基于低深度全基因组测序分析内江猪群体结构和遗传多样性

doi:10.11843/j.issn.0366-6964.2023.06.010

摘要/Abstract

摘要： 旨在更好的了解内江猪群体结构和遗传多样性,更好的保护和利用内江猪遗传资源。本研究基于低深度全基因组测序,检测了133头(12头公猪,121头母猪)内江猪的SNP,按照SNP检出率大于90%和95%获得两组SNP数据,分别编号为NJ90和NJ95。针对两组数据,通过群体分层、遗传距离、亲缘关系、家系划分等方法分析了内江猪群体结构,通过等位基因频率、有效等位基因数、多态性标记比、杂合度等指标估计了群体遗传多样性,最后利用不同方法评估了群体近交系数。结果显示:1)NJ90共包含135 760个SNPs位点,其等位基因频率为0.87,有效等位基因数为1.27,多态性标记比为0.76,观测杂合度为0.15,期望杂合度为0.21;NJ95包含32 266个SNPs位点,其等位基因频率为0.79,有效等位基因数为1.44,多态性标记比为0.74,观测杂合度为0.30,期望杂合度为0.31。2)NJ90结果显示群体平均遗传距离为0.20,平均亲缘系数为0.9%;NJ95结果显示群体平均遗传距离为0.25,平均亲缘系数为0.7%。3)根据NJ90公猪和群体聚类以及亲缘关系,可将群体分为6个家系。4)根据SNP纯合性评估群体近交系数,NJ90和NJ95平均近交系数分别为0.27和0.01;根据连续性纯合片段估计群体近交系数,NJ90和NJ95平均近交系数分别为6.65%和0.02%。综上所述,内江猪群体具有较为丰富的遗传多样性,群体遗传距离和亲缘关系较远,近交程度较低,根据公猪聚类和亲缘关系可以分为6个家系,具有较好的遗传资源保护和开发利用的基础。同时低深度重测序对比SNP芯片能够更大范围覆盖基因组信息,对群体遗传多样性分析和遗传结构分析更具参考价值。

关键词: 低深度全基因组测序, 内江猪, 群体结构, 遗传多样性

Abstract: The study aimed to better understand the population structure and genetic diversity of Neijiang pigs, make a better protection and utilization of the genetic resources of Neijiang pig. The SNPs of 133 Neijiang pigs (12 boars, 121 sows) was detected based on low-coverage whole genome sequencing. Two sets of SNPs data, with different calling rates 90% or 95%, were obtained and named as NJ90 and NJ95. Population structure of Neijiang pigs was analyzed by population stratification, genetic distance, genetic relationship and boar genome family. The population genetic diversity was estimated by allele frequency, effective number of alleles, proportion of polymorphic and heterozygosity. Finally, the inbreeding coefficient of population was evaluated by different methods. The results showed as follows: 1) NJ90 contained 135 760 SNPs. The allele frequency, effective number of alleles, and proportion of polymorphic were 0.87, 1.27, and 0.76, respectively. The observed heterozygosity and expected heterozygosity were 0.15 and 0.21. NJ95 contained 32 266 SNPs. The allele frequency, effective allele number, and proportion of polymorphic were 0.79, 1.44, and 0.74, respectively. The observed heterozygosity and expected heterozygosity was 0.30 and 0.31. 2) The results of NJ90 showed that the average genetic distance of the population was 0.20, and the average kinship coefficient was 0.9%. The results of NJ95 showed that the average genetic distance of the population was 0.25, and the average kinship coefficient was 0.7%. 3) According to the boars of NJ90 and population clustering as well as kinship, the population could be divided into 6 families. 4) According to SNPs homozygosity, the average inbreeding coefficients of NJ90 and NJ95 were 0.27 and 0.01, respectively. According to the runs of homozygosity, the average inbreeding coefficients of NJ90 and NJ95 were 6.65% and 0.02%. In conclusion, the Neijiang pigs are relative abundant in genetic diversity, far in genetic distance and kinship, and low in inbreeding degree. The population can be divided into 6 families according to boars clustering and kinship analysis, which has a good basis for the protection, exploration and utilization of genetic resource. Moreover, low-coverage whole genome sequencing can cover genome information in a wider range, which is of more reference value for population genetic diversity and genetic structure analysis.

Key words: low-coverage whole genome sequencing, Neijiang pig, population structure, genetic diversity

中图分类号:

S828.2

赵真坚, 王书杰, 陈栋, 姬祥, 申琦, 余杨, 崔晟頔, 王俊戈, 陈子旸, 唐国庆. 基于低深度全基因组测序分析内江猪群体结构和遗传多样性[J]. 畜牧兽医学报, 2023, 54(6): 2297-2307.

ZHAO Zhenjian, WANG Shujie, CHEN Dong, JI Xiang, SHEN Qi, YU Yang, CUI Shengdi, WANG Junge, CHEN Ziyang, TANG Guoqing. Population Structure and Genetic Diversity Analysis of Neijiang Pigs Based on Low-coverage Whole Genome Sequencing[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(6): 2297-2307.

