74-plex SNPs复合检测体系在中国人群中的族群推断研究

doi:10.16288/j.yczz.19-252

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Hereditas(Beijing) ›› 2020, Vol. 42 ›› Issue (3): 296-308.doi: 10.16288/j.yczz.19-252

• Research Article • Previous Articles Next Articles

The ancestry inference of Chinese populations using 74-plex SNPs system

Yang Liu^1,², Changchun Sun^1,², Mi Ma^2,³, Ling Wang², Wenting Zhao², Quan Ma², Anquan Ji², Jing Liu²(), Caixia Li^1,²()

^1. Shanxi Medical University, Taiyuan 036000, China
^2. Key Laboratory of Forensic Genetics, Beijing Engineering Research Center of Crime Scene Evidence Examination, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing 100038, China
^3. Production and Construction Corps of Seventh Division Public Security Bureau, Kuitun 833200, China

Received:2019-11-13 Revised:2020-01-13 Online:2020-03-20 Published:2020-01-17
Contact: Liu Jing,Li Caixia E-mail:biojing@yeah.net;licaixia@tsinghua.org.cn
Supported by:
Supported by the National Natural Science Foundation of China No(81772027);National Key R&D Program of China No(2017YFC0803501);National Science and Technology Resources Sharing Service Platform Project No(YCZYPT[2017]01-3);Fundamental Research Funds for Institute of Forensic Science Nos(2017JB025);Fundamental Research Funds for Institute of Forensic Science Nos(2017JB026);Fundamental Research Funds for Institute of Forensic Science Nos(2019JB011)

Abstract

Abstract:

A panel of ancestry informative SNPs (AISNPs) can be used to analyze the genetic components of a population and infer the ancestral origin of a DNA sample. Previously, we have selected a 74-AISNPs panel and used it to infer the ancestry of unknown individuals in the following ten geographical regions: Sub-Saharan Africa, North Africa, Europe, Pacific, Americas, Southwest Asia, South Asia, North Asia, East Asia and Southeast Asia. We have also established a 74-plex SNPs assay based on SEQUENOM system. In the present study, we genotyped 1371 individuals from 14 populations of China using this multiplex assay, and validated its ability to infer the ancestry in Chinese populations. Firstly, based on the reference database of 3628 individuals from 57 world populations, Structure and Heatmap were employed to evaluate the population differentiation capacity. The training data include 1654 individuals from 14 Chinese populations and 3 populations from 1K Genome, which are not included in the reference database. Then the likelihood ratio and ancestry components were analyzed for individual ancestry assignment using the 74-plex SNPs. The minimum amount of DNA required for a full genotype of the 74 SNPs is 1.5 ng, which is applicable for forensic analysis. The results demonstrate that this system can be used in differentiating the population from ten geographical regions. The ancestry inference accuracy for EUR/SAFR/AME population is 95.4%, 71.0% for East Asia and 66.4% for Southeast Asia respectively. The ancestry inference inclusive rate for EUR/SAFR/AME population is 1.06%, 17.9% for East Asia and 33.3% for Southeast Asia respectively. The results suggest that this method can be used in forensic investigations of criminal cases.

Key words: forensic genetics, AISNPs, ancestry inference, Chinese populations

Yang Liu, Changchun Sun, Mi Ma, Ling Wang, Wenting Zhao, Quan Ma, Anquan Ji, Jing Liu, Caixia Li. The ancestry inference of Chinese populations using 74-plex SNPs system[J]. Hereditas(Beijing), 2020, 42(3): 296-308.

Figures/Tables 7

Table 1

Table 2

The information of SNP loci in each well"

