Biomarkers for asd and uses thereof

Abstract

The application relates to a biomarker for ASD and application thereof, and belongs to the technical field of disease gene detection and diagnosis. The biomarker comprises 283 SNP sites. The risk of ASD can be predicted through the method of selecting specific risk gene mutation sites, accumulating weights according to risk benefits, and multi-gene risk scoring, a certain judgment basis is provided for clinical diagnosis of ASD, pathogenicity judgment of main genes such as gene and CNV detection is avoided, time and workload are saved, and the method has the characteristics of high prediction and evaluation accuracy.

Description

Technical Field

[0001] This invention relates to the field of disease gene detection and diagnostic technology, and in particular to a biomarker for ASD and its application. Background Technology

[0002] Autism spectrum disorders (ASD), characterized by social communication impairments, have a significant impact on the mental and physical health of patients and their families. The current global prevalence of ASD is approximately 0.6%-1%. ASD diagnosis is crucial for targeted treatment. However, the specific pathogenic mechanisms of ASD are currently unclear. The mainstream view is that it is mainly related to genetics and environment. Data shows that ASD is a highly heritable disease, with a heritability of approximately 80% (74-93%). Diagnosis is generally determined by detecting CNVs and gene mutations in patients. Current research has identified over 1000 genes that may be associated with the genetic risk of ASD. Most of these genes are closely related to normal neural development and connections between different functional areas of the brain, such as mutations and copy number variations in genes like FMR1, SHANK3, and MECP2. Known ASD-related genes or CNVs can be found on the SFARI website: https: / / www.sfari.org / resource / sfari-gene / . However, the large number of ASD-related pathogenic or susceptibility genes illustrates the heterogeneity of ASD gene abnormalities, contributing to the current difficulties in diagnosis. Therefore, the diagnosis of ASD currently relies mainly on phenotypic assessments such as behavioral scoring, with genetic testing serving only as an auxiliary diagnostic tool.

[0003] Due to the genetic heterogeneity of ASD, the detection rate of susceptibility gene mutations for ASD is extremely low, less than 20%, which makes diagnosing ASD difficult. Furthermore, genetic testing for ASD often requires detecting CNVs and harmful mutations in related genes, necessitating the use of whole-genome microarrays or whole-genome sequencing to scan the entire genome for CNVs. Rare mutations require detection using whole-extraction or whole-genome sequencing methods, significantly increasing testing costs. Simultaneously, the vast majority of ASD patients do not have detectable known related gene mutations, resulting in a situation where even with testing, genetic diagnosis is not always possible.

[0004] Furthermore, mutation detection of genes and CNVs involves thousands of genes and the entire genome, and whole-external sequencing, microarray CNV detection, or whole-genome sequencing all require high testing costs. Therefore, developing a cost-effective product that can help diagnose ASD has significant market application value. Summary of the Invention

[0005] Therefore, it is necessary to provide a biomarker for ASD to address the problems of low detection rate and high cost of ASD gene testing. Detection of this biomarker can better analyze and obtain an individual's risk of developing ASD.

[0006] A biomarker for ASD includes the following gene loci:

[0007] Research has found that the genetic abnormalities of classic ASD may originate from multiple common gene variants, such as single nucleotide variants (SNVs), including de novo and hereditary variants. These variants were previously considered "non-pathogenic" and had no clear clinical significance, but it is now speculated that they may increase an individual's genetic susceptibility to ASD through quantitative trait inheritance or cumulative effects. Some of these genes have a significant effect, called major genes, while others have a small effect, called minor genes. If an individual has a large number of variant genes, or includes abnormalities in major genes, the genetic susceptibility is high, and vice versa.

[0008] Based on the above research foundation and considerations, the inventors believe that ASD may be a polygenic risk factor caused by the accumulation of multiple variations. Therefore, by selecting specific combinations of risk gene mutation sites and weighting them according to their risk benefits, we use a polygenic risk score to predict the risk of developing ASD, which can provide a certain basis for clinical diagnosis of ASD. The above-mentioned biomarkers are combinations of gene sites that can better predict the risk of ASD.

