022, 24(1):76.
[38]	JUDGE M M, KELLEHER M M, KEARNEY J F, et al.Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle[J].Animal, 2017, 11(6):938-947.
[39]	SEO D, CHO S, MANJULA P, et al.Identification of target chicken populations by machine learning models using the minimum number of SNPs[J].Animals (Basel), 2021, 11(1):241.
[40]	BERTOLINI F, GALIMBERTI G, SCHIAVO G, et al.Preselection statistics and Random Forest classification identify population informative single nucleotide polymorphisms in cosmopolitan and autochthonous cattle breeds[J].Animal, 2018, 12(1):12-19.
[41]	SCHIAVO G, BERTOLINI F, GALIMBERTI G, et al.A machine learning approach for the identification of population-informative markers from high-throughput genotyping data:application to several pig breeds[J].Animal, 2020, 14(2):223-232.
[42]	CHO E, CHO S, KIM M, et al.Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach[J].J Anim Sci Technol, 2022, 64(5):830-841.
[43]	KUMAR H, PANIGRAHI M, CHHOTARAY S, et al.Comparative analysis of five different methods to design a breed-specific SNP panel for cattle[J].Anim Biotechnol, 2021, 32(1):130-136.
[44]	GAO J, SUN L W, ZHANG S S, et al.Screening discriminating SNPs for Chinese indigenous pig breeds identification using a random forests algorithm[J].Genes (Basel), 2022, 13(12):2207.