反应孔	序号	SNP编号	染色体	位置	等位基因	反应孔	序号	SNP编号	染色体	位置	等位基因
Well 1	1	rs10496971	2	145769943	G/T	Well 2	38	rs1513056	12	17407792	A/G
Well 1	2	rs10511828	9	28628500	C/T	Well 2	39	rs174574	11	61600342	A/C
Well 1	3	rs10512572	17	69512099	A/G	Well 2	40	rs17822931	16	48258198	C/T
Well 1	4	rs10516441	4	100307167	A/G	Well 2	41	rs1876482	2	17362568	A/G
Well 1	5	rs12913832	15	28365618	A/G	Well 2	42	rs192655	6	90518278	A/G
Well 1	6	rs1426654	15	48426484	A/G	Well 2	43	rs1950993	14	58238687	G/T
Well 1	7	rs1572018	13	41715282	C/T	Well 2	44	rs2006996	9	117592638	C/T
Well 1	8	rs16891982	5	33951693	C/G	Well 2	45	rs2125345	17	73782191	C/T
Well 1	9	rs17028973	4	100322786	C/T	Well 2	46	rs2238151	12	112211833	C/T
Well 1	10	rs1800414	15	28197037	C/T	Well 2	47	rs3118378	1	68849687	A/G
Well 1	11	rs1871428	6	168665760	A/G	Well 2	48	rs37369	5	35037115	C/T
Well 1	12	rs2033111	17	53788280	A/G	Well 2	49	rs3814134	9	127267689	A/G
Well 1	13	rs2241894	4	100266133	C/T	Well 2	50	rs4833103	4	38815502	A/C
Well 1	14	rs2242480	7	99361466	C/T	Well 2	51	rs6451722	5	43711378	A/G
Well 1	15	rs2702414	4	179399523	A/G	Well 2	52	rs647325	1	18170886	A/G
Well 1	16	rs2899826	15	74734500	A/G	Well 2	53	rs6990312	8	110602317	G/T
Well 1	17	rs316598	5	2364626	C/T	Well 2	54	rs7226659	18	40488279	G/T
Well 1	18	rs3737576	1	101709563	C/T	Well 2	55	rs7238445	18	49781544	A/G
Well 1	19	rs3811801	4	100244319	A/G	Well 2	56	rs7554936	1	151122489	C/T
Well 1	20	rs3827760	2	109513601	A/G	Well 2	57	rs8035124	15	92105708	A/C
Well 1	21	rs385194	4	85309078	A/G	Well 2	58	rs917115	7	28172586	C/T
Well 1	22	rs459920	16	89730827	C/T	Well 2	59	rs9319336	13	27624356	C/T
Well 1	23	rs4908343	1	27931698	A/G	Well 3	60	rs1229984	4	100239319	C/T
Well 1	24	rs671	12	112241766	A/G	Well 3	61	rs174570	11	61597212	C/T
Well 1	25	rs6754311	2	136707982	C/T	Well 3	62	rs2024566	22	41697338	A/G
Well 1	26	rs734873	3	147750355	A/G	Well 3	63	rs2166624	13	42579985	A/G
Well 1	27	rs7997709	13	34847737	C/T	Well 3	64	rs2814778	1	159174683	C/T
Well 1	28	rs8003942	14	105971670	A/G	Well 3	65	rs2986742	1	6550376	C/T
Well 1	29	rs8113143	19	33652247	A/C	Well 3	66	rs310644	20	62159504	C/T
Well 1	30	rs870347	5	6845035	A/C	Well 3	67	rs4670767	2	37941396	G/T
Well 1	31	rs9522149	13	111827167	C/T	Well 3	68	rs6054605	20	744570	A/G
Well 2	32	rs10108270	8	4190793	A/C	Well 3	69	rs735480	15	45152371	C/T
Well 2	33	rs10236187	7	139447377	A/C	Well 3	70	rs7722456	5	170202984	C/T
Well 2	34	rs1040404	1	168159890	A/G	Well 3	71	rs7745461	6	21911616	A/G
Well 2	35	rs10513300	9	120130206	C/T	Well 3	72	rs798443	2	7968275	A/G
Well 2	36	rs11652805	17	62987151	C/T	Well 3	73	rs8021730	14	67886781	G/T
Well 2	37	rs13400937	2	79864923	G/T	Well 3	74	rs818386	16	65406708	C/T

