A quantitative coding method for SNP data based on allele frequency

Abstract

The present invention discloses a method for quantitatively encoding SNP data based on allele variation frequency. The SNP data quantitative encoding method provided by the present invention quantitatively encodes the homozygous genotype of the main variant gene, the homozygous genotype of the secondary gene, and the heterozygous genotype of the secondary gene based on the frequency of the secondary variant gene of each variety or sample under the SNP site, so that the encoded SNP data has clear statistical and professional significance, is compatible with traditional statistical analysis methods, and is easier to perform SNP site evaluation and sample similarity analysis. Experiments have shown that the SNP data quantitative encoding method provided by the present invention can realize intuitive and convenient comparison and classification evaluation of similarity or dissimilarity between samples and sites, the professional significance of the data is clearer, the data analysis method is richer, it is not limited by statistical software in the field of biotechnology, and is applicable to a wider range of classical statistical methods and software, and the evaluation of samples and sites is more flexible, intuitive, and comprehensive.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to a SNP data quantization encoding method based on allele frequency. Background Art

[0002] Molecular markers are an important tool in molecular genetics research and can be widely used in the fields of molecular marker-assisted breeding, genetic similarity comparison of crop varieties, variety characteristics identification and protection, crop germplasm resource diversity analysis, variety kinship research, etc. The universal research of DNA molecular markers initially focused on SSR markers with rich polymorphism. As a new type of molecular marker developed in recent years, SNP markers have the advantages of high genetic stability, co-dominance, rich content, wide distribution and are often associated with important crop trait genes. They have important biological significance. With the development of high-throughput sequencing technology, the new generation of molecular markers SNPs have gradually received attention in molecular marker research in recent years due to their rich content, wide distribution and strong representativeness. They have gradually been widely used in the fields of crop variety detection and identification and variety protection.

[0003] SNP fingerprint data, also known as "presence / absence" data, exhibits dimorphic characteristics. Statistical analysis and evaluation of SNP loci and sample quality are based on variations in the frequency of gene occurrence across different samples and loci. At the allele level, SNP data is a special type of bidirectional ordinal categorical data consisting of only two categories, 0 and 1. At the genotype level, a single SNP locus may have three genotypes. Modern high-throughput testing platforms such as KASP can categorize test results into types (e.g., AA, TT, and AT) based on the dimorphic nature of SNP markers. Current methods for processing SNP typing data typically convert typing data into binary coded data (0, 1 data) according to the format requirements of specialized software. Biotechnology-specific software is then used to calculate built-in parameters such as genetic similarity coefficients, detection rates, PIC values, MAF values, and heterozygosity rates. These parameters are then used to evaluate and select core loci and compare sample similarity (or heterozygosity). Existing specialized software is diverse, with varying functionality and data format requirements. For example, Structure software and NTSYSpc software are used to calculate genetic similarity coefficients and cluster analysis (Li Lechen et al., 2019), Tassel software is used to calculate indicators such as genetic distance and PIC value (Li Niannian et al., 2019), Powermarker software is used to organize SNP typing data and convert them into a 0, 1 binary data format, and then NTSYSpc software is used to calculate genetic similarity coefficients. Tassel software is used to perform principal component analysis on varieties (Liu Yike et al., 2020). Current statistical methods for SNP data all convert SNP typing data into a (0 1) binary data format, that is, the homozygous genotype is recorded as (1 0), the heterozygous genotype is recorded as (1 1), and the other homozygous genotype is recorded as (0 1). The fingerprint map of the variety is constructed, and then the site and sample evaluation method is performed with the help of specialized statistical software. In essence, this method follows the SSR data conversion method and treats dimorphic SNP data as a specific type of SSR data in which multiple genes may exist under the same marker. The main advantage of this method of converting SNP typing data into a "0 1" binary data format and analysis method is that it can follow the ideas, methods, indicators and software of SSR data analysis. Researchers can organize and import the data according to the software requirements to obtain the parameter calculation results set by the software. At the same time, it also has the following disadvantages: (1) The converted data has no statistical significance, that is, the SNP fingerprint data is converted into "0 1 type" "presence or absence" attribute data, which has no "quantity" meaning of data size and cannot be used for intuitive statistical analysis. (2) It is highly dependent on professional software and must rely on professional software to generate specific evaluation indicators, while the data itself cannot be analyzed and evaluated based on traditional statistical methods. Professional software usually has limited functions and complex operations, and requires special learning and training to master its use.(3) The existing SNP development software is still very limited and difficult to operate for researchers who are not bioinformatics professionals. Summary of the Invention

