KR20190139480A - Full siblings analysis using Genotyping - Google Patents

Abstract

Provided is a blood relationship analysis method using genotyping, which comprises: step (A) of analyzing, based on a marker, a genotype of entities whose likelihood is to be checked; step (B) of inputting a genotyping result data as a result of the step (A); step (C) of inputting an allele frequency from the genotyping result data of the step (B); step (D) of comparing whether an allelotype is matched between the entities whose likelihood is to be checked, setting an identity by descent (IBD) formula in accordance with the number of cases with which the allelotype is matched, and applying an IBD constant in accordance with the number of cases of the likelihood to the set IBD formula, and thus extracting a likelihood ratio; and step (E) of confirming a probability result of the highest value of the step (D) to determine the likelihood. According to the present invention, an analysis process is simplified such that analysis can be performed in a short time, thereby increasing correctness, efficiency, simplicity and specialty in business, and providing a correct result in family line confirmation, selection breeding and discharge effect examination (sibling analysis for a discharged seed).

Description

Full siblings analysis using genotyping

The present invention analyzes the kinship relationship with higher reliability by using the accumulated allele frequency data for the results of analysis of the microsatellite marker of the simple sequence repeat (SSR) region. The present invention relates to a method for analyzing typical relationships using genotyping.

Since it has been found that all biological traits in living organisms are determined by genetic information consisting of chemicals called nucleic acids (DNAs), many new research methods have been developed for understanding genetic information and for industrial and medical use of genetic information.

DNA marker is a term that refers to a short part of DNA on the chromosome and is called a molecular label or DNA label. In experimental terms, it is mainly used for two purposes. The first is the identification of individuals by genotype differences, which makes it possible to estimate progeny transmission patterns such as paternal or maternal inheritance of specific chromosomal sites on the chromosome. The second is to indicate the location of specific DNA parts on the chromosome. This makes it possible to link DNA fragments contained in the DNA library into one large unit.

One of the most important uses of DNA markers is genotyping. Genotyping refers to the analysis of the shape of DNA polymorphisms for specific loci, through which the gene transfer patterns of these loci can be determined. Most genotyping is performed by electrophoresis using agarose or acrylamide gels to determine genotypes based on DNA band mobility differences according to DNA size and sequence variation.

As such, DNA marker is a basic tool for the study of utilization of genetic information, and various DNA markers have been developed in animal molecular breeding methods to select genotypes, not phenotypes, to identify genes, identify superior genes or disease genes, understand gene function, It is used for selection of molecular marker dependence.

The most important factor for gene mapping, phenotype and genotype association analysis is the acquisition of DNA markers with large differences between individuals. Various DNA markers have been developed for this purpose, and major DNA markers according to the test method and the form of DNA polymorphism include SSLP, SNP, RFLP, RAPD, and AFLP.

The usefulness of DNA markers increases as the discriminating power between alleles inherited from the father and mother lines increases, and the discriminating power increases as the polymorphism of the marker increases, so the usefulness of the marker is ultimately determined by the degree of marker polymorphism. There are two methods to express the DNA marker polymorphism. The heterozygosity (H) of the marker and the polymorphism information content (PIC) are measured.

Currently, a system for confirming paternity and kinship using a simple repeat base sequence marker, an internationally validated DNA analysis method, is widely used for not only humans but also livestock and plants. The paternity analysis method is widely used in the fisheries field recently, and the genetic analysis data is continuously increasing.

However, there are no analysis programs and methods for identifying full-sibs in kinship, so there is little inaccuracy and error in household identification, selection breeding, and survey of discharge effects (sibling analysis of discharge seeds). This high result is being used.

In addition, the cumulative allele frequency data on the results of the analysis of the microsatellite markers of the simple sequence repeat (SSR) region used for analysis cannot be applied. And there are many difficulties in analyzing the blood relationship. Although there is an analysis program used to confirm the typical sales relationship in Korea, the resulting value is not suitable because it is not a comparison between individuals or a typical sales relationship verification method for confirming the relationship.

