KR102120775B1 - A method of predicting metabolism phenotype using SNP - Google Patents

Abstract

The present invention relates to a method for predicting a metabolic phenotype using monobasic polymorphism. Although metabolic syndrome has no symptoms or has only one risk factor, it interacts with each other and is highly likely to develop chronic diseases such as hypertension, diabetes, hyperlipidemia, and obesity. This is not being done. On the other hand, according to the recent reports that each of the factors related to metabolism show a difference in the basic metabolic level based on the individual's genotype, efforts to analyze the single nucleotide polymorphism of the individual gene and use it to manage the individual metabolic syndrome have been made. ought.
The present invention has been devised to solve the problems in the prior art as described above, and relates to a method for predicting a metabolic phenotype using a single base polymorphism. The method of predicting the metabolic phenotype of the present invention enables accurate diagnosis of individual metabolic levels, and thus is expected to be widely used in the medical and cosmetic fields.

Description

A method of predicting metabolism phenotype using SNP}

The present invention relates to a method for predicting metabolic phenotype using gene polymorphism.

Since metabolic syndrome was named by Reaven GM in 1988, metabolic syndrome has become a common health problem in primary care. The prevalence of metabolic syndrome in the primary care is known to reach about 30%, and the prevalence increased with age and the severity of obesity. The medical significance of the metabolic syndrome is revealed from the fact that the case with metabolic syndrome has a higher mortality rate of cardiovascular disease than the case without metabolic syndrome. Since the obesity population is gradually increasing in Korea, and the prevalence of cardiovascular diseases such as hypertension, diabetes, and hyperlipidemia is rapidly increasing, considering the fact that the mortality rate due to cardiovascular disease ranks second among men and women, the recent invention of metabolic syndrome Efforts to manage body metabolic indicators have continued. However, even if there are no symptoms or only one risk factor, metabolic syndrome interacts with each other, so it is highly possible to express chronic diseases such as hypertension, diabetes, hyperlipidemia, and obesity. However, it is not being done properly. On the other hand, according to recent reports that each of the factors related to metabolism shows a difference in the basic metabolic level based on the individual genotype, the individual metabolic syndrome is analyzed by analyzing the single-nucleotide polymorphism (SNP) of the individual gene. Efforts are being made to use it for management.

On the other hand, the single nucleotide polymorphism of an individual gene refers to a genetic change or variation that shows a difference between one nucleotide sequence (A, T, G, and C) in a DNA nucleotide sequence. Because it shows the mutation, it is mainly used for DNA fingerprint analysis. Using a single-base polymorphism analysis, it is possible to consider differences in basic metabolic abilities inherent in individual genes, so that complex metabolic syndrome can be managed more effectively.

The present invention has been devised to solve the problems in the prior art as described above, and relates to a method for predicting a metabolic phenotype using a single base polymorphism. The method of predicting the metabolic phenotype of the present invention enables accurate diagnosis of individual metabolic levels, and thus is expected to be widely used in the medical and cosmetic fields.

The present invention has been devised to solve the problems in the prior art as described above, and relates to a method for predicting a metabolic phenotype using a single base polymorphism.

However, the technical problem to be achieved by the present invention is not limited to the above-mentioned problems, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

Hereinafter, various embodiments described herein are described with reference to the drawings. In the following description, for the full understanding of the present invention, various specific details, such as specific forms, compositions and processes, have been described. However, certain embodiments may be practiced without one or more of these specific details, or in combination with other known methods and forms. In other instances, well-known processes and manufacturing techniques are not described in specific details in order not to unnecessarily obscure the present invention. Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, form, composition or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, the context of “in one embodiment” or “an embodiment” expressed in various places throughout this specification does not necessarily represent the same embodiment of the invention. Additionally, special features, shapes, compositions, or properties can be combined in any suitable way in one or more embodiments.

Unless otherwise specified in the specification, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention pertains.

In one embodiment of the present invention, "genetic information" is a broad concept that collectively refers to all information encoded as a nucleotide sequence of DNA. In the present invention, "genetic information" includes nucleotide polymorphism information of an individual. .

In one embodiment of the present invention "single nucleotide polymorphism (single nucleotide polymorphism, SNP)" is a general mutation that appears in one of several DNA bases in a single region of the chromosome, about 3 million SNPs in the human genome. Is present and appears in about one form per 500 to 1,000 bases, and it is estimated that about 200,000 of them are cSNPs present in the gene making the protein. SNPs are high in frequency, stable, and distributed throughout the genome, resulting in individual genetic diversity. In other words, some people have adenine (A) in certain parts of the DNA chain, while others have cytosine (C). The function of each gene can be changed by these minute differences (SNP), and these things interact to make people with different shapes and different susceptibility to different diseases. In other words, if you can find the genetic difference between people who get hepatitis and those who don't, you can figure out the function of why the sensitivity to hepatitis changes. The ultimate goal of human genome research is to use it to develop drugs that can be used to prevent or treat hepatitis. As a result, the world's largest pharmaceutical companies and genome research institutes decided that SNP could provide the original information of new drug development in the future, and formed the SNP consortium (TSC) to jointly form the long-term ideal of humanity. To accelerate her dream, she is focusing on SNP research. However, even if numerous SNPs have been developed, SNPs alone have no meaning. In other words, if there is no object to compare and analyze SNP, it is useless. Therefore, domestic pharmaceutical companies and research institutes secure comparison targets (patient DNA and clinical data) for diseases such as heart disease, dementia, AIDS, etc., which they have a lot, and determine which SNPs are associated with which diseases. Efforts are being made to build a database.

In one embodiment of the present invention, "research database" refers to a data pool for predicting the expression of a specific phenotype by comparing phenotype-SNP association results, but the research data may be a clinical or academic paper. When the data in the data pool is derived from a paper, the data pool is the paper's unique number (PMID), subject, research method, research period, research results, journal information, and repeatability of the research. Information may be included, and the age, gender information of the subject to be studied, and family tree information about spouses, children, parents, and cousins, and environmental information may be included.

In one embodiment of the present invention, "gene database" means a data pool for predicting expression of a specific phenotype by comparing phenotype-SNP association results, and a chromosome number and gene of a specific SNP associated with a specific phenotype Gene information including stomach and allele information may be included in the data pool. In particular, race information of an object to be analyzed may be an important factor in data stored in a genetic database, but is not limited thereto.

In one embodiment of the present invention, "algorithm" refers to processes that computer programming must perform to solve a given problem. When a machine is processed in a certain order, it is an algorithm for the purpose. In general, knowing an algorithm can be processed by converting it into a computer program. In the present specification, the algorithm derives, but is not limited to, the probability that a particular phenotype will be expressed by comparing genetic information of a specific individual with information stored in a research database and a genetic database.

