WO2022105629A1 - Method for screening snp sites for detecting contamination level of sample and method for detecting contamination level of sample - Google Patents

Abstract

A method for screening SNP sites for detecting the contamination level of a sample and a method for detecting the contamination level of a sample, which relate to the technical field of biological sequencing. The screening method comprises: obtaining SNP sites that have a population mutation frequency of 30% to 70% in a target region as candidate marker sites; dividing a region between a starting site and an terminating site among the candidate marker sites present on a single chromosome into a plurality of selection regions, the selection regions having a length of 0.7 to 1.3 Mb; and if there are two or more candidate marker sites in the selection regions, then selecting sites that have an allele frequency in the selection regions of 40% to 60% and different genotypes most in line with the Hardy-Weinberg equilibrium as marker sites, and removing other candidate markers within the regions. The marker sites screened on the basis of the screening method may be used to detect the contamination level of the sample, and has relatively low detection costs and relatively high detection accuracy.

Description

A screening method for SNP sites for detecting sample contamination level and a method for detecting sample contamination level

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure requires a Chinese patent application with application number 202011321699.3 and titled "A screening method for SNP loci for detecting sample contamination levels and a method for detecting sample contamination levels" filed with the China Patent Office on November 23, 2020 , the entire contents of which are incorporated by reference in this disclosure.

technical field

The present disclosure relates to the technical field of biological sequencing, and in particular, to a screening method for SNP sites used to detect the contamination level of a sample and a method for detecting the contamination level of the sample.

Background technique

Next-generation sequencing (NGS) technology has developed rapidly due to its high-throughput and low-cost characteristics and has been applied to genome sequencing of tumor clinical samples. However, multi-sample parallel library construction and sequencing leads to the problem of data contamination between samples. Tumor clinical samples mostly adopt a tumor-control paired design, and control samples are used to filter the germline mutations of the corresponding samples present in the tumor samples. Often, contamination of tumor samples results in a large number of heterologous contaminating germline mutations being incorrectly identified as somatic mutations, resulting in a large number of false-positive mutations in the detected somatic mutations. At the same time, heterologous contamination reduces the purity of tumor samples, which in turn reduces the sensitivity of detection of somatic mutations in tumor samples. Therefore, accurate detection of contamination levels in tumor clinical samples is an indispensable quality control step.

Whole genome sequencing (Whole Genome Sequence, WGS) and whole exome sequencing (Whole Exome Sequence, WES) can cover a certain amount of population polymorphism single nucleotide polymorphism (Single Nucleotide Polymorphism, SNP) loci. Based on prior population frequencies and wild-type and mutant-type information of SNP loci in tumor samples and control samples in paired samples, the estimation of sample contamination levels has been successfully applied in whole-genome sequencing and whole-exome sequencing. Existing representative methods include ConEst, Conpair, and VerifyBamID, all of which use Bayesian methods, use the marker sites covered by WGS/WES, and have a large number of markers. ConEst and Conpair both used homozygous loci among them, compared to VerifyBamID's use of fully covered SNP loci. Due to the prevalence of gene copy number variation CNVs in tumor samples, the heterozygous sites of tumor samples affected by CNVs will deviate from the mutation abundance (Variation Alle Fraction, VAF) in paired samples, resulting in VerifyBamID's estimate of the contamination level of the sample. May be too high.

Although WGS and WES contribute to a more comprehensive detection and understanding of tumor gene mutations, their high sequencing costs limit the depth of sample sequencing, and no effective panel-based marker site contamination prediction method has been reported. As a necessary quality control module for tumor genetic testing, contamination level prediction is an important guarantee for the reliability of tumor sample genetic testing results.

Therefore, it is necessary and urgent to develop a set of marker locus screening and contamination level prediction methods suitable for different panels. If you simply add all the markers in the WES range to the large Panel, taking the Conpair algorithm as an example, it needs to cover 7387 sites in the WES range. If you design a 120bp probe for each marker, you will need to cover at least 0.886Mb of interval size. , obviously will greatly increase the Panel size and cost. At the same time, when there are too few labeled sites, the Conpair algorithm performs poorly. The contamination prediction algorithm disclosed in the present disclosure makes it possible to accurately predict the contamination level of a sample only by relying on the marker sites covered at the beginning of the design of the large panel.

SUMMARY OF THE INVENTION

The present disclosure provides a method for screening SNP sites for detecting sample contamination levels, and obtaining SNP sites with population mutation frequencies of 30% to 70% in a target region as candidate marker sites;

dividing the region between the starting site and the ending site in the candidate marker sites existing on a single chromosome into multiple selection regions, and the length of the selection region is 0.7-1.3Mb;

If there are two or more candidate marker loci in the selection region, the locus with the allele frequency of 40% to 60% in the selection region and the different genotypes most in line with Hardy-Wingerberg equilibrium is selected as Mark the site and remove other candidate marked sites within the selected region.

In some embodiments, the loci with different genotypes most in line with Hardy-Wingerberg equilibrium refer to: the locus with the smallest locus disequilibrium coefficient U in the selected region, the locus disequilibrium coefficient The formula for calculating U is as follows:

U=(S ₀ -0.25) ² +(S ₁ -0.5) ² +(S ₂ -0.25) ² ; wherein, S ₀ , S ₁ and S ₂ are wild type, mutant heterozygous and mutant homozygous, respectively Population frequency in the target area.

In some embodiments, when there are multiple sites that best fit the Hardy-Wingerberg equilibrium in one of the selected regions, any one of them is selected.

In some embodiments, the SNP candidate loci are SNP loci with a population mutation frequency of 30%-70% based on the genomes of N cases of uncontaminated negative samples.

In some embodiments, the N≧50.

In some embodiments, the N≧100.

In some embodiments, the SNP candidate loci are loci with an occurrence frequency of 40% to 60% in the gene database.

In some embodiments, the source of the sample to be tested is selected from tumors and genetic diseases.

In some embodiments, the type of the sample to be tested is selected from plasma samples, leukocyte samples, tissue samples, oral mucosa samples, saliva samples, cerebrospinal fluid samples, pleural fluid samples, ascites samples, and buccal swab samples.

The present disclosure also provides a method for detecting the contamination level of a sample, comprising: screening the contamination marker loci obtained by using the screening method for SNP loci for detecting the contamination level of a sample as described above.

In some embodiments, according to the aforementioned method for detecting the contamination level of a sample, comprising: using the contamination marker locus whose genotype is homozygous genotype in the target area of the sample to be tested and/or the non-contamination control sample as a pure The combined marker sites; the obtained VAF difference of mutation abundance of the sample to be tested and the VAF difference distribution of the contaminated samples under different pollution levels were tested by rank sum test respectively, and the P values under different pollution levels were obtained respectively;

Wherein, the mutation abundance VAF difference of the sample to be tested is: the absolute value of the VAF difference between the sample to be tested and the uncontaminated negative control sample at the homozygous marker site;

The VAF difference distribution of the contaminated samples under different pollution levels is: the absolute value distribution of the VAF difference at the homozygous marker site between the contaminated samples with different levels of pollution sources and the uncontaminated negative control samples respectively.

In some embodiments, according to the aforementioned method for detecting the contamination level of a sample, it includes: determining the maximum value of the P value under different pollution levels, and determining the pollution level corresponding to the maximum value of the P value as the contamination level of the sample to be tested.

In some embodiments, the pollution ratios of the different pollution levels are selected from 0.01% to 99.9%.

