KR101881812B1 - Biomarker for predicting of training response - Google Patents

Abstract

The present invention provides: a single nucleotide polymorphism marker capable of predicting aerobic exercise reactivity as a biomarker; a method for providing information to predict the aerobic exercise reactivity by using the marker; a composition for predicting the aerobic exercise reactivity including a probe or a primer on the marker; and a kit for predicting the aerobic exercise reactivity, including the probe or the primer on the marker. The present invention also provides: a single nucleotide polymorphism marker capable of predicting anaerobic exercise reactivity as a biomarker; a method for providing information to predict the anaerobic exercise reactivity by using the marker; a composition for predicting the anaerobic exercise reactivity, including a probe or a primer on the marker; and a kit for predicting the anaerobic exercise reactivity, including the probe or the primer on the marker. According to the present invention, as the aerobic exercise reactivity and the anaerobic exercise reactivity of an individual can be predicted, an exercise program appropriate for an individual can be suggested before or during an exercise.

Description

Biomarker for predicting of training response

The present invention is a biomarker, a single base polymorphic marker capable of predicting aerobic exercise reactivity; A method of providing information for predicting aerobic exercise reactivity using the marker; A composition for predicting aerobic exercise reactivity including a probe or primer for the marker; And it relates to a kit for predicting aerobic exercise reactivity comprising a probe or primer for the marker.

The present invention also provides a single base polymorphic marker capable of predicting anoxic movement reactivity as a biomarker; A method of providing information for predicting anaerobic exercise reactivity using the marker; A composition for predicting anaerobic exercise reactivity comprising a probe or a primer for the marker; And it relates to a kit for predicting anaerobic exercise reactivity comprising a probe or primer for the marker.

With the development of science and the spread of computers, the physical activity of modern people has decreased, and modern people have suffered from lack of exercise. Lack of exercise is known to be an important risk factor for all adult diseases, such as hypertension, diabetes, obesity, heart disease, and hyperlipidemia, causing a decrease in immunity as well as a decrease in physical strength. Accordingly, the importance of exercise has begun to be emphasized in order to maintain and improve health, and in fact, many modern people feel the necessity of exercise to reinforce physical strength. Exercise is becoming important not only to promote health, but also to develop a beautiful body. In fact, there is a growing trend of people looking for exercise to lose weight.

Since the effect of such exercise can be obtained by steadily performing exercise over a certain period of time, modern people are investing a lot of time and money in exercise.

Meanwhile, even when the same time and effort are invested, the exercise effect may vary according to the physical characteristics of the exerciser. Therefore, in order to obtain an effective exercise effect, the necessity of predicting the exercise responsiveness according to the physical characteristics of each exerciser is emerging.

International Patent Publication No. 2010-028256 discloses a biomarker for predicting exercise reactivity expressed by such maximum oxygen intake.

WO 2010-028256 A

An object of the present invention is to provide a single base polymorphic marker capable of effectively predicting the kinetic reactivity to anoxic / anoxic exercise.

The present inventors assume that if the exercise effect according to the individual's genetic characteristics can be predicted in advance, it is assumed that it will be possible to propose a personalized exercise program capable of achieving excellent exercise effect in time and cost-effective manner. The present invention was completed by discovering a specific single nucleotide polymorphism marker having a significant correlation with anoxic movement reactivity.

Accordingly, the present invention provides the use of a single nucleotide polymorphic marker capable of predicting the exercise reactivity in oxygen/anoxic exercise.

The present invention is a marker for predicting exercise reactivity, including one or more single-base polymorphic markers selected from single-base polymorphic markers capable of predicting exercise reactivity in oxygen/anaerobic exercise, a method for predicting exercise reactivity using the marker , A method of providing information for predicting exercise reactivity using the marker, and a composition for predicting anoxic exercise reactivity including a probe or primer for the marker, and a composition for predicting exercise reactivity or a probe or primer for the marker. Provides a kit for predicting anaerobic exercise response.

I. How to predict aerobic exercise response

Specifically, the present invention is to predict the aerobic reactivity of an individual, comprising identifying the base of the polymorphic site of one or more single nucleotide polymorphic markers selected from the single nucleotide polymorphic markers of the present invention from a nucleic acid sample isolated from the individual. Provides a way to provide information for the purpose.

The present invention also includes identifying the base of the polymorphic site of one or more single nucleotide polymorphic markers selected from the single nucleotide polymorphic markers of the present invention from a nucleic acid sample isolated from the subject, Provides a way to present information.

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

The term'polymorphism' refers to the coexistence of one or more forms in a nucleic acid including exons and introns, or a portion thereof. 'Single Nucleotide Polymorphism'or'SNP' refers to a case in which two or more single bases exist at a specific position in a gene. Most SNPs have two alleles. For one allele of the polymorphism, having two identical alleles is called homozygous for the allele (having the same base at the SNP position), and having two different alleles is heterozygous for the allele (SNP Have different bases at the position). Preferred SNPs have two or more alleles exhibiting an incidence of at least 1%, more preferably at least 10% or 20% in the selected population. In the present invention, the SNP name is indicated by rs ID. The rs ID refers to the official SNP (Reference SNP, rs) ID assigned to each unique SNP in the National Center for Biotechnological Information (NCBI) .

The term'allele' refers to an alternative form of a gene comprising introns, exons, intron/exon junctions and 3'and/or 5'untranslated regions associated with a gene or a portion thereof. In general, an allele refers to several types of a gene that exist at the same locus on a homologous chromosome. Alleles of a particular gene may differ by a single nucleotide or several nucleotides from each other and may include substitutions, deletions and insertions of nucleotides.

The term “marker”, “polymorphic marker” or “single nucleotide polymorphic marker” refers to a genomic polymorphic site of a gene. Each polymorphic marker has two or more sequence variations characteristic of a particular allele at the polymorphic site.

In this specification, the sequence list was prepared according to the Korean Intellectual Property Office Notice No. 2013-01 [Nucleate group sequence list or amino acid sequence list preparation standard]. In the nucleic acid sequence, the symbol r represents g (guanine) or a (adenine), and the symbol y represents t (thymine)/u (uracil) or c (cytosine). The symbol m represents a or c, and k represents g or t/u. The symbol s represents g or c, and the symbol w represents a or t/u.

In the present specification, the term'aerobic exercise' refers to an exercise in which oxygen is supplied to the muscles by continuing at a relatively low intensity. Examples of aerobic exercise include walking, jogging, aerobics, jumping rope, swimming, biking, mountain climbing, and the like. 'Anaerobic exercise' refers to an exercise of sufficient intensity to trigger lactic acid formation in the absence or lack of oxygen. Examples of anaerobic exercises include push-ups, weight lifting, squats, and the like.

The term'training response' refers to the ability of a living body related to a response to exercise, and refers to the responsiveness that a living body shows to exercise, that is, training sensitivity. For example, when the exercise responsiveness is high, the level of changes in the living body that occurs in response to exercise, such as the maximum oxygen intake or the increase in muscle strength, is high. "Aerobic exercise reactivity" is an exercise reactivity to aerobic exercise, but is not limited thereto, but may be expressed as aerobic capacity and/or maximum oxygen intake. The'anaerobic exercise reactivity' is an exercise reactivity to anoxic exercise, but is not limited thereto, but may be expressed as an increase in anaerobic ability and/or muscle strength.