Accordingly, Korean Unexamined Patent Publication No. 10-2017-0068347 discloses a method of predicting maternal blood relationship and exercise ability of horses, and Korean Unexamined Patent Publication No. 2002-0081704 discloses a method of evaluating blood ties and a DNA typing kit using the same. Although it is published in relation to the prior document, based on the markers of the present invention (A) analyzing the genotypes of individuals to determine the blood relationship; Inputting genotyping result data as a result of step (a); Inputting an allele frequency from the data of genotyping results of step (b); Compares allele types between individuals to be related to blood relationship, sets the IBD formula according to the number of cases where the allele is matched, and sets the IBD according to the number of cases related to the blood relationship in the set IBD formula. Extracting a likelihood ratio by applying a constant (d); The configuration consisting of the step (e) of determining the kinship relationship by checking the probability result of the highest value of the step (d) does not disclose the difference.

Korean Patent Laid-Open Publication No. 10-2017-0068347 discloses a first step of extracting a DNA sequence of a follicle and a follicle, and a second step of performing PCR using a primer capable of analyzing a maternal lineage with the DNA extracted in the first step. The third step of identifying the eight haplo groups corresponding to the DNA sequence by performing sequence analysis of the regulatory region of the mitochondrial DNA, the product of the PCR for identifying the maternal lineage of the follicle and the mother Single base polymorphisms related to maternal lineage and motor activity prediction (SNP, Single) to analyze the peaks of the DNA sequences of follicles and mothers to determine which haplo groups belong to them to predict maternal lineage analysis and motor performance A method of predicting maternal kinship and motor performance in horses using -nucleotide polymorphism is disclosed. Korean Laid-Open Patent Publication No. 2002-0081704 relates to a method for checking blood kinase in the absence of father DNA by comparing allele-repeated DNA on X chromosome, and a DNA typing kit for blood quench using the same. In the method of evaluating the blood ties of the present invention, the STRs on the X chromosome of the grandmother and the granddaughter can be confirmed to be paternity even in the absence of the father's DNA. By observing whether each subject shares the chromosome STR allele, the identity of the father can be determined, thus comparing the alleles of the chromosome STR, which can be useful for identifying the kinship between women, even in the absence of father DNA. A method of appraising and a DNA typing kit using the same are disclosed. Korean Patent No. 10-1533792 relates to an autosomal analysis method of a next generation sequencing-based human subject, comprising: (a) D3S1358, TH01, D21S11, D18S51, PentaE, D5S818, D13S317, D7S820, D16S539, Human c-fms proto-oncogene for CSF-1 receptor gene (CSF1PO), PentaD, von Willebrandfactor A (vWA), D8S1179, Human thyroid peroxidase gene (TPOX), Human fibrin performing multiplex amplification using respective primers that complementarily bind to an alpha chain) and an ameloogenin locus; And (b) determining the short tandem repeat (STR) allele of the locus using the NGS data of the multiplex amplification product of step (a) to identify the human subject with a gene. An autosomal analysis method of a human subject is disclosed. Korean Patent Registration No. 10-1147691 compares genetic information of two or more individuals to determine paternity, blood relationship, and non-kinship relationship such as parent child relationship (generation 1) through generation (generation). Disclosed is a blood relationship determination device using genetic information comparison that can provide accurate household information.

Currently, it is not developed about full-sib analysis software using the result of genotyping analysis in fisheries or kinship analysis method using it. In order to solve the above problems using the analytical method, the present invention is to provide a method for analyzing the typical solvent relationship applicable to the fisheries field.

As a means for solving the above problems, the present invention comprises the steps of analyzing the genotype of the individuals to determine the relative relationship based on the marker (A); Inputting genotyping result data as a result of step (a); Inputting an allele frequency from the data of genotyping results of step (b); Compares allelic types between individuals seeking to identify typical sales relationships, sets the IBD formula according to the number of cases where the allelic types match, and sets the number of cases for the typical sales relationship to the set IBD formula Extracting the likelihood ratio by applying the IBD constant according to the step (d); After calculating the average value and standard deviation for the log median value [Log10 (median LR)] using the results of the typical sales relationship index extracted in the step (d) in Micro Excel, (E) determining the typical sales relationship by investigating false-positive rate and false-negative rate based on the likelihood ratio value ranging from 1 to 100,000. Provide a relationship analysis method.