[0004] Therefore, the purpose of the present invention is to provide a convenient calculation method for SNP data quantification coding based on allele frequency and SNP site and sample evaluation parameters, which converts SNP binary data into quantitative data with clear statistical and professional significance, can be applied to a wider range of classical statistical methods and software, and can perform more comprehensive and intuitive analysis of sample and site evaluations, providing scientific research and technical personnel with a simpler and more convenient method that is applicable to general data processing software such as MS Excel, thereby breaking away from the constraints of numerous cumbersome, complex, and limited-function dedicated software, making SNP data analysis more convenient, efficient, free, and convenient.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for quantifying and encoding SNP data based on allele frequency may include the following steps:

[0007] (1) SNP data quantization precoding.

[0008] Single Nucleotide Polymorphism (SNP) refers to a change in the DNA sequence caused by a single nucleotide alteration—A, T, C, or G. The Minor Allele Frequency (MAF) based on SNP typing data is the frequency of the allele with the second highest variation frequency, reflecting the polymorphism of the SNP locus.

[0009] When the minor allele of each SNP locus is unclear, it is assumed that any allele (such as A) at a certain SNP locus is the minor allele, and the other allele (such as G) is the major allele. The homozygous genotype of the assumed minor allele (such as AA) is numbered 2, the other homozygous genotype (such as GG) is numbered 0, and the heterozygous genotype (such as AG) is numbered 1. Missing values are represented by blanks. This converts the original attribute data composed of nucleotide letters into an m×n data matrix composed of 0, 1, and 2 numbers (m and n represent the number of varieties or samples and the number of SNP loci, respectively), completing the precoding of SNP data.

[0010] (2) SNP data quantification and coding correction. Based on the above m×n data matrix, the hypothetical MAF value (Hypothetical Minor Allele Frequency, H_MAF) of each SNP site is calculated to test the authenticity of the hypothetical minor allele. If the H_MAF value is less than 0.50, the hypothesis is accepted; if the H_MAF value is ≥0.50, the hypothesis is rejected. The numbers 0 and 2 in the column where the corresponding SNP site is located are swapped so that all numbers 2 and 0 in the matrix represent the homozygous genotype of the minor allele and major allele, respectively, thus completing the SNP data quantification and coding process.

[0011] The calculation formula of the H_MAF value of the j-th SNP site in MS Excel is as follows:

[0012] H_MAF j =SUM(SNP 1j :SNP mj ) / (COUNT(SNP 1j :SNP mj )*2)*100%…………(1)

[0013] Among them, H_MAF j represents the H_MAF value of the jth SNP site, m is the variety or sample size, SNP 1j 、SNP mj They represent the quantization codes of the 1st row and jth column and the mth row and jth column of the quantization coding matrix respectively.

[0014] Among them, the site evaluation parameters mainly include minor allele frequency (MAF), major allele frequency (MAAF), polymorphism information content (PIC), loci heterozygosity rate (LHR), call frequency (CF), etc. The calculation formula of the site evaluation parameters based on the quantitative coding of SNP data in Excel is as follows:

[0015] MAF j =SUM(SNP 1j :SNP mj ) / ((COUNT(SNP 1j :SNP mj )*2)*100%…………(2)

[0016] MAAF j =1-MAF j…………(3)

[0017]

[0018] LHR j =COUNTIF(SNP 1j :SNP mj ,1) / COUNT(SNP 1j :SNP mj )*100%…………(5)

[0019] CF j =(1-COUNTBLANK(SNP 1j :SNP mj ) / m)*100%…………(6)

[0020] Among them, MAF j 、MAAF j , PIC j , LHR j CF j They represent the MAF value, MAAF value, PIC value, LHR value, and CF value of the j-th SNP site, respectively. m is the variety or sample size, and SNP 1j 、SNP mj They represent the quantization codes of the 1st row and jth column and the mth row and jth column of the quantization coding matrix respectively.