'Aerobic capacity' refers to the ability to convert energy during aerobic processes in the process of energy conversion, and is related to the ability to carry oxygen such as breathing, circulation, and blood, or to the use of oxygen by tissues, and is closely related to systemic endurance. There is. Decreased aerobic capacity can lead to fatigue and ischemic heart disease. Aerobic capacity can be raised through aerobic exercise and can generally be assessed as maximum oxygen intake. 'Maximal oxygen consumption (maximal oxygen uptake; V'O2max)' refers to the maximum oxygen consumption in a unit of time measured during exercise. The maximum oxygen intake is used as an index to determine aerobic capacity or cardiopulmonary endurance. In addition, the'increased amount of maximum oxygen intake' refers to the rate of change (%) of the maximum oxygen intake increased after exercising during a predetermined exercise period compared to before the individual exercised.

'Anaerobic capacity' is a concept that contrasts with aerobic capacity, and refers to the ability to convert energy in an anaerobic process without using oxygen. Anaerobic ability can be raised through anaerobic exercise, and can be evaluated as an increase in muscle strength. The'muscle strength increase' refers to an increase in muscle strength (muscle strength) caused by muscle contraction, and may be expressed as a peak torque. Torque means the force that rotates an object, and'peak torque' means the maximum torque value. In addition, the'increased amount of peak torque' refers to a rate of change (%) of the peak torque increased after exercising during a predetermined exercise period compared to before the individual exercised.

The term'subject' refers to a subject for predicting the kinetic reactivity of aerobic/anaerobic exercise, and the'nucleic acid sample' refers to a sample containing a nucleic acid (DNA or RNA) obtained from an individual. Nucleic acid samples include blood, urine, hair, amniotic fluid, cerebrospinal fluid collected from an individual; Or skin, muscle, oral mucosa or conjunctival mucosa; It can be obtained from any source containing nucleic acids, such as tissue samples from the placenta, gastrointestinal tract or other organs. Methods for obtaining a nucleic acid sample from an individual are well known in the art, and known methods can be used without limitation.

From a nucleic acid sample isolated from an individual, a polymorphic site of a single nucleotide polymorphic marker may be amplified or hybridized with a probe capable of detecting a single nucleotide polymorphic marker to identify the base of the polymorphic site.

For example, polymorphic sites can be amplified using PCR, ligase chain reaction (LCR), transcription amplification, self-maintained sequence replication, and nucleic acid-based sequence amplification (NASBA).

The identification of the base of the polymorphic site is sequencing analysis, hybridization by microarray, allele specific PCR, dynamic allele-specific hybridization (DASH), PCR extension analysis, SSCP. , PCR-RFLP analysis, TaqMan technique, SNPlex platform (Applied Biosystems), mass spectrometry (e.g., Sequenom's MassARRAY system), mini-sequencing method, Bio-Plex system (BioRad), CEQ and SNPstream System (Beckman), Molecular Inversion Probe array technology (eg, Affymetrix GeneChip), and BeadChip (HumanOmni2.5-8 from Illumina), and the like. For example, the base of the polymorphic site can be identified using a SNP chip. The SNP chip refers to one of the DNA microarrays that can identify each base of hundreds of thousands of SNPs at once.

The TaqMan method includes the steps of (1) designing and producing a primer and a TaqMan probe to amplify a desired DNA fragment; (2) labeling probes of different alleles with FAM dyes and VIC dyes (Applied Biosystems); (3) using the DNA as a template and performing PCR using the primers and probes; (4) after the PCR reaction is completed, analyzing and confirming the TaqMan assay plate with a nucleic acid analyzer; And (5) determining the genome types of the polynucleotides of step (1) from the analysis result.

Sequencing analysis may be performed using a conventional method for determining the base sequence, and may be performed using an automated genetic analyzer. In addition, allele-specific PCR refers to a PCR method for amplifying a DNA fragment in which the SNP is located with a primer set including a primer designed with a base in which the SNP is located as the 3'end. The principle of the method is, for example, when a specific base is substituted from A to G, a primer containing A as a 3'end base and a reverse primer capable of amplifying a DNA fragment of an appropriate size are designed and PCR In the case of performing the reaction, when the base at the SNP position is A, the amplification reaction is normally performed and a band at the desired position is observed. When the base is substituted with G, the primer can complementarily bind to the template DNA. This is due to the fact that the amplification reaction is not performed properly because the 3'end cannot perform complementary bonding. DASH may be performed by a conventional method, and preferably may be performed by a method by a prince or the like.

In PCR extension analysis, a DNA fragment containing a base in which a single base polymorphism is located is first amplified with a primer pair, and then all nucleotides added to the reaction are dephosphorylated to inactivate, and a SNP-specific extension primer, dNTP mixture , Didioxynucleotide, reaction buffer, and DNA polymerase are added to perform a primer extension reaction. At this time, the extension primer uses the base immediately adjacent to the 5'direction of the base where the SNP is located as the 3'end, and the dNTP mixture excludes nucleic acids having the same base as the didioxynucleotide, and the didioxynucleotide represents the SNP. It is selected from one of the base types. For example, when there is a substitution from A to G, when a mixture of dGTP, dCTP and TTP and ddATP are added to the reaction, the primer at the base where the substitution occurs is extended by DNA polymerase, and after several bases, A Primer extension reaction is terminated by ddATP at the position where the base first appears. If the substitution has not occurred, since the extension reaction is terminated at that position, it is possible to determine the type of base representing the SNP by comparing the length of the extended primers.

At this time, as a detection method, in the case of fluorescently labeled extension primers or dodioxynucleotides, the SNP can be detected by detecting fluorescence using a genetic analyzer (eg, ABI's Model 3700, etc.) used for general nucleotide sequence determination. The SNP can be detected by measuring the molecular weight using a matrix assisted laser desorption ionization-time of flight (MALDI-TOF) technique in the case of using a non-labeled extension primer and a dodioxynucleotide.

The single nucleotide polymorphic markers shown in Table 1 can be classified into aerobic exercise responders and non-responders, and aerobic exercise responders, aerobic exercise normal responders and aerobic exercise high responders, depending on the intensity of the reaction in aerobic exercise. These are the markers.

[Table 1]

Specifically, a single base polymorphic marker capable of discriminating aerobic exercise reactivity is,

Rs11051548 on human chromosome 12; Rs2542729 on human chromosome 18; Rs1451462 on human chromosome 2; Rs11096663 on human chromosome 2; Rs6570913 on human chromosome 6; Rs13060995 on human chromosome 3; Rs12613181 on human chromosome 2; Rs1626492 on human chromosome 6; Rs2417760 on human chromosome 12; Rs10072122 on human chromosome 5; And rs1056233 of human chromosome 2 may be selected from the group consisting of.