At present, the analysis method of kinship and identity by descent (IBD) constants, which are used in forensics, are applied to the software for typical sales relations and household identification of fish species. However, in the case of fish, since alleles have more alleles than humans, the probability of finding the same allele is lowered because the marker's discrimination ability is increased. In addition, there are many cases where the half brother relationship index or the index of the other relationship has a high probability even when they are actually siblings, and statistical criteria for the probability of misjudgment are not set separately.

Accordingly, the present invention has the effect of specifying a specific relationship to be analyzed and calculating and calculating a statistical normal distribution result for false positives and false voice rates and making an accurate determination based on this.

Performing genotyping using the developed marker of the present invention, and calculating the cumulative allele frequency data is indiscriminately different alleles in the use of the marker or frequency of the specific allele among the alleles biased Markers can be avoided.

The exponential calculation method for confirming the typical sales relationship according to an embodiment of the present invention is to compare the alleles of the two objects for comparison in the same way as the general paternity index calculation method and the allele frequency and blood relationship confirmation formula according to the match Applying an Identity by descent (IBD) constant.

In addition, the mean and standard deviation of the log median value [Log10 (median LR)] of the likelihood ratio using the calculated likelihood ratio calculated according to the exemplary embodiment of the present invention. And calculate the false-positive rate and false-negative rate based on the likelihood ratio value in the range of 1 to 100,000 in each relationship.

The present invention can produce a large amount of genotyping analysis results up to 1 million, as well as the analysis of a large number of individuals (1000x1000) at one time has the effect of saving time, cost, labor.

In addition, in the past, the analysis requires not only specialized skills but also takes a lot of time, but the present invention solves problems of accuracy, efficiency, simplicity, and professionalism by enabling analysis in a short time due to the simplification of the analysis process. .

In addition, statistical verification of the full-sib kinship analysis, which is difficult to analyze in the fisheries field, can provide highly reliable and accurate results, and systematically establish a database of cumulative genotyping results. It can be managed and used. Thus, it is possible to provide accurate results in household identification, selection breeding, and discharge effect investigation (sibling analysis of discharged seeds) through accurate full-sib confirmation.

Figure 1 shows a schematic diagram of the method of analyzing the typical sales relationship using genotyping of the present invention.
2 shows a typical sales relation index calculation formula.
3 shows software for inputting genotyping results for population F ₀ and population F ₁ for which genotyping has been completed.
Figure 4 shows the result of extracting the allele frequency for each marker.
5 shows software according to an embodiment capable of inputting an allele frequency value.
FIG. 6 shows a likelihood ratio calculated for a marker according to Example 1 of the present invention.
7 illustrates a typical sales relationship index value stored in the system according to step (d).
Figure 8 shows the results of analyzing the distribution of the relationship between the probability of the typical sisterhood (Likelihood ratio) in the actual sibling relationship according to Experimental Example 1.
FIG. 9 shows evaluation results of a Likelihood ratio distribution using a false-positive rate and a false-negative rate according to Experimental Example 1. FIG.
The kinetic probability equation of FIG. 10 is shown.

Hereinafter, described in detail with the accompanying drawings of the present invention. Figure 1 shows a schematic diagram of the method of analyzing the typical sales relationship using genotyping of the present invention. (A) analyzing the genotypes of the individuals to be related to the markers based on the markers; Inputting genotyping result data as a result of step (a); Inputting an allele frequency from the data of genotyping results of step (b); Compares allelic types between individuals seeking to identify typical sales relationships, sets the IBD formula according to the number of cases where the allelic types match, and sets the number of cases for the typical sales relationship to the set IBD formula Extracting the likelihood ratio by applying the IBD constant according to the step (d); After calculating the average value and standard deviation for the log median value [Log10 (median LR)] using the results of the typical sales relationship index extracted in the step (d) in Micro Excel, (E) determining a typical sales relationship by investigating false-positive rate and false-negative rate based on the likelihood ratio value ranging from 1 to 100,000.

Genotyping step (a) of the present invention is to extract the DNA from the sample of the population F ₀ and group F ₁ and PCR using a marker, and then classified by size using an automatic base sequencing device using the PCR product After electrophoresis, a standard allele ladder for each marker is produced, and all the objects are scored using an analysis program and classified by size and type of marker.