Table 2

Fig. 1

Fig. 2

Fig. 3

Table 3

Fig. 4

References 35

[1]	Tishkoff SA, Kidd KK . Implications of biogeography of human populations for 'race' and medicine. Nat Genet, 2004,36(11 Suppl):S21-S27.
[2]	Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramachandran S, Cann HM, Barsh GS, Feldman M, Cavalli-Sforza LL, Myers RM . Worldwide human relationships inferred from genome-wide patterns of variation. Science, 2008,319(5866):1100-1104.
[3]	Hellenthal G, Busby GBJ, Band G, Wilson JF, Capelli C, Falush D, Myers S . A genetic atlas of human admixture history. Science, 2014,343(6172):747-751.
[4]	Sudmant PH, Mallick S, Nelson BJ, Hormozdiari F, Krumm N, Huddleston J, Coe BP, Baker C, Nordenfelt S, Bamshad M, Jorde LB, Posukh OL, Sahakyan H, Watkins WS, Yepiskoposyan L, Abdullah MS, Bravi CM, Capelli C, Hervig T, Wee JT, Tyler-Smith C, van Driem G, Romero IG, Jha AR, Karachanak-Yankova S, Toncheva D, Comas D, Henn B, Kivisild T, Ruiz-Linares A, Sajantila A, Metspalu E, Parik J, Villems R, Starikovskaya EB, Ayodo G, Beall CM, Di Rienzo A, Hammer MF, Khusainova R, Khusnutdinova E, Klitz W, Winkler C, Labuda D, Metspalu M, Tishkoff SA, Dryomov S, Sukernik R, Patterson N, Reich D, Eichler EE. Global diversity, population stratification,selection of human copy-number variation. Science, 2015, 349M(6253): aab3761.
[5]	Phillips C . Forensic genetic analysis of bio-geographical ancestry. Forensic Sci Int Genet, 2015,18:49-65.
[6]	Santos C, Phillips C, Oldoni F, Amigo J, Fondevila M, Pereira R, Carracedo Á, Lareu MV . Completion of a worldwide reference panel of samples for an ancestry informative Indel assay. Forensic Sci Int Genet, 2015,17:75-80.
[7]	Halder I, Shriver M, Thomas M, Fernandez JR, Frudakis T . A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications. Hum Mutat, 2010,29(5):648-658.
[8]	Kosoy R, Nassir R, Tian C, White PA, Butler LM, Silva G, Kittles R, Alarcon-Riquelme ME, Gregersen PK, Belmont JW, De La Vega FM, Seldin MF, . Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America. Hum Mutat, 2010,30(1):69-78.
[9]	Nievergelt CM, Maihofer AX, Shekhtman T, Libiger O, Wang X, Kidd KK, Kidd JR . Inference of human continental origin and admixture proportions using a highly discriminative ancestry informative 41-SNP panel. Investig Genet, 2013,4(1):13.
[10]	Frudakis T, Venkateswarlu K, Thomas MJ, Gaskin Z, Ginjupalli S, Gunturi S, Ponnuswamy V, Natarajan S, Nachimuthu PK . A classifier for the SNP-based inference of ancestry. J Forensic Sci, 2003,48(4):771-782.
[11]	Phillips C, Salas A, Sánchez JJ, Fondevila M, Gómez-Tato A, Álvarez-Dios J, Calaza M, de Cal MC, Ballard D, Lareu MV, Carracedo A. Inferring ancestral origin using a single multiplex assay of ancestry-informative marker SNPs. Forensic Sci Int Genet, 2007,1(3-4):273-280.
[12]	Wei YL, Wei L, Zhao L, Sun QF, Jiang L, Zhang T, Liu HB, Chen JG, Ye J, Hu L, Li CX . A single-tube 27-plex SNP assay for estimating individual ancestry and admixture from three continents. Int J Legal Med, 2015,130(1):27-37.
[13]	Wei L, Wei YL, Sun QF, Wang YY, Li CX . The development of a 27-plex SNP multiplex system. Chin J Foren Med, 2016,31(1):13-17.
	魏丽, 魏以梁, 江丽, 孙启凡, 王英元, 李彩霞 . 27-plex SNPs复合扩增检测体系构建与应用评价. 中国法医学杂志, 2016,31(1):13-17.
[14]	Jiang L, Sun QF, Ma Q, Zhao WT, Liu J, Zhao L, Ji AQ, Li CX . Optimization and validation of analysis method based on 27-plex SNP panel for ancestry inference. Hereditas (Beijing), 2017,39(2):166-173.
	江丽, 孙启凡, 马泉, 赵雯婷, 刘京, 赵蕾, 季安全, 李彩霞 . 27-plex SNP种族推断方法的优化及验证. 遗传, 2017,39(2):166-173.
[15]	Li CX, Pakstis AJ, Jiang L, Wei YL, Sun QF, Wu H, Bulbul O, Wang P, Kang LL, Kidd JR, Kidd KK . A panel of 74 AISNPs: Improved ancestry inference within Eastern Asia. Forensic Sci Int Genet, 2016,23:101-110.
[16]	Ma M, Liu J, Hu S, Zhang T, Zhou H, Feng BQ, Liu HB, Li B, Li CX . The validation study of 74-plex SNP assay for ancestry inference. Chin J Foren Med, 2019,34(4):324-329.
	马咪, 刘京, 胡胜, 张涛, 周浩, 冯保强, 刘海渤, 李蓓, 李彩霞 . 74重SNP族群来源推断体系准确性验证研究. 中国法医学杂志, 2019,34(4):324-329.