In one embodiment of the present invention "metabolism (metabolism)" refers to all material changes, such as the decomposition or synthesis of substances occurring in an organism, assimilation of polymer compounds using low-molecular organic or inorganic substances absorbed from the surroundings On the contrary, it is categorized into catabolism, which decomposes polymer compounds into low molecular organic or inorganic substances. Metabolic syndrome is a condition in which an energy balance in an organism is broken due to an abnormality in metabolic processes in the organism. More specifically, metabolic syndrome refers to a set of abnormal conditions such as an increase in body fat, an increase in blood pressure, an increase in blood sugar, and an abnormality in lipids in the blood, which increases the risk of cerebrovascular diseases and diabetes. In the presence of metabolic syndrome, the risk of cardiovascular disease is more than doubled, and the onset of diabetes is increased more than 10 times. Metabolic syndrome is not a single disease, but a comprehensive disease caused by the addition of genetic predisposition and environmental factors.

In one embodiment of the present invention, (a) a first calculation unit for calculating the frequency of each genotype of a specific gene; (b) a second calculation unit for calculating a target phenotype characteristic index for phenotype prediction; (c) a third calculation unit for calculating a target phenotypic characteristic index according to each genotype combination of a subject from all genes used; (d) a fourth calculation unit for calculating a frequency according to each genotype combination of a subject from all genes used; (e) a fifth calculation unit for calculating a weight target phenotype characteristic index; (f) a sixth calculation unit for calculating an average target phenotypic characteristic index according to a genotype combination of a population-based subject; And (g) a determination unit for determining a target phenotype, providing a phenotype prediction apparatus using a single base polymorphism, wherein the calculation in step (a) is characterized in that the sum of frequencies per genotype is 1 Provided is a phenotype prediction apparatus using a single base polymorphism, and the calculation in step (b) provides a phenotype prediction apparatus using a single base polymorphism, characterized in that it is derived from a research database or a genetic database. The calculation in step c) provides a phenotype prediction apparatus using single nucleotide polymorphism, characterized in that all target phenotypic characteristic indicators for each genotype constituting the genotype combination are summed, and the calculation in step (d) is genotype combination It provides a phenotype prediction apparatus using a single base polymorphism characterized by multiplying the frequency for each genotype constituting the, and the calculation in step (e) is "target phenotype characteristic index * genotype according to the genotype combination of all genotype combinations" Provides an apparatus for predicting phenotype using single base polymorphism, which is calculated by adding up the frequency according to combination, or by adding “(-1)*target phenotypic characteristic index according to genotype combination*frequency according to genotype combination”, , The calculation in step (f) using "single base polymorphism, characterized in that it is calculated as "(target phenotypic characteristic index according to the genotype-weighted target phenotypic characteristic index)/|weighted target phenotypic characteristic index|*100". Provided is a phenotype prediction device.

The target phenotype of the phenotype prediction apparatus using the single-base polymorphism is not limited thereto, but is preferably blood pressure, blood sugar, body mass index, triglyceride, or cholesterol.

When the target phenotype is blood pressure, the single base polymorphism is polymorphism of the ATPase plasma membrane Ca2+ transporting 1 (ATP2B1) gene rs17249754, the polymorphism of the NT5C2 (5'-nucleotidase, cytosolic II) gene rs11191593, and CYP17A1 (cytochrome P450 family 17 Characterized in that at least one polymorphism selected from the group consisting of the gene polymorphism rs3824755 of subfamily A member 1), the determination in step (g) is based on the calculated value in step (f) being 0 The higher the value, the higher the blood pressure, the lower the value, the lower the blood pressure.

When the target phenotype is blood sugar, the monobasic polymorphism is composed of the polymorphism rs560887 of the G6PC2 (glucose-6-phosphatase catalytic subunit 2) gene, the polymorphism of the MTNR1B (melatonin receptor 1B) gene rs10830963, and the polymorphism rs4607517 in chromosome 7 It characterized in that it is any one or more polymorphism selected from the group, the determination in step (g), the higher the numerical value based on the calculated value in step (f) is 0, the type of blood sugar maintenance is smooth, numerical value It is judged that the lower is, the more difficult to maintain blood sugar.

When the target phenotype is a body mass index, the single nucleotide polymorphism is characterized by polymorphism rs10767664 of the brain derived neurotrophic factor (BDNF) gene, or polymorphism rs17782313 in chromosome 18, and the determination in step (g) is: If the calculated value in step (f) is less than -50, the body mass index is low, and if the body mass index is -50 to 50, the body mass index is normal, and if it is above 50, the body mass index is determined to be high.

When the target phenotype is triglyceride, the monobasic polymorphism is characterized by polymorphism rs2293571, or rs780092 of the GCKR (glucokinase regulator) gene, and the determination in step (g) is in step (f). If the calculated value is less than -20, a triglyceride type is low, if it is -20 to 20, a triglyceride type is normal, and if it is greater than 50, a triglyceride type is determined.

When the target phenotype is cholesterol, it is characterized in that it is calculated by dividing it into a high-density lipoprotein and a low-density lipoprotein.

When the target phenotype is a high-density lipoprotein, the single-base polymorphism of the MYL2 (myosin light chain 2) gene polymorphism rs3782889, LIPG (lipase G, endothelial type) gene polymorphism rs3813082, and CETP (Cholesteryl Ester Transfer Protein) gene The polymorphism is characterized in that it is at least one polymorphism selected from the group consisting of rs6499861, and the determination in step (g) is, when the calculated value in step (f) is less than -1, a high-density lipoprotein is a low type,- If it is 1 to 1, it is characterized by determining that the high density lipoprotein is a normal type, and if it is more than 1, the high density lipoprotein is determined to be a high type.

When the target phenotype is a low-density lipoprotein, the single-base polymorphism is characterized in that the polymorphism rs3782889 of the myosin light chain 2 (MYL2) gene, or the polymorphism rs3813082 of the LIPG (lipase G, endothelial type) gene, and (g) The determination in the step is to determine that the low-density lipoprotein is a low-density type if the calculated value in the step (f) is less than -0.1, a low-density lipoprotein is a normal type, and a high-density low-density lipoprotein is higher than 0.1. It is characterized by.

In another embodiment of the present invention, (a) calculating the frequency by genotype of a specific gene; (b) calculating a target phenotypic characteristic index for phenotypic prediction; (c) calculating a target phenotypic characteristic index according to each genotype combination of a subject from all genes used; (d) calculating a frequency according to each genotype combination of the subject from all the genes used; (e) calculating a weight target phenotype characteristic index; (f) calculating an average target phenotypic characteristic index according to the genotype combination of the population-based subject; And (g) determining a target phenotype; provides a phenotype prediction method using single base polymorphism, wherein the target phenotype is blood pressure, blood sugar, body mass index, triglyceride, or cholesterol. It provides a phenotype prediction method using a, and when the target phenotype is cholesterol, provides a phenotype prediction method using a single nucleotide polymorphism characterized in that it is calculated by dividing into a high density lipoprotein and a low density lipoprotein.