Above,

Rs11051548 of human chromosome 12 is A;

Rs2542729 of human chromosome 18 is C;

Rs1451462 on human chromosome 2 is C;

Rs11096663 on human chromosome 2 is G;

Rs6570913 of human chromosome 6 is G;

Rs13060995 on human chromosome 3 is G;

Rs12613181 on human chromosome 2 is C;

Rs1626492 of human chromosome 6 is G;

Rs2417760 on human chromosome 12 is A;

Rs10072122 on human chromosome 5 is G; or

If rs1056233 of human chromosome 2 is C,

Compared with the case of having other bases on the allele, the base exhibits a high index of aerobic exercise reactivity.

As described above, the aerobic exercise reactivity may be expressed as a maximum oxygen intake.

Therefore, the present invention

From a nucleic acid sample isolated from a subject, a method of providing information for predicting aerobic exercise reactivity in a subject comprising identifying the base of the polymorphic site of the following single nucleotide polymorphic marker is provided:

The single base polymorphism marker,

Rs11051548 on human chromosome 12; Rs2542729 on human chromosome 18; Rs1451462 on human chromosome 2; Rs11096663 on human chromosome 2; Rs6570913 on human chromosome 6; Rs13060995 on human chromosome 3; Rs12613181 on human chromosome 2; Rs1626492 on human chromosome 6; Rs2417760 on human chromosome 12; Rs10072122 on human chromosome 5; And it is at least one selected from the group consisting of rs1056233 of human chromosome 2,

Above,

Rs2542729 of human chromosome 12 is A;

Rs2542729 of human chromosome 18 is C;

Rs1451462 on human chromosome 2 is C;

Rs11096663 on human chromosome 2 is G;

Rs6570913 of human chromosome 6 is G;

Rs13060995 on human chromosome 3 is G;

Rs12613181 on human chromosome 2 is C;

Rs1626492 of human chromosome 6 is G;

Rs2417760 on human chromosome 12 is A;

Rs10072122 on human chromosome 5 is G; or

When rs1056233 of human chromosome 2 is C, it is an index of high aerobic response,

The aerobic exercise reactivity is expressed as the maximum oxygen intake.

One of the single nucleotide polymorphic markers may be used, one or more of the markers may be used in combination, or all of the markers may be used in combination. As the number of markers to be combined increases, the discriminant ability of predicting motor response may be improved.

In one embodiment, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, the single base polymorphic markers for predicting the aerobic exercise reactivity, It can be used in combination of 10 or more and all 11 of them.

For example, in the aerobic exercise reactivity experiment, among 11 polymorphic markers capable of discriminating non-responders, normal responders, and high-responders, the discriminant power of each marker was at most 53%, but when all 11 were used, About 91% of discriminant power was obtained. The results were similar in the anaerobic exercise response experiment. In the anaerobic exercise reactivity experiment, of the 8 polymorphic markers that can discriminate between the non-responder, normal and high-responder group, the discriminant power of each marker was up to 54%, but when all 8 were used, about 82% I was able to get discrimination.

In one embodiment of the present invention, the aerobic exercise reactivity of an individual may be classified into a non-reactive aerobic exercise group, a normal aerobic exercise reaction group, or a high aerobic exercise response group.

As described in the following examples, the method of providing information for predicting aerobic exercise reactivity of an individual of the present invention is a single base polymorphism capable of predicting aerobic exercise reactivity based on a model for predicting aerobic exercise reactivity obtained through clinical trials. It is based on the selection of markers.

'Aerobic exercise reactivity prediction model' refers to a mathematical model capable of predicting the level of increase in maximum oxygen uptake according to the difference in single nucleotide polymorphism observed in an individual.

The'aerobic exercise reactivity prediction model' is constructed according to the main SNP extraction process of the prediction model as shown in FIG. 1 and is described in detail in Examples.

The method of predicting an individual's aerobic exercise reactivity through a pre-built'aerobic exercise reactivity prediction model' begins by scoring the genotype of each SNP determined as an aerobic exercise reactivity prediction marker.

Since the genome type of each SNP is composed of a combination of two alleles, there are three types of genome type per SNP. Through the'Aerobic Exercise Reactivity Prediction Model', the allele with a low aerobic exercise reactivity was set as A, and the allele with a high aerobic exercise reactivity was set as B. For example, homozygous for the allele with low aerobic exercise reactivity. The score can be scored with 0 points for case (AA genomic type), 1 point for heterozygous (AB genomic type), and 2 points for homozygous alleles with high aerobic activity (BB genomic type). have.

When each SNP has the same influence on the aerobic exercise reactivity, the sum (X) of the genome-type scores of each individual can be calculated according to the following [Equation 1].

[Equation 1]

If each SNP has a different influence on the aerobic exercise responsiveness, the sum (X) of the genome-type scores of each individual can be calculated according to the following [Equation 2].

[Equation 2]

In Equations 1 and 2,

X is the sum of the genomic score calculated in the individual, and is 0≤X≤2n or 0≤X≤2,

G _j is the genomic type score ∈ {0, 1, 2} observed at the j-th SNP of the individual,

W _j represents the weight of the j-th SNP,

n represents the number of SNPs constituting the predictive model.

W _j is based on the use of semi R2 of each SNP calculated in multiple linear repression, and W _j is 1 if the weights of all SNPs are the same.

The individual does not respond to aerobic exercise according to where the sum (X) of the genome-type scores of each individual obtained according to [Equation 1] or [Equation 2] belongs to one of the cut-off intervals preset through the aerobic exercise reactivity prediction model, It can be determined that it belongs to the normal aerobic exercise response group and the high aerobic exercise response group.

For example, C0 and C1 are the cut-off values to determine the aerobic response group and the aerobic non-responder group, respectively, through a model for predicting aerobic activity response, and to determine the normal aerobic response group and the high aerobic exercise group. When we say that it represents the cutoff value for

If X≤C0, aerobic exercise non-response group,

When C0<X<C1, aerobic exercise moderate responder,

If X≥C1, it may be determined that you are in the aerobic high-responder group.

Another way to classify an individual's aerobic exercise reactivity into aerobic non-responsive group, aerobic moderate response group, or aerobic high-responsive group

According to [Equation 1] or [Equation 2] above, the sum (X) of the genome type score of the individual is obtained,

According to the following [Equation 3] or [Equation 4], the predicted increase rate (Y) of the maximum oxygen intake is obtained,

If Y≤Q0, aerobic exercise non-responsive group,

If Q0<Y<Q1, aerobic exercise moderate responder,

If Y≥Q1, it may be performed by a method involving determining that it belongs to the high aerobic exercise group.

[Equation 3]

Y=a+bX

[Equation 4]

In Equation 3,

X is the sum of the genomic score calculated in the individual, and is 0≤X≤2n or 0≤X≤2,

Y represents the predicted rate of increase in maximal oxygen intake,

a and b are the intercept and slope values of the linear regression equation obtained through the predictive model, where a represents the regression constant, and b represents the influence of X on Y.

In Equation 4,

G _j is the genomic type score ∈ {0, 1, 2} observed at the j-th SNP of the individual,

c and d _j are the intercept and slope values of the multilinear regression equation obtained through the predictive model, where c represents the regression constant, and d _j represents the influence of G _{j on Y.}

The intercept and slope of Equations 3 and 4 can be estimated through a linear regression method for the relationship between the genome score and the increase in maximal oxygen intake.

