CN110885888B - SNP marker combination for deducing different geographical region populations of Asia - Google Patents

SNP marker combination for deducing different geographical region populations of Asia Download PDF

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CN110885888B
CN110885888B CN201811043117.2A CN201811043117A CN110885888B CN 110885888 B CN110885888 B CN 110885888B CN 201811043117 A CN201811043117 A CN 201811043117A CN 110885888 B CN110885888 B CN 110885888B
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陈华
石承民
赵石磊
刘琪
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Abstract

The invention belongs to the field of biotechnology, and particularly discloses an SNP marker combination for deducing people in different geographical regions of Asia, wherein specific information of contained SNP molecular markers is shown in Table 1. The SNP marker combination provided by the invention can distinguish and infer islands of east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia, and the distinguishing accuracy of the SNP marker combination with different capacities can reach 90.04%, 95.05% and 96.19% respectively.

Description

SNP marker combination for deducing different geographical region populations of Asia
Technical Field
The invention belongs to the field of biotechnology, and particularly relates to a SNP marker combination for deducing people in different geographical areas (islands of east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia) of Asia.
Background
Asia is the most widely-populated continent, accounting for about 30% of the terrestrial land area, and has a 60% distribution of the world population. Meanwhile, Asia is also an area with a plurality of nationalities, various language families and complex religious education. Although different populations are currently characterized by a transnational distribution, historically the same ethnic, linguistic, and religious populations have long-term colonized distributions with prominent regional characteristics. The evolutionary characteristic of the population enables different people to have clear geographic ancestry, and the population genetics basis of population geographic ancestry deduction is formed.
Large-scale population genome studies have shown that genetic variation in the genome can be reliably inferred using the geographic and ethnic origins of individuals. For example, cluster analysis of genomic level data can unambiguously reflect intercontinental origin in a population. In europe, genomic level genetic variation reveals a high degree of population structure overlapping with international administrative maps, suggesting that genetic variation may reflect the geographic origin of the population. However, the high-density, genomic-level genotyping described above is poorly suited for analysis of most forensic samples; only trace amounts of highly degraded DNA could be obtained from these samples. Therefore, screening of highly informative molecular marker combinations from the genome for human ancestry is crucial for efficient inference of human ancestry.
In recent years, the population flows across regions and boundaries are becoming more frequent due to the extreme imbalance of regional economic development. Particularly, with the continuous high-speed development of Chinese economy and the increasing promotion of comprehensive national force, a large number of people are introduced into China from different regions through different approaches, and enter various industries of society. To some extent, these foreign populations also pose significant risks to social security and national security. Because the people in the same region of Asia are overlapped with the people in China to a certain extent in the aspects of nationality, language and religious belief and are highly similar to the people in China in the aspect of physical and morphological characteristics, the inference on the geographical ancestry of the people in Asia is particularly important in judicial investigation. Reliable identification of different geographical populations will effectively improve the progress of the survey and the efficiency of the investigation.
The following characteristics of Single Nucleotide Polymorphism (SNP) make it an ideal molecular marker for population ancestral inference in forensic research: first, SNP markers are the most abundant markers in the human genome, with an average of 1 SNP site every 1250 bases; secondly, the SNP marker is stable, has a full-range allele frequency mode and has the potential of ancestral inference; thirdly, linkage disequilibrium exists among different SNP markers in the genome, and information redundancy is realized when ancestral inference is carried out, so that the number of SNP sites needing to be analyzed can be reduced by reducing the information redundancy degree among the SNP markers; fourthly, SNP markers require short amplified fragments for genotyping, and are particularly suitable for analysis of degraded DNA samples.
However, whole genome SNP analysis is costly, has a large demand for DNA samples, and is difficult to adapt to forensic applications and genetic analysis of very large sample populations. Therefore, the result of the genome-wide SNP analysis is needed, an efficient SNP molecular marker reference system is constructed, and the efficient SNP molecular marker reference system is used for reliably distinguishing and identifying the human population so as to meet the needs of forensic and population genetic analysis. The characteristics of forensic identification samples are combined, a set of simple, efficient and accurate molecular markers suitable for analyzing trace samples and deducing individual sources are provided, and the efficiency of investigation work is greatly improved.
Disclosure of Invention
To solve the problems of the prior art, the invention aims to provide a mark combination for deducing the people in different geographical areas of Asia.
In order to realize the purpose of the invention, the technical scheme of the invention is as follows:
the invention firstly provides a SNP marker combination (SNP reference system) for deducing people in different geographical regions of Asia, which is called BIG-AsianTag-24panel for short, and comprises the following 24 SNP molecular markers:
rs3131522、rs10888768、rs3792006、rs260690、rs11123128、rs10191411、rs7638187、rs9837708、rs1675497、rs7669241、rs4712358、rs9395112、rs2168587、rs13232911、rs7002043、rs11038167、rs174570、rs4408369、rs9285110、rs9522149、rs1834640、rs6500380、rs4789182、rs3746807。
the average classification accuracy of BIG-AsianTag-24panel on island populations of east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia can reach 90.04%.