Group F ₀ and group F ₁ according to the present invention refer to an individual to be tested for full-sib. The marker may preferably be a microsatellite marker and the PCR may be a multi-amplification polymerase chain reaction (Mutiplex-PCR).

As used herein, the term "marker" refers to a nucleotide sequence used as a reference point when identifying genetically unspecified genetic loci.

As used herein, the term "multiplex PCR" refers to a PCR technique for amplifying several genes simultaneously, unlike a single PCR for amplifying one gene for one template DNA. In multiplex PCR, multiple primer pairs are included in a single PCR mixture. It is preferable to indicate the size range of amplification products specific to different DNA sequences so that the sizes do not overlap each other.

In the multiplex PCR technique, since different primer pairs are put in one tube and reacted, inhibition may occur between primers. Therefore, when applying to multiplex PCR, it is important to select primers for genes to be amplified. Do. In addition, since the appropriate binding temperature may be different for each of the primers included, the multiplex PCR application requires optimization of the binding temperature suitable for multiplex PCR so that all pairs of PCR primers included in a single PCR reaction can be efficiently bound.

As used herein, "primer" generally refers to oligonucleotides, and the term primers as defined herein refers to DNA strands that are ready for the synthesis of DNA. DNA polymerase cannot synthesize DNA from scratch without primers. DNA polymerases can only extend the DNA strands present in the reaction used as a template to direct the sequence of nucleotides to which the complementary strands are assembled. And at the appropriate temperature and pH conditions can serve as a starting point for the synthesis. Preferably, the primer is deoxyribonucleotide and single chain. Primers used in the present invention may include natural dNMP (ie, dAMP, dGMP, dCMP and dTMP), modified nucleotides or synthetic nucleotides. In addition, the primer may also include ribonucleotides.

The genotyping result data input step (b) is a step of inputting genotyping data (Allele scoring data) data determined based on the marker value of step (a).

An allele frequency value input step (c) may include inputting an allele frequency obtained from the data of the genotyping result of step (b).

The typical sales relations index extraction step (d) of the present invention comprises the steps of comparing the allelic types of the group F ₀ and the group F ₁ of each marker; Setting an IBD formula according to the case where the compared allele types match; Extracting a typical sales relationship index (likelihood ratio) by applying the IBD constant according to the typical sales relationship between the group F ₀ and the group F ₁ to the set IBD formula.

Table 1 below shows a case (CASE) according to whether the alleles of each locus of the group F ₀ and the group F ₁ (CASE) and the corresponding kinship formulas (kinship Formulas). P represents the allele frequency. Table 2 below shows Identity by descent coefficients (IBD constants) according to the relationship between the parent group and the child group.

Kinship Formulas Parent group (F ₀ ) allele type Child group (F ₁ ) allele type Frequency case 1 AB AB Φ ₂ + 0.5Φ ₁ (P _A + P _B ) + 2Φ ₀ P _A P _B case 2 AA AA Φ ₂ + Φ ₁ P _A + Φ ₀ P _A ² case 3 AA AB Φ ₁ P _B + 2Φ ₀ P _A P _B case 4 AB AA 0.5Φ ₁ P _A + Φ ₀ P _A ² case 5 AB AC 0.5Φ ₁ P _C + 2Φ ₀ P _A P _C case 6 AB CD 2Φ ₀ P _C P _D case 7 AA BB Φ ₀ P _B ² case 8 AA BC 2Φ ₀ P _B P _C case 9 BC AA Φ ₀ P _A ²

 Identity by descent coefficients Relationship Φ ₂ Φ ₁ Φ ₀ Parent-child 0 One 0 Full siblings 1/4 1/2 1/4 Half siblings 1/2 1/2 0 Grandparent-grandchild 1/2 1/2 0 Uncle-nephew 1/2 1/2 0 First cousins 3/4 1/4 0 Second cousins 15/16 1/16 0 Unrelated 0 0 One

Comparing the allele-type match whether the allele of the group F ₀ and each position of the group F ₁ of each marker is compared, and setting the frequency formula according to the case of the match Include. If the types of the alleles match both, if only one matches, both do not match, the value is calculated. The calculation method is calculated by varying the applied value by assigning and comparing the alphabet for each allele according to the case in Table 1.