[0021] Sample evaluation parameters mainly include sample heterozygosity rate of sample (SHR), missing rate (MR), number of identical and / or different loci between samples, and genetic distance (GD) or genetic similarity (GS) between samples. The calculation formula of sample evaluation parameters based on SNP data quantification coding in Excel is as follows:

[0022] SHR i =COUNTIF(SNP i1 :SNP in ,1) / COUNT(SNP il :SNP in )*100%…………(7)

[0023] MR i =(1-COUNTBLANK(SNP i1 :SNP in) / n)*100%…………(8)

[0024] N ii′ =COUNTIF((SNP i1 -SNP i′1 ):(SNP in -SNP i′n ),0)…………(9)

[0025] GD ii′ =SUM(ABS(SNP i1 -SNP i′1 ):ABS(SNP in -SNP i′n )) / 2n…………(10)

[0026] GS ii′ =1-GD ii′ …………(11)

[0027] Among them, SHR i , MR i They represent the heterozygous rate and data missing rate of the SNP site of the i-th sample, n is the number of SNP sites, SNP i1 、SNP i′1 、SNP in 、SNP i′n Respectively represent the quantization codes of the quantization coding matrix at row i and column 1, row i′ and column 1, row i and column n, and row i′ and column n; N ii′ is the number of identical sites between varieties i and i′, GD ii′ is the genetic distance between varieties i and i′, GS ii′ is the genetic similarity coefficient between varieties i and i′.

[0028] Through the above-mentioned calculation formulas in MS Excel for the evaluation parameters of the quantified coding sites based on SNP data (MAF value, PIC value, heterozygosity rate, detection rate, etc.) and the calculation formulas in MS Excel for the evaluation parameters of the quantified coding varieties or samples based on SNP data (heterozygosity rate, deletion rate, number of identical sites between samples, genetic distance or genetic similarity coefficient, etc.), parameter calculation in Excel after SNP quantification coding can be realized.

[0029] The present invention has the following advantages:

[0030] (1) The quantitatively coded SNP data has clear statistical and professional significance. The codes 0, 1, and 2 are the frequencies of occurrence of the minor allele.

[0031] (2) It provides a simple, convenient, intuitive, efficient and widely applicable analysis method for calculating site and sample evaluation parameters based on SNP data. Based on the quantitative coding, parameters such as MAF, PIC, heterozygosity rate, and deletion rate of samples and SNP sites can be directly calculated in general software such as MS Excel, without relying on cumbersome professional software to complete the calculation process.

[0032] (3) Similarities or differences can be directly compared and classified between samples and sites because the data of 0, 1, and 2 in different sites have the same definition and meaning, representing the frequency of minor alleles, and thus polymorphism and gene distribution.

[0033] (4) The statistical analysis of encoded SNP data is no longer restricted by specialized software, achieving a good connection between SNP data and traditional statistical software. This method largely makes up for the tediousness and limitations of "01 type" data encoding and analysis. It can be applied to a wider range of classical statistical methods and software, and can conduct more comprehensive and intuitive analysis of sample and locus evaluation. This method can play an important role in the process of SNP data processing and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the method for calculating SNP data quantification coding and SNP site and sample evaluation parameters of the present invention.

[0035] Figure 2 This is a frequency distribution diagram of SNP site evaluation parameters based on coded data in the implementation steps; in the figure, A: PIC value; B: MAF value; C: detection rate; D: site heterozygosity rate.

[0036] Figure 3 This is a frequency distribution diagram of the variety sample evaluation parameters based on the coded data in the implementation steps. In the figure, A: proportion of the number of identical sites; B: genetic similarity coefficient; C: missing rate; D: sample heterozygosity rate. DETAILED DESCRIPTION

[0037] The present invention will be described in detail below through specific embodiments. These embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0038] Throughout the specification and claims, the words "including" and "comprising" are open-ended terms and should be interpreted as meaning "including, but not limited to." The specification subsequently describes preferred embodiments of the present invention. However, this description is intended for general purposes only and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the appended claims.

[0039] Example 1

[0040] A method for quantifying and encoding SNP data based on allele frequency comprises the following steps:

[0041] (1) SNP data quantization precoding.

[0042] When the minor allele of each SNP locus is unclear, it is assumed that any allele (such as A) at a certain SNP locus is the minor allele, and the other allele (such as G) is the major allele. The homozygous genotype of the assumed minor allele (such as AA) is numbered 2, the other homozygous genotype (such as GG) is numbered 0, and the heterozygous genotype (such as AG) is numbered 1. Missing values are represented by blanks. This converts the original attribute data composed of nucleotide letters into an m×n data matrix composed of 0, 1, and 2 numbers (m and n represent the number of varieties or samples and the number of SNP loci, respectively), completing the precoding of SNP data.

[0043] (2) SNP data quantification coding correction.

[0044] Based on the above m×n data matrix, the hypothetical MAF value (Hypothetical MinorAllele Frequency, H_MAF) of each SNP locus is calculated to test the authenticity of the hypothesized minor allele. If the H_MAF value is less than 0.50, the hypothesis is accepted; if the H_MAF value is ≥0.50, the hypothesis is rejected. The numbers 0 and 2 in the column corresponding to the SNP locus are swapped so that all numbers 2 and 0 in the matrix represent the homozygous genotype of the minor allele and major allele, respectively, thus completing the SNP data quantification encoding process.