Q0 and Q1 are predetermined cut-off values obtained in advance through the aerobic exercise reactivity prediction model described above, where Q0 is a cut-off value that separates the aerobic exercise non-responder group and the aerobic exercise responder, and Q1 is the aerobic exercise normal response group and aerobic exercise response group. This is the cutoff value that distinguishes the high-responder group.

A more reliable aerobic exercise response is predicted by combining the result of predicting the aerobic exercise reactivity of the individual based on the sum of each individual's genomic score (X) and the predicted increase of the maximum oxygen intake (Y). This could be possible.

The present invention also provides a polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs11051548 of human chromosome 12 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 1: TGTGAATGCA GTAGCCCTGCRACAATAGCTCTGATAACCAA);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs2542729 of human chromosome 18 or a polynucleotide complementary thereto (eg, a polynucleotide of SEQ ID NO: 2: ATGGCTCAGT CAGTTGATGTYAGCCGGCCCTGGTCATTGGC);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs1451462 of human chromosome 2 or a polynucleotide complementary thereto (polynucleotide of SEQ ID NO: 3: TGGAGACTCCAGATCTTTCAYAGGGCATTTC ATCCTCAGAA);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs11096663 of human chromosome 2 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 4: CCTAGAAATC TCCAAGGCCTRTGTTAAAACC ATCCCTTACC);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs6570913 of human chromosome 6 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 5: TCCCGAATGA ATCAATTCACRATGATTATTCGCATTTTGAA);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs13060995 of human chromosome 3 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 6: cacagccatg gaaccaaagcDaggcatctacaagccaaggc);

Polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs12613181 of human chromosome 2 or a polynucleotide complementary thereto (e.g., polynucleotide of SEQ ID NO: 7: AATCGAAAGC ATATAGAGAAYATGAGGAAGCATGAAAAGTC)

Polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs1626492 of human chromosome 6 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 8: TCATGAACTA CTTCTGAGTTRTTTACTACTGATTTGTGGGG)

Polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs2417760 of human chromosome 12 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 9: AATCTCAGCT CTACTTGTAAMTCACTGTGATGCCTTAGGTG)

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs10072122 of human chromosome 5 or a polynucleotide complementary thereto (e.g., a polynucleotide of SEQ ID NO: 10: GGGTCAAACACAGTAAATGARTGATTTCTGTAAGTATTAGA) and

One or more single nucleotide polymorphisms selected from the group consisting of a polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs1056233 of human chromosome 2 or a polynucleotide complementary thereto (e.g., polynucleotide of SEQ ID NO: 11: ATGAAAATCAACCATGTTTTMTCTCACCCTCTCTCTTTTGT) It provides a composition for predicting the aerobic activity of an individual comprising a probe capable of detecting a marker or a primer capable of amplifying the single nucleotide polymorphic marker.

In addition, the present invention

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs11051548 of human chromosome 12 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs2542729 of human chromosome 18 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs1451462 of human chromosome 2 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs11096663 of human chromosome 2 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs6570913 of human chromosome 6 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs13060995 of human chromosome 3 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs12613181 of human chromosome 2 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs1626492 of human chromosome 6 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs2417760 of human chromosome 12 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs10072122 of human chromosome 5 or a polynucleotide complementary thereto; And

A probe capable of detecting one or more single nucleotide polymorphism markers selected from the group consisting of a polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs1056233 of human chromosome 2 or a polynucleotide complementary thereto, or the single nucleotide polymorphism It provides a kit for predicting an individual's aerobic exercise response, including a primer capable of amplifying a marker.

In the present specification, the term'probe' refers to a substance capable of predicting the sensitivity of the aerobic/anaerobic exercise by specifically hybridizing with a polymorphic site of a gene, and the specific method of such genetic analysis is not particularly limited. It may be by any method for detecting genes known in the art to which the invention belongs.

In the present specification, the term'primer' is a material capable of specifically amplifying a polynucleotide of a single nucleotide polymorphism marker, and a template complementary to a base sequence having a short free 3'terminal hydroxyl group (free 3'hydroxyl group). ) Refers to a short sequence that can form a base pair and serves as a starting point for template strand copying. Primers can initiate DNA synthesis in the presence of a reagent for polymerization (ie, DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates at an appropriate buffer and temperature. By performing PCR amplification, the kinetic reactivity can be predicted through whether or not a desired product is produced. The PCR conditions, the length of the sense and antisense primers can be modified based on those known in the art. The appropriate length of the primer may vary depending on the purpose of use, and may be, for example, 15 to 30 nucleotides. The primer sequence need not be completely complementary to the template, but must be sufficiently complementary to hybridize to the template.

Probes or primers can be chemically synthesized using the phosphoramidite solid support method, or other well known methods. Such nucleic acid sequences can also be modified using a number of means known in the art. Non-limiting examples of such modifications include methylation, encapsulation, substitution of one or more homologs of natural nucleotides, and modifications between nucleotides, e.g., uncharged linkers (e.g., methyl phosphonate, phosphorester, phosphoro Amidates, carbamates, etc.) or with charged linkers (eg phosphorothioate, phosphorodithioate, etc.).

In the present invention, the kit may be used without limitation as long as it is a kit capable of detecting or identifying a single nucleotide polymorphism marker. For example, the kit may be a DNA chip kit or a real time-polymerase chain reaction (RT-PCR) kit. The RT-PCR kit includes, in addition to each primer pair specific for the gene of the polymorphic marker, a test tube or other suitable container, reaction buffer (varies in pH and magnesium concentration), deoxynucleotides (dNTPs), Taq-polymerase and Enzymes such as reverse transcriptase, DNase, RNAse inhibitors, DEPC-water, sterile water, and the like may be included. In addition, a primer pair specific to a gene used as a quantitative control may be included. Using an RT-PCR kit, polymorphic markers can be identified by measuring the mRNA expression level of a single base polymorphic marker. A DNA chip kit is a gridded array of nucleic acid species attached to a generally flat solid support plate, typically a glass surface that is not larger than a microscope slide, in which nucleic acids are uniformly arranged on the chip surface. It is a tool that enables mass-parallel analysis by causing multiple hybridization reactions between the nucleic acid on the image and the complementary nucleic acid contained in the solution treated on the surface of the chip.

The kit of the present invention also includes a microarray. The microarray may include DNA or RNA polynucleotides. The microarray may be formed of a conventional microarray, except that the polynucleotide of the polymorphic marker of the present invention is included in the probe polynucleotide.

A method of preparing a microarray by immobilizing a probe polynucleotide on a substrate is well known in the art. The probe polynucleotide refers to a polynucleotide capable of hybridizing, and refers to an oligonucleotide capable of sequence-specific binding to a complementary strand of a nucleic acid. The probe of the present invention is an allele-specific probe, and a polymorphic site exists in a nucleic acid fragment derived from two members of the same species, and hybridizes to a DNA fragment derived from one member, but does not hybridize to a fragment derived from another member. . In this case, hybridization conditions show a significant difference in hybridization strength between alleles, and must be sufficiently stringent to hybridize to only one of the alleles. This can lead to good hybridization differences between different allelic types.