2 shows a typical sales relation index calculation formula. In the formula of calculating the likelihood ratio, H _p refers to a value to which the frequency formula obtained according to the relationship shown in Table 2 is applied when two individuals are related to each other. The typical sales relationship includes the identification of full siblings as the relationship between group F ₀ and group F ₁ . H _d refers to Unrelated when two individuals are related to each other.

The correlation verification verification algorithm analyzes the genotype through a value (Allele score) for the marker for confirmation of the kinship relationship, establishes an identity by descent constant, and extracts an allele frequency. Compare the alleles at each marker and set the appropriate frequency.

Then, by applying the Φ value of the full siblings portion in Table 1 in the IBD constants (Identity by descent coefficients) of Table 2 can be calculated as shown in Figure 6 for each marker. In this case, if the number of markers compared for calculating the correlation coefficient in the result value using the software is less than 50%, it is determined that there is no kinship relationship except the comparison target, and the number of markers can be used for comparison. If more than 50%, the analysis goes to the next step.

The typical sales relationship determination step (e) is a logarithm of the normal distribution created by examining the false-positive rate and the false-negative rate, and using the result of the kinship index extracted in the step (d). After calculating the mean and standard deviation for the median value [Log10 (median LR)] in Micro Excel, the value is based on the likelihood ratio of 1 to 100,000 in the full-sib. The method may be applied to the normal distribution chart to determine a full-sib relationship.

The normal distribution is prepared as follows. An individual population having a typical sales relationship and a population having no typical sales relationship are selected, and the relative relationship index is calculated by performing steps (a) to (d) from the population.

The normal distribution probability for the typical sales relationship index calculated from the population with typical sales relation and the population without typical sales relation was calculated to prepare a normal distribution graph for Log ₁₀ values, and is shown in FIG. 8 using the normal distribution graph. As can be seen, the false positive rate and false negative rate values for the log reference value can be calculated.

As an example, if the determination is made based on Log ₁₀ 0, this means that there is a possibility of false-negative 3.671% and false-positive 2.747%. Therefore, these results can be used to minimize and optimize the false positive and false negative errors for the determination of the typical sales relationship.

As an example, the group F ₀ and the group F ₁ are set to the average value and standard deviation of the log median [Log10 (median LR)] using the results of the typical sales relationship index extracted in steps (a) to (d). After calculating with Excel, the normal distribution is applied based on the likelihood ratio value in the range of 1 to 100,000 in a full-sib. False Positive Rate and False Negative Rate on the Normal Distribution Map Normal distribution also has no typical sales relationship when the kinetic index applied to the central axis and the normal distribution is close to the false positive rate. (Full-sib) relationship can be determined.

Hereinafter, specific examples related to the typical sales relationship analysis method using genotyping of the present invention will be described.

Example 1 Typical Sales Relationship Analysis Using Flounder Genotyping

(A) Genotyping stage

The child group of the experimental fish according to the present invention was carried out genotyping step by setting the flounder individuals of the typical sales relationship confirmed to be produced in the same parent group F ₀ and group F ₁ , the experimental example of the present invention This is to confirm whether the typical sales relationship is established. The typical relationship refers to a relationship composed of siblings (full-sib) produced from the same parent in the family according to the general breeding.

(B) Input step of genotyping results

3 shows software for inputting genotyping results for population F ₀ and population F ₁ for which genotyping has been completed. As an example, the result value was saved as a CSV file and the result value was input to the software through the open-file selection-check process of sample 1 and 2 parts.

(C) Entering allele frequency values

4 shows a result of extracting allele frequencies for each marker, and FIG. 5 shows software according to an embodiment of the present invention for inputting allele frequencies. Calculate the number of alleles using genotyping results for all individuals, generate allele frequencies by dividing by the total number for each allele, and sum the frequencies for each allele to yield 1.0.