[0045] Then, use the following formula to calculate the parameters in Excel after SNP quantification coding. The specific formula involved is as follows:

[0046] The calculation formula of the H_MAF value of the j-th SNP site in MS Excel is as follows:

[0047] H_MAF j =SUM(SNP 1j :SNP mj ) / (COUNT(SNP 1j :SNP mj )*2)*100%…………(1)

[0048] Among them, H_MAF j represents the H_MAF value of the jth SNP site, m is the variety or sample size, SNP 1j 、SNP mj They represent the quantization codes of the 1st row and jth column and the mth row and jth column of the quantization coding matrix respectively.

[0049] Among them, the site evaluation parameters mainly include minor allele frequency (MAF), major allele frequency (MAAF), polymorphism information content (PIC), loci heterozygosity rate (LHR), call frequency (CF), etc. The calculation formula of the site evaluation parameters based on the quantitative coding of SNP data in Excel is as follows:

[0050] MAF j =SUM(SNP 1j :SNP mj ) / ((COUNT(SNP 1j :SNP mj )*2)*100%…………(2)

[0051] MAAF j =1-MAF j …………(3)

[0052]

[0053] LHR j =COUNTIF(SNP 1j :SNP mj ,1) / COUNT(SNP 1j :SNP mj )*100%…………(5)

[0054] CF j =(1-COUNTBLANK(SNP 11j :SNP mj ) / m)*100%…………(6)

[0055] Among them, MAF j 、MAAF j , PIC j , LHR j CF j They represent the MAF value, MAAF value, PIC value, LHR value, and CF value of the j-th SNP site, respectively. m is the variety or sample size, and SNP 1j 、SNP mj They represent the quantization codes of the 1st row and jth column and the mth row and jth column of the quantization coding matrix respectively.

[0056] Sample evaluation parameters mainly include sample heterozygosity rate of sample (SHR), missing rate (MR), number of identical and / or different loci between samples, and genetic distance (GD) or genetic similarity (GS) between samples. The calculation formula of sample evaluation parameters based on SNP data quantification coding in Excel is as follows:

[0057] SHR i =COUNTIF(SNP i1 :SNP in ,1) / COUNT(SNP i1 :SNP in )*100%…………(7)

[0058] MR i =(1-COUNTBLANK(SNP i1 :SNP in ) / n)*100%…………(8)

[0059] N ii′ =COUNTIF((SNP i1 -SNP i′1 ):(SNP in -SNP i′n ),0)…………(9)

[0060] GD ii′ =SUM(ABS(SNP i1 -SNP i′1 ):ABS(SNP in -SNP i′n )) / 2n…………(10)

[0061] GS ii′ =1-GD ii′ …………(11)

[0062] Among them, SHR i , MR i They represent the heterozygous rate and data missing rate of the SNP site of the i-th sample, n is the number of SNP sites, SNP i1 、SNP i′1 、SNP in 、SNP i′nRespectively represent the quantization codes of the quantization coding matrix at row i and column 1, row i′ and column 1, row i and column n, and row i′ and column n; N ii′ is the number of identical sites between varieties i and i′, GD ii′ is the genetic distance between varieties i and i′, GS ii′ is the genetic similarity coefficient between varieties i and i′.

[0063] Application Examples

[0064] The following is a specific application example to further illustrate the above coding method. The SNP fingerprint data is statistically analyzed in MS Excel as follows:

[0065] 1. Materials and Methods

[0066] 1.1 Data Source

[0067] The sample SNP fingerprint data is derived from the National Maize Variety Standard Sample Fingerprint Database. The data includes 966 maize cultivars, including CF024, Anyu 13, and Badan 25, and fingerprint information for 200 SNP loci, numbered MSNP00001 to MSNP00200. The data are presented in the SNP-specific dimorphic format of AA, AT, TT, AG, CT, and AC. Missing values are indicated by "--." The data structure is detailed in Table 1.