In some embodiments, the contamination marker site includes at least one of (1) to (60) in the following table:

Numbering	rsid	chromosome	Location	reference base	mutated base
1	rs2294714	chr1	6257826	T	C
2	rs7532151	chr1	89388944	A	C
3	rs1800880	chr1	156846120	C	T
4	rs11543979	chr1	201981218	C	G
5	rs1136410	chr1	226555302	A	G
6	rs1128919	chr2	148657117	G	A
7	rs1048108	chr2	215674224	G	A
8	rs7652776	chr3	2741024	G	C
9	rs6443222	chr3	9163267	C	T
10	rs2251219	chr3	52584787	T	C
11	rs3218651	chr3	121208176	T	C
12	rs3749234	chr3	176765269	T	C
13	rs17497475	chr4	19242239	C	A
14	rs1870377	chr4	55972974	T	A
15	rs3756122	chr4	143235865	C	G
16	rs2565007	chr5	53820267	C	A
17	rs13184586	chr5	161119125	C	G
18	rs56188706	chr5	180057356	C	G
19	rs2230653	chr6	26056604	G		A
20	rs4607417	chr6	41978274	C	T
twenty one	rs2243384	chr6	117678083	A	G
twenty two	rs2077647	chr6	152129077	T	C
twenty three	rs62456182	chr7	6038722	T	C
twenty four	rs2813838	chr7	24149292	C	G
25	rs2227983	chr7	55229255	G	A
26	rs41736	chr7	116435768	C	T
27	rs2267708	chr7	124392512	C	T
28	rs1293288	chr8	11718528	T	C
29	rs1805794	chr8	90990479	C		G
30	rs7839934	chr8	144941181	G	C
31	rs7026388	chr9	8518143	T	C
32	rs7023954	chr9	21816758	G	A
33	rs357564	chr9	98209594	G	A
34	rs17114803	chr10	104386934	T	C

35	rs9344	chr11	69462910	G	A
36	rs641936	chr11	94197260	A	G
37	rs543840	chr11	115764486	G	A
38	rs734075	chr12	4497047	C	A
39	rs7955902	chr12	40645257	C		A
40	rs2290103	chr12	78531156	A	G
41	rs2259820	chr12	121435342	C	T
42	rs9534262	chr13	32936646	T	C
43	rs4883918	chr13	73350079	T	C
44	rs17655	chr13	103528002	G	C
45	rs7328030	chr13	112269444	C	A
46	rs2231301	chr14	23777099	G	A
47	rs2241119	chr14	81558965	A	G
48	rs1130233	chr14	105239894	C	T
49	rs2305030	chr15	41805237	C	T
50	rs2229765	chr15	99478225	G	A
51	rs2230930	chr17	1782957	C	T
52	rs1799966	chr17	41223094	T	C
53	rs3744093	chr17	56492800	T	C
54	rs4647887	chr17	74558806	A	G
55	rs663651	chr18	42456653	G	A
56	rs2075607	chr19	1222012	G	C
57	rs2302603	chr19	17941294	T	C
58	rs8113496	chr19	29820529	A	G
59	rs2242522	chr19	36228954	G	T
60	rs9617050	chr22	50708731	A	G

.

In some embodiments, the contamination marker sites include at least 10 of (1) to (60) in the above table;

In some embodiments, the contamination marker sites include at least 20 of (1) to (60) in the above table.

In some embodiments, the contamination marker site includes at least one of 1) to 30) in the following table, or includes at least one of 1) to 30) in the table below and the contamination marker site ( Combination of at least one of 1)～(60):

Numbering	rsid	chromosome	Location	reference base	mutated base
1)	rs3219489	chr1	45797505	C	G
2)	rs2298258	chr1	162737116	C	G
3)	rs3747636	chr1	204403659	A	G
4)	rs2227982	chr2	242793433	G	A
5)	rs11466512	chr3	30713126	T	A
6)	rs2227931	chr3	142222284	A	G
7)	rs1350191	chr4	154807324	C	T
8)	rs2043112	chr5	38955796	G	A
9)	rs3733875	chr5	176637240	G	T
10)	rs915894	chr6	32190390	T	G
11)	rs1801270	chr6	36651971	C	A
12)	rs12055782	chr6	128312033	A	G
13)	rs9639168	chr7	13978809	T	C
14)	rs2074566	chr7	26224668	C	T
15)	rs1129293	chr7	106513011	C	T
16)	rs3808565	chr8	26227640	A	G
17)	rs4244612	chr8	145741702	C	G

18)	rs290223	chr9	93639846	C	G
19)	rs2071313	chr11	64572602	G	A
20)	rs974144	chr11	85968623	C	T
twenty one)	rs11062385	chr12	427575	A	G
twenty two)	rs3741622	chr12	49425978	T	C
twenty three)	rs2075784	chr12	133263825	G	A
twenty four)	rs1805097	chr13	110435231	C	T
25)	rs9549365	chr13	113907391	A	G
26)	rs2240308	chr17	63554591	G	A
27)	rs1567962	chr17	78919558	C	T
28)	rs1799817	chr19	7125297	G	A
29)	rs12461253	chr19	31769763	G	A
30)	rs2076578	chr22	41569609	C	T

The present disclosure also provides a kit for detecting the contamination level of a sample, which includes a reagent for detecting a target SNP site, the target SNP site is selected from the aforementioned SNP site for detecting the contamination level of the sample method to screen the obtained marker sites.

In some embodiments, the contamination marker site includes at least one of (1) to (60) in the above table.

In some embodiments, the contamination marker sites include at least 10 of (1) to (60) in the above table.

In some embodiments, the contamination marker sites include at least 20 of (1) to (60) in the above table.

The present disclosure provides a composition for detecting a level of contamination of a sample, comprising a reagent for detecting a target SNP site, the target SNP site being the method for detecting a level of contamination in a sample described in any one of the above The contamination marker loci obtained by the screening method of the SNP locus.

The present disclosure provides a detection system comprising:

storage devices and processing devices;

When the processing device runs the program in the storage device, the screening method for detecting the SNP site of the sample contamination level as described in any one of the above or the method for detecting sample contamination as described in any one of the above is performed. horizontal method.

The present disclosure also provides an electronic device comprising a memory and a processor, when the processor runs a computer program in the memory, executes the SNP loci for detecting the contamination level of a sample as described in the previous embodiments A screening method or method for detecting the level of contamination in a sample as described in the previous examples.

The present disclosure also provides use of the kit, the composition, the detection system or the electronic device for tumor gene detection.

In some embodiments, according to the screening method described above, an acquisition module is used to acquire SNP sites with a population mutation frequency of 30%-70% in the target region as candidate marker sites;

Using a region division module to divide the region between the start site and the end site in the candidate marker sites existing on a single chromosome into a plurality of selection regions, and the length of the selection region is 0.7-1.3Mb;

Use the selection module to carry out, if there are two or more candidate marker sites in the selection region, select the allele frequency in the selection region to be 40% to 60% and the different genotypes are most consistent with Hardy-Wingerberg. The balanced sites are used as marker sites, and other candidate marker sites within the selected region are removed.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present disclosure more clearly, the following briefly introduces the accompanying drawings that need to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present disclosure, and therefore do not It should be regarded as a limitation of the scope, and for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is the flow chart of the method for detecting sample contamination level in embodiment 3;

Fig. 2 is the VAF difference distribution diagram under different pollution levels in Test Example 1;

Fig. 3 is the number distribution map of homozygous marker sites in Test Example 1;

4 is a graph showing the performance results of the detection performance of the method of Example 3 of the present disclosure in Test Example 1;

FIG. 5 is a graph showing the performance results of the detection methods of Examples 4 and 5 in Test Example 2. FIG.

Detailed ways

In order to make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below. However, the following embodiments are only examples of the present disclosure, and do not represent or limit the protection scope of the present disclosure, and the protection scope of the present disclosure is subject to the claims. If the specific conditions are not indicated in the examples, it is carried out in accordance with the conventional conditions or the conditions suggested by the manufacturer. The reagents or instruments used without the manufacturer's indication are conventional products that can be purchased from the market.

The features and properties of the present disclosure will be further described in detail below with reference to the embodiments.

Definition of Terms

As used herein, the "SNP" herein refers to a single nucleotide polymorphism, mainly referring to a DNA sequence polymorphism caused by variation of a single nucleotide at the genome level, among the variations that can be inherited by humans The most common one.

As used herein, "Hardy-Weinberg Equilibrium" in English is Hardy-Weinberg equilibrium, which can refer to ideally, the frequency of each allele is genetically stable, that is, the gene is maintained balance.