[0009] In one embodiment, the biomarker includes the following SNP risk alleles: 1:9347278:G:A;1:9755273:G:A;1:22523128:AT:A;1:44935688:A:AT;1:45056293:G:A;1:56798308:A:G;1:58647705:C:A;1:63529371:C:A;1:86465821:A:G;1:87629310:G:A;1:99505519:G:A;1:111088759:G:A;1:145536082:G:A;1:145547963:G:A;1:161219361:G:A;1:161658658:G:A;1:162320978:C:T;1:190061806:C:A;1:194232033:G:A;1:205740437:AAAAG:A;1:221465080:G:GTAATT;1:223180844:C:T;1:229826077:ATGGT:A;1:231968283:G:A;1:232787053:G:A;1:232858468:T:C;1:238273454:T:C;2:2644858:A:C;2:5680859:TA:T;2:9071427:A:C;2:11540489:T:C;2:24243127:T:C;2:37465344:G:T;2:52163147:T:C;2:76533082:A:G;2:76800193:C:A;2:80080287:C:A;2:101490450:G:GT;2:113382684:G:A;2:119042700:G:A;2:124450103:C:T;2:136464965:C:T;2:173038064:A:G;2:179260382:A:G;2:189851887:C:T;2:191234995:C:T;2:224558743:T:G;3:11670061:A:G;3:13273299:C:T;3:23017012:C:T;3:24734617:G:A;3:70967199:T:C;3:73296564:C:T;3:112379369:C:A;3:151478769:T:C;3:159719132:A:G;3:197812186:A:G;4:4261796:A:G;4:6422476:A:C;4:19902437:A:C;4:23871590:A:G;4:26486816:CA:C;4:26494411:C:T;4:32270753:CTTG:C;4:35129586:C:T;4:42875791:C:T;4:58166176:A:G;4:63390201:C:T;4:111121841:A:G;4:134893186:G:A;4:175065243:C:T;4:179940751:T:C;4:182847899:C:T;4:184256979:A:G;4:185856878:A:G;5:61092625:C:A;5:61882534:C:T;5:63977000:G:A;5:72526505:C:T;5:76683468:TCTC:T;5:79074229:T:C;5:135384092:C:T;5:144012547:CA:C;5:145813950:C:T;5:153304569:G:A;5:155323482:CAA:C;5:157032783:C:A;5:172829796:CT:C;5:173136667:G:A;6:1490104:G:T;6:10035866:C:A;6:13161138:C:T;6:13940796:A:C;6:17432373:G:A;6:21901658:A:C;6:46118413:A:G;6:56297437:G:A;6:61977078:C:T;6:71117655:G:A;6:73899718:C:CA;6:93916121:C:A;6:123469051:A:AC;6:125475645:G:T;6:130955699:A:G;6:151618825:C:T;6:164065525:A:G;7:7804891:A:G;7:14360034:A:G;7:14592662:T:G;7:22046220:C:A;7:29206680:C:A;7:32061610:T:C;7:45459733:G:A;7:49202075:C:T;7:69755860:A:G;7:87986355:A:G;7:88231767:G:A;7:89621864:G:T;7:130876979:A:AAATAT;7:133391858:A:G;8:1588469:G:A;8:3725241:A:G;8:8338592:G:A;8:9097737:TA:T;8:14318719:A:G;8:14601428:C:T;8:19032221:G:A;8:19187642:A:G;8:24897820:T:G;8:32595943:T:TAC;8:39112600:T:C;8:59314113:T:G;8:59810877:C:T;8:60251078:T:C;8:63602687:C:T;8:98623084:G:A;8:115484185:T:TCACA;8:116921178:ATTG:A;9:7280488:G:A;9:9777805:A:C;9:27173180:C:T;9:28228958:A:G;9:29517349:A:G;9:30043590:G:A;9:32537141:T:G;9:80935588:TGTCCATCCATCCAACC(SEQ ID