[0068] Table 1 Excel table structure of SNP data of national corn standard samples

[0069]

[0070] 1.2 SNP data quantification encoding method

[0071] Given the limitations of converting SNP typing data to a "0 1" data format, and considering that statistical analysis and evaluation of SNP sites and sample quality are based on the frequency variation of genes in different samples and sites, the present invention adopts a "0 1 2" data conversion method instead of the original "0 1" data conversion method. The "0 1 2" data conversion method encodes the homozygous genotype (e.g., AA) of the major gene of each variety or sample at each SNP site as 0, the heterozygous genotype (e.g., AT) as 1, the homozygous genotype (e.g., TT) of the minor gene as 2, and leaves missing values blank (Table 2).

[0072] Table 2 Excel table of the national corn standard sample SNP fingerprint database "0 1 2 type" data

[0073]

[0074] 1.3 Calculation methods of SNP loci and variety evaluation parameters

[0075] The SNP sample data includes 966 varieties and 200 loci (the data area is adjusted according to actual conditions).

[0076] 1.3.1 Calculation method of site evaluation parameters in Excel based on "012" type data. Taking the first site SNP001 as an example, the parameters of the remaining sites can be achieved by copying the formula (Table 3):

[0077] C969, standard deviation (SD) = STDEV.S (C2: C967) = 0.64

[0078] C970, number of minor genes (MAN) = SUM (C2: C967) = 885

[0079] C971, minor gene frequency (MAF) = SUM(C2:C967) / (COUNT(C2:C967)*2) = 0.48

[0080] C972, major gene frequency (MAAF) = 1-SUM(C2:C967) / (COUNT(C2:C967)*2) = 0.52

[0081] C973, polymorphism information content (PIC) = 1-(MAAF^ 2 +MAF^ 2 )=1-C972^2-C971^2=0.50

[0082] C974, detection rate (CF) = (1-COUNTBLANK(C2:C967) / 966)*100 = 95.0%

[0083] C975, heterozygosity rate (%) = COUNTIF (C2: C967, 1) / COUNT (C2: C967) * 100 = 59.2%

[0084] Table 3 Excel table of evaluation parameters of the national corn standard sample SNP fingerprint database "0 1 2 type" data sites

[0085]

[0086] N_minor: number of minor genes, MAF: minor gene frequency; MAAF: major gene frequency

[0087] 1.3.2 The calculation method of variety evaluation parameters in Excel based on "0 1 2" type data is as follows: Taking the comparison between variety CF024 in the first row and the rest of the varieties as an example, the comparison between the rest of the varieties can be achieved by copying the formula (Table 4):

[0088] The number of identical sites between GU3, CF024 and Anyu13 is {=SUM(($C$2:$GT$2=C3:GT3)*1)}=78

[0089] (Shift+Ctrl+Enter)

[0090] Genetic distance of GV3, CF024 / Anyu13 = SUM((abs(C$2-C3):abs(GT$2-GT3)) / 200 / 2 = 0.36

[0091] Genetic similarity coefficient of GW3, CF024 / Anyu13 = 1-genetic distance = 1-GV3 = 0.64

[0092] GX2, CF024 deletion rate (%) = COUNTBLANK (C2: GT2) / 200 * 100 = 6.0%

[0093] Heterozygosity rate (%) of GY2, CF024 = COUNTIF (C2: GT2, 1) / COUNT (C2: GT2) * 100 = 53.2%

[0094] Table 4 Excel table of variety evaluation parameters for national corn standard sample SNP “0 1 2 type” data

[0095]

[0096] 2. Quantitative coding of gene SNP data and variety evaluation results

[0097] 2.1 Analysis of main evaluation indicators of SNP sites

[0098] SNP evaluation is based on allele frequency, with key indicators including PIC, minor gene frequency, detection rate, and heterozygosity rate. The goal of site evaluation and screening is to identify more differences between varieties using fewer sites.

[0099] 2.1.1 Polymorphism information content of SNP sites

[0100] The PIC value refers to the polymorphism information content of a site, which depends on the number of alleles in the test population and their frequency distribution. Its value is equal to 1 minus the sum of the squares of allele frequencies. Based on the quantitative coding introduced in this invention, the calculation results based on Excel show that ( Figure 2A), the PIC values of the 200 SNP loci ranged from 0.32 to 0.50, with an average of 0.46. Of the 200 SNP loci, 140 had PIC values between 0.46 and 0.50; 19.5% of the loci had PIC values between 0.41 and 0.45; 8.5% of the loci had PIC values between 0.36 and 0.40; and 2.0% of the loci had PIC values below 0.35. A PIC greater than 0.35 is generally used as the standard for SNP core markers (Wei Qingzhen et al., 2019). Among the 200 SNPs in this maize standard sample, only four loci had PIC values less than 0.35: SNP044, SNP128, SNP156, and SNP185. Therefore, 98% of the 200 SNP molecular markers in the maize standard sample met the selection criteria for core markers.