参考文献 41

[1]	陈帅.内江猪品种特性及生态养殖[J].四川畜牧兽医,2014,41(9):45.CHEN S.Neijiang pig breed characteristics and ecological breeding[J].Sichuan Animal & Veterinary Sciences,2014, 41(9):45.(in Chinese)
[2]	段诚中,唐显作,李宴群.内江猪的选育及利用[J].猪业科学,2008,25(12):92-94.DUAN C Z,TANG X Z,LI Y Q.Selection and utilization of Neijiang pigs[J].Swine Industry Science,2008,25(12):92-94.(in Chinese)
[3]	黄礼光,王希龙,王峰,等.内江猪在海南的引种繁育与杂交利用[J].四川畜牧兽医,2003,30(4):23-24.HUANG L G,WANG X L,WANG F,et al.Study on the introducing reproduction and crossbreeding of Neijiang pig in Hainan[J].Sichuan Animal & Veterinary Sciences,2003,30(4):23-24.(in Chinese)
[4]	夏运红,胡红文,黄跃成,等.浅析内江猪种质资源保护与利用现状及发展对策[J].中国猪业,2021,16(6):90-92.XIA Y H,HU H W,HUANG Y C,et al.The current situation of the protection,utilization and development of Neijiang pigs[J].China Swine Industry,2021,16(6):90-92.(in Chinese)
[5]	JOAQUIM L B,CHUD T C S,MARCHESI J A P,et al.Genomic structure of a crossbred Landrace pig population[J].PLoS One,2019,14(2):e0212266.
[6]	CAI Z X,SARUP P,OSTERSEN T,et al.Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds[J].J Anim Sci,2020,98(7):skaa229.
[7]	DELPUECH E,ALIAKBARI A,LABRUNE Y,et al.Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency[J].Genet Sel Evol,2021,53(1):49.
[8]	LIU Z,SUN H,LAI W,et al.Genome-wide re-sequencing reveals population structure and genetic diversity of Bohai Black cattle[J].Anim Genet,2022,53(1):133-136.
[9]	MCKAY S D,SCHNABEL R D,MURDOCH B M,et al.An assessment of population structure in eight breeds of cattle using a whole genome SNP panel[J].BMC Genet,2008,9:37.
[10]	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.
[11]	IHESHIULOR O O M,WOOLLIAMS J A,YU X J,et al.Within-and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels[J].Genet Sel Evol,2016,48:15.
[12]	AL KALALDEH M,GIBSON J,DUIJVESTEIJN N,et al.Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep[J].Genet Sel Evol,2019,51(1):32.
[13]	AL-MAMUN H A,CLARK S A,KWAN P,et al.Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep[J].Genet Sel Evol,2015,47:90.
[14]	ZHOU M L,WANG G F,CHEN M H,et al.Genetic diversity and population structure of sheep (Ovis aries) in Sichuan,China[J].PLoS One,2021,16(9):e0257974.
[15]	刘家鑫,魏霞,邓天宇,等.绵羊全基因组ROH检测及候选基因鉴定[J].畜牧兽医学报,2019,50(8):1554-1566.LIU J X,WEI X,DENG T Y,et al.Genome-wide scan for run of homozygosity and identification of corresponding candidate genes in sheep populations[J].Acta Veterinaria et Zootechnica Sinica,2019,50(8):1554-1566.(in Chinese)
[16]	CENDRON F,MASTRANGELO S,TOLONE M,et al.Genome-wide analysis reveals the patterns of genetic diversity and population structure of 8 Italian local chicken breeds[J].Poult Sci,2021,100(2):441-451.
[17]	LIU R R,XING S Y,WANG J,et al.A new chicken 55K SNP genotyping array[J].BMC Genomics,2019,20(1):410.
[18]	RAJKUMAR U,PRINCE L L L,RAJARAVINDRA K S,et al.Analysis of (co) variance components and estimation of breeding value of growth and production traits in Dahlem Red chicken using pedigree relationship in an animal model[J].PLoS One,2021,16(3):e0247779.
[19]	CHAT V,FERGUSON R,MORALES L,et al.Ultra low-coverage whole-genome sequencing as an alternative to genotyping arrays in genome-wide association studies[J].Front Genet,2022,12:790445.
[20]	LOU R N,JACOBS A,WILDER A P,et al.A beginner's guide to low-coverage whole genome sequencing for population genomics[J].Mol Ecol,2021,30(23):5966-5993.
[21]	ZHOU B,HO S S,ZHANG X L,et al.Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis[J].J Med Genet,2018,55(11):735-743.
[22]	RUSTAGI N,ZHOU A B,WATKINS W S,et al.Extremely low-coverage whole genome sequencing in South Asians captures population genomics information[J].BMC Genomics,2017,18(1):396.