Further, on the basis of the BIG-AsianTag-24panel, in order to improve the average classification accuracy of the SNP reference system, the invention supplements 24 new SNP molecular markers on the basis of the BIG-AsianTag-24panel to form a new marker combination (SNP reference system), which is called BIG-AsianTag-48panel for short and comprises the following 48 SNP molecular markers:
rs3131522、rs10888768、rs3792006、rs260690、rs11123128、rs10191411、rs7638187、rs9837708、rs1675497、rs7669241、rs4712358、rs9395112、rs2168587、rs13232911、rs7002043、rs11038167、rs174570、rs4408369、rs9285110、rs9522149、rs1834640、rs6500380、rs4789182、rs3746807、rs6588412、rs10494447、rs888574、rs17033180、rs4495184、rs6465485、rs10096702、rs16879442、rs3864668、rs7836463、rs1414202、rs6481038、rs1881731、rs1902689、rs3995780、rs11027293、rs3741259、rs17067700、rs17323696、rs2224442、rs753357、rs3091402、rs12443685、rs138908。
the average classification accuracy of BIG-AsianTag-48panel on island populations of east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia can reach 95.05%.
Further, on the basis of the BIG-AsianTag-48panel, in order to improve the average classification accuracy of the SNP reference system, the invention supplements 10 new SNP molecular markers on the basis of the BIG-AsianTag-48panel to form a new marker combination (SNP reference system), which is called BIG-AsianTag-58panel for short and comprises the following 58 SNP molecular markers:
rs3131522、rs10888768、rs3792006、rs260690、rs11123128、rs10191411、rs7638187、rs9837708、rs1675497、rs7669241、rs4712358、rs9395112、rs2168587、rs13232911、rs7002043、rs11038167、rs174570、rs4408369、rs9285110、rs9522149、rs1834640、rs6500380、rs4789182、rs3746807、rs6588412、rs10494447、rs888574、rs17033180、rs4495184、rs6465485、rs10096702、rs16879442、rs3864668、rs7836463、rs1414202、rs6481038、rs1881731、rs1902689、rs3995780、rs11027293、rs3741259、rs17067700、rs17323696、rs2224442、rs753357、rs3091402、rs12443685、rs138908、rs2760519、rs10190125、rs1388612、rs2243290、rs1204250、rs4733581、rs10512188、rs7131166、rs2000926、rs1323910。
the average classification accuracy of the BIG-AsianTag-58panel on the population in different geographical regions of Asia can reach 96.19%.
The specific information of the SNP molecular markers of the present invention is shown in Table 1:
TABLE 1 SNPs information of Asian panel
Figure BDA0001792571620000041
Figure BDA0001792571620000051
Further, the present invention provides the use of the aforementioned SNP marker combination in any one of:
(1) constructing a DNA chip or a multiple PCR genotype analysis or other applicable kit;
(2) identifying the identities of people in different geographical areas of Asia;
(3) and analyzing the ancestral source of the population in different geographical areas of Asia.
It should be noted that, those skilled in the art can design a primer (a set of primers) or a gene chip by applying conventional technical means according to the specific information of the SNP molecular markers involved in the SNP marker combination of the invention, which is not limited in the present invention, but the set of primers or the gene chip designed according to the SNP marker combination provided by the present invention for detecting any one of the SNP marker combinations described above belong to the protection scope of the present invention.
The primer set or gene chip can be applied to any one of the following aspects:
(1) constructing a DNA chip or a multiple PCR genotype analysis or other applicable kit;
(2) identifying the identities of people in different geographical areas of Asia;
(3) and analyzing the ancestral source of the population in different geographical areas of Asia.
The different geographical regions of asia referred to herein are islands of east asia, west asia, south asia, north asia, middle asia and southeast asia.
The invention has the beneficial effects that:
according to the invention, by extracting SNP marker combinations with high information graduation from massive genome data, reference systems which can be used for distinguishing east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia island crowds are formed, and the distinguishing accuracy of the reference systems with different sizes can reach 90.04%, 95.05% and 96.19% respectively.
The efficient SNP molecular marker combination constructed by the invention is used for deducing the geographical ancestors of different Asia populations (east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia islands), and can meet the needs of forensic and medical genetic analysis.
Drawings
FIG. 1 is a graph showing the variation of the average classification accuracy of SNP numbers in example 2.
FIG. 2 is a comparison of the results of PCA plots for all 349381 SNPs in example 2, BIG-AsianTag-58panel, BIG-AsianTag-48panel, and BIG-AsianTag-24 panel.