As the microarray, a known microarray may be used without limitation, and a method of manufacturing a microarray is well known in the art. In addition, hybridization of nucleic acids on microarrays and detection of hybridization results are also well known in the art. For example, the result of hybridization may be detected by labeling a nucleic acid sample with a labeling substance capable of generating a detectable signal such as a fluorescent substance, hybridizing on a microarray, and detecting a signal generated from the labeling substance.

II. How to predict anaerobic exercise reactivity

The single-base polymorphic markers shown in Table 2 can be classified into an anoxic exercise responder group and an anoxic exercise response group, and an anoxic exercise response group according to the reaction intensity, and an anoxic exercise normal response group and an anoxic exercise high response group in anoxic exercise. These are the markers.

[Table 2]

Specifically, a single base polymorphic marker capable of discriminating an anaerobic exercise reactivity is,

Rs10072841 on human chromosome 5; Rs6564267 on human chromosome 16; Rs17044554 on human chromosome 2; Rs1341439 on human chromosome 13; Rs10756546 on human chromosome 9; Rs4522375 on human chromosome 15; Rs10045249 on human chromosome 5; And rs7154161 of human chromosome 14.

Above,

Rs10072841 on human chromosome 5 is C;

Rs6564267 on human chromosome 16 is G;

Rs17044554 on human chromosome 2 is T;

Rs1341439 on human chromosome 13 is A;

Rs10756546 of human chromosome 9 is C;

Rs4522375 on human chromosome 15 is T;

Rs10045249 on human chromosome 5 is A; or

When rs7154161 of human chromosome 14 is T, the base may be an index having a high anaerobic kinetic reactivity as compared to the case of having other bases on the allele.

As described above, the anaerobic motion reactivity may be expressed as a peak torque.

Therefore, the present invention

From a nucleic acid sample isolated from an individual, a method of providing information for predicting an anaerobic locomotor reactivity in an individual, comprising identifying the base of the polymorphic site of the following single base polymorphic marker is provided:

The single base polymorphism marker,

Rs10072841 on human chromosome 5; Rs6564267 on human chromosome 16; Rs17044554 on human chromosome 2; Rs1341439 on human chromosome 13; Rs10756546 on human chromosome 9; Rs4522375 on human chromosome 15; Rs10045249 on human chromosome 5; And one or more selected from the group consisting of rs7154161 of human chromosome 14,

Above,

Rs10072841 on human chromosome 5 is C;

Rs6564267 on human chromosome 16 is G;

Rs17044554 on human chromosome 2 is T;

Rs1341439 on human chromosome 13 is A;

Rs10756546 of human chromosome 9 is C;

Rs4522375 on human chromosome 15 is T;

Rs10045249 on human chromosome 5 is A; or

When rs7154161 of human chromosome 14 is T, it is an index of high anaerobic response,

The anaerobic motion reactivity is expressed as a peak torque.

One of the single nucleotide polymorphic markers may be used, one or more of the markers may be used in combination, or all of the markers may be used in combination. As the number of markers to be combined increases, the discriminant ability of predicting motor response may be improved.

In one embodiment, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, all of 8 or more single base polymorphic markers for predicting the anaerobic kinetic reactivity may be used in combination. .

In one embodiment of the present invention, the anaerobic exercise reactivity of the individual may be classified into an anoxic exercise non-reactive group, an anoxic exercise normal response group, or an anoxic exercise high response group.

As described in the following examples, the method of providing information for predicting the anaerobic exercise reactivity of an individual of the present invention is a single base polymorphism capable of predicting the anaerobic exercise reactivity based on the anaerobic exercise reactivity prediction model obtained through clinical trials. It is based on the selection of markers.

The'anaerobic exercise reactivity prediction model' refers to a mathematical model capable of predicting the level of muscle strength increase that can be expressed, for example, as an increase in peak torque according to the difference in single-base polymorphism observed in an individual.

The'anaerobic exercise reactivity prediction model' is constructed according to the main SNP extraction process constituting the prediction model as shown in FIG. 2, and details are described in detail in the following examples.

The method of predicting an individual's anaerobic exercise reactivity through a pre-built'anaerobic exercise reactivity prediction model' begins by scoring the genotype of each SNP determined as an anaerobic exercise reactivity prediction marker.

Since the genome type of each SNP is composed of a combination of two alleles, there are three types of genome type per SNP. Through the'anaerobic exercise reactivity prediction model', the allele with a low aerobic exercise reactivity was set as A, and the allele with a high anoxic reactivity was set as B. For example, the homozygous of the allele with a low anaerobic reactivity. The score can be scored with 0 points for case (AA genome type), 1 point for heterozygous (AB genome type), and 2 points for homozygous case (BB genome type) of alleles with high anaerobic locomotor reactivity. have.

At this time, since each SNP may have a different influence on the anaerobic exercise reactivity, the sum (X) of the genome-type scores of each individual can be calculated according to the following [Equation 2].

When each SNP has the same influence on the aerobic exercise reactivity, the sum (X) of the genome-type scores of each individual can be calculated according to the following [Equation 1].

[Equation 1]

If each SNP has a different influence on the aerobic exercise responsiveness, the sum (X) of the genome-type scores of each individual can be calculated according to the following [Equation 2].

[Equation 2]

In Equations 1 and 2,

X is the sum of the genomic score calculated in the individual, and is 0≤X≤2n or 0≤X≤2,

G _j is the genomic type score ∈ {0, 1, 2} observed at the j-th SNP of the individual,

W _j represents the weight of the j-th SNP,

n represents the number of SNPs constituting the predictive model.

W _j is based on the use of semi R2 of each SNP calculated in multiple linear repression, and W _j is 1 if the weights of all SNPs are the same.

Depending on where the sum (X) of the genome-type scores of each individual obtained according to [Equation 2] belongs to one of the cut-off intervals preset through the anaerobic exercise reactivity prediction model, the individual is an anoxic exercise non-responder group, an anaerobic exercise normal response group, It can be determined as belonging to the anaerobic exercise high response group.

For example, R0 and R1 are the cut-off values to determine the anoxic exercise reactive group and the anoxic exercise non-reactive group respectively set in advance through the anaerobic exercise reactivity prediction model, and the anaerobic exercise normal response group and the anaerobic exercise high responder group. When we say that it represents the cutoff value for

When X≤R0, anoxic exercise non-response group,

When R0<X<R1, the anaerobic exercise normal responder,

If X≥R1, it may be determined that you are in the anaerobic exercise high response group.

Another way to classify the individual's anaerobic exercise reactivity into anoxic exercise non-responder group, anoxic exercise moderate response group, or anoxic exercise high response group

According to [Equation 1] or [Equation 2] above, the sum (X) of the genome type score of the individual is obtained,

Obtain the predicted increase rate (Z) of the peak torque according to the following [Equation 5] or [Equation 6],

When Z≤S0, anoxic exercise non-response group,

When S0<Z<S1, the anaerobic exercise normal responder,

If Z≥S1, it may be performed by a method including determining that it belongs to the anaerobic exercise high response group.