[17]	Ren P, Liu J, Zhao H, Fan XP, Xu YC, Li CX . Construction of a rapid microfluidic-based SNP genotyping (MSG) chip for ancestry inference. Forensic Sci Int Genet, 2019,41:145-151.
[18]	Clendenen TV, Rendleman J, Ge W, Koenig KL, Wirgin I, Currie D, Shore RE, Kirchhoff T, Zeleniuch-Jacquotte A . Genotyping of single nucleotide polymorphisms in DNA isolated from serum using sequenom MassARRAY technology. PLoS One, 2015,10(8):e0135943.
[19]	Falush D, Stephens M, Pritchard JK . Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes, 2007,7(4):574-578.
[20]	Liu J, Li S, Jang L, Zhao L, Zhao WT, Feng L, Liu HB, Ji AQ, Li CX . DNA Ancestry Analyzer: an automatic program for ancestry inference of unknown individuals. Life Sci Res, 2018,22(1):3-7, 41.
	刘京, 李盛, 江丽, 赵蕾, 赵雯婷, 丰蕾, 刘海渤, 季安全, 李彩霞 . 对于未知来源个体进行族群推断的自动分析系统. 生命科学研究, 2018,22(1):3-7, 41.
[21]	Phillips C, McNevin D, Kidd KK, Lagacé R, Wootton S, de la Puente M, Freire-Aradas A, Mosquera-Miguel A, Eduardoff M, Gross T, Dagostino L, Power D, Olson S, Hashiyada M, Oz C, Parson W, Schneider PM, Lareu MV, Daniel . MAPlex - A massively parallel sequencing ancestry analysis multiplex for Asia-Pacific populations. Forensic Sci Int Genet, 2019,42:213-226.
[22]	Sun K, Yun LB, Zhang C, Shao CC, Gao TZ, Zhao ZQ, Hou YP, Xie JH, Tang QQ . Evaluation of 12 Multi-InDel markers for forensic ancestry prediction in Asian populations. Forensic Sci Int Genet, 2019,43:102155.
[23]	Xu Y, Zhang XL, Zhang QC, Cui YQ, Zhou H, Zhu H . Genetic relationship between ancient Khitan and modern Daur. J Jilin Univ(Sci Ed), 2006,44(6):997-1000.
	许月, 张小雷, 张全超, 崔银秋, 周慧, 朱泓 . 古代契丹与现代达斡尔遗传关系分析. 吉林大学学报(理学版), 2006,44(6):997-1000.
[24]	Zhu H . The ethnic type and related issues of the Khitay. Acta Sci Natl Univ Neimongol(Hum Soc Sci), 1991(2):36-41.
	朱泓 . 契丹族的人种类型及其相关问题. 内蒙古大学学报(哲学社会科学版), 1991, ( 2):36-41.
[25]	Fan H . Relationship among 28 Chinese populations in western and southern of China based on STR loci. J Kunming Med Univ, 2006.
	范浩 . 应用STR位点研究中国西、南部28个民族群体族源关系. 昆明医学院, 2006.
[26]	Du R, Xiao C, Cavalli-Sforza LL . Genetic distances between Chinese populations calculated on gene frequencies of 38 loci. Sci China C Life Sci, 1997,40(6):613-621.
[27]	Gayden T, Mirabal S, Cadenas AM, Lacau H, Simms TM, Morlote D, Chennakrishnaiah S, Herrera RJ . Genetic insights into the origins of Tibeto-Burman populations in the Himalayas. J Hum Genet, 2009,54(4):216-223.
[28]	Zhao YB, Yu CC, Zhou H . Study on the origin and development of the Han Chinese. Jilin Norm Univ J(Nat Sci Ed), 2012,33(4):45-49.
	赵永斌, 于长春, 周慧 . 汉族起源与发展的遗传学探索. 吉林师范大学学报(自然科学版), 2012,33(4):45-49.
[29]	Huang YZ . The historical migration of the Han population and the color pattern of the southern Han folk songs. Musicol China, 1989(4):36-48.
	黄允箴 . 汉族人口的历史迁徙与南方汉族民歌的色彩格局. 中国音乐学, 1989, ( 4):36-48.
[30]	Xu SH, Yin XY, Li LS, Jin WF, Lou HY, Yang L, Gong XH, Wang HY, Shen YP, Pan XD, He YG, Yang YJ, Wang Y, Fu WQ, An Y, Wang JC, Tan JZ, Qian J, Chen XL, Zhang X, Sun YF, Zhang XJ, Wu BL, Jin L . Genomic dissection of population substructure of Han Chinese and its implication in association studies. Am J Hum Genet, 2009,85(6):762-774.
[31]	Wang YS . Historical evolution of the Han population and its survival area. Hist Teach, 2010, ( 6):3-7.
	王跃生 . 汉族人口及其生存区域的历史演变. 历史教学(下半月刊), 2010, ( 6):3-7.
[32]	Mo L, Wang CL . Analysis on the characteristics of Guangxi Jing population. Stud Ethn Guangxi, 1990, ( 3):17-22.
	莫龙, 王春林 . 广西京族人口特点浅析. 广西民族研究, 1990, ( 3):17-22.
[33]	Jin TB, Gao Y, Chen T, Yan HX, Li SB . Genetic relationships of 15 populations of Guangxi province. J Xi'an Jiaotong Univ (Med Sci), 2004,25(5):422-424, 429.
	金天博, 高雅, 陈腾, 阎春霞, 李生斌 . 广西地区15个不同民族人群的群体遗传学关系. 西安交通大学学报 (医学版), 2004,25(5):422-424, 429.
[34]	Tang JP, Yu X, Jiang FH, Yu XJ . Analyzing population differentiation between Hall and other population of Guangxi. Int J Genet, 2008,31(6):409-412.
	唐剑频, 于昕, 蒋丰慧, 于晓军 . 广西汉族群体与其他群体的群体差异分析. 国际遗传学杂志, 2008,31(6):409-412.
[35]	Qian YP . Rearch on genetic diversity of five Yunnan ethnic groups in China[Dissertation]. Peking Union Medical College, 1999.
	钱亚屏 , 中国云南5个民族的遗传多样性研究[学位论文]. 中国协和医科大学, 1999.