NO.1):T;9:91189753:C:T;9:95679005:C:T;9:103249058:A:G;9:120512867:T:C;9:123708286:CA:C;10:5388149:T:G;10:26956626:T:C;10:29972550:A:G;10:48524247:C:A;10:48628441:T:C;10:51564487:A:AAAAAG;10:82702830:A:G;10:99770333:A:G;10:105214932:T:C;10:107441454:T:C;10:108530976:GT:G;10:121030127:C:T;10:128081737:A:C;11:3173813:T:C;11:19604832:G:T;11:21431441:G:A;11:21915113:G:A;11:42490143:G:A;11:47298201:C:T;11:51215645:C:A;11:51533354:A:G;11:60266616:AT:A;11:63598690:A:G;11:72263122:G:A;11:75124132:C:T;11:81582575:A:C;11:86957273:C:A;11:94679823:G:A;11:99367849:A:G;11:115025905:CTTTTTTTTTTTTTT(SEQ ID NO.2):C;11:128467030:G:A;11:130264515:G:A;12:526482:A:G;12:18205835:T:C;12:31112670:A:G;12:39070505:A:G;12:46830041:G:A;12:47906340:G:A;12:52489247:T:C;12:97867811:A:G;12:116056547:A:C;12:121887194:A:G;13:24441156:GCCAACACTTC(SEQ ID NO.3):G;13:30689441:G:A;13:64906150:CTT:C;13:75040462:T:C;13:77039814:G:A;13:102123480:A:C;13:104279232:T:C;13:104358672:T:C;13:107553786:C:T;14:22763063:G:A;14:49069193:G:A;14:54221497:A:C;14:55330566:G:GTTGTTTGTTTGT(SEQ ID NO.4);14:56225235:C:T;14:58305649:T:C;14:62580347:C:T;14:70734632:T:C;14:80783560:ATAT:A;14:85363140:G:A;14:86756037:T:G;14:93186594:C:T;14:93191471:T:G;14:101873354:A:C;15:22787103:T:C;15:22891783:A:C;15:23024381:T:TGC;15:23043159:C:T;15:23046404:T:C;15:24228689:G:A;15:40828922:C:T;15:60474270:G:A;15:63196148:G:T;16:19699108:G:A;16:24536229:T:C;16:24865425:T:C;16:56734322:G:A;16:68902047:CA:C;16:73550229:C:T;17:3430919:G:A;17:4875566:T:C;17:19406538:A:G;17:55314589:A:G;17:59245970:G:A;17:60239913:C:T;17:70613349:G:A;17:71431091:TA:T;17:75802486:TC:T;18:4057334:T:C;18:5735028:T:G;18:7352006:C:T;18:19260817:G:GTA;18:22588929:A:C; 18:22936880:A:G; 18:25046875:A:G; 18:32724651:CACAG:C; 18:33762467 :CACTT:C;18:34168491:A:G;18:41776545:C:T;18:41903035:T:G;18:57381093:C:T;18:5931 4950: A: G; 18: 72543115: C: T; 19: 4978203: T: C; 19: 16109849: C: T; 3067:G:A; 19:46338774:G:A; 19:47014935:T:C; 19:56247627:G:A; 20:12354280:C:T; 20:1918 2064:T:C; 20:38619473:G:GTT; 20:41851935:G:A; 20:48971714:T:C; 20:51247346:T:C; 20:6 1317786:G:A; 21:14838552:A:G; 21:28929393:C:T; 21:35570842:G:T; 21:46228165:T:C; 21:4 6646005:T:C; 21:47476701:A:C; 22:18181869:A:G; 22:22926137:C:T; 22:26190467:G:T; 22: 29237026:C:CA; 22:34531560:CTT:C; 22:36020403:C:T; 22:37677949:C:A; 22:42240069:C:T; In each SNP risk allele, the chromosome number and locus location are indicated before and after the first colon, respectively, and the bases before and after the third colon are indicated before and after the mutation, respectively.