As used herein, "wild type" as used herein may refer to an unmutated genotype; "mutant heterozygous" refers to a pair of alleles in which one gene is mutant and the other is wild type; "mutant heterozygous" "Homozygous" can refer to the presence of mutations in both alleles.

As used herein, the "mutation abundance" described herein is VAF, Variant allele fraction, also known as Variant allele frequency (variant allele frequency), which can refer to the percentage of mutation reads (read length) in the total reads during the sequencing process The ratio of , that is, the calculation formula can be:

VAF=Allele Depth/Total Depth. Among them, Allele Depth is the coverage depth of reads (read length) of each locus in the genome supporting the mutant genotype, and Total Depth is the total reads coverage depth of this locus.

As used herein, the "rank sum test" described herein, also known as the Wilcoxon rank sum test or rank sum test, is a nonparametric test that does not depend on the type of population distribution and does not measure population parameters. Statistical methods for statistical inference.

As used herein, "allele frequency" as used herein means if: (a) a particular locus is present on a chromosome, (b) there is a gene at that locus, (c) each of the The somatic cells of an individual have n of this specific locus (for example, there are two of this specific locus in the cells of a diploid organism), (d) the gene has alleles or variants; then the allele frequency is: etc. The percentage of all alleles at a particular locus in this population of an allele.

As used herein, the "Fastq format" refers to a textual format in which biological sequences, such as nucleic acid sequences, and corresponding quality assessments are stored.

Technical solutions

First, an embodiment of the present disclosure provides a method for screening SNP sites for detecting the level of contamination in a sample, which can be applied to an electronic device for performing the following steps: obtaining a population mutation frequency of 30% in a target area ~70% of SNP sites are used as candidate marker sites;

Divide the region between the start site and the end site in the candidate marker sites existing on a single chromosome into multiple selection regions, and the length of the selection region is 0.7-1.3Mb; if the selection region If there are two or more candidate marker loci in the selected region, the locus with the allele frequency of 40% to 60% in the selected region and the different genotypes most in line with Hardy-Wingerberg equilibrium is selected as the marker locus, and the Other candidate marker sites within the selected region are removed.

After a series of studies, it was found that, based on covering the SNP candidate loci in the genomes of N uncontaminated negative samples, removing the loci that significantly shifted the Hardy-Wingerberg equilibrium of different genotypes and the adjacent loci that may be linked, it is possible to obtain Contamination marker sites for detection of sample contamination levels.

It should be noted that "extracting SNP sites with population mutation frequency in the target region ranging from 30% to 70%" refers to extracting any frequency point value or all or part of the frequency range whose mutation frequency falls within the range of 30% to 70%. For SNP sites, for example, extract all or part of the site with a mutation frequency of 40% to 60%, extract all or part of the site with a mutation frequency of 45% to 60%, or extract a mutation frequency of 45% to 60% 55% of all or part of the site; or all or part of the site with a mutation frequency of 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% . In some embodiments, the allele frequency may be 40%-60%, 45%-60%, 40%-55%, or 45%-55%, such as 40%, 45%, 50%, 55% and any of 60%, optionally 50%.

"Target region" is any region desired to be characterized by the contamination level detection method of the present disclosure, including but not limited to the target region for sequencing and the coverage region of the panel on the genome, and the like.

Information on "SNP loci" and their mutation frequencies in the population can be obtained by sequencing or public or commercial databases. There is no specific limitation on the database, and an existing gene database can be selected. In some embodiments, the gene database may be selected from at least one of: gnomAD, 1000G, ExAC and popfreq_max_20150413 databases.

Selecting candidate sites with "0.7-1.3Mb" as the length of the selection region, on the one hand, can effectively avoid the occurrence of linked adjacent sites between candidate sites; , reducing the number of labeled sites for lower cost and more efficient detection. In some embodiments, the length of the selected region can be 0.8-1.3Mb, 0.7-1.2Mb, 1.0-1.3Mb, or 0.8-1.0Mb, such as can be 0.7Mb, 0.8Mb, 0.9Mb, 1.0Mb, 1.1Mb, Any length of 1.2Mb and 1.3Mb is used for division as the length of the selection area.

In some embodiments, a site with a mutation heterozygosity ratio of 30% to 70% is selected as a candidate marker site, and the mutation heterozygosity ratio can be 40% to 70%, 30% to 60%, or 50% to 60%. %, such as any of 31%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, and 70%. The site mutation frequency of the above mutation heterozygous ratio is more stable, and using it as a candidate marker site can increase the effectiveness of subsequent contamination level prediction.

In some embodiments, the source of the sample to be tested is selected from tumors, genetic diseases.

In some embodiments, genetic disorders include, but are not limited to, chromosomal disorders, dominant disorders, recessive disorders, monogenic disorders, polygenic disorders, X-linked disorders.

In some embodiments, the type of sample to be tested is selected from plasma samples, leukocyte samples, tissue samples, oral mucosa samples, saliva samples, cerebrospinal fluid samples, pleural fluid samples, ascites samples, and buccal swab samples.

In some typical embodiments, the SNP candidate loci are SNP loci with a population mutation frequency of 30%-70% in the genomes of N cases of uncontaminated negative samples. The negative sample here can be understood as a wild-type sample, which can refer to a negative control sample corresponding to the sample to be tested and without contamination. For example, when the sample to be tested is a tumor sample, the negative sample is a corresponding uncontaminated healthy individual sample.

In some typical embodiments, the N≧50; optionally, the N≧100; optionally, the N≧500. For example, N is 50-100, 50-500, 100-500, 200-500, or 300-500.

In some typical embodiments, when there are multiple sites that are most in line with Hardy-Wingerberg equilibrium in each of the selected regions, any one of them is selected to reduce or avoid contamination in the labeled sites Linked loci appear.

In some typical embodiments, the locus with the different genotypes most in line with Hardy-Wingerberg equilibrium refers to the locus with the smallest locus disequilibrium coefficient U in the selected region, the locus The calculation formula of the unbalance coefficient U is as follows:

U=(S ₀ -0.25) ² +(S ₁ -0.5) ² +(S ₂ -0.25) ² ; wherein, S ₀ , S ₁ and S ₂ are wild type, mutant heterozygous and mutant homozygous, respectively Population frequency in the target area. It should be noted that "wild type" refers to a genotype without mutation.

Embodiments of the present disclosure also provide an electronic device, which includes a memory and a processor, when the processor runs a computer program in the memory, executes the SNP for detecting the contamination level of a sample described in any of the foregoing embodiments site screening method.

The electronic device may include a memory, a processor, a bus, and a communication interface, and the memory, the processor, and the communication interface are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, these elements may be electrically connected to each other through one or more buses or signal lines. The processor may process information and/or data related to target identification to perform one or more functions described in this disclosure.

In some embodiments, the memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM) , Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Read-Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

In some embodiments, the processor may be an integrated circuit chip with signal processing capabilities. In some embodiments, the processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it may also be a digital signal processor (Digital Signal Processing, DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

Each component in the electronic device can be implemented by hardware, software or a combination thereof. In practical applications, the electronic device can be a server, a cloud platform, a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (personal digital assistant, PDA), wearable electronic devices, virtual reality devices and other devices, so the embodiments of the present disclosure do not limit the types of electronic devices.

Embodiments of the present disclosure also provide a method for detecting the contamination level of a sample, which includes: using the screening method for SNP loci for detecting the contamination level of a sample according to any of the foregoing embodiments to screen the contamination marker loci obtained point.

By confirming and detecting the contamination marker sites screened by the above screening method, the contamination level of the sample can be predicted. Compared with the prior art, the detected marker sites are less, the cost is lower, and the contamination level of the sample can be predicted. prediction results are more accurate.

In some embodiments, the method further comprises detecting the contaminating marker site. Specifically, there is no restriction on the method of site detection. For example, it can be carried out by any one of primers, probes and chips. As long as the sample contamination level is detected by detecting the above-mentioned contamination marker sites, in this paper within the scope of public protection.