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代码	人群信息	数量	样本来源
EIC	内蒙古鄂温克族人	35	本实验室
DIC	内蒙古达斡尔族人	34	本实验室
MIC	内蒙古蒙古族人	135	本实验室
TUQ	青海土族人	116	本实验室
CCT	西藏藏族人	143	本实验室
CHQ	青海汉族人	100	本实验室
CHL	青岛汉族人	94	本实验室
CHN	河南汉族人	107	本实验室
HGC	广西汉族人	79	本实验室
CHG	广东汉族人	46	本实验室
HCM	广东梅州客家汉族人	50	本实验室
DGC	广西侗族人	155	本实验室
CDY	云南傣族人	93	本实验室
KGC	广西京族人	184	本实验室
ESN	非洲尼日利亚人	99	千人基因组
FIN	芬兰人	99	千人基因组
PEL	秘鲁人	85	千人基因组

测试人群（代码）	来源区域	预测第1位的人群(LR>10)							不排除 (LR≤10)	错误	样本数量
测试人群（代码）	来源区域	撒哈拉以南的非洲	北非	欧洲	东亚	东北亚	东南亚	美洲	不排除 (LR≤10)	错误	样本数量
内蒙古达斡尔族(DIC)	EA(东亚)				19 (0.42)	8 (0.45)			4	3	34
内蒙古鄂温克族(EIC)	EA(东亚)				24 (0.56)	3(0.31)			3	5	35
内蒙古蒙古族(MIC)	EA(东亚)				59 (0.31)	37 (0.56)	2		26	13	135
青海土族(TUQ)	EA(东亚)				76 (0.26)	3 (0.63)			20	17	116
青海汉族(CHQ)	EA(东亚)				74 (0.41)	3 (0.46)			5	18	100
西藏藏族(CTT)	EA(东亚)				71 (0.14)	17 (0.78)	2		36	19	143
河南汉族(CHN)	EA(东亚)				87 (0.65)	1 (0.15)	2 (0.15)		1	19	107
青岛汉族(CHL)	EA(东亚)				85 (0.61)	(0.28)	1		0	9	94
广东汉族(CHG)	EA(东亚)				13 (0.49)		13 (0.45)		20		46
广西汉族(HGC)	EA(东亚)				13 (0.31)		30 (0.60)		36		79
广东客家汉族(HCM)	EA(东亚)				17(0.38)		15 (0.53)		17	1	50
广西侗族(DGC)	SEA(东南亚)				27 (0.32)		65 (0.62)		63		155
广西京族(KGC)	SEA(东南亚)				16 (0.21)		109 (0.73)		58	1	184
云南傣族(CDY)	SEA(东南亚)				6 (0.18)		64 (0.76)		23		93
非洲尼日利亚人 (ESN)	S-AFR(撒哈拉以南的非洲)	99 (0.98)							0		99
芬兰人(FIN)	EUR(欧洲)			99 (0.93)					0		99
秘鲁人(PEL)	AME(美洲)		1			3		72(0.74)	3	10	85

The ancestry inference of Chinese populations using 74-plex SNPs system

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