[0010] The present invention also discloses the application of the above-mentioned biomarkers in the preparation of reagents for the auxiliary diagnosis of ASD.

[0011] It is understood that the reagents and methods mentioned above can be those commonly used in this field, and can be used to detect the specific biomarker gene information.

[0012] The present invention also discloses a panel for assisting in the diagnosis of ASD, comprising reagents for detecting the aforementioned biomarkers.

[0013] Understandably, the panel described above can be designed according to the panel design commonly used in this field. The key is that the selected combination of gene loci to be detected is the biomarker described above.

[0014] This invention also discloses a detection system for assisting in the diagnosis of ASD, comprising: The detection device is used to detect the aforementioned biomarkers in the sample to be tested and obtain the site information of each SNP. The analysis device acquires the effect value of each SNP, sums them to obtain the total effect value, compares the total effect value with a preset threshold, and determines the risk of ASD. An output device for outputting the above-mentioned ASD risk results.

[0015] The above-mentioned detection system, after obtaining the site information of each SNP, accumulates the effect values ​​of different SNPs to obtain the total effect value. The risk of ASD is assessed by accumulating the total effect value. It can reasonably integrate major and minor genes together to achieve a better assessment effect.

[0016] In one embodiment, the total effect value is calculated in the analytical apparatus according to the following formula: Total effect size = β1x1 + β2x2 + ... + βkxk + ... + βnxn Where: βk is the effect value of each SNP, x k denoted as the number of risk alleles for each SNP in the sample to be tested, where n is 283 SNPs.

[0017] In one embodiment, the effect size of each SNP is taken from the range of ±10% of the following β values:

[0018] or The effect size of each SNP is taken from the following β value ± 10% range:

[0019] In one embodiment, the effect values ​​are taken from the range of β value ±5%, β value ±3%, β value ±2%, β value ±1%, and β value ±0.5%.

[0020] Understandably, SNP effect values ​​can be obtained by analyzing large sample sizes, but using the aforementioned SNP effect values ​​has the advantage of high assessment accuracy.

[0021] In one embodiment, the analysis device compares and judges according to the following method: when the total effect value is greater than or equal to a threshold, it is judged as a high risk of ASD.

[0022] In one embodiment, the threshold is 7-10. Understandably, this threshold can be determined based on population sample sequencing data and a comprehensive clinical assessment. After conducting experiments with a large number of samples, the inventors have concluded that using the above conditions, the probability of ASD diagnosed as high-risk is relatively high and generally fairly evenly distributed, demonstrating good efficacy.

[0023] This invention also discloses a method for detecting ASD for non-diagnostic and non-therapeutic purposes, comprising the following steps: Genetic testing: Detects the above-mentioned biomarker gene information in the sample to be tested, and obtains the site information of each SNP; Effect value analysis: Obtain the effect value of each SNP mentioned above, sum them up to obtain the total effect value, compare the total effect value with a preset threshold to determine the risk of ASD.

[0024] Compared with the prior art, the present invention has the following beneficial effects: The biomarker for ASD of this invention identifies a combination of 283 ASD risk loci through screening and gene linkage of risk genes. The cumulative ASD risk is calculated using the utility values ​​of these risk alleles, and combined with the PRS score range, the individual's ASD risk is assessed, achieving an AUC value as high as 0.9423. Combining these biomarkers into a panel requires only the detection of 283 SNP loci, significantly reducing the original testing costs and saving on expenses.

[0025] Furthermore, the risk of an individual with ASD can be determined by summing the risk utility value of each locus, eliminating the need for pathogenicity determination of major genes such as gene and CNV testing, saving time and workload, and improving the accuracy of prediction and assessment. Attached Figure Description

[0026] Figure 1 This is a flowchart of the experimental method in Example 1; Figure 2 This refers to the total effect value in each test group and validation set in Examples 2 and 4, with the best effect value being BETA best. Figure 3 The total effect value of BETA all in each test group and validation set in Examples 3 and 4 is the overall effect value. Figure 4 This is the ROC curve of the validation set in Example 4. Detailed Implementation

[0027] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] Unless otherwise specified, all reagents used in the following examples are commercially available; and all methods used in the following examples are conventional methods unless otherwise specified.

[0030] Example 1 A biomarker for ASD was discovered through the following method.