In some typical embodiments, the method further comprises: using the contamination marker locus whose genotype is homozygous genotype in the target region of the sample to be tested and/or the uncontaminated control sample as the homozygous marker locus; The rank sum test was performed on the VAF difference of the obtained mutation abundance of the sample to be tested and the VAF difference distribution of the contaminated samples under different pollution levels, and the P values under different pollution levels were obtained respectively.

Wherein, the VAF difference value of the mutation abundance of the sample to be tested is: the absolute value of the VAF difference value of the sample to be tested and the uncontaminated negative control sample at the homozygous marker site. In some embodiments, the sample to be tested is a sample that may be contaminated or has incorporated a source of contamination (heterologous data), such as a tumor sample, and the negative control sample is a sample of a healthy individual without contamination.

The VAF difference distribution of the contaminated samples at different pollution levels is: the absolute value distribution of the VAF difference at the homozygous marker site between the contaminated samples with different levels of pollution sources and the uncontaminated negative control sample respectively.

Specifically, the above-mentioned "use the contamination marker site whose genotype is homozygous genotype in the target region of the sample to be tested and/or the uncontaminated control sample as the homozygous marker site" means: based on the sample to be tested and/or According to the genotype information of each contamination marker locus existing on the target region of the non-contamination control sample, the contamination marker loci whose genotype is wild type or mutant homozygous type are selected as the homozygous marker locus. "The genotype information of each contamination marker locus present on the target region of the test sample and/or the non-contamination control sample" can be obtained by existing genetic testing methods.

In some typical embodiments, the contamination marker locus whose genotype is homozygous genotype in the target region of the uncontaminated control sample is used as the homozygous marker locus.

In some typical embodiments, the contamination marker sites whose genotypes are homozygous genotypes in the target regions of the test sample and the uncontaminated control sample are used as homozygous marker sites. "The contamination marker site whose genotype is homozygous genotype in the target area of the sample to be tested and the uncontaminated control sample is regarded as a homozygous marker site" means that the genotype in the target area of the sample to be tested is a homozygous genotype The contamination marker site is merged with the contamination marker site whose genotype is homozygous genotype in the target region of the non-polluting control sample, and is combined as a homozygous marker site. Uncontaminated control samples can include, but are not limited to, white blood cell samples.

In some embodiments, the method further includes constructing contamination samples with different contamination levels: dividing the samples into samples to be contaminated and uncontaminated control samples, adding contamination sources with different contamination ratios to the samples to be contaminated to obtain different contaminations Proportion (mass ratio) of contaminated samples. The samples to be contaminated or control samples can be selected from leukocyte samples or existing cell line samples. The source of contamination in the contaminated sample can be selected from other leukocyte or cell line samples than the "sample to be contaminated".

In some typical embodiments, the pollution ratios of the different pollution levels are selected from 0.01% to 99.9%. In some embodiments, the pollution ratio of the same pollution level can be 0.01%-95%, 1%-99.9%, 10%-90%, 30%-70% or 40%-60%, for example, the different pollution levels can be 0.01%, 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75% , 80%, 85%, 90%, 95% and 99.9%.

During detection, the span range of different pollution levels can be: 0 to 1%, and a set of pollution samples is set at every 0.1% interval (denoted as 0 to 1%, every 0.1%); the span range of different pollution levels can be: 1% to 10%, set a set of pollution samples at 0.5% intervals; the span range of different pollution levels can also be: 10% to 100%, set a set of pollution samples at 1% intervals, and so on.

In some embodiments, statistical analysis is calculated using SPSS 10.0 statistical analysis software. All statistical tests were two-tailed.

In some typical embodiments, the method includes: determining the maximum value of the P value under different pollution levels, and determining the pollution level corresponding to the maximum value of the P value as the pollution level of the sample to be tested. The P value can reflect the degree of agreement between the VAF difference of the sample and the theoretical distribution, and the larger the P value, the better the agreement.

In some typical embodiments, the contamination marker site includes at least one of (1) to (60) in Table 1;

Table 1 Contamination marker sites

Numbering	rsid	chromosome	Location	reference base	mutated base
1	rs2294714	chr1	6257826	T	C
2	rs7532151	chr1	89388944	A	C
3	rs1800880	chr1	156846120	C	T
4	rs11543979	chr1	201981218	C	G
5	rs1136410	chr1	226555302	A	G
6	rs1128919	chr2	148657117	G	A
7	rs1048108	chr2	215674224	G	A
8	rs7652776	chr3	2741024	G	C
9	rs6443222	chr3	9163267	C	T
10	rs2251219	chr3	52584787	T	C
11	rs3218651	chr3	121208176	T	C
12	rs3749234	chr3	176765269	T	C
13	rs17497475	chr4	19242239	C	A
14	rs1870377	chr4	55972974	T	A
15	rs3756122	chr4	143235865	C	G
16	rs2565007	chr5	53820267	C	A
17	rs13184586	chr5	161119125	C	G
18	rs56188706	chr5	180057356	C	G
19	rs2230653	chr6	26056604	G		A
20	rs4607417	chr6	41978274	C	T
twenty one	rs2243384	chr6	117678083	A	G
twenty two	rs2077647	chr6	152129077	T	C
twenty three	rs62456182	chr7	6038722	T	C
twenty four	rs2813838	chr7	24149292	C	G
25	rs2227983	chr7	55229255	G	A
26	rs41736	chr7	116435768	C	T
27	rs2267708	chr7	124392512	C	T
28	rs1293288	chr8	11718528	T	C
29	rs1805794	chr8	90990479	C		G
30	rs7839934	chr8	144941181	G	C
31	rs7026388	chr9	8518143	T	C
32	rs7023954	chr9	21816758	G	A

33	rs357564	chr9	98209594	G	A
34	rs17114803	chr10	104386934	T	C
35	rs9344	chr11	69462910	G	A
36	rs641936	chr11	94197260	A	G
37	rs543840	chr11	115764486	G	A
38	rs734075	chr12	4497047	C	A
39	rs7955902	chr12	40645257	C		A
40	rs2290103	chr12	78531156	A	G
41	rs2259820	chr12	121435342	C	T
42	rs9534262	chr13	32936646	T	C
43	rs4883918	chr13	73350079	T	C
44	rs17655	chr13	103528002	G	C
45	rs7328030	chr13	112269444	C	A
46	rs2231301	chr14	23777099	G	A
47	rs2241119	chr14	81558965	A	G
48	rs1130233	chr14	105239894	C	T
49	rs2305030	chr15	41805237	C	T
50	rs2229765	chr15	99478225	G	A
51	rs2230930	chr17	1782957	C	T
52	rs1799966	chr17	41223094	T	C
53	rs3744093	chr17	56492800	T	C
54	rs4647887	chr17	74558806	A	G
55	rs663651	chr18	42456653	G	A
56	rs2075607	chr19	1222012	G	C
57	rs2302603	chr19	17941294	T	C
58	rs8113496	chr19	29820529	A	G
59	rs2242522	chr19	36228954	G	T
60	rs9617050	chr22	50708731	A	G

In some typical embodiments, the contamination marker sites include at least 10 of (1) to (60) in Table 1; optionally, the contamination marker sites include (1) in Table 1 At least 20 of ~(60).

In some embodiments, before screening the marker sites, the method further comprises: a library building step and/or a sequencing step of the sample. The library building and sequencing steps of the sample can be implemented with reference to the existing library building and sequencing steps, respectively.

Embodiments of the present disclosure also provide an electronic device, which includes a memory and a processor, when the processor runs a computer program in the memory, the processor executes the method for detecting a contamination level of a sample described in any of the foregoing embodiments .

In addition, an embodiment of the present disclosure also provides a kit for detecting a contamination level of a sample, comprising a reagent for detecting a target SNP site, the target SNP site being the screening method described in any of the foregoing embodiments Screened marker sites.

In some typical embodiments, the contamination marker site includes at least one of (1) to (60) in Table 1.

In some typical embodiments, the contamination marker sites include at least 10 of (1) to (60) in Table 1.

In some typical embodiments, the contamination marker sites include at least 20 of (1) to (60) in Table 1.

In some typical embodiments, the contamination marker sites include at least 30 of (1) to (60) in Table 1.