[Equation 5]

Z=e+fX

[Equation 6]

In Equation 5,

X is the sum of the genomic score calculated in the individual, and is 0≤X≤2n or 0≤X≤2,

e and f are the intercept and slope values of the linear regression equation obtained through the predictive model, where e is the regression constant, and f is the influence of X on Z.

In Equation 6,

G _j is the genomic type score ∈ {0, 1, 2} observed at the j-th SNP of the individual,

g and h _j are the intercept and slope values of the multilinear regression equation obtained through the predictive model, where g is the regression constant, and h _j is the influence of _{G j on Z.}

The intercept and slope of Equations 5 and 6 can be estimated through a linear regression method for a relationship between a dielectric score and an increase in peak torque.

S0 and S1 are predetermined cutoff values obtained in advance through the anaerobic exercise reactivity prediction model described above, respectively, S0 is a cutoff value that distinguishes the anoxic exercise non-responder group and the anoxic exercise response group, and S1 is the anaerobic exercise normal response group and anoxic exercise response group. This is the cutoff value that distinguishes the high-responder group.

By combining the result of predicting the anaerobic exercise reactivity of the individual based on the sum of the genomic scores of each individual (X) and the predicted increase of the peak torque (Z), a more reliable prediction of the anaerobic exercise reactivity can be obtained. It could be possible.

The present invention also,

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs10072841 of human chromosome 5 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 12: CTAGTAATACAACTGTTTAGYACCTGGCTTATACTATTAAC);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs6564267 of human chromosome 16 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 13: CTTTTGCCCAGGCTGTCAGCKCTTTGGCCCCAGTTAATGAC);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs17044554 of human chromosome 2 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 14: GAGGACACCACAGAGGAGCTKAGAGCTTGTCAGCTACCACC);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs1341439 of human chromosome 13 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 15: AATAGTTGTT TCTATTTCRTAGAGGTGTTGTGAAGAGTA);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs10756546 of human chromosome 9 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 16: TTACAGCATCAGAATGTTTAYATCTGCACAATTCTTTGATT);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs4522375 of human chromosome 15 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 17: aaattatCtttggggatacaYatgcaggttcattacttagg);

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs10045249 of human chromosome 5 or a polynucleotide complementary thereto (eg, polynucleotide of SEQ ID NO: 18: CTGGCAAGTCCAAAATCAGCRGGGTAGGCTAGTAGGCTGGA); And

One or more single nucleotide polymorphisms selected from the group consisting of a polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs7154161 of human chromosome 14 or a polynucleotide complementary thereto (e.g., polynucleotide of SEQ ID NO: 19: GCTGCATCCTCCTCGCTGTGYGAAAGAGACCGTAGATTTTA) It provides a composition for predicting the anaerobic kinetic reactivity of an individual comprising a probe capable of detecting a marker or a primer capable of amplifying the single nucleotide polymorphic marker.

In addition, the present invention

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs10072841 of human chromosome 5 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs6564267 of human chromosome 16 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs17044554 of human chromosome 2 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs1341439 of human chromosome 13 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs10756546 of human chromosome 9 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences comprising rs4522375 of human chromosome 15 or a polynucleotide complementary thereto;

A polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs10045249 of human chromosome 5 or a polynucleotide complementary thereto, and

A probe capable of detecting one or more single nucleotide polymorphism markers selected from the group consisting of a polynucleotide consisting of 5 to 100 consecutive nucleic acid sequences including rs7154161 of human chromosome 14 or a polynucleotide complementary thereto, or the single nucleotide polymorphism It provides a kit for predicting the anaerobic exercise reactivity of an individual comprising a primer capable of amplifying a marker.

In addition, the method of providing information for predicting anaerobic exercise reactivity, a composition for predicting anoxic exercise reactivity of an individual, and a kit for predicting anoxic exercise reactivity of an individual may apply mutatis mutandis to the matters previously described in the method for predicting aerobic exercise reactivity. .

In the present invention, the provision of information for predicting the exercise reactivity may be made through a computer program. For example, the result of base identification of the polymorphic site of the single base polymorphic marker of the target individual is output through a computer-readable program, and as a result, in the case of an individual with high aerobic activity reactivity, an appropriate aerobic exercise program can be presented. In the case of individuals with high anaerobic exercise response, an appropriate anaerobic exercise program can be suggested. Accordingly, by presenting a personalized exercise program according to the genetic characteristics of each individual, it is possible to maximize the exercise effect.

In accordance with the present invention, single nucleotide polymorphisms, which significantly affect the _{maximum O 2} intake (V'O ₂ max) and knee peak torque, which are the main evaluation variables for the aerobic/anaerobic exercise reactivity, SNPs) were identified. Using these single nucleotide polymorphisms, it is possible to predict an individual's aerobic/anaerobic exercise reactivity, so it is possible to present an exercise program suitable for the characteristics of the individual before or during exercise.

1 and 2 show the main SNP extraction processes of the'Aerobic Exercise Reactivity Prediction Model' and the'Anoxic Exercise Reactivity Prediction Model', respectively.
_{3 shows the sample distribution of the% change in V'O 2} max and the% change in knee peak torque after completion of the 9-week HIT program.
Figure 4 shows a Manhattan plot of significant SNPs (p-value <0.01) across 22 autosomes. The X axis represents the chromosomes and the Y axis represents the p-values converted to -log 10. The SNP of FIG. 4A is _{significant in the linear regression analysis for the% change in V'O 2} max, and the SNP in FIG. 4B is significant in the linear regression analysis for the% change in knee peak torque.
_{5 shows the average value of the% change in V'O 2} max observed in the genotype of 11 SNPs and the upper 95% confidence interval.
6 shows the average value of the% change in knee peak torque observed in the genotype of 8 SNPs and the upper 95% confidence interval.
7 shows the average value of the% change in the exercise response (Y axis) and the upper 95% confidence interval. The X axis represents three different ranges determined by the cutoff of the total Ginotype score. The number of subjects within each score range is indicated by n in the diagram.

Hereinafter, the present invention will be described in detail through examples. The following examples are only illustrative of the present invention, and the scope of the present invention is not limited to the following examples. The present embodiments are provided to complete the disclosure of the present invention and to fully inform the scope of the invention to those of ordinary skill in the art to which the present invention belongs, and the present invention will be defined by the scope of the claims. Only.

[ Example ]

79 healthy volunteers participated in the HIT program. A genome-wide association study (GWAS) based on 2,391,739 SNPs was performed to identify SNPs significantly related to the increase in _{V'O 2 max and knee peak torque after 9 weeks of HIT program execution.} . In order to predict the two types of kinetic reactivity, two independent SNP sets were determined using linear regression analysis and repeated binary logistic regression analysis. False discovery rate analysis and permutation verification were performed to avoid false-positive findings.

The protocol for this study was approved by the Institutional Review Board (IRB) of Kyunghee University Hospital. The method and process of this study are in accordance with the Good Clinical Practice Guidelines and the Declaration of Helsinki.