[0101] 2.1.2 Minimum allele frequency

[0102] Allele frequency is an indicator of gene diversity or the richness of the gene pool. The minimum allele frequency (MAF) based on the SNP typing database refers to the frequency of the gene with the second highest frequency, reflecting the polymorphism of the gene pool. Based on the quantitative coding introduced in this invention, the calculation results based on Excel show that ( Figure 2 B) The MAF values for the 200 SNP loci in the maize standard sample ranged from 0.20 to 0.50, with an average of 0.38. The MAF values for the 200 SNP loci ranged from 0.36 to 0.50, accounting for 66% of the total loci. Loci ranging from 0.20 to 0.25 accounted for 4.5% of the total loci, including SNP128, SNP185, SNP044, SNP156, SNP186, SNP016, SNP066, SNP097, and SNP069. Seven other loci had MAF values of 0.50, indicating that the frequencies of the major and minor genes were equal, representing the highest polymorphism. These loci were SNP095, SNP116, SNP179, SNP052, SNP049, SNP030, and SNP143.

[0103] 2.1.3 Detection rate

[0104] The call frequency refers to the success rate of typing a certain SNP in all samples, which is an indicator reflecting the stability, sensitivity and reliability of the SNP marker. Based on the quantitative coding introduced in this invention, the calculation results based on Excel show that ( Figure 2C) The detection rates of 200 SNP molecular markers in maize standard samples ranged from 87.9% to 98.1%, with an average of 94.6%. The detection rates were primarily between 93% and 97%, accounting for 80% of the total loci; 15% of the loci were between 90% and 93%. Loci with detection rates above 97% and below 90% accounted for 4% and 1%, respectively. Loci with detection rates > 97% included SNP044, SNP156, SNP011, SNP066, SNP073, SNP114, SNP178, and SNP056, while those with detection rates < 90% included SNP099 and SNP195. The number of sites with a detection rate above 95% accounted for 43.5%. Compared with the standard of detection rate >95% (Li Lechen et al., 2019), the detection rate of SNP sites in the corn standard sample library was relatively low, with more than half of the sites (56.5%) failing to reach 95%.

[0105] 2.1.4 Heterozygosity rate

[0106] The loci heterozygosis rate refers to the ratio of the number of heterozygous genotypes successfully typed by SNP for all samples to the total number of genotypes. Based on the quantitative coding introduced in this invention, the calculation results based on Excel show that ( Figure 2 D), the heterozygosity rate of 200 SNP molecular markers ranged from 32.4% to 68.4%, with an average of 49.5%; the main distribution was between 40% and 60%, accounting for 82% of the total number of sites; the sites distributed between 30% and 40% accounted for 9.5%; the sites with a detection rate between 60% and 70% accounted for 6.5%.

[0107] 2.2 Analysis of main evaluation indicators of varieties (or samples)

[0108] The main evaluation indicators of varieties (or samples) include the number of identical sites between samples, genetic distance, genetic similarity coefficient, data missing rate and genotype heterozygosity rate.

[0109] 2.2.1 Number of identical or different sites in samples

[0110] The number of identical SNP sites or the number of different sites between samples of each variety is an important indicator for measuring the similarity / dissimilarity of samples. Based on the quantitative coding introduced in this invention, taking the similarity comparison between the first sample (CF024) and the remaining 965 varieties as an example, the calculation results based on Excel show that (1) the number of identical sites between CF024 and Zhengdan 958 and Yulong 6 is 194, accounting for 97.0%; the number of different sites is 6, accounting for 3%. (2) The number of identical sites between CF024 and Chidan 661, Yuqing 9, Chuangqi 0203, Wanjia 14, Jinshan 15, and Jinboshi 658 is more than 190, accounting for more than 95%; the number of different sites is 10 or less, accounting for 5% or less. (3) The varieties with 180-189 identical sites with CF024 include 19 varieties such as Suyu 1, Jifeng 223, and Derui 66, and the number of identical sites accounts for more than 90%. (4) The number of identical sites between the other varieties and CF024 ranged from 54 to 179, accounting for 27% to 89.5%. Figure 3 A), the majority of samples had the number of identical sites between 30-49%, accounting for about 83%; samples between 50-89% accounted for about 12.7%; samples with the number of identical sites above 90% accounted for about 3%, of which samples with the number of identical sites above 96% accounted for 0.7%.