Hereinafter, the present invention will be described in detail step by step.

By measuring the metabolic status of individuals by analyzing single-nucleotide polymorphism (SNP) of individual genes, it is possible to consider the difference in basic metabolic abilities inherent in individual genes, so it is possible to manage complex metabolic syndrome more effectively Do. The present invention relates to a method for predicting a metabolic phenotype using a single base polymorphism, and the method for predicting a metabolic phenotype of the present invention enables accurate diagnosis of individual metabolic levels, and thus is expected to be widely used in the medical and cosmetic fields.

1 is a view showing the classification of the determination of the blood pressure and blood sugar phenotype prediction algorithm using a single base polymorphism, according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating a decision classification of a BMI and triglyceride phenotype prediction algorithm using monobasic polymorphism according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating a decision classification of an HDL and LDL phenotype prediction algorithm using a single base polymorphism according to an embodiment of the present invention.
4 is a view showing a classification of cholesterol risk determination using a single base polymorphism according to an embodiment of the present invention.

Hereinafter, the present invention will be described in more detail through examples. These examples are only for explaining the present invention in more detail, it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples according to the gist of the present invention. .

Throughout the specification, when a part “includes” a certain component, this means that other components may be further included rather than excluding other components unless specifically stated to the contrary.

Example 1. Metabolic phenotype prediction algorithm design using single base polymorphism

Example 1-1. collecting data

First, target phenotypic characteristic indicators are selected from the research database. The above research database is not limited as long as it can search for academic papers such as PubMed and Google scholar. Type information such as control studies, meta-analysis, family studies, and cohort studies are derived from the collected papers, and the data are selected in the order of population data and good significance.

Genotyping frequencies are collected based on accredited data such as Hapmap 3 and 1000 Genomes project.

Example 1-2. Gene phenotype index model design

Gene phenotypic indexes are calculated by the following steps 1-5.

The first step is a process of calculating the frequency according to the combination of the subject's genotype. It is calculated that the sum of frequencies of all genotype combinations of all the genes used is 1. Table 1 shows the specific calculation formula.

Step 2 is a process of calculating target phenotypic characteristic indicators according to the genotype combination of the subject. The target phenotypic characteristic indicators of each genotype constituting the genotype combination are summed. Table 2 shows the specific calculation formula.

Step 3 is the process of calculating the metabolic characteristics index of the population. According to the genotype combination of all genes used, the metabolic characteristic index is generalized according to the nationality and race. Table 3 shows the specific calculation formula.

Step 4 is a process of calculating a weighted average target phenotype characteristic index. For each genotype combination of all genes, the "target phenotypic characteristic index according to the genotype combination * frequency according to the genotype combination" is added. In some cases, it is also possible to multiply "-1" further.

Step 5 is a process of calculating an average target phenotypic characteristic index according to the genotype combination of the population-based subject. Table 4 shows the specific calculation formula.

The metabolic phenotype prediction result of the subject is derived from the calculation process of steps 1 to 5. The target phenotypic characteristic index for predicting the metabolic phenotype is set to the G value of each genotype. The G value is the combined effect of all genes at all loci affecting the phenotypic properties, and is actually interpreted as the average phenotype of the individual with the same genotype. The G value can be derived from a study database or a genetic database. Detailed measurement elements for comprehensive metabolic phenotype prediction are set to "blood pressure", "blood sugar", "body mass index (BMI)", "triglyceride", and "cholesterol". Blood pressure and blood sugar are divided into two stages, and body mass index (BMI) and triglyceride are classified into three stages. Guides for classifying the elements are shown in FIGS. 1, 2, and Table 5.

Classification Step-by-step guide Stage 1 Stage 2 Stage 3 Blood pressure Low blood pressure High blood pressure Blood sugar Difficulty maintaining blood sugar Blood sugar maintenance smoothly Body Mass Index (BMI) Low BMI BMI is usually High BMI Triglyceride Low triglycerides Triglycerides are usually High triglycerides

The criteria for applying the guide in Table 5 are specifically, in the case of blood pressure, when the PP (pulse pressure) value according to the genotype is high, the blood pressure is high, and when the value is low, the blood pressure is determined to be low. The determination reference value is 0. In the case of blood sugar, the higher the blood sugar level according to the genotype, the better the blood sugar maintenance is, and the lower the blood sugar maintenance is. The determination reference value is 0. In the case of body mass index (BMI), it is determined according to the BMI value according to the genotype. The judgment reference value is judged to be low if the value is less than -50, normal if it is from -50 to 50, and high if it is greater than 50. In the case of triglycerides, it is determined according to the triglyceride concentration according to the genotype. The judgment reference value is judged to be low if the value is less than -20, normal if it is from -20 to 20, and high if it is larger than 20.

In the case of cholesterol, high density lipoprotein (HDL) and low density lipoprotein (LDL) are first derived, and cholesterol results are determined therefrom. The final results of HDL and LDL are divided into three stages, and the results of HDL and LDL are comprehensively considered when determining cholesterol, and the higher the HDL level and the lower the LDL level, the better, the lower the HDL level and the higher the LDL level, the more dangerous group. To judge. Guides for classifying HDL and LDL steps are shown in FIGS. 3 and 6.

Classification Step-by-step guide Stage 1 Stage 2 Stage 3 HDL Low HDL levels HDL levels moderate High HDL levels LDL Low LDL levels LDL levels moderate High LDL value

The criteria for applying the guide in Table 6 above are specifically determined according to HDL values according to genotype in the case of HDL. The determination reference value is usually determined as low if the value is less than -1, normal if it is -1 to 1, and high when it is greater than 1. In the case of LDL, it is determined according to the LDL value according to the genotype. The judgment reference value is judged to be low if the value is less than -0.1, normal if it is from -0.1 to 0.1, and high if it is greater than 0.1. Cholesterol is determined as shown in Fig. 4 and Table 7 according to the HDL and LDL determination results.

Good usually danger Very dangerous HDL=High, LDL=Low HDL=High, LDL=High HDL=High, LDL=Moderate HDL=normal, LDL=low
HDL=low, LDL=low HDL=normal, LDL=high
HDL=moderate, LDL=moderate
HDL=low, LDL=high
HDL=low, LDL=normal

Example 2. Prediction of metabolic phenotype using monobasic polymorphism

Using the algorithm designed in Example 1, the subject's metabolic phenotype was determined.