In some typical embodiments, the contamination marker sites include 20, 30, 40, 50, 60, 70, 80, 90 of (1) to (60) in Table 1 one, 100.

In some typical embodiments, the type of reagent is selected from at least one of probes, primers and chips.

Embodiments of the present disclosure also provide a composition for detecting a contamination level of a sample, which includes a reagent for detecting a target SNP site, where the target SNP site is a screening method for a SNP site for detecting the contamination level of a sample Screened contamination marker sites.

In some typical embodiments, the contamination labeling site includes at least one of (1) to (60) in the above table; optionally, the contamination labeling site includes (1) to (60) in the above table at least 10 of ; optionally, the contamination marker sites include at least 20 of (1) to (60) in the above table.

Embodiments of the present disclosure also provide a detection system, which includes:

storage devices and processing devices;

When the processing device runs the program in the storage device, a screening method for SNP loci for detecting the contamination level of the sample or a method for detecting the contamination level of the sample is performed.

Embodiments of the present disclosure also provide the use of the kit, composition, detection system or electronic device for tumor gene detection.

In some embodiments, an acquisition module is used to acquire SNP sites with a population mutation frequency of 30%-70% in the target region as candidate marker sites;

Using a region division module to divide the region between the start site and the end site in the candidate marker sites existing on a single chromosome into a plurality of selection regions, and the length of the selection region is 0.7-1.3Mb;

Use the selection module to carry out, if there are two or more candidate marker sites in the selection region, select the allele frequency in the selection region to be 40% to 60% and the different genotypes are most consistent with Hardy-Wingerberg. The balanced sites are used as marker sites, and other candidate marker sites within the selected region are removed.

Embodiments of the present disclosure provide a method for screening SNP sites for detecting contamination levels of samples and a method for detecting contamination levels of samples. The screening method includes obtaining SNP sites with population mutation frequencies of 30% to 70% in a target area. As a candidate marker site; the region between the start site and the end site in the candidate marker site existing on a single chromosome is divided into multiple selection regions, and the length of the selection region is 0.7-1.3Mb ; If there are two or more candidate marker sites in the selected region, select the sites with an allele frequency of 40% to 60% in the selected region and with different genotypes most in line with Hardy-Wingerberg equilibrium as a marker site, and remove other candidate marker sites within the selected region. The marker loci screened based on the above screening method can be used to detect the contamination level of the sample, and compared with the prior art, the detection cost is lower and the detection accuracy is higher.

Example

Example 1

A method for screening SNP sites for detecting sample contamination levels, specifically comprising the following steps:

(1) Using the popfreq_max_20150413 database, extract sites with population frequencies of 40% to 60% as SNP candidate sites.

(2) Extract the genotype information of SNP candidate loci in N cases (500) leukocyte samples (negative samples without contamination). Sites with population mutation frequencies of 40% to 60% were selected as candidate marker sites.

At the same time, the region between the starting site and the ending site in the candidate sites existing on each chromosome is divided into multiple selection regions, and the length of each selection region is 1Mb; if the If there are more than 2 candidate marker sites in the selected region, select the site with the allele frequency of 50% and the smallest site unbalance coefficient U (if there are multiple minimum sites, choose one of them) as the contamination Mark the locus, remove other candidate marker loci in the selected region, and combine the selected contamination marker loci on all chromosomes to obtain the final contamination marker locus collection.

The calculation formula of the above-mentioned position imbalance coefficient U is as follows:

U=(S ₀ -0.25) ² +(S ₁ -0.5) ² +(S ₂ -0.25) ² ; wherein, S ₀ , S ₁ and S ₂ are wild type, mutant heterozygous and mutant homozygous, respectively Proportion in the genome of 500 leukocyte samples.

Example 2

A group of SNP loci (Panel) used to detect the contamination level of the sample, which was screened by the screening method of Example 1, specifically including the 60 SNP loci in Table 1 above.

Example 3

A method for detecting the contamination level of a sample, the flow chart refers to FIG. 1 , and specifically includes the following steps.

(1) Construction of VAF difference distribution of pollution samples under different pollution levels:

1.1 Sample database construction: randomly select 1 of 500 tumor tissue or plasma samples as the contaminated sample, and then select 1 as the pollution source, and simulate the pollution sources (heterologous data) with different pollution levels in the tumor samples. Repeat 500 times horizontally. Obtain contamination samples with different contamination levels. 50ng of the contaminated samples and control samples (uncontaminated) with different pollution levels were taken respectively for subsequent library construction experiments. The construction of the library mainly includes the following steps:

a. The sample is interrupted and the ends are repaired; b. The above-mentioned repaired DNA fragments are connected by joints; c. The products after the above-mentioned joints are connected are subjected to PCR amplification to obtain a sufficient amount of DNA fragments with joints, which are Pre-Library. d. Purify the pre-library with magnetic beads, and perform concentration determination and fragment quality inspection; e. Perform probe hybridization on the pre-library; f. Use streptavidin magnetic beads to capture the probe-bound samples; g. Amplify the DNA fragments captured by the magnetic beads by PCR to obtain a sufficient amount of tagged DNA fragments, which is the final library; h. Purify the final library with magnetic beads, and carry out concentration determination and fragment quality inspection, using qPCR Quantify.

1.2 Collecting the next-generation sequencing data of the samples: Based on the library obtained from the establishment, the multi-exon probe in the target region was used to capture, and the gene sequencer was used to perform 150bp Pair-End mode sequencing using the standard operating procedures of the case instrument (Read1:151; Read2:151 ; Index1:8, Index2:8), and finally obtain the next-generation sequencing data in Fastq format as raw data.

1.3 Data split quality control: use bcl2fastq to split the data, use the quality control software fastp to perform data quality control, and obtain high-quality data (clean data) for paired samples.

1.4 Data alignment: Use bwa software to align the clean data to the hg19 sequence of the reference genome to obtain the alignment information of each sequencing segment (read), and then use the gencore software to compare the results for deduplication and base correction.

1.5 Screening of marker sites: The pollution marker sites provided in Example 2 were selected as the pollution marker sites.

1.6 Calculation of VAF difference at homozygous marker sites:

The method for obtaining homozygous marker loci: based on the genotype information of each contamination marker locus on the genome of the sample to be tested and the uncontaminated control sample, the genotype on the genome of the sample to be tested is wild-type or homozygous for mutation The contamination marker loci of the genotype were merged with the contamination marker loci of wild type or mutation homozygous genotype on the genome of the uncontaminated control sample as the homozygous marker locus.

Through the pysam software package, the VAF values of the test sample and the control sample at each contamination marker site were obtained respectively, and the absolute value of the VAF difference between the tumor sample (test sample) at the homozygous marker site and the control sample was calculated. The reference distribution of the absolute value of the VAF difference between the contaminated sample and the control sample at the homozygous marker site under the corresponding pollution level was obtained.

(2) Rank sum test: Use the reference distribution of the VAF difference between the test sample and the control sample at the homozygous marker site and the absolute value of the VAF difference between the contaminated sample and its control sample at the homozygous site under different pollution levels. Rank sum test, calculation of P-value (P-value) (two-tailed test).

(3) Prediction of pollution levels:

The contamination level of the sample to be tested is confirmed based on the largest P value.

It should be noted that the selection of the source of the sample to be tested in this example is only exemplary, and the embodiments of the present disclosure can be applied to almost all diseases involving human sources, including but not limited to tumors and genetic diseases (such as chromosomal diseases, Dominant genetic disease, recessive genetic disease, single-gene genetic disease, polygenic genetic disease, X-linked genetic disease), can realize the detection of the contamination level of the sample.

In addition, the sample type selection in this embodiment is only exemplary, and the sample types that can be selected in the embodiments of the present disclosure can also be samples such as leukocyte samples, oral mucosa samples, saliva samples, cerebrospinal fluid samples, pleural fluid samples, ascites samples, Oral swab sample.

Example 4

A method for detecting the contamination level of a sample is roughly the same as the method provided in Example 3, except that the selected contamination marker sites are 30, which are all selected from the contamination marker sites in Table 1, and the information of the sites is specific. As shown in table 2.