[0111] 2.2.2 Genetic distance or genetic similarity coefficient between samples

[0112] The genetic distance or genetic similarity coefficient between samples is an indicator that compares the degree of genetic similarity / difference between samples at the allele level. On the basis of the quantitative coding introduced in the present invention, taking the similarity comparison between the first sample (CF024) and the remaining 965 varieties as an example, the calculation results based on Excel showed that (1) the genetic similarity coefficients of Zhengdan 958, Yulong 6, Chidan 661, Chuangqi 0203 and Jinboshi 658 with CF024 were 0.98 and above; the genetic similarity coefficients of Yuqing 9, Wanjia 14, Jinshan 15, Suyu 1, Jifeng 223, Derui 66, Xuntian 969 and Deyu 4 with CF024 were 0.96 and above; the genetic similarity coefficients of Xuntian 2008, Houde 198, Xuntian 19, Neidan 402, Zaoyu 8, Junshi 8, Jindan 55, Jipin 7, Lianchuang 3, Luzhong 99118 and Lianchuang 6 with CF024 were above 0.95. (2) The varieties with a genetic similarity coefficient of 0.98 or above to CF024 accounted for 0.5%; the varieties with a genetic similarity coefficient of 0.95 or above to CF024 accounted for 2.5%; the varieties with a genetic similarity coefficient of 0.90 or above to CF024 accounted for 7.8%; the remaining varieties had a genetic similarity coefficient of less than 0.90 to CF024, accounting for 92.2%. Overall ( Figure 3B), the genetic similarity coefficients are mostly distributed between 0.6-0.69, accounting for about 72%; the samples with genetic similarity coefficients between 0.7-0.89 account for about 14%; the samples with genetic similarity coefficients above 0.9 account for about 7.7%, of which the samples with genetic similarity coefficients above 0.96 account for 1.3%.

[0113] 2.2.3 Missing rate of sample data

[0114] The data missing rate of each variety or sample is an indicator of DNA sample quality. Based on the quantitative coding introduced in this invention, the calculation results based on Excel showed that the missing rate of 966 corn standard samples at 200 SNP sites ranged from 0% to 48%, with an average of 5.4%; more than half (53%) of the samples had a missing rate between 0.5% and 4.5%, 9.3% of the varieties had a missing rate of 0, and a total of 62.3% of the samples had a missing rate below 5%. The number of samples with missing rates between 5.0% and 10.0%, 10% and 20%, 21% and 30% or more accounted for 23.7%, 9.5%, 2.4% and 2.1% respectively. There were 90 samples with a missing rate of 0, and 8 samples with a missing rate greater than 40%, including Wanqingsi No. 1, Changchengsiyu No. 7, Liaodan 566, Sanbei 23, Zhayu No. 1, Liaozuo No. 1, Xunqing 938 and Ruide No. 1. In addition, the missing rates of 13 varieties, including Zhangdan 206, Shannong 202, Ruide 2, Nongfu 8, Pudan 5, Mengdong 18, Wandan 14, Liaonuo 1, Shentian 6, Zhongxing 7, Lianyu 15, Zunyan 2 and Hezuo 2, are also above 30%. There are 135 varieties with missing rates above 10%. Figure 3 C), the site missing rate is mostly distributed between 0.5-4.5%, accounting for about 53%; the samples with a site missing rate between 5-10% account for about 23.7%; the samples with a site missing rate between 10-20% account for about 9.5%; the samples with no missing values account for 9.3%; the samples with 21-30% missing values account for 2.4%; and the samples with more than 30% missing values account for 2.1%.

[0115] 2.2.4 Analysis of sample genotype heterozygosity

[0116] On the basis of the quantitative coding introduced in the present invention, the calculation results based on Excel show that the 966 standard corn samples are mainly hybrids, and their genotype heterozygosity is also relatively high, ranging from 0-73.1%, with an average of 54.9%; it is mainly distributed between 40-60%, accounting for 94% of the total; varieties below 40% and above 60% account for 4.7% and 1.2% respectively. Among them, the site heterozygosity of Jidan 79 is 0, all are homozygous genotypes, and should be homozygous inbred lines; the site heterozygosity of Shannong 202 and Baotian 3 is less than 10%; the site heterozygosity of Zhayu 1, Shengtian 1 and Changcheng Shiyu 7 is less than 20%; and the site heterozygosity of varieties such as Sanbei 23, Ping'an 14, Ludan 26, Xunqing 938, Jinji 3, Jidan 517, Shenyu 28, Liaodan 28, Shenhai 18, Liangshan 1, Shenyu 17 and Liangshan 2 is greater than 60%. Overall ( Figure 3 D), the heterozygosity rate of SNP sites in the samples was mostly distributed between 40-60%, accounting for 94%; the samples with a heterozygosity rate between 30-40% accounted for about 3.8%; the samples with a heterozygosity rate between 60-75% accounted for about 1.2%; and the samples with a heterozygosity rate below 30% accounted for 0.9%.