[23]	ZAN Y J,PAYEN T,LILLIE M,et al.Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders:a cost-efficient approach[J].Genet Sel Evol,2019,51(1):44.
[24]	DRUET T,MACLEOD I M,HAYES B J.Toward genomic prediction from whole-genome sequence data:impact of sequencing design on genotype imputation and accuracy of predictions[J].Heredity (Edinb),2014,112(1):39-47.
[25]	MEUWISSEN T,GODDARD M.Accurate prediction of genetic values for complex traits by whole-genome resequencing[J]. Genetics,2010,185(2):623-631.
[26]	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.
[27]	刘彬,沈林園,陈映,等.基于SNP芯片分析青峪猪保种群体的遗传结构[J].畜牧兽医学报,2020,51(2):260-269.LIU B,SHEN L Y,CHEN Y,et al.Analysis of genetic structure of conservation population in Qingyu pig based on SNP Chip[J].Acta Veterinaria et Zootechnica Sinica,2020,51(2):260-269.(in Chinese)
[28]	戴丽荷,褚晓红,陈晓宇,等.利用SNP标记分析淳安花猪群体遗传多样性和群体结构[J].养猪,2021(6):59-64.DAI L H,CHU X H,CHEN X Y,et al.Analysis of genetic diversity and genetic structure in Chun'an spotted pigs conserved population based on SNP Chip[J].Swine Production,2021(6):59-64.(in Chinese)
[29]	袁娇,徐国强,周翔,等.基于SNP芯片监测通城猪的保种效果[J].畜牧兽医学报,2022,53(8):2514-2523.YUAN J,XU G Q,ZHOU X,et al.SNP Chip-based monitoring of population conservation effect of Tongcheng pigs[J].Acta Veterinaria et Zootechnica Sinica,2022,53(8):2514-2523.(in Chinese)
[30]	时坤鹏,刘莹,张志勇,等.基于SNP芯片分析安庆六白猪群体遗传结构[J].中国畜牧杂志,2022,58(8):136-140.SHI K P,LIU Y,ZHANG Z Y,et al.Analasis of genetic structure of Anqing Liubai pig based on SNP chip[J].Chinese Journal of Animal Science,2022,58(8):136-140.(in Chinese)
[31]	李晓,崔超,王源,等.基于SNP芯片对里岔黑猪进行遗传多样性与遗传结构分析的研究[J].中国畜牧杂志,2022, 58(11):117-122.LI X,CUI C,WANG Y,et al.Research on genetic diversity and genetic structure analysis of Licha black pig based on SNP chip[J].Chinese Journal of Animal Science,2022,58(11):117-122.(in Chinese)
[32]	王余北,肖莲梅,何帅涵,等.基于50K SNP芯片信息的枣庄黑盖猪群体遗传结构分析[J].中国畜牧杂志,2022, 58(9):168-171,178.WANG Y B,XIAO L M,HE S H,et al.Genetic structure analysis of Zaozhuang black-capped pigs population based on 50K SNP chip[J].Chinese Journal of Animal Science,2022,58(9):168-171,178.(in Chinese)
[33]	DAVIES R W,KUCKA M,SU D W,et al.Rapid genotype imputation from sequence with reference panels[J].Nat Genet, 2021, 53(7):1104-1111.
[34]	BROWNING B L,BROWNING S R.A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals[J].Am J Hum Genet,2009,84(2):210-223.
[35]	MCGIVNEY B A,HAN H G,CORDUFF L R,et al.Genomic inbreeding trends,influential sire lines and selection in the global Thoroughbred horse population[J].Sci Rep,2020,10(1):466.
[36]	WANG J.Marker-based estimates of relatedness and inbreeding coefficients:an assessment of current methods[J].J Evol Biol,2014,27(3):518-530.
[37]	GOMEZ-RAYA L,RODRÍGUEZ C,BARRAGÁN C,et al.Genomic inbreeding coefficients based on the distribution of the length of runs of homozygosity in a closed line of Iberian pigs[J].Genet Sel Evol,2015,47:81.
[38]	DADOUSIS C,ABLONDI M,CIPOLAT-GOTET C,et al.Genomic inbreeding coefficients using imputed genotypes: assessing different estimators in Holstein-Friesian dairy cows[J].J Dairy Sci,2022,105(7):5926-5945.
[39]	GURGUL A,SZMATOŁA T,TOPOLSKI P,et al.The use of runs of homozygosity for estimation of recent inbreeding in Holstein cattle[J].J Appl Genet,2016,57(4):527-530.
[40]	刘刚,孙飞舟,朱芳贤,等.连续性纯合片段在畜禽基因组研究中的应用[J].遗传,2019,41(4):304-317.LIU G,SUN F Z,ZHU F X,et al.Runs of homozygosity and its application on livestock genome study[J].Hereditas (Beiijng),2019,41(4):304-317.(in Chinese)
[41]	CABALLERO A,VILLANUEVA B,DRUET T.On the estimation of inbreeding depression using different measures of inbreeding from molecular markers[J].Evol Appl,2021,14(2):416-428.