Detailed Description
The present invention is further illustrated by the following examples. It is to be understood that the following examples are given for illustrative purposes only and are not intended to limit the scope of the present invention. Various modifications and alterations of this invention will become apparent to those skilled in the art without departing from the spirit and scope of this invention.
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified.
Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
Example 1
In this example, genomic level SNP data of 2276 samples were screened from the thousand human genome project (1000GP), EGDP, HGDP, SGDP, SSIP, SSMP database for six geographical populations of islands in east asia, west asia, south asia, north asia, central asia, and southeast asia. The population and sample size contained in each region are shown in table 1. Through integration and merging of data from different sources, site quality control and individual screening, an original data set consisting of 349381 SNP points of 2128 non-related individuals is formed and used for constructing a subsequent regional ancestral indicative SNP combination.
1000GP the 1000genome Project, thousand human genome Project, A global reference for human genetic variation, Nature 526(7571) and (2015) 68-74.
EGDP: estonian biological Human Genome Diversity Panel, the Genomic analysis on simulation events during the cloning of the European of Eurasia, Nature 538(7624) (2016) 238-242.
HGDP: human Genome Diversity Project, A Human Genome Diversity cell line panel, Science 296(5566) (2002) 261-.
SGDP: simons Genome Diversity Project, The Simons Genome Diversity Project, 300genomes from 142 differential publications, Nature 538(7624) (2016) 201-.
SSIP: singapore Sequencing Indian Project, Singapore Indian Sequencing Project, insight into the Genetic Structure and Diversity of 38South Asian Indians from Deep wheel Sequencing, PLoS Genetics,10,5(2014-5-15)10 (2014) e 1004377.
SSMP: singapore Sequencing Malay Project, Singapore Malayne Sequencing Project, Deep floor-genome Sequencing of 100 souutheast Asians Malays, American journal of human genetics 92(1), (2013) 52-66.
TABLE 2 sample sources
Figure BDA0001792571620000081
This example illustrates the extraction of BIG-AsianTag-24panel, BIG-AsianTag-48panel, and BIG-AsianTag-58panel from a total of 349381 SNPs in the above-described sample.
The AsianTag construction process is as follows, and the method specifically comprises the following steps:
(1) data segmentation:
taking a known genetic marker database as a sample, and segmenting sample data according to genetic information difference to obtain four types: (west, south, middle-north-east-south); (east-southeast asia, middle asia-north asia); (central asia, north asia); (east asia, southeast asia).
(2) And (3) data filtering:
calculating F of genetic markers in four classes respectivelySTThe genetic markers in the four classes are sorted in a descending order, and the top 20000 genetic markers sorted in each class are reserved;
(3) selecting a genetic marker: the AIM-SNPTag method was used to pick 100 markers from 20000 genetic markers in the four classes of data, forming four MaC pools. The four MaC pools were then fused to obtain the final SNP marker combination. Setting the fusion termination threshold as: 1) the average accuracy rate (AAC) of the SNP marker combination reaches 0.90; 2) the number of SNPs in the SNP marker combination reaches 100. Finally, the termination condition 2) was satisfied when the number of SNPs was 100).
(4) In 100 SNP combinations, extracting the first 24 SNPs to construct a BIG-AsianTag-24panel panel, wherein the ancestral inference accuracy is 90.04%; extracting the first 48 SNPs to construct a BIG-AsianTag-48panel panel, wherein the ancestral inference accuracy is 95.05%; the BIG-AsianTag-58panel is constructed by extracting the first 58 SNPs, and the ancestral inference accuracy is 96.19 percent.
Example 2
Example 1 a combination of SNPs that is favorable for ancestral inference was extracted from a total of 349381 SNPs. The algorithm can balance the ancestral inference ability of the SNPs themselves and the information overlap between different SNPs to obtain the best combined inference effect. The screened SNPs are sequentially added, and the average classification accuracy AAC is calculated, and the obtained curve is shown in FIG. 1. The classification accuracy AC is defined as the ratio of the number of correctly classified samples to the total number of test samples,
Figure BDA0001792571620000091
the average classification accuracy (AAC) is defined as the average of 1000 repeated AC values under randomly selected test sets.
In this example, the performance of the SNP reference systems obtained in examples 1 to 3 was evaluated in three ways. The first way is to directly compare the true and predicted ancestors; the second way is to calculate the commonly used indexes in the classification problem, including Sensitivity (Sensitivity), Specificity (Specificity), Positive Predictive Value (PPV), and Negative Predictive Value (NPV); the third way is to visually analyze the performance of the SNP reference frame by PCA plot.