[0117] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for quantifying and encoding SNP data based on allele frequency, characterized in that: The method comprises the following steps: (1) Quantitative precoding of SNP data: When the minor allele of each SNP site is unclear, it is assumed that any allele at a certain SNP site is the minor allele, and the other allele is the major allele; the homozygous genotype of the assumed minor allele is numbered 2, the homozygous genotype of the major allele is numbered 0, and the heterozygous genotype is numbered 1. Missing values are represented by blanks, thereby converting the original attribute data composed of nucleotide letters into an m×n data matrix composed of 0, 1, and 2 numbers, where m and n represent the number of varieties or samples and the number of SNP sites, respectively, to complete the precoding of SNP data; (2) SNP data quantification and coding correction: Based on the above m × n data matrix, the assumed MAF value of each SNP site, i.e., H_MAF value, is calculated to test the authenticity of the assumed minor allele; if the H_MAF value is <0.50, the assumption is accepted; if the H_MAF value is ≥0.50, the assumption is rejected, and the numbers 0 and 2 in the column where the corresponding SNP site is located are swapped so that all numbers 2 and 0 in the matrix represent the homozygous genotype of the minor allele and major allele, respectively, thereby completing the SNP data quantification and coding process; The calculation formula of the H_MAF value of the j-th SNP site in MS Excel is as follows: H_MAF j =SUM(SNP 1j :SNP mj ) / (COUNT(SNP 1j :SNP mj )*2)*100%…………(1) Among them, H_MAF j represents the H_MAF value of the jth SNP site, m is the variety or sample size, SNP 1j 、SNP mj They represent the quantization codes of the 1st row and jth column and the mth row and jth column of the quantization coding matrix respectively.

2. The SNP data quantification encoding method based on allele frequency according to claim 1, characterized in that: The evaluation parameters of SNP sites include minor gene frequency MAF, major gene frequency MAAF, polymorphism information content PIC, site heterozygosity rate LHR, and detection rate CF. The formula in Excel is as follows: MAF j =SUM(SNP 1j :SNP mj ) / ((COUNT(SNP 1j :SNP mj )*2)*100%…………(2) EXCUSE ME j =1-MAF j …………(3) LHR j =COUNTIF(SNP 1j :SNP mj ,1) / COUNT(SNP 1j :SNP mj )*100%…………(5) CF j =(1-COUNTBLANK(SNP 1j :SNP mj ) / m)*100%…………(6) Among them, MAF j 、MAAF j , PIC j , LHR j CF j They represent the MAF value, MAAF value, PIC value, LHR value, and CF value of the j-th SNP site, respectively. m is the variety or sample size, and SNP 1j 、SNP mj They represent the quantization codes of the 1st row and jth column and the mth row and jth column of the quantization coding matrix respectively.

3. The SNP data quantification encoding method based on allele frequency according to claim 1, characterized in that: Sample evaluation parameters include sample gene heterozygosity rate (SHR), data missing rate (MR), number of identical sites and / or number of different sites (N) between samples, genetic distance (GD) between samples, and genetic similarity coefficient (GS). The calculation formula in Excel is as follows: SHR i =COUNTIF(SNP i1 :SNP in ,1) / COUNT(SNP i1 :SNP in )*100%…………(7) MR i =(1-COUNTBLANK(SNP i1 :SNP in ) / n)*100%…………(8) N ii′ =COUNTIF((SNP i1 -SNP i′1 ):(SNP in -SNP i′n ),0)…………(9) GD ii′ =SUM(ABS(SNP i1 -SNP i′1 ):ABS(SNP in -SNP i′n )) / 2n………(10) GS ii′ =1-GD ii′ …………(11) Among them, SHR i , MR i They represent the heterozygous rate and data missing rate of the SNP site of the i-th sample, n is the number of SNP sites, SNP i1 、SNP i′1 、SNP in 、SNP i′n Respectively represent the quantization codes of the quantization coding matrix at row i and column 1, row i′ and column 1, row i and column n, and row i′ and column n; N ii′ is the number of identical sites between varieties i and i′, GD ii′ is the genetic distance between varieties i and i′, GS ii′ is the genetic similarity coefficient between varieties i and i′.

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