[0031] 1. ASD sample collection and mutation data processing To find a gene mutation panel for auxiliary diagnosis of ASD assessment, we first collected mutation data from ASD samples.

[0032] In this embodiment, sequencing data from CytoScan HD chips were collected from 591 ASD patients (ASD group) and 500 non-ASD patients (control group). CytoScan HD chips are chromosome microarray chips manufactured by Thermo Fisher Scientific, which can detect CNVs (copy number variations) throughout the genome and also contain approximately 750,000 SNP markers.

[0033] We extracted SNPs for each individual using Thermo Fisher Scientific's CHAS software and compiled them into a VCF format file representing the population mutations. Since the microarray data only provides fewer than 750,000 SNP markers, we needed to perform whole-genome imputation to expand the SNP range. We used a website for this purpose (Westlake Imputation Server, https: / / imputationserver.westlake.edu.cn / help / help / help.html), which provides over 9,000 haplotypes and more than 40 million autosomal mutations, effectively providing reliable and accurate whole-genome imputation data.

[0034] Following conventional filling techniques in this field, after whole-genome filling, we used the filtering conditions of MAF>1% and R2>0.8 (i.e., sites with a minimum allele frequency greater than 1% and a filling reliability of approximately 80%) to filter all sites, obtaining 4,291,808 high-quality SNP sites.

[0035] 2. Determination of SNP combinations and calculation of their effect sizes We randomly selected samples from the ASD group and the control group at a ratio of 8:2, and performed basic data analysis and validation analysis, which were repeated 10 times.

[0036] (1) Risk effect value assessment of all SNPs on ASD.

[0037] We first performed genome-wide association analysis on all samples, including 591 ASD patients and 500 controls. Using the Firth logistic regression model in Regenie v3.1.1 software, we obtained the effect value of each SNP risk allele using the Firth likelihood ratio test. Then, based on the aforementioned 10 replicates of randomly extracted baseline samples (8 / 10, i.e., 414 ASD patients and 350 controls), we used the same method to obtain the risk effect assessment values ​​(BETA, β) for ASD prevalence for all SNPs in 10 replicates.

[0038] (2) Polygenic risk assessment test Using PRSice 2.3.1.e software, polygenic risk assessment was performed on 10 replicate test samples (2 / 10, i.e., 177 ASD patients and 150 normal control samples). 9837 different SNP combinations were randomly calculated, and the R² of each combination was used for evaluation using linear regression. The optimal SNP combination for each of the 10 tests was obtained, and ROC curves were used to verify its efficacy. The test results are shown in Table 1.

[0039] Table 1. Number and effectiveness of the best SNP combinations in the test set

[0040] As shown in Table 1, the average ROC of the 10 random test sets can reach 0.7221, with the highest value reaching 0.7548. Compared with the less than 20% detection rate of pathogenic gene mutations, it has good predictive efficacy.

[0041] 3. Performance verification of SNP combinations To identify the optimal SNP combination, we performed a union of the best SNP combinations from the above test sets, obtaining 283 SNP combinations, which serve as the optimal SNP combination panel of this invention. For ease of study, the effect sizes (BETA best) of the risk alleles of these SNPs for ASD in their respective tests and their overall effect sizes (BETA all) are shown in the table below.

[0042] Table 2. Effect values ​​of each SNP

[0043] Note: In the above SNP descriptions, the first colon indicates the chromosome number and locus location, respectively, and the third colon indicates the base before and after the mutation, respectively. For example, "1:9347278:G:A" means that this SNP is the 9347278th base on chromosome 1, which was G before the mutation and A after the mutation; "1:205740437:AAAAG:A" means that this SNP is the 205740437th base on chromosome 1, which was AAAAG before the mutation and A after the mutation. The "freq" above refers to the frequency of occurrence in the population.

[0044] The risk alleles of the aforementioned SNPs are biomarkers used to assist in the diagnosis and assessment of ASD.

[0045] Example 2 This embodiment uses the best effect value (BETA best) of the 283 SNPs obtained in Example 1 in each group of tests. By using the combination of the 283 SNPs, the total effect value is calculated according to the following formula: Total Effect =β1x1+β2x2+…+β k x k +…+β n x n In the formula, β k For each SNP, the effect size, x k The number of risk alleles at this SNP locus in the sample to be tested is n, where n is 283 SNPs.