Table 2 Contamination marker sites

rsid	chromosome	Location	reference base	mutated base
rs2302603	chr19	17941294	T	C
rs2077647	chr6	152129077	T	C
rs1800880	chr1	156846120	C	T
rs4647887	chr17	74558806	A	G
rs6443222	chr3	9163267	C	T
rs11543979	chr1	201981218	C	G
rs56188706	chr5	180057356	C	G
rs2290103	chr12	78531156	A	G
rs1048108	chr2	215674224	G	A
rs2813838	chr7	24149292	C	G
rs7328030	chr13	112269444	C	A
rs2241119	chr14	81558965	A	G
rs1799966	chr17	41223094	T	C
rs1130233	chr14	105239894	C	T
rs3218651	chr3	121208176	T	C
rs17114803	chr10	104386934	T	C
rs1870377	chr4	55972974	T	A
rs641936	chr11	94197260	A	G
rs2242522	chr19	36228954	G	T
rs9344	chr11	69462910	G	A
rs3744093	chr17	56492800	T	C
rs4607417	chr6	41978274	C	T
rs7026388	chr9	8518143	T	C
rs9617050	chr22	50708731	A	G
rs17655	chr13	103528002	G	C
rs13184586	chr5	161119125	C	G
rs3756122	chr4	143235865	C	G
rs62456182	chr7	6038722	T	C
rs7532151	chr1	89388944	A	C

rs2305030

chr15

41805237

C

T

Example 5

A method for detecting the contamination level of a sample, which is roughly the same as the method provided in Example 3, except that there are 60 contamination marker sites, of which 30 are selected from Table 1, and the other 30 are different from Table 1. Sites (bold sites are marked sites different from Table 1, which are other sites with a mutant heterozygosity ratio of 40% to 60%). Please refer to Table 3 for specific contamination marker sites.

Table 3 Contamination marker sites

rsid	chromosome	Location	reference base	mutated base
rs2294714	chr1	6257826	T	C
rs3219489	chr1	45797505	C	G
rs7532151	chr1	89388944	A	C
rs2298258	chr1	162737116	C	G
rs3747636	chr1	204403659	A	G
rs1136410	chr1	226555302	A	G
rs1128919	chr2	148657117	G	A
rs1048108	chr2	215674224	G	A
rs2227982	chr2	242793433	G	A
rs7652776	chr3	2741024	G	C
rs11466512	chr3	30713126	T	A
rs3218651	chr3	121208176	T	C
rs2227931	chr3	142222284	A	G
rs3749234	chr3	176765269	T	C
rs1870377	chr4	55972974	T	A
rs1350191	chr4	154807324	C	T
rs2043112	chr5	38955796	G	A
rs2565007	chr5	53820267	C	A
rs3733875	chr5	176637240	G	T
rs56188706	chr5	180057356	C	G
rs2230653	chr6	26056604	G	A
rs915894	chr6	32190390	T	G
rs1801270	chr6	36651971	C	A
rs12055782	chr6	128312033	A	G
rs2077647	chr6	152129077	T	C
rs9639168	chr7	13978809	T	C
rs2074566	chr7	26224668	C	T
rs2227983	chr7	55229255	G	A
rs1129293	chr7	106513011	C	T
rs2267708	chr7	124392512	C	T
rs3808565	chr8	26227640	A	G
rs1805794	chr8	90990479	C	G
rs4244612	chr8	145741702	C	G
rs7026388	chr9	8518143	T	C
rs290223	chr9	93639846	C	G
rs17114803	chr10	104386934	T	C
rs2071313	chr11	64572602	G	A
rs974144	chr11	85968623	C	T
rs11062385	chr12	427575	A	G
rs3741622	chr12	49425978	T	C
rs2290103	chr12	78531156	A	G

rs2075784	chr12	133263825	G	A
rs4883918	chr13	73350079	T	C
rs1805097	chr13	110435231	C	T
rs9549365	chr13	113907391	A	G
rs2241119	chr14	81558965	A	G
rs1130233	chr14	105239894	C	T
rs2305030	chr15	41805237	C	T
rs2229765	chr15	99478225	G	A
rs2230930	chr17	1782957	C	T
rs1799966	chr17	41223094	T	C
rs2240308	chr17	63554591	G	A
rs1567962	chr17	78919558	C	T
rs663651	chr18	42456653	G	A
rs2075607	chr19	1222012	G	C
rs1799817	chr19	7125297	G	A
rs2302603	chr19	17941294	T	C
rs12461253	chr19	31769763	G	A
rs2076578	chr22	41569609	C	T
rs9617050	chr22	50708731	A	G

Test Example 1

The detection effect of the method of Embodiment 3 is compared with that of the prior art Conpair.

First, a simulated pollution sample was constructed, and the method of Example 3 was used to detect the pollution level of the simulated sample. The steps of constructing a reference distribution include: randomly selecting 1 of 500 cell line samples as a contaminated sample (a known uncontaminated sample), randomly selecting other leukocyte or cell line samples as the contamination source, and carrying out different contamination ratios based on mass ratios. 500 pollution samples and their control samples (samples without pollution sources) were randomly selected under each pollution level to obtain the VAF difference distribution, please refer to Figure 2.

Then, real samples under different contamination levels are constructed, and the real samples are clinical samples that may be contaminated. Select other cell line samples as contamination sources to be spiked into real samples to obtain real samples under different contamination levels. Adopt the method of embodiment 3 and existing Conpair method respectively (method comes from: Bergmann, Ewa A., et al. "Conpair:concordance and contamination estimator for matched tumor-normal pairs." Bioinformatics 20:3196-3198.) Contamination level detection is performed on real samples with different contamination levels. The labeling site information of the two methods is shown in Table 4.

Table 4 Detection sites

method	number of marks
Conpair	7387
Methods of the present disclosure	60

Please refer to FIG. 2 for the distribution map of the number of homozygous marker sites covered by the detection method provided in Example 3. Among them, the real data refers to the distribution of homozygous marker sites obtained when detecting real samples; the simulated data is a randomly generated binomial distribution random number (simulation parameter n is 60, p is 0.5 Binomial distribution B (60,0.5)). It can be seen from the results that the number distribution of homozygous marker sites obtained by screening is consistent with the theoretical distribution, indicating that the screening conditions for contaminated marker sites are effective and close to the ideal situation.

The detection method of Example 3 and the partial detection results of the pollution level of the Conpair sample are shown in Table 5.

Table 5 Comparison of test results

sample name	refer to	The present disclosure (Example 3) predicts	Conpair forecast
PRE-0_005N93B66	0.005	0.003	0.56492
PRE-0_005N94B68	0.005	0.003	0.54717
PRE-0_005N95B78	0.005	0.004	0.54601
PRE-0_01N93B86	0.01	0.008	0.5604
PRE-0_01N94B94	0.01	0.009	0.55138

PRE-0_01N95B51	0.01	0.008	0.55441
PRE-0_02N93B59	0.02	0.015	0.99297
PRE-0_02N94B60	0.02	0.02	0.55951
PRE-0_02N95B61	0.02	0.015	0.56807
PRE-0_03N93B66	0.03	0.025	0.56421
PRE-0_03N94B68	0.03	0.03	0.55602
PRE-0_03N95B78	0.03	0.025	0.56495
PRE-0_05N93B86	0.05	0.04	0.55515
PRE-0_05N94B94	0.05	0.05	0.52593
PRE-0_05N95B51	0.05	0.05	0.57246
PRE-0_1N93B59	0.1	0.1	0.52641
PRE-0_1N94B60	0.1	0.1	0.51767
PRE-0_1N95B61	0.1	0.1	0.55821

As can be seen from Figure 3 and Table 5, the method of Example 3 of the present disclosure needs to cover less marker sites. Compared with the limitation of more than 1000 marker sites in existing tools, it can be flexibly screened or embedded in different panel products. Flexible and extensive application.

For the performance of the detection method of Embodiment 3, please refer to FIG. 4 . Specifically, A in FIG. 4 is the performance result of the method of Embodiment 3 in the detection of simulated contaminated samples, and B in FIG. 4 is the performance result of the method of Embodiment 3 in the detection of real samples in different polluted waters.