The allele frequency value extracted by the above method can be input to the software according to the embodiment of the present invention. In the function for the software according to the present invention, Save as saves the selected allele frequency file under another name (Excel), Data selects the selected allele frequency file view, selects the stored allele frequency file, and creates A file can be generated to calculate and select allele frequencies using genotyping data of all individuals.

(D) Extraction of Full siblings index

In the step (b), the object 1 of the group F ₀ and the object ₁ of the group F ₁ are subjected to a 1: 1 comparison. When comparing the alleles in each marker as shown in Table 1, using the appropriate frequency and applying the Φ value of the full siblings portion in the Identity by descent coefficients of Table 2 to Table 1, the typical sales relationship index for each marker is It is calculated as shown in FIG.

FIG. 6 shows a likelihood ratio calculated for a marker according to Example 1 of the present invention. The typical sales relation index was calculated by applying the calculated Identity by descent (IBD) constant and allele frequency.

Typical sales relation index was calculated according to the formula shown in FIG. The calculation is based on the ratio of the probability of a given genotype to a given genotype in a particular population, under the two opposing assumptions of a relative kinship (related, H _p ) and an unrelated kin (unrelated, H _d ). The index was calculated. 7 illustrates a typical sales relationship index value stored in the system according to step (d).

(E) Decision-making process

Using the likelihood ratio result, the average value and standard deviation for the log median [Log10 (median LR)] are calculated in Micro Excel as shown in FIG. 8, and 1 in the full-sib. The false-positive rate and false-negative rate are investigated based on the likelihood ratio value in the range of ~ 100000. False Positive Rate and False Negative Rate in the Normal Distribution Map Normal distribution also shows that there is no typical sales relationship when the kinetic index applied to the central axis and the normal distribution is close to the false positive rate, and when there is close to the false negative rate, the typical sales relationship is typical. Full-sib relationships can be determined.

FIG. 8 shows the result of analyzing the distribution relationship probability for the value of a typical medium relationship (Likelihood ratio) in the actual sibling relationship according to Experimental Example 1. FIG. For example, if the distance from the coordinate position of the kinetic index value applied to the normal distribution is located closer to the central axis -1.98 of the false negative rate than the central axis 3.15 of the false positive rate, there is a typical sales relationship. sib) relationship can be determined.

FIG. 9 shows evaluation results of a Likelihood Ratio distribution using a false-positive rate and a false-negative rate according to Experimental Example 1. FIG. Finally, the result of the typical sales relationship analysis, group F ₀ and group F ₁ The full-sib determination between the two can be determined by appropriately using the kinship index and the kinship probability of FIG.

The typical sales relationship derived according to the embodiment of the present invention was determined to have a sibling relationship (Full-sib) with a probability of 99.999%, and in fact, the experimental group F ₀ and group F ₁ are typical sale relationships. Is considered to have a verification effect and high reliability.

Existing fish farming has reduced growth, frequent disease outbreaks, malformations, population size, and positivity due to unscientific management and reduced genetic diversity, increased inbreeding and genetic bottlenecks. It is difficult to solve the above problems by developing technologies such as feed, facilities, and distribution. In this regard, the kinship analysis using the genotype analysis of the present invention can provide more rapid and highly reliable kinship analysis results, so that it can be utilized in many parts, such as a survey of congestion rate, genetic diversity analysis, household identification, and selection sarcoma. There is a possibility.

Claims (2)

(A) analyzing the genotypes of the individuals to be related to the markers based on the markers; Inputting genotyping result data as a result of step (a); Inputting an allele frequency from the data of genotyping results of step (b);
Compares allelic types between individuals to be related to blood relationship, sets the IBD formula according to the number of cases where the allele is matched, and sets the IBD according to the number of cases related to the related IBD formula. Extracting a likelihood ratio by applying a constant (d);
Correlation relationship analysis method using genotype analysis consisting of the step (e) of determining the relative relationship by checking the probability result of the highest value of step (d) The method of claim 1, wherein the determination of blood relationship in the step (e) is performed based on the calculated likelihood ratio value and the mean and standard deviation of the log median of the likelihood ratio [Log10 (median LR)]. Calculation of deviations and kinship analysis using genotyping, which examines false-positive rates and false-negative rates based on likelihood ratio values ranging from 1 to 100,000 in each relationship. Way

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