[0046] Based on this, we calculated the ROC of the total effect size for the validation population (177 ASD patients and 150 normal controls in 10 randomized trials), as follows: Figure 1 As shown in Table 3.

[0047] Table 3. Power of the 283 SNP Panel in each test group, weighted by the best effect value (BETA best).

[0048] As shown in Table 3, the average ROC of each test set reached 0.9398, the total effect size ranged from -20 to +50 or +60, the average was about 10, the median was about 9, and the probability of ASD with a total effect size greater than the median was about 89.20%. Overall, the effect size was relatively evenly distributed and had the efficacy of assisting in the diagnosis of ASD.

[0049] Example 3 In this embodiment, the risk effect value BETA all of the 283 SNPs obtained in Example 1 was used. By using the combination of the 283 SNPs, referring to the method in Example 2, we calculated the ROC of the total effect value of the same validation population (177 ASD patients and 150 normal control samples in 10 randomizations), as shown in Table 4.

[0050] Table 4. Power of the 283 SNP Panel in each test group, weighted by the total effect value BETA all.

[0051] The results above are not significantly different from those in Example 2, but the overall average ROC is 0.9423, which is higher than in Example 2. Furthermore, the range of weighted risk effect values ​​is relatively narrower, with the average value decreasing to around 7 and the median to around 6. The average prevalence of total effect values ​​greater than the median is 88.96%, which is generally more evenly distributed and has the efficacy of assisting in the diagnosis of ASD, and is slightly better than in Example 2.

[0052] Example 4 To further verify the reliability of the ASD risk assessment in this invention, we used BETA best and BETA all data to test datasets from different sources, including 7 patients with ASD and 405 other data samples (which may contain samples with ASD, depending on the prevalence of ASD, ranging from 2-4%). ​​These datasets were used as the validation set for analysis. The results are as follows: Figure 2-4 As shown.

[0053] The results show that the total effect sizes of the normal population (control group) calculated by BETA best and BETA all ranged from -17.08 to 25.75 and -10.35 to 17.607, respectively, with medians of 5.61 and 3.72, respectively. These values ​​were all within the tested range and significantly lower than those of the ASD group.

[0054] The total effect sizes of the seven ASD sample data were: 7.807–21.172 calculated using BETA best, with a median of 13.328; and 5.247–13.647 calculated using BETA all, with a median of 8.282, which are basically consistent with the distribution of the ASD group.

[0055] In this embodiment, ROC analysis was performed on 405 population samples and 7 ASD data. The ROC values ​​calculated using BETA Best and BETA All were 0.804 (95% CI: 0.681~0.9261) and 0.801 (95% CI: 0.695~0.9064), respectively, showing little difference. See details below. Figure 4 .

[0056] In summary, by using the SNP of this invention as a biomarker and a weighted algorithm, the risk of ROC can be analyzed and evaluated through weighted effect values, and the predictive requirements for auxiliary diagnosis can be met.