It can be seen from the results that in the simulated pollution samples, the coefficient of determination of the correlation between the analytical predicted value and the theoretical value is 0.9984, and in the real sample, the coefficient of determination of the correlation between the analytical predicted value and the theoretical value is 0.9987. Conpair does not perform well in real sample detection. Compared with Conpair, the predicted value of the method of Embodiment 3 of the present disclosure is closer to the real value. It should be noted that Example 3 of the present disclosure is only an exemplary illustration of the implementation of the present disclosure, and does not represent or limit the protection scope of the present disclosure, and methods using other embodiments of the present disclosure can also be obtained and implemented. Example 3 is similar to the predicted results.

Test Example 2

The detection effects of the methods provided in Examples 3, 4 and 5 were compared.

Detection method

61 samples with known contamination levels were tested using the methods provided in Examples 3, 4 and 5. Please refer to Table 6 for the test results.

Table 6 Test results

sample number	Example 5 Predicted value	Predicted value of Example 3	Example 4 Predicted value	Theoretical pollution level
1	0.002	0.004	0.004	0.005
2	0.004	0.002	0.004	0.005
3	0.004	0.005	0.003	0.005
4	0.006	0.009	0.015	0.01
5	0.007	0.007	0.009	0.01
6	0.009	0.007	0.003	0.01
7	0.015	0.02	0.03	0.02
8	0.015	0.015	0.02	0.02
9	0.015	0.02	0.015	0.02
10	0.02	0.025	0.035	0.03
11	0.02	0.025	0.025	0.03
12	0.03	0.03	0.02	0.03
13	0.035	0.05	0.05	0.05
14	0.05	0.04	0.04	0.05
15	0.05	0.05	0.035	0.05
16	0.08	0.1	0.12	0.1
17	0.09	0.09	0.1	0.1
18	0.11	0.11	0.08	0.1
19	0.87	0.91	0.92	0.9
20	0.89	0.88	0.91	0.9

twenty one	0.89	0.91	0.88	0.9
twenty two	0.9	0.97	0.99	0.95
twenty three	0.94	0.93	0.95	0.95
twenty four	0.93	0.95	0.94	0.95
25	0.94	0.98	1	0.97
26	0.97	0.96	0.97	0.97
27	0.96	0.98	0.94	0.97
28	0.97	1	1	0.98
29	0.97	0.96	0.98	0.98
30	0.97	0.98	0.96	0.98
31	0.99	1	1	0.99
32	0.99	1	1	0.99
33	0.99	1	0.98	0.99
34	0.19	0.19	0.18	0.2
35	0.25	0.27	0.25	0.25
36	0.29	0.3	0.3	0.3
37	0.25	0.27	0.27	0.25
38	0.1	0.11	0.1	0.1
39	0.1	0.11	0.11	0.1
40	0.15	0.16	0.16	0.15
41	0.21	0.22	0.22	0.2
42	0.29	0.32	0.31	0.3
43	0.38	0.4	0.36	0.4
44	0.1	0.1	0.09	0.1
45	0.14	0.16	0.15	0.15
46	0.2	0.21	0.21	0.2
47	0.3	0.31	0.3	0.3
48	0.39	0.41	0.38	0.4
49	0.81	0.84	0.89	0.8
50	0.74	0.77	0.82	0.75
51	0.69	0.72	0.76	0.7
52	0.75	0.78	0.81	0.75
53	0.88	0.94	0.97	0.9
54	0.85	0.88	0.91	0.85
55	0.78	0.81	0.84	0.8
56	0.7	0.73	0.78	0.7
57	0.62	0.64	0.7	0.6
58	0.85	0.89	0.94	0.85
59	0.8	0.83	0.89	0.8
60	0.69	0.73	0.77	0.7
61	0.6	0.64	0.7	0.6

For the performance of the detection method in Embodiment 4, please refer to B in FIG. 5 , and for the performance of the detection method provided in Embodiment 5, please refer to A in FIG. 5 .

It can be seen from the results that the coefficient of determination of the correlation between the predicted value and the theoretical value of the detection method in Example 4 is 0.9937, and the coefficient of determination of the correlation between the predicted value and the theoretical value of the detection method of Example 5 is 0.9993. It can be seen that the performance phenotype change after randomly replacing 30 markers at the marker site (Example 5) is small (0.9987vs 0.9993), indicating that as long as the screening conditions of the marker site are met, the performance of any 60rs marker site is stable.

When the number of markers was reduced to 30 (Example 4), the prediction performance decreased, and the coefficient of determination decreased to 0.9937, which indicated that the prediction performance would decrease when the number of markers was reduced, but the prediction of contamination by 30 markers was still feasible.

The above descriptions are only typical embodiments of the present disclosure, and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included within the protection scope of the present disclosure.

Industrial Applicability

Embodiments of the present disclosure provide a method for screening SNP sites for detecting the level of contamination of a sample and a method for detecting the level of contamination in a sample. The marker sites screened out based on the above screening method can be used for detecting the level of contamination in a sample, which is consistent with the present Compared with other technologies, the detection cost is lower, the detection accuracy is higher, and it has a wide range of application value. At the same time, the marker loci screened based on the above screening method have stable performance in detecting the contamination level of the sample, and have great practical value.

Claims (16)

1. A method for screening SNP sites for detecting the pollution level of a sample, characterized in that the SNP sites whose population mutation frequency is 30% to 70% in a target area are obtained as candidate marker sites;

   dividing the region between the starting site and the ending site in the candidate marker sites existing on a single chromosome into multiple selection regions, and the length of the selection region is 0.7-1.3Mb;

   If there are two or more candidate marker loci in the selection region, the locus with the allele frequency of 40% to 60% in the selection region and the different genotypes most in line with Hardy-Wingerberg equilibrium is selected as Mark the site, and remove other candidate marker sites within the selected region.
2. The method for screening SNP sites for detecting sample contamination levels according to claim 1, wherein the sites with different genotypes most in line with Hardy-Wingerberg equilibrium refer to: in the selection region For the site with the smallest site unbalance coefficient U, the calculation formula of the site unbalance coefficient U is as follows:

   U=(S ₀ -0.25) ² +(S ₁ -0.5) ² +(S ₂ -0.25) ² ; wherein, S ₀ , S ₁ and S ₂ are wild type, mutant heterozygous and mutant homozygous, respectively Population frequency in the target area.
3. The method for screening SNP sites for detecting the level of contamination in samples according to claim 2, characterized in that, in one of the selected regions, there are multiple sites that are most in line with Hardy-Wingerberg equilibrium , select any one of them;

   Preferably, the SNP candidate loci are SNP loci with a population mutation frequency of 30% to 70% in the genomes of N cases of uncontaminated negative samples;

   Preferably, the N≥50;

   Preferably, the N≧100.
4. The method for screening SNP loci for detecting sample contamination level according to any one of claims 1 to 3, wherein the SNP candidate locus is a locus with an occurrence frequency of 40% to 60% in a gene database point.
5. The method for screening SNP sites for detecting contamination levels of samples according to any one of claims 1 to 4, wherein the source of the sample to be tested is selected from tumors and genetic diseases;

   Preferably, the type of the sample to be tested is selected from plasma samples, white blood cell samples, tissue samples, oral mucosa samples, saliva samples, cerebrospinal fluid samples, pleural fluid samples, ascites samples, and oral swab samples.
6. A method for detecting the contamination level of a sample, characterized in that it comprises: using the screening method for SNP sites for detecting the contamination level of a sample according to any one of claims 1 to 5 to screen the contamination marker positions obtained point.
7. The method for detecting the contamination level of a sample according to claim 6, characterized in that the method comprises: adding the contamination whose genotype is a homozygous genotype in the target area of the sample to be tested and/or the non-contamination control sample The marker site was used as a homozygous marker site; the rank sum test was performed on the VAF difference of the obtained mutation abundance of the sample to be tested and the VAF difference distribution of the contaminated samples under different pollution levels, and the P values under different pollution levels were obtained respectively. ;