<How to build a model for predicting exercise reactivity>

Clinical trial participants and HIT program

A total of 123 Korean men and women aged 30-60 years were recruited to participate in this clinical trial. All participants had no experience with normal exercise within the previous 3 months and had a body mass index (BMI) of ^{16-32 kg/m 2.} We examined their medical history, vital signs (systolic and diastolic blood pressure, and pulse rate), 12-lead electrocardiogram (ECG), and performed routine laboratory tests (blood analysis, clinical chemistry, and urine analysis).

An ergometer bike (Aerobike 75XL-II, COMBI, Japan) was used to perform the 9-week HIT program. The intensity of this exercise was adjusted for each participant based _{on the HR and V'O 2} max measured in their basic trait tests. Each participant exercised for 9 weeks for 3 minutes 40 seconds per session in HR set to 60-84% of their baseline V'O _{2 max.} One training session consists of 40 seconds of warm-up, 20 seconds of HIT, 40 sets of rest, 20 seconds of HIT, 40 seconds of rest, 20 seconds of HIT, and finally 40 seconds of cool-down. All participants performed this training three times a week (1 session/day, 3 sessions/week), and their performance was recorded in the exercise diary immediately after completion of the training. Participants could voluntarily withdraw from the program at any time. Participants who performed less than 21 sessions were eliminated from this study. At the basic test, the 6-week test, and the final 9-week test, participants visited the national fitness center (NFC, Korea) and measured the following evaluation variables for exercise reactivity related to their exercise reactivity: V'O ₂ max, anaerobic. Anaerobic threshold, tidal volume, oxygen intake (V'O ₂ ), carbon dioxide emissions (V'CO ₂ ), and respiratory exchange ratio of participants. In addition, as part of the muscle strength test, the flexor and extensor peak torque at the knee, and the agonist/antagonist ratio were measured. The flexor and extensor peak torques of each individual's knee were measured at 60°/sec using Biodex System 3 (Biodex Medical Systems, USA); Measurements were made on the left and right knees and averaged. The main evaluation variables for exercise response were determined by V'O ₂ max (unit: ml/min/kg) and knee flexor peak torque (unit: NM/kg). Participants were guided by professional trainers prior to conducting the HIT program.

Definition of actual subgroups for motor reactivity

Quantification of exercise reactivity is the rate of change in volume _{of V'O 2 max (% change in V'O 2} max) from baseline (baseline) to the end of the 9-week HIT program, and% increase in knee flexor peak torque (knee It was defined as% change of peak torque). A clustering analysis was used to define three subgroups representing three different motor responses: non-responders, moderate responders, and high responders. Subjects with a% change in exercise response <0% were included in the non-responder subgroup. The remaining subjects were divided into moderate-responders or high-responders using a 2-means clustering algorithm: the first centers of the two subgroups were the minimum (S1) and maximum (S2) of the% change in motor reactivity. Accordingly, subjects clustered around S1 and S2, and were therefore defined as moderate and high responders, respectively.

SNP Gino Typing And To GWAS About the data Pre-processing

Template DNA was prepared from whole blood samples using the High Pure PCR Template Preparation kit (Roche Diagnostics, Mannheim, Germany) according to the manufacturer's instructions. Whole blood cells were lysed using binding buffer and protease K, and genomic DNA was extracted using isopropanol and inhibitor removal buffer. The total DNA was eluted with 40uL of elution buffer, and then stored at 70°C until SNP genotyping. GWAS SNPs were genotyped using HumanOmni2.5-8 BeadChips (Illumina, Inc., USA). Genotype calling was performed using GenomeStudio software (Illumina, Inc., USA), and after checking the logR ratio and waviness, a Genotype with a call rate> 0.99 was selected. Monomorphic SNPs accounting for 41.8% of the total 2,391,739 SNPs were filtered out. 7,270 (0.3%) SNPs with missing genotypes ≥ 10% and 225,440 SNPs with minor allele frequency (MAF) <5% (9.4%) were filtered out. The Hardy-Weinberg equilibrium (HWE) test was performed on the remaining SNPs, and 26,588 SNPs with a p-value of <0.001 (1.1%) were filtered out. After performing the pre-processing of the data, 1,131,703 SNPs (47.3%) remained, and these were used to conduct the GWAS study. For these SNPs, Annotation information such as location and gene information on chromosomes was obtained from human genome version 19, and databases for annotation, visualization and integrated discovery (DAVID) were used for functional annotation of related genes (Huang et al. al . 2009a; Huang et al . 2009b).

SNP for quantitative score Genotype Coding

An analysis of the relationship between alleles and motor reactivity was performed; The homozygous genotype consisting of alleles that are beneficial to motor reactivity was coded as 2 points, and the homozygous genotype consisting of alleles that are unfavorable to motor reactivity was coded as 0. The heterozygous genotype was coded as 1 point. Using these coded values, we analyzed the additional genetic influence of SNPs on motor reactivity.

Statistical analysis

The basic characteristics of the measured values were expressed as mean±standard deviation (SD), and N (%). Each of these represents a continuous and categorical variable. Two sample t-tests, or Wilcoxon signed rank tests were performed to test the difference between the initial baseline measurements and the measurements measured after the 9-week HIT program. 1 and 2 show the overall analysis process from data preprocessing to selection of genetic markers used to construct a model for predicting aerobic and anaerobic exercise reactivity. We performed linear regression analysis and logistic regression analysis consecutively for GWAS SNP in order to identify SNPs significantly related to both continuous and categorical training responses. The% change in V'O ₂ max and the% change in knee peak torque were used as the continuous exercise responsiveness, and three subgroups, non-responders, moderate responders, and high responders, were used as categorical exercise responsiveness. All analyzes were corrected by gender, age and _{baseline quantification of V'O 2} max and knee peak torque. Linear regression analysis was performed to evaluate the influence of the SNP genotype on the quantitative increase in motor reactivity. In addition, logistic regression analysis was used to evaluate the force of the SNP genotype that divides the subject into subgroups with different motor reactivity. In binary logistic regression analysis, the first was the response group vs. For the non-responder group, the second was the high responder vs. Usually applied for the responder group. The significance level for p-value was set to 0.01 for both linear regression and logistic regression analysis. The q-value cutoff for FDR (False Discovery Rate) and the p-value cutoff for the permutation test (n=2,000) were set to 0.1 and 0.01, respectively, to minimize false positives. To identify the SNP that best fits the regression model among the candidate markers, the backward variable selection method and the forward variable selection method were successively applied based on the Akaike Information Criterion (AIC). In addition, SNPs that maximize the classification accuracy were additionally discovered by adding SNPs that fall behind in the backward and forward variable selection methods to the regression model one by one. Accordingly, we selected these SNPs as the final genetic markers to divide subjects into three subgroups of different motor reactivity.