Example 2-1. Blood pressure phenotype prediction

For prediction of blood pressure phenotype, polymorphism of ATP2B1 (ATPase plasma membrane Ca2+ transporting 1) gene rs17249754 (89666809, G>A), polymorphism of NT5C2 (5'-nucleotidase, cytosolic II) gene rs11191593 (103179458, T>C), and CYP17A1 (cytochrome P450 family 17 subfamily A member 1) gene polymorphism rs3824755 (102836092, G>A) was used.

The frequency and G value for each genotype of the genes derived from the research database and gene data are shown in Table 8 below.

RS number genotype frequency
(frequency) G value
(value) rs17249754 AA 0.78251716 53.1 AC 0.20416568 54.4 CC 0.01331716 63.2 rs11191593 TT 0.662596 51.3 CT 0.302808 50.1 CC 0.034596 49.5 rs3824755 GG 0.008464 -9.328 CG 0.167072 -4.664 CC 0.824464 0

The blood pressure phenotype was predicted by substituting the frequency and G value for each genotype of Table 8 in steps 1 to 5 of Example 1. Table 9 shows the specific derivation process.

rs11191593 rs17249754 rs3824755 Combination frequency Combination value (-1)* Combination value * Combination frequency COMPARE TO
GLOBAL MEAN (%) result TT AA CC 0.00686909 -0.6 0.004121457 -481.3953488 Step 1 TT AA GC 0.02919365 -0.32 0.009341968 -210.0775194 Step 1 TT AA GG 0.03101825 -0.04 0.00124073 61.24031008 Stage 3 TT GA CC 0.02551378 -0.2 0.005102756 -93.79844961 Step 1 TT GA GC 0.10843356 0.08 -0.008674685 177.5193798 Stage 3 TT GA GG 0.11521066 0.36 -0.041475837 448.8372093 Stage 3 TT GG CC 0.02369137 0.2 -0.004738273 293.7984496 Stage 3 TT GG GC 0.10068831 0.48 -0.048330387 565.1162791 Stage 3 TT GG GG 0.10698133 0.76 -0.081305808 836.4341085 Stage 3 TC AA CC 0.00482693 -0.98 0.004730393 -849.6124031 Step 1 TC AA GC 0.02051446 -0.7 0.01436012 -578.2945736 Step 1 TC AA GG 0.02179661 -0.42 0.009154577 -306.9767442 Step 1 TC GA CC 0.0179286 -0.58 0.010398589 -462.0155039 Step 1 TC GA GC 0.07619656 -0.3 0.022858967 -190.6976744 Step 1 TC GA GG 0.08095884 -0.02 0.001619177 80.62015504 Stage 3 TC GG CC 0.01664799 -0.18 0.002996638 -74.41860465 Step 1 TC GG GC 0.07075395 0.1 -0.007075395 196.8992248 Stage 3 TC GG GG 0.07517607 0.38 -0.028566906 468.2170543 Stage 3 CC AA CC 0.00084797 -1.36 0.001153245 -1217.829457 Step 1 CC AA GC 0.00360389 -1.08 0.003892202 -946.5116279 Step 1 CC AA GG 0.00382913 -0.8 0.003063308 -675.1937984 Step 1 CC GA CC 0.00314962 -0.96 0.003023634 -830.2325581 Step 1 CC GA GC 0.01338588 -0.68 0.009102399 -558.9147287 Step 1 CC GA GG 0.0142225 -0.4 0.005689 -287.5968992 Step 1 CC GG CC 0.00292465 -0.56 0.001637802 -442.6356589 Step 1 CC GG GC 0.01242975 -0.28 0.003480329 -171.3178295 Step 1 CC GG GG 0.01320661 0 0 100 Step 1 TOTAL AVERAGE -0.1032

In Table 9, "combination frequency" represents "the product of frequencies for each genotype of the corresponding column", and "combination value" represents "sum of values for each genotype of the corresponding column". "(-1)*combination value*combination frequency" is "(-1)*combination frequency of that column*combination value of that column", "COMPARE TO GLOBAL MEAN (%)" is "(combination value of that column-total average ) / total absolute value of total average * 100".

Example 2-2. Blood sugar phenotype prediction

For prediction of blood sugar phenotype, polymorphism of the G6PC2 (glucose-6-phosphatase catalytic subunit 2) gene rs560887 (168906638, T>C), polymorphism of the MTNR1B (melatonin receptor 1B) gene rs10830963 (92975544, C>G), and chromosome 7 The polymorphism rs4607517 (44196069, G>A), which is included in but does not belong to a specific gene and is specified by position 44196069, was used.

The frequency and G value for each genotype of the genes derived from the research database and the gene date are shown in Table 10 below.

RS number genotype frequency
(frequency) G value
(value) rs560887 CC 0.49 -0.084 CT 0.42 -0.042 TT 0.09 0 rs10830963 GG 0.09 -0.068 GC 0.42 -0.034 CC 0.49 0 rs4607517 AA 0.0256 -0.068 AG 0.2688 -0.034 GG 0.7056 0

The frequency and G value for each genotype of Table 10 were substituted in steps 1 to 5 of Example 1 to predict the blood sugar phenotype. The specific derivation process is shown in Table 11.

rs560887 rs10830963 rs4607517 Combination frequency Combination value Combination value * Combination frequency COMPARE TO
GLOBAL MEAN (%) result CC GG AA 0.00112896 -0.22 -0.000248371 -144.2273535 Step 1 CC GG AG 0.01185408 -0.186 -0.002204859 -106.4831261 Step 1 CC GG GG 0.03111696 -0.152 -0.004729778 -68.73889876 Step 1 CC GC AA 0.00526848 -0.186 -0.000979937 -106.4831261 Step 1 CC GC AG 0.05531904 -0.152 -0.008408494 -68.73889876 Step 1 CC GC GG 0.14521248 -0.118 -0.017135073 -30.9946714 Step 1 CC CC AA 0.00614656 -0.152 -0.000934277 -68.73889876 Step 1 CC CC AG 0.06453888 -0.118 -0.007615588 -30.9946714 Step 1 CC CC GG 0.16941456 -0.084 -0.014230823 6.74955595 Stage 3 CT GG AA 0.00096768 -0.178 -0.000172247 -97.60213144 Step 1 CT GG AG 0.01016064 -0.144 -0.001463132 -59.85790409 Step 1 CT GG GG 0.02667168 -0.11 -0.002933885 -22.11367673 Step 1 CT GC AA 0.00451584 -0.144 -0.000650281 -59.85790409 Step 1 CT GC AG 0.04741632 -0.11 -0.005215795 -22.11367673 Step 1 CT GC GG 0.12446784 -0.076 -0.009459556 15.63055062 Stage 3 CT CC AA 0.00526848 -0.11 -0.000579533 -22.11367673 Step 1 CT CC AG 0.05531904 -0.076 -0.004204247 15.63055062 Stage 3 CT CC GG 0.14521248 -0.042 -0.006098924 53.37477798 Stage 3 TT GG AA 0.00020736 -0.136 -2.8201E-05 -50.97690941 Step 1 TT GG AG 0.00217728 -0.102 -0.000222083 -13.23268206 Step 1 TT GG GG 0.00571536 -0.068 -0.000388644 24.51154529 Stage 3 TT GC AA 0.00096768 -0.102 -9.87034E-05 -13.23268206 Step 1 TT GC AG 0.01016064 -0.068 -0.000690924 24.51154529 Stage 3 TT GC GG 0.02667168 -0.034 -0.000906837 62.25577265 Stage 3 TT CC AA 0.00112896 -0.068 -7.67693E-05 24.51154529 Stage 3 TT CC AG 0.01185408 -0.034 -0.000403039 62.25577265 Stage 3 TT CC GG 0.03111696 0 0 100 Stage 3 TOTAL AVERAGE -0.09008