   Wherein, the mutation abundance VAF difference value of the sample to be tested is: the absolute value of the VAF difference value of the sample to be tested and the uncontaminated negative control sample at the homozygous marker site;

   The VAF difference distribution of the contaminated samples under different pollution levels is: the absolute value distribution of the VAF difference at the homozygous marker site between the contaminated samples with different levels of pollution sources and the uncontaminated negative control samples respectively.
8. The method for detecting the pollution level of a sample according to claim 7, wherein the method comprises: determining the maximum value of the P value under different pollution levels, and determining the pollution level corresponding to the maximum value of the P value as the test to be measured the contamination level of the sample;

   Preferably, the pollution ratios of the different pollution levels are selected from 0.01% to 99.9%.
9. The method for detecting the contamination level of a sample according to claim 6, wherein the contamination marker site comprises at least one of (1) to (60) in the following table;

   Numbering rsid chromosome Location reference base mutated 1 rs2294714 chr1 6257826 T C 2 rs7532151 chr1 89388944 A C 3 rs1800880 chr1 156846120 C T 4 rs11543979 chr1 201981218 C G 5 rs1136410 chr1 226555302 A G 6 rs1128919 chr2 148657117 G A 7 rs1048108 chr2 215674224 G A 8 rs7652776 chr3 2741024 G C 9 rs6443222 chr3 9163267 C T 10 rs2251219 chr3 52584787 T C 11 rs3218651 chr3 121208176 T C 12 rs3749234 chr3 176765269 T C

   13 rs17497475 chr4 19242239 C A 14 rs1870377 chr4 55972974 T A 15 rs3756122 chr4 143235865 C G 16 rs2565007 chr5 53820267 C A 17 rs13184586 chr5 161119125 C G 18 rs56188706 chr5 180057356 C G 19 rs2230653 chr6 26056604 G A 20 rs4607417 chr6 41978274 C T twenty one rs2243384 chr6 117678083 A G twenty two rs2077647 chr6 152129077 T C twenty three rs62456182 chr7 6038722 T C twenty four rs2813838 chr7 24149292 C G 25 rs2227983 chr7 55229255 G A 26 rs41736 chr7 116435768 C T 27 rs2267708 chr7 124392512 C T 28 rs1293288 chr8 11718528 T C 29 rs1805794 chr8 90990479 C G 30 rs7839934 chr8 144941181 G C 31 rs7026388 chr9 8518143 T C 32 rs7023954 chr9 21816758 G A 33 rs357564 chr9 98209594 G A 34 rs17114803 chr10 104386934 T C 35 rs9344 chr11 69462910 G A 36 rs641936 chr11 94197260 A G 37 rs543840 chr11 115764486 G A 38 rs734075 chr12 4497047 C A 39 rs7955902 chr12 40645257 C A 40 rs2290103 chr12 78531156 A G 41 rs2259820 chr12 121435342 C T 42 rs9534262 chr13 32936646 T C 43 rs4883918 chr13 73350079 T C 44 rs17655 chr13 103528002 G C 45 rs7328030 chr13 112269444 C A 46 rs2231301 chr14 23777099 G A 47 rs2241119 chr14 81558965 A G 48 rs1130233 chr14 105239894 C T 49 rs2305030 chr15 41805237 C T 50 rs2229765 chr15 99478225 G A 51 rs2230930 chr17 1782957 C T 52 rs1799966 chr17 41223094 T C 53 rs3744093 chr17 56492800 T C 54 rs4647887 chr17 74558806 A G 55 rs663651 chr18 42456653 G A 56 rs2075607 chr19 1222012 G C 57 rs2302603 chr19 17941294 T C 58 rs8113496 chr19 29820529 A G

   59 rs2242522 chr19 36228954 G T 60 rs9617050 chr22 50708731 A G

   ;

   Preferably, the contamination marker sites include at least 10 of (1) to (60) in the above table;

   Preferably, the contamination marker sites include at least 20 of (1) to (60) in the above table.
10. The method for detecting the contamination level of a sample according to claim 6, wherein the contamination marker site includes at least one of 1) to 30) in the following table or includes 1) to 30 in the following table. A combination of at least one of ) and at least one of the contamination marker sites (1) to (60) of claim 9:

    Numbering rsid chromosome Location reference base mutated base 1) rs3219489 chr1 45797505 C G 2) rs2298258 chr1 162737116 C G 3) rs3747636 chr1 204403659 A G 4) rs2227982 chr2 242793433 G A 5) rs11466512 chr3 30713126 T A 6) rs2227931 chr3 142222284 A G 7) rs1350191 chr4 154807324 C T 8) rs2043112 chr5 38955796 G A 9) rs3733875 chr5 176637240 G T 10) rs915894 chr6 32190390 T G 11) rs1801270 chr6 36651971 C A 12) rs12055782 chr6 128312033 A G 13) rs9639168 chr7 13978809 T C 14) rs2074566 chr7 26224668 C T 15) rs1129293 chr7 106513011 C T 16) rs3808565 chr8 26227640 A G 17) rs4244612 chr8 145741702 C G 18) rs290223 chr9 93639846 C G 19) rs2071313 chr11 64572602 G A 20) rs974144 chr11 85968623 C T twenty one) rs11062385 chr12 427575 A G twenty two) rs3741622 chr12 49425978 T C twenty three) rs2075784 chr12 133263825 G A twenty four) rs1805097 chr13 110435231 C T 25) rs9549365 chr13 113907391 A G 26) rs2240308 chr17 63554591 G A 27) rs1567962 chr17 78919558 C T 28) rs1799817 chr19 7125297 G A 29) rs12461253 chr19 31769763 G A 30) rs2076578 chr22 41569609 C T .
11. A kit for detecting the contamination level of a sample, characterized in that it comprises a reagent for detecting a target SNP site, wherein the target SNP site is the one described in any one of claims 1 to 5 for detecting The contamination marker loci obtained by the screening method of the SNP locus of the sample contamination level;

    Preferably, the contamination marker site includes at least one of (1) to (60) in the above table;

    Preferably, the contamination marker sites include at least 10 of (1) to (60) in the above table;

    Preferably, the contamination marker sites include at least 20 of (1) to (60) in the above table.
12. A composition for detecting the contamination level of a sample, characterized in that it comprises a reagent for detecting a target SNP site, wherein the target SNP site is the one described in any one of claims 1 to 5 for detecting The contamination marker loci obtained by the screening method of the SNP locus of the sample contamination level;

    Preferably, the contamination marker site includes at least one of (1) to (60) in the above table;

    Preferably, the contamination marker sites include at least 10 of (1) to (60) in the above table;

    Preferably, the contamination marker sites include at least 20 of (1) to (60) in the above table.
13. A detection system, characterized in that it includes:

    storage devices and processing devices;

    When the processing device runs the program in the storage device, it executes the screening method for SNP sites for detecting the contamination level of the sample according to any one of claims 1 to 5 or any one of claims 6 to 11 The described method for detecting the level of contamination in a sample.
14. An electronic device, characterized in that it includes a memory and a processor, and when the processor runs a computer program in the memory, executes the method for detecting the contamination level of a sample according to any one of claims 1 to 5. The screening method for SNP sites or the method for detecting the contamination level of a sample according to any one of claims 6 to 11.
15. Use of the kit according to claim 11 , the composition according to claim 12 , the detection system according to claim 13 or the electronic device according to claim 14 in tumor gene detection.
16. The screening method according to claim 1, wherein an acquisition module is used to acquire SNP sites with a population mutation frequency of 30% to 70% in the target region as candidate marker sites;

    Using a region division module to divide the region between the start site and the end site in the candidate marker sites existing on a single chromosome into a plurality of selection regions, and the length of the selection region is 0.7-1.3Mb;

    Use the selection module to carry out, if there are two or more candidate marker sites in the selection region, select the allele frequency in the selection region to be 40% to 60% and the different genotypes are most consistent with Hardy-Wingerberg. The balanced sites are used as marker sites, and other candidate marker sites within the selected region are removed.

Images