The total genotype score was calculated by summing the encoded genotype scores of all marker SNPs, and this was used to predict the motor response subgroup of the subject. The cutoff of the total genotype score that distinguishes between the three different subgroups was calculated through an iterative process that minimizes classification errors into 3x3 contingency tables consisting of the actual exercise reactivity subgroup and the predicted exercise reactivity subgroup (Table 3 and classification below). See accuracy formula). All two score combinations that can come from the calculated minimum genotype score to maximum genotype score were used as genotype score cutoff candidates. For example, if the set cutoff is C1 and C2, respectively, people with the minimum gene score ~ C1 are predicted as non-responders, those with C1 ~ C2 are usually responders, and those with C2 ~ maximum gene scores are predicted as high responders. The three predicted response groups are compared with the actual three response groups to calculate classification accuracy, that is, discriminant power (expressed as discrimination accuracy or prediction accuracy). When the classification accuracy calculation for all the cutoff combinations is completed, the cutoff with the highest classification accuracy among them is finally used to predict the exercise reactivity.

[Table 3]

The reliability of the prediction results was estimated through the leave-one-out cross-validation (LOOCV) procedure, bootstrapping (n=1,000), and randomization testing (n=1,000). All aggregate analyzes were conducted in R language ver. 3.1.1 (R Foundation for Statistical Computing, Vienna, Austria).

<Results of building a model for predicting exercise reactivity>

Basic characteristics of HIT program measurement

The final analysis data set included participants who completed at least 21 sessions of clinical trials; Participants meeting this classification criterion were 79 out of a total of 128 subjects (51 males and 28 females). Table 4 summarizes the basic characteristics of these 79 subjects.

[Table 4]

Several measurements _{, such as V'O 2} max, knee peak torque, systolic blood pressure (SBP), diastolic blood pressure (DBP), and evaluation of the homeostasis model of beta cell function (HOMA), were significantly greater than baseline values after a 9-week HIT program. It was different (p-value <0.05, Table 5).

[Table 5]

Single SNP analysis

_{3 shows the sample distribution of the% change in V'O 2} max and the% change in knee peak torque after completion of the 9-week HIT program. Approximately 24% (n=19) and 15% (n=12) of subjects in V'O _{2 max and knee peak torque, respectively, showed a change of zero or negative.} On the other hand, approximately 29% (n=23) of subjects showed results exceeding 18% and 24% changes in _{V'O 2 max and knee peak torque, respectively.} The average% changes in V'O ₂ max and knee peak torque were 9.5% and 15.2%, respectively (data not shown).

Linear regression analysis was performed for each GWAS SNP and corrected for age, gender and _{baseline measurements of V'O 2} max or knee peak torque. From a total of 1,131,703 SNPs remaining after data pre-processing, we found that 11,773 SNPs and 11,258 SNPs were _{significantly associated with the% change in V'O 2} max and the% change in knee peak torque, respectively (p value < 0.01). The distribution of the analysis results is shown in the Manhattan plot of FIG. 4. Using these SNPs, binary logistic regression analysis including FDR analysis was performed. From 11,773 _{SNPs linearly correlated with the% change in V'O 2} max, 259 SNPs and 650 SNPs, respectively, were dichotomous and It was significantly related (p value <0.01, and q value <0.1). Complete information on the selected SNP is as described in Tables 1 and 2.

Genetic for multiple SNP analysis and motor reactivity prediction model Marker

In order to further reduce false positives, a permutation test was performed on the selected SNP in the single SNP analysis (n=2,000, and p value <0.01), and the filtered SNP was used for multivariate logistic regression analysis. After sequentially applying the forward variable selection method and the backward variable selection method, 11 SNPs were selected as final markers predicting% change in _{V'O 2 max.} In this case, 6 SNPs were selected to identify responders versus non-responders, while the remaining 5 SNPs were selected to identify high responders versus moderate responders. Table 1 provides detailed information on these SNPs.

The _{11 SNPs selected to predict the% change in V'O 2} max accounted for 20.3% of the observed total variance: the contribution of each single SNP varied from 0.6% to 5.2% (contribution was It is the value of multiplying the partial R2 value in Table 1 by 100). 5 and 6 show the average value of the% change in _{V'O 2 max and the 95% confidence interval for each genotype; In all SNPs, the% change in V'O 2} max was significantly different according to the genotype (a = 0.05).

[Table 6]

The SNP most strongly correlated with the% change in _{V'O 2 max was rs11051548.} This SNP is located in the intron of the antagonist of mitotic exit network 1 homolog (AMN1) gene. rs2542729 includes 20 nucleotide sequences before and after the 30568418 sequence of chromosome 18. rs1451462 is located at the LOC105369165 gene site. rs13060995 is located in the intergenic region of two genes: SLIT-ROBO Rho GTPase activating protein 3 (SRGAP3) and LOC100288831. rs6570913 is located in the intron of the uronyl-2-sulfotransferase (UST) gene. rs11096663 is located at the LOC105373465 gene site. rs12613181 is located in the intron of the potassium channel, voltage gated eag related subfamily H, member 7 (KCNH7) gene.

On the other hand, as can be seen in Table 2, 8 SNPs selected to predict the% change in knee peak torque accounted for 21.8% of the observed total variance. The contribution of each single SNP varied from 1.1% to 7.9% (the contribution is the partial R2 value in Table 2 multiplied by 100). The average value of the% change in knee peak torque for each genotype and the 95% confidence interval are as shown in FIGS. 6 and 7.

[Table 7]

All 8 SNPs showed significant differences in the% change in knee peak torque according to genotype (a = 0.05). rs10072841 was most strongly associated with the% change in knee peak torque, and this SNP is located in the intron of the FAT atypical cadherin 2 (FAT2) gene. Other strongly related SNPs include: rs6564267 is included in the intron of the GABA(A) receptor-associated protein like 2 (GABARAPL2) gene. rs17044554 contains 20 nucleotide sequences before and after the 22995383 nucleotide sequence of chromosome 2. rs1341439 includes 20 nucleotide sequences before and after the 103706322 nucleotide sequence of chromosome 13. rs4522375 includes 20 nucleotide sequences before and after nucleotide sequence 23957540 of chromosome 15. rs7154161 includes 20 nucleotide sequences before and after the 51387550 nucleotide sequence of chromosome 14.

SNP genotype for prediction of motor reactivity subgroups Scoring Construction of the system

Two types of SNP genotype scoring systems were constructed, respectively, for% change in V'O _{2 max and% change in knee peak torque.} As can be seen in Tables 1 and 2, selected SNPs were used as predictive markers to divide subjects into three subgroups. In order to reflect the quantitative contribution of the genotype to the motor responsiveness, the individual genotype of each SNP was coded as 0, 1, or 2 according to the procedure described in the method section. Then, the sum of the genotype scores (i.e., total genotype score) was used to predict the subject's motor response subgroup. _{The genetic scoring system used to measure the% change in V'O 2} max and the% change in knee peak torque divides the subjects' motor reactivity subgroups into non-responders, moderate responders, or high responders.

The cutoff of the total genotype score to identify the three subgroups was determined as described in the Methods section. The distribution of the% change in the subject's motor response is shown in FIG. 7. Here, _{the average value of the% change in V'O 2} max was -4.2%, 8.0%, and 28.0% for each subrange of the total genotype score. The average values of the% change in peak torque were -2.7%, 15.7%, and 42.7% for each subrange of the total genotype score.

prediction Estimating accuracy for the model And internal verification

[Table 8]

Aerobic Exercise Reactivity Prediction Model

[Table 9]