In Table 11, "combination frequency" represents "the product of frequencies for each genotype of the corresponding column", and "combination value" represents "sum of values for each genotype of the corresponding column". "Combination value*Combination frequency" is "Combination frequency* of that column" and "COMPARE TO GLOBAL MEAN (%)" is "(Combination of the column value-total average) / Integer absolute value of total average * 100 Is counted as ".

Example 2-3. Body Mass Index (BMI) phenotype prediction

For the prediction of body mass index phenotype, the polymorphism of the brain derived neurotrophic factor (BDNF) gene rs10767664 (27704439, T>A), which is included in chromosome 18 and is located near the MC4R (melanocortin 4 receptor) gene, but does not belong to a specific gene, The polymorphism rs17782313 (60183864, T>C) specified by position 60183864 was used.

The frequency and G value for each genotype of the genes derived from the research database and gene data are shown in Table 8 below.

RS number genotype frequency
(frequency) G value
(value) rs17782313 CC 0.058564 0.576 TC 0.366872 0.288 TT 0.574564 0 rs10767664 TT 0.3364 0 TA 0.4872 0.14 AA 0.1764 0.28

The blood pressure phenotype was predicted by substituting the frequency and G value for each genotype of Table 12 in steps 1 to 5 of Example 1. Table 13 shows the specific derivation process.

rs17782313 rs10767664 Combination frequency Combination value Combination value * Combination frequency COMPARE TO
GLOBAL MEAN (%) result CC TT 0.01970093 0.576 0.011347735 124.1314905 Stage 3 CC TA 0.02853238 0.716 0.020429185 178.6078944 Stage 3 CC AA 0.01033069 0.856 0.00884307 233.0842983 Stage 3 TC TT 0.12341574 0.288 0.035543733 12.06574524 Stage 2 TC TA 0.17874004 0.428 0.076500736 66.54214917 Stage 3 TC AA 0.06471622 0.568 0.036758813 121.0185531 Stage 3 TT TT 0.19328333 0 0 -100 Step 1 TT TA 0.27992758 0.14 0.039189861 -45.52359607 Stage 2 TT AA 0.10135309 0.28 0.028378865 8.95280787 Stage 2 TOTAL AVERAGE 0.256992

In Table 13, "combination frequency" represents "the product of frequencies for each genotype of the column", and "combination value" represents "sum of values for each genotype of the column". "Combination value*Combination frequency" is "Combination frequency* of that column" and "COMPARE TO GLOBAL MEAN (%)" is "(Combination of the column value-total average) / Integer absolute value of total average * 100 Is counted as ".

Example 2-4. Triglyceride phenotype prediction

For prediction of triglyceride phenotype, polymorphisms rs2293571 (27506613, G>A) and rs780092 (27520287, A>G) of the GCKR (glucokinase regulator) gene were used.

The frequency and G value for each genotype of the genes derived from the research database and gene dating are shown in Table 14 below.

RS number genotype frequency
(frequency) G value
(value) rs2293571 CC 0.7744 0.04 TC 0.2112 0.04 TT 0.0144 0 rs780092 TT 0.459684 0.1 CT 0.436632 0.05 CC 0.103684 0

The frequency and G value of each genotype in Table 14 were substituted in steps 1 to 5 of Example 1 to predict the blood sugar phenotype. The specific derivation process is shown in Table 15.

rs2293571 rs780092 Combination frequency Combination value Combination value * Combination frequency COMPARE TO
GLOBAL MEAN (%) result CC TT 0.35597929 0.14 0.049837101 30.56778333 Stage 3 CC CT 0.33812782 0.09 0.030431504 -16.06356786 Stage 2 CC CC 0.08029289 0.04 0.003211716 -62.69491905 Step 1 TC TT 0.09708526 0.14 0.013591937 30.56778333 Stage 3 TC CT 0.09221668 0.09 0.008299501 -16.06356786 Stage 2 TC CC 0.02189806 0.04 0.000875922 -62.69491905 Step 1 TT TT 0.00661945 0.1 0.000661945 -6.73729762 Stage 2 TT CT 0.0062875 0.05 0.000314375 -53.36864881 Step 1 TT CC 0.00149305 0 0 -100 Step 1 TOTAL AVERAGE 0.107224

In Table 15, "combination frequency" represents "the product of frequencies for each genotype of the corresponding column", and "combination value" represents "sum of values for each genotype of the corresponding column". "Combination value*Combination frequency" is "Combination frequency* of that column" and "COMPARE TO GLOBAL MEAN (%)" is "(Combination of the column value-total average) / Integer absolute value of total average * 100 Is counted as ".

Example 2-5. Cholesterol phenotype prediction

Example 2-5-1. High-density lipoprotein (HDL) phenotype prediction

For prediction of high-density lipoprotein (HDL) phenotype, the polymorphism rs3782889 (110912851, A>G) of the MYL2 (myosin light chain 2) gene, the polymorphism rs3813082 (49561673, A>G) of the LIPG (lipase G, endothelial type) gene, and CETP (Cholesteryl Ester Transfer Protein) gene polymorphism rs6499861 (56957583, C>G) was used.

The frequency and G value for each genotype of the genes derived from the study database and gene data are shown in Table 16 below.

RS number genotype frequency
(frequency) G value
(value) rs3813082 AA 0.78251716 53.1 AC 0.20416568 54.4 CC 0.01331716 63.2 rs3782889 TT 0.662596 51.3 CT 0.302808 50.1 CC 0.034596 49.5 rs6499861 GG 0.008464 -9.328 CG 0.167072 -4.664 CC 0.824464 0

The frequency and G value for each genotype in Table 16 were substituted in steps 1 to 5 of Example 1 to predict the blood sugar phenotype. Table 17 shows the detailed derivation process.

rs3813082 rs3782889 rs6499861 Combination frequency Combination value Combination value * Combination frequency COMPARE TO
GLOBAL MEAN (%) result AA TT GG 0.00438852 95.072 0.417225616 -8.15728208 Step 1 AA TT CG 0.08662562 99.736 8.639692745 -3.651702768 Step 1 AA TT CC 0.4274786 104.4 44.62876568 0.853876544 Stage 2 AA CT GG 0.00200557 93.872 0.188266453 -9.316522041 Step 1 AA CT CG 0.03958812 98.536 3.900855067 -4.810942728 Step 1 AA CT CC 0.19535877 103.2 20.16102505 -0.305363416 Stage 2 AA CC GG 0.00022914 93.272 0.021372076 -9.896142021 Step 1 AA CC CG 0.00452297 97.936 0.442961307 -5.390562708 Step 1 AA CC CC 0.02231986 102.6 2.29001758 -0.884983396 Stage 2 AC TT GG 0.001145 96.372 0.110346376 -6.90143879 Step 1 AC TT CG 0.02260139 101.036 2.283554416 -2.395859478 Step 1 AC TT CC 0.11153296 105.7 11.78903436 2.109719835 Stage 3 AC CT GG 0.00052327 95.172 0.049800641 -8.06067875 Step 1 AC CT CG 0.01032889 99.836 1.031195308 -3.555099438 Step 1 AC CT CC 0.05097084 104.5 5.326452664 0.950479874 Stage 2 AC CC GG 5.9784E-05 94.572 0.005653884 -8.64029873 Step 1 AC CC CG 0.00118008 99.236 0.117106648 -4.134719418 Step 1 AC CC CC 0.00582345 103.9 0.605056419 0.370859894 Stage 2 CC TT GG 7.4685E-05 105.172 0.00785482 1.599654252 Stage 3 CC TT CG 0.00147423 109.836 0.161923099 6.105233564 Stage 3 CC TT CC 0.00727499 114.5 0.832985825 10.61081288 Stage 3 CC CT GG 3.4131E-05 103.972 0.003548714 0.440414292 Stage 2 CC CT CG 0.00067372 108.636 0.073190784 4.945993604 Stage 3 CC CT CC 0.00332469 113.3 0.376686945 9.451572916 Stage 3 CC CC GG 3.8995E-06 103.372 0.000403103 -0.139205688 Stage 2 CC CC CG 7.6973E-05 108.036 0.008315908 4.366373624 Stage 3 CC CC CC 0.00037985 112.7 0.042808806 8.871952936 Stage 3 TOTAL AVERAGE 103.5161003

In Table 17, "combination frequency" represents "the product of frequencies for each genotype of the corresponding column", and "combination value" represents "sum of values for each genotype of the corresponding column". "Combination value*Combination frequency" is "Combination frequency* of that column" and "COMPARE TO GLOBAL MEAN (%)" is "(Combination of the column value-total average) / Integer absolute value of total average * 100 Is counted as ".

Example 2-5-2. Low Density Lipoprotein (LDL) phenotype prediction

For prediction of low-density lipoprotein (LDL) phenotype, the polymorphism rs3782889 (110912851, A>G) of the myosin light chain 2 (MYL2) gene and the polymorphism rs3813082 (49561673, A>G) of the LIPG (lipase G, endothelial type) gene are used. Did. The frequency and G value for each genotype of the genes derived from the study database and gene data are shown in Table 18 below.

RS number genotype frequency
(frequency) G value
(value) rs3813082 AA 0.78251716 117.7 AC 0.20416568 118.3 CC 0.01331716 99 rs3782889 TT 0.662596 116.5 CT 0.302808 116.5 CC 0.034596 117.8

The frequency and G value for each genotype of Table 18 were substituted in steps 1 to 5 of Example 1 to predict the blood sugar phenotype. Table 19 shows the detailed derivation process.

rs3813082 rs3782889 Combination frequency Combination value Combination value * Combination frequency COMPARE TO
GLOBAL MEAN (%) result AA TT 0.51849274 234.2 121.4309997 0.034835651 Stage 2 AA CT 0.23695246 234.2 55.49426524 0.034835651 Stage 2 AA CC 0.02707196 235.5 6.375447444 0.590110144 Stage 3 AC TT 0.13527936 234.8 31.76359441 0.291116186 Stage 3 AC CT 0.061823 234.8 14.51604069 0.291116186 Stage 3 AC CC 0.00706332 236.1 1.667648876 0.84639068 Stage 3 CC TT 0.0088239 215.5 1.901549792 -7.952574369 Step 1 CC CT 0.00403254 215.5 0.869012927 -7.952574369 Step 1 CC CC 0.00046072 216.8 0.099884197 -7.397299876 Step 1 TOTAL AVERAGE 234.1184433

In Table 19, "combination frequency" represents "the product of frequencies for each genotype of the corresponding column", and "combination value" represents "sum of values for each genotype of the corresponding column". "Combination value*Combination frequency" is "Combination frequency* of that column" and "COMPARE TO GLOBAL MEAN (%)" is "(Combination of the column value-total average) / Integer absolute value of total average * 100 Is counted as ".

Example 2-5-3. Cholesterol phenotype prediction

Cholesterol is determined according to the results of HDL and LDL determination as shown in FIG. 4 and Table 7 above.

The specific parts of the present invention have been described in detail above, and it is obvious that for those skilled in the art, these specific techniques are only preferred embodiments, and the scope of the present invention is not limited thereto. Therefore, the substantial scope of the present invention will be defined by the appended claims and equivalents thereof.

Claims (24)