[0057] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0058] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. The application of a biomarker for ASD in the preparation of reagents for the auxiliary diagnosis of ASD, characterized in that, Whole genome data were obtained from Westlake Imputation Server; This biomarker includes the following gene loci: 。 2. The application according to claim 1, characterized in that, This biomarker includes the following SNP risk alleles: 1:9347278:G:A;1:9755273:G:A;1:22523128:AT:A;1:44935688:A:AT;1:45056293:G:A;1:56798308:A:G;1:58647705:C:A;1:63529371:C:A;1:86465821:A:G;1:87629310:G:A;1:99505519:G:A;1:111088759:G:A;1:145536082:G:A;1:145547963:G:A;1:161219361:G:A;1:161658658:G:A;1:162320978:C:T;1:190061806:C:A;1:194232033:G:A;1:205740437:AAAAG:A;1:221465080:G:GTAATT;1:223180844:C:T;1:229826077:ATGGT:A;1:231968283:G:A;1:232787053:G:A;1:232858468:T:C;1:238273454:T:C;2:2644858:A:C;2:5680859:TA:T;2:9071427:A:C;2:11540489:T:C;2:24243127:T:C;2:37465344:G:T;2:52163147:T:C;2:76533082:A:G;2:76800193:C:A;2:80080287:C:A;2:101490450:G:GT;2:113382684:G:A;2:119042700:G:A;2:124450103:C:T;2:136464965:C:T;2:173038064:A:G;2:179260382:A:G;2:189851887:C:T;2:191234995:C:T;2:224558743:T:G;3:11670061:A:G;3:13273299:C:T;3:23017012:C:T;3:24734617:G:A;3:70967199:T:C;3:73296564:C:T;3:112379369:C:A;3:151478769:T:C;3:159719132:A:G;3:197812186:A:G;4:4261796:A:G;4:6422476:A:C;4:19902437:A:C;4:23871590:A:G;4:26486816:CA:C;4:26494411:C:T;4:32270753:CTTG:C;4:35129586:C:T;4:42875791:C:T;4:58166176:A:G;4:63390201:C:T;4:111121841:A:G;4:134893186:G:A;4:175065243:C:T;4:179940751:T:C;4:182847899:C:T;4:184256979:A:G;4:185856878:A:G;5:61092625:C:A;5:61882534:C:T;5:63977000:G:A;5:72526505:C:T;5:76683468:TCTC:T;5:79074229:T:C;5:135384092:C:T;5:144012547:CA:C;5:145813950:C:T;5:153304569:G:A;5:155323482:CAA:C;5:157032783:C:A;5:172829796:CT:C;5:173136667:G:A;6:1490104:G:T;6:10035866:C:A;6:13161138:C:T;6:13940796:A:C;6:17432373:G:A;6:21901658:A:C;6:46118413:A:G;6:56297437:G:A;6:61977078:C:T;6:71117655:G:A;6:73899718:C:CA;6:93916121:C:A;6:123469051:A:AC;6:125475645:G:T;6:130955699:A:G;6:151618825:C:T;6:164065525:A:G;7:7804891:A:G;7:14360034:A:G;7:14592662:T:G;7:22046220:C:A;7:29206680:C:A;7:32061610:T:C;7:45459733:G:A;7:49202075:C:T;7:69755860:A:G;7:87986355:A:G;7:88231767:G:A;7:89621864:G:T;7:130876979:A:AAATAT;7:133391858:A:G;8:1588469:G:A;8:3725241:A:G;8:8338592:G:A;8:9097737:TA:T;8:14318719:A:G;8:14601428:C:T;8:19032221:G:A;8:19187642:A:G;8:24897820:T:G;8:32595943:T:TAC;8:39112600:T:C;8:59314113:T:G;8:59810877:C:T;8:60251078:T:C;8:63602687:C:T;8:98623084:G:A;8:115484185:T:TCACA;8:116921178:ATTG:A;9:7280488:G:A;9:9777805:A:C;9:27173180:C:T;9:28228958:A:G;9:29517349:A:G;9:30043590:G:A;9:32537141:T:G;9:80935588:TGTCCATCCATCCAACC(SEQ ID NO.1):T;9:91189753:C:T;9:95679005:C:T;9:103249058:A:G;9:120512867:T:C;9:123708286:CA:C;10:5388149:T:G;10:26956626:T:C;10:29972550:A:G;10:48524247:C:A;10:48628441:T:C;10:51564487:A:AAAAAG;10:82702830:A:G;10:99770333:A:G;10:105214932:T:C;10:107441454:T:C;10:108530976:GT:G;10:121030127:C:T;10:128081737:A:C;11:3173813:T:C;11:19604832:G:T;11:21431441:G:A;11:21915113:G:A;11:42490143:G:A;11:47298201:C:T;11:51215645:C:A;11:51533354:A:G;11:60266616:AT:A;11:63598690:A:G;11:72263122:G:A;11:75124132:C:T;11:81582575:A:C;11:86957273:C:A;11:94679823:G:A;11:99367849:A:G;11:115025905:CTTTTTTTTTTTTTT(SEQ ID NO.2):C;11:128467030:G:A;11:130264515:G:A;12:526482:A:G;12:18205835:T:C;12:31112670:A:G;12:39070505:A:G;12:46830041:G:A;12:47906340:G:A;12:52489247:T:C;12:97867811:A:G;12:116056547:A:C;12:121887194:A:G;13:24441156:GCCAACACTTC(SEQ ID NO.3):G;13:30689441:G:A;13:64906150:CTT:C;13:75040462:T:C;13:77039814:G:A;13:102123480:A:C;13:104279232:T:C;13:104358672:T:C;13:107553786:C:T;14:22763063:G:A;14:49069193:G:A;14:54221497:A:C;14:55330566:G:GTTGTTTGTTTGT(SEQ ID NO.4);14:56225235:C:T;14:58305649:T:C;14:62580347:C:T;14:70734632:T:C;14:80783560:ATAT:A;14:85363140:G:A;14:86756037:T:G;14:93186594:C:T;14:93191471:T:G;14:101873354:A:C;15:22787103:T:C;15:22891783:A:C;15:23024381:T:TGC;15:23043159:C:T;15:23046404:T:C;15:24228689:G:A;15:40828922:C:T;15:60474270:G:A;15:63196148:G:T;16:19699108:G:A;16:24536229:T:C;16:24865425:T:C;16:56734322:G:A;16:68902047:CA:C;16:73550229:C:T;17:3430919:G:A;17:4875566:T:C;17:19406538:A:G;17:55314589:A:G;17:59245970:G:A;17:60239913:C:T;17:70613349:G:A;17:71431091:TA:T;17:75802486:TC:T;18:4057334:T:C;18:5735028:T:G;18:7352006:C:T;18:19260817:G:GTA;18:22588929:A:C;18:22936880:A:G;18:25046875:A:G;18:32724651:CACAG:C;18:33762467:CACTT:C;18:34168491:A:G;18:41776545:C:T;18:41903035:T:G;18:57381093:C:T;18:59314950:A:G;18:72543115:C:T;19:4978203:T:C;19:16109849:C:T;19:19370917:C:T;19:31123067:G:A;19:46338774:G:A;19:47014935:T:C;19:56247627:G:A;20:12354280:C:T;20:19182064:T:C;20:38619473:G:GTT;20:41851935:G:A;20:48971714:T:C;20:51247346:T:C;20:61317786:G:A;21:14838552:A:G;21:28929393:C:T;21:35570842:G:T;21:46228165:T:C;21:46646005:T:C;21:47476701:A:C;22:18181869:A:G;22:22926137:C:T;22:26190467:G:T;22:29237026:C:CA;22:34531560:CTT:C;22:36020403:C:T;22:37677949:C:A;22:42240069:C:T; In each SNP risk allele, the chromosome number and locus location are indicated before and after the first colon, respectively, and the bases before and after the third colon are indicated before and after the mutation, respectively.

3. A detection composition for assisting in the diagnosis of ASD, characterized in that, Includes reagents for detecting biomarkers in the application described in claim 1 or 2.

4. A detection system for assisting in the diagnosis of ASD, characterized in that, include: A detection device for detecting biomarkers in the application described in claim 1 or 2 in a sample to be tested, and obtaining site information for each SNP; The analysis device acquires the effect value of each SNP, sums them to obtain the total effect value, compares the total effect value with a preset threshold, and determines the risk of ASD. An output device is used to output the above-mentioned ASD risk results; In the analytical apparatus, the total effect value is calculated according to the following formula: Total effect size = β1x1 + β2x2 + ... + β k x k +…+β n x n Where: β k For each SNP, the effect size, x k n represents the number of risk alleles for each SNP in the sample to be tested, where n is 283 SNPs. The effect value for each SNP is taken from the following β values: or The effect value for each SNP is taken from the following β values: In the analysis device, the following comparison and judgment method is used: when the total effect value is greater than or equal to a threshold, it is judged as a high risk of ASD; the threshold is 7-10.

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