(1) Comparing true and predicted ancestors
TABLE 3 BIG-AsianTag-24panel (AAC 90.04%)
Figure BDA0001792571620000101
TABLE 4 BIG-AsianTag-48panel (AAC 95.05%)
Figure BDA0001792571620000102
TABLE 5 BIG-AsianTag-58panel (AAC 96.19%)
Figure BDA0001792571620000103
(2) General classification performance index
True TP true Positive (A population is identified as A population)
False negative FN false negative (A population is identified as non-A population)
False positive FP false positive (non-A population is identified as A population)
True negative TN (non-A population is identified as non-A population)
Sensitivity: sensitivity TP/(TP + FN)
Specificity: specificity TN/(FP + TN)
Positive predictive value: PPV is TP/(TP + FP)
Negative predictive value: NPV (TN/(TN + FN)
TABLE 6 BIG-AsianTag-24panel (AAC. 90.04%) Performance
Figure BDA0001792571620000111
TABLE 7 BIG-AsianTag-48panel (AAC. 95.05%) Performance
Figure BDA0001792571620000112
TABLE 8 BIG-AsianTag-58panel (AAC 96.19%) Performance
Figure BDA0001792571620000113
(3) Principal component analysis
The effect of the SNP reference system was verified by principal component analysis. Principal component analysis is a commonly used data feature extraction method. When the SNP is used for deducing the ancestral source of an individual, each SNP of the individual is equivalent to one ancestral attribute label of the individual (each SNP of the individual is equivalent to one clue for deducing the ancestral source of the individual), and a plurality of the ancestral attribute labels are combined together to form a panel, so that the ancestral source deduction of the individual can be effectively carried out.
PCA charts of 349381 SNPs, BIG-AsianTag-58panel, BIG-AsianTag-48panel, and BIG-AsianTag-24panel were compared, as shown in FIGS. 2(1) to (4).
In the figure, the field symbols represent western Asia individuals, the plus sign symbols represent middle Asia individuals, the triangle symbols represent northern Asia individuals, the circle symbols represent southern Asia individuals, the Mi symbols represent east Asia individuals, and the rectangle represents southeast Asia individuals. In FIG. 2(1), all symbols of the same type are grouped together without crossing between symbols of different types, indicating that all SNPs contain enough information to distinguish samples of different populations; FIGS. 2(2), (3), (4) are similar to FIG. 2(1), and only a very small portion of individuals cross in the first and second principal component spaces between different groups, but the cross individuals can still be clearly distinguished in other principal component spaces, which shows that although there are only 24-58 SNPs, the contained information is enough to effectively deduce the group to which the individual belongs. In addition, the performance of BIG-AsianTag-58panel was superior to that of BIG-AsianTag-48panel, and the performance of BIG-AsianTag-48panel was superior to that of BIG-AsianTag-24panel (tables 6-8), in terms of four criteria, sensitivity, specificity, positive predictive value and negative predictive value.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (6)

1. The primer set or the gene chip for deducing the population in different geographical regions of Asia is characterized in that the primer set or the gene chip is used for detecting the following SNP marker combinations, and the SNP marker combinations consist of the following SNP sites: rs3131522, rs10888768, rs3792006, rs260690, rs11123128, rs10191411, rs7638187, rs9837708, rs1675497, rs7669241, rs4712358, rs9395112, rs2168587, rs13232911, rs7002043, rs11038167, rs174570, rs4408369, rs9285110, rs9522149, rs1834640, rs6500380, rs4789182, and rs 3746807.
2. The set of primers or gene chip of claim 1, wherein said SNP marker set further comprises the following SNP sites: rs6588412, rs10494447, rs888574, rs17033180, rs4495184, rs6465485, rs10096702, rs16879442, rs3864668, rs7836463, rs1414202, rs6481038, rs1881731, rs1902689, rs3995780, rs 11027227293, rs3741259, rs17067700, rs17323696, rs2224442, rs753357, rs3091402, rs12443685, rs 138908.
3. The set of primers or gene chip of claim 2, wherein said SNP marker set further comprises the following SNP sites: rs2760519, rs10190125, rs1388612, rs2243290, rs1204250, rs4733581, rs10512188, rs7131166, rs2000926, rs 1323910.
4. The set of primers or gene chip of any one of claims 1 to 3, wherein the different geographical regions of Asia include islands of east Asia, west Asia, south Asia, north Asia, middle Asia and east Asia.
5. Use of a SNP marker set as set forth in any one of claims 1 to 4 in any one of:
(1) constructing a DNA chip or a multiple PCR genotype analysis or other applicable kit;
(2) identifying the identities of people in different geographical areas of Asia;
(3) and analyzing the ancestral source of the population in different geographical areas of Asia.
6. Use of a set of primers or gene chip according to any one of claims 1 to 3 in any one of:
(1) constructing a DNA chip or a multiple PCR genotype analysis or other applicable kit;
(2) identifying the identities of people in different geographical areas of Asia;
(3) and analyzing the ancestral source of the population in different geographical areas of Asia.
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