(A) a first calculation unit for calculating the frequency according to the genotype combination of the subject;
(b) a second calculation unit for calculating metabolic characteristic indicators according to the genotype combination of the subject;
(c) a third calculation unit for calculating an indicator of metabolic characteristics of the population;
(d) a fourth calculation unit for calculating the weighted metabolic characteristic index;
(e) a fifth calculation unit for calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And
(f) a metabolic phenotype prediction apparatus using a single base polymorphism comprising: a determination unit for determining a metabolic phenotype,
The first calculation unit is calculated by the following equation (1),
The second calculation unit is calculated by Equation 2 below,
The third calculation unit is calculated by the following equation (3),
The fourth calculator is calculated by Equation 4 or Equation 5 below,
The fifth calculation unit is calculated by the following equation (6),
The metabolic phenotype is blood pressure,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The single-base polymorphism is the polymorphism of the ATP2B1 (ATPase plasma membrane Ca2+ transporting 1) gene rs17249754, the polymorphism of the NT5C2 (5'-nucleotidase, cytosolic II) gene rs11191593, and the CYP17A1 (cytochrome P450 family 17 subfamily A member 1) gene polymorphism rs3824755 Is
The determination is characterized in that the output value in step (e) is based on 0, the higher the value, the higher the blood pressure, and the lower the value, the lower the blood pressure.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) a first calculation unit for calculating the frequency according to the genotype combination of the subject;
(b) a second calculation unit for calculating metabolic characteristic indicators according to the genotype combination of the subject;
(c) a third calculation unit for calculating an indicator of metabolic characteristics of the population;
(d) a fourth calculation unit for calculating the weighted metabolic characteristic index;
(e) a fifth calculation unit for calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And
(f) a metabolic phenotype prediction apparatus using a single base polymorphism comprising: a determination unit for determining a metabolic phenotype,
The first calculation unit is calculated by the following equation (1),
The second calculation unit is calculated by Equation 2 below,
The third calculation unit is calculated by the following equation (3),
The fourth calculator is calculated by Equation 4 or Equation 5 below,
The fifth calculation unit is calculated by the following equation (6),
The metabolic phenotype is blood sugar,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The monobasic polymorphism is that of the polymorphism rs560887 of the G6PC2 (glucose-6-phosphatase catalytic subunit 2) gene, the polymorphism of the melantonin receptor 1B (MTNR1B) gene rs10830963, and the polymorphism rs4607517 in chromosome 7,
The determination is characterized in that the output value in step (e) is determined based on 0, the higher the value is, the smoother the blood sugar maintenance is, and the lower the value is, the more difficult the blood sugar maintenance is.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) a first calculation unit for calculating the frequency according to the genotype combination of the subject;
(b) a second calculation unit for calculating metabolic characteristic indicators according to the genotype combination of the subject;
(c) a third calculation unit for calculating an indicator of metabolic characteristics of the population;
(d) a fourth calculation unit for calculating the weighted metabolic characteristic index;
(e) a fifth calculation unit for calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And
(f) a metabolic phenotype prediction apparatus using a single base polymorphism comprising: a determination unit for determining a metabolic phenotype,
The first calculation unit is calculated by the following equation (1),
The second calculation unit is calculated by Equation 2 below,
The third calculation unit is calculated by the following equation (3),
The fourth calculator is calculated by Equation 4 or Equation 5 below,
The fifth calculation unit is calculated by the following equation (6),
The metabolic phenotype is a body mass index,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The monobasic polymorphism is the polymorphism rs10767664 of the brain derived neurotrophic factor (BDNF) gene, and the polymorphism rs17782313 in chromosome 18,
The determination is characterized in that when the calculated value in step (e) is less than -50, the body mass index is a low type, when the body mass index is -50 to 50, the body mass index is a normal type, and if it is more than 50, the body mass index is a high type. Device.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) a first calculation unit for calculating the frequency according to the genotype combination of the subject;
(b) a second calculation unit for calculating metabolic characteristic indicators according to the genotype combination of the subject;
(c) a third calculation unit for calculating an indicator of metabolic characteristics of the population;
(d) a fourth calculation unit for calculating the weighted metabolic characteristic index;
(e) a fifth calculation unit for calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And
(f) a metabolic phenotype prediction apparatus using a single base polymorphism comprising: a determination unit for determining a metabolic phenotype,
The first calculation unit is calculated by the following equation (1),
The second calculation unit is calculated by Equation 2 below,
The third calculation unit is calculated by the following equation (3),
The fourth calculator is calculated by Equation 4 or Equation 5 below,
The fifth calculation unit is calculated by the following equation (6),
The metabolic phenotype is triglyceride,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The monobasic polymorphisms are polymorphisms rs2293571 and rs780092 of the GCKR (glucokinase regulator) gene,
The determination is characterized in that when the calculated value in step (e) is less than -20, the type is low triglyceride, -20 to 20 is triglyceride normal, and if it is above 20, triglyceride is high. Device.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) a first calculation unit for calculating the frequency according to the genotype combination of the subject;
(b) a second calculation unit for calculating metabolic characteristic indicators according to the genotype combination of the subject;
(c) a third calculation unit for calculating an indicator of metabolic characteristics of the population;
(d) a fourth calculation unit for calculating the weighted metabolic characteristic index;
(e) a fifth calculation unit for calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And
(f) a metabolic phenotype prediction apparatus using a single base polymorphism comprising: a determination unit for determining a metabolic phenotype,
The first calculation unit is calculated by the following equation (1),
The second calculation unit is calculated by Equation 2 below,
The third calculation unit is calculated by the following equation (3),
The fourth calculator is calculated by Equation 4 or Equation 5 below,
The fifth calculation unit is calculated by the following equation (6),
The metabolic phenotype is a high density lipoprotein,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The single-base polymorphism is that of the polymorphism rs3782889 of the myosin light chain 2 (MYL2) gene, the polymorphism rs3813082 of the LIPG (lipase G, endothelial type) gene, and the polymorphism rs6499861 of the Cholesteryl Ester Transfer Protein (CETP) gene,
The determination is characterized in that when the output value in step (e) is less than -1, the high-density lipoprotein is a low type, if -1 to 1, the high-density lipoprotein is a normal type, and if it is more than 1, the high-density lipoprotein is determined as a high type. Device.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) a first calculation unit for calculating the frequency according to the genotype combination of the subject;
(b) a second calculation unit for calculating metabolic characteristic indicators according to the genotype combination of the subject;
(c) a third calculation unit for calculating an indicator of metabolic characteristics of the population;
(d) a fourth calculation unit for calculating the weighted metabolic characteristic index;
(e) a fifth calculation unit for calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And
(f) a metabolic phenotype prediction apparatus using a single base polymorphism comprising: a determination unit for determining a metabolic phenotype,
The first calculation unit is calculated by the following equation (1),
The second calculation unit is calculated by Equation 2 below,
The third calculation unit is calculated by the following equation (3),
The fourth calculator is calculated by Equation 4 or Equation 5 below,
The fifth calculation unit is calculated by the following equation (6),
The metabolic phenotype is a low density lipoprotein,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The single-base polymorphism is that of the polymorphism rs3782889 of the myosin light chain 2 (MYL2) gene, and the polymorphism rs3813082 of the LIPG (lipase G, endothelial type) gene,
The determination is characterized in that when the calculated value in step (f) is less than -0.1, the low-density lipoprotein is a low-type, if -0.1 to 0.1, the low-density lipoprotein is a normal type, and if it exceeds 0.1, the low-density lipoprotein is a high-type. Device.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

The method according to any one of claims 1 to 6,
The calculation in step (a) is characterized in that the sum of the frequencies of genotypes of a specific gene is 1.
The method according to any one of claims 1 to 6,
The device is characterized in that the phenotypic mean value (G value) of the individual having the same genotype is derived from a study database or a genetic database.
(A) calculating the frequency according to the genotype combination of the subject;
(B) calculating a metabolic characteristic index according to the genotype combination of the subject;
(c) calculating a metabolic characteristic index of the population;
(d) calculating a weighted metabolic characteristic index;
(e) calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And,
(f) determining a metabolic phenotype; as a metabolic phenotype prediction method using a single base polymorphism comprising:
Step (a) is calculated by Equation 1 below,
Step (b) is calculated by Equation 2 below,
Step (c) is calculated by Equation 3 below,
Step (d) is calculated by Equation 4 or Equation 5 below,
Step (e) is calculated by Equation 6 below,
The metabolic phenotype is blood pressure,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The single-base polymorphism is the polymorphism of the ATP2B1 (ATPase plasma membrane Ca2+ transporting 1) gene rs17249754, the polymorphism of the NT5C2 (5'-nucleotidase, cytosolic II) gene rs11191593, and the CYP17A1 (cytochrome P450 family 17 subfamily A member 1) gene polymorphism rs3824755 Is
The determination is characterized in that, as the calculated value in step (e) is 0, the higher the value, the higher the blood pressure, and the lower the value, the lower the blood pressure.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) calculating the frequency according to the genotype combination of the subject;
(B) calculating a metabolic characteristic index according to the genotype combination of the subject;
(c) calculating a metabolic characteristic index of the population;
(d) calculating a weighted metabolic characteristic index;
(e) calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And,
(f) determining a metabolic phenotype; as a metabolic phenotype prediction method using a single base polymorphism comprising:
Step (a) is calculated by Equation 1 below,
Step (b) is calculated by Equation 2 below,
Step (c) is calculated by Equation 3 below,
Step (d) is calculated by Equation 4 or Equation 5 below,
Step (e) is calculated by Equation 6 below,
The metabolic phenotype is blood sugar,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The monobasic polymorphism is that of the polymorphism rs560887 of the G6PC2 (glucose-6-phosphatase catalytic subunit 2) gene, the polymorphism of the melantonin receptor 1B (MTNR1B) gene rs10830963, and the polymorphism rs4607517 in chromosome 7,
The determination is characterized in that it is determined that the higher the value in step (e) is based on 0, the smoother the blood sugar maintenance is, and the lower the value is, the more difficult the blood sugar maintenance is.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) calculating the frequency according to the genotype combination of the subject;
(B) calculating a metabolic characteristic index according to the genotype combination of the subject;
(c) calculating a metabolic characteristic index of the population;
(d) calculating a weighted metabolic characteristic index;
(e) calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And,
(f) determining a metabolic phenotype; as a metabolic phenotype prediction method using a single base polymorphism comprising:
Step (a) is calculated by Equation 1 below,
Step (b) is calculated by Equation 2 below,
Step (c) is calculated by Equation 3 below,
Step (d) is calculated by Equation 4 or Equation 5 below,
Step (e) is calculated by Equation 6 below,
The metabolic phenotype is a body mass index,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The monobasic polymorphism is the polymorphism rs10767664 of the brain derived neurotrophic factor (BDNF) gene, and the polymorphism rs17782313 in chromosome 18,
The determination is characterized in that when the calculated value in step (e) is less than -50, the body mass index is a low type, when the body mass index is -50 to 50, the body mass index is a normal type, and if it is more than 50, the body mass index is a high type. Way.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) calculating the frequency according to the genotype combination of the subject;
(B) calculating a metabolic characteristic index according to the genotype combination of the subject;
(c) calculating a metabolic characteristic index of the population;
(d) calculating a weighted metabolic characteristic index;
(e) calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And,
(f) determining a metabolic phenotype; as a metabolic phenotype prediction method using a single base polymorphism comprising:
Step (a) is calculated by Equation 1 below,
Step (b) is calculated by Equation 2 below,
Step (c) is calculated by Equation 3 below,
Step (d) is calculated by Equation 4 or Equation 5 below,
Step (e) is calculated by Equation 6 below,
The metabolic phenotype is triglyceride,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The monobasic polymorphisms are polymorphisms rs2293571 and rs780092 of the GCKR (glucokinase regulator) gene,
The determination is characterized in that when the calculated value in step (e) is less than -20, the type is low triglyceride, -20 to 20 is triglyceride normal, and if it is above 20, triglyceride is high. Way.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) calculating the frequency according to the genotype combination of the subject;
(B) calculating a metabolic characteristic index according to the genotype combination of the subject;
(c) calculating a metabolic characteristic index of the population;
(d) calculating a weighted metabolic characteristic index;
(e) calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And,
(f) determining a metabolic phenotype; as a metabolic phenotype prediction method using a single base polymorphism comprising:
Step (a) is calculated by Equation 1 below,
Step (b) is calculated by Equation 2 below,
Step (c) is calculated by Equation 3 below,
Step (d) is calculated by Equation 4 or Equation 5 below,
Step (e) is calculated by Equation 6 below,
The metabolic phenotype is a high density lipoprotein,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The single-base polymorphism is that of the polymorphism rs3782889 of the myosin light chain 2 (MYL2) gene, the polymorphism rs3813082 of the LIPG (lipase G, endothelial type) gene, and the polymorphism rs6499861 of the Cholesteryl Ester Transfer Protein (CETP) gene,
The determination is characterized in that when the output value in step (e) is less than -1, the high-density lipoprotein is a low type, if -1 to 1, the high-density lipoprotein is a normal type, and if it is more than 1, the high-density lipoprotein is determined as a high type. Way.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

(A) calculating the frequency according to the genotype combination of the subject;
(B) calculating a metabolic characteristic index according to the genotype combination of the subject;
(c) calculating a metabolic characteristic index of the population;
(d) calculating a weighted metabolic characteristic index;
(e) calculating an average metabolic characteristic index according to a genotype combination of a population-based subject; And,
(f) determining a metabolic phenotype; as a metabolic phenotype prediction method using a single base polymorphism comprising:
Step (a) is calculated by Equation 1 below,
Step (b) is calculated by Equation 2 below,
Step (c) is calculated by Equation 3 below,
Step (d) is calculated by Equation 4 or Equation 5 below,
Step (e) is calculated by Equation 6 below,
The metabolic phenotype is a low density lipoprotein,
The metabolic characteristic index is an average phenotypic value (G value) of an individual having the same genotype,
The single-base polymorphism is that of the polymorphism rs3782889 of the myosin light chain 2 (MYL2) gene, and the polymorphism rs3813082 of the LIPG (lipase G, endothelial type) gene,
The determination is characterized in that when the calculated value in step (f) is less than -0.1, the low-density lipoprotein is a low-type, if -0.1 to 0.1, the low-density lipoprotein is a normal type, and if it exceeds 0.1, the low-density lipoprotein is a high-type. Way.
[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

[Equation 6]

The method according to any one of claims 9 to 14,
The calculation in step (a) is characterized in that the sum of the frequencies of genotypes of a specific gene is 1.
The method according to any one of claims 9 to 14,
The method, characterized in that the average phenotypic value (G value) of the individual having the same genotype is derived from a study database or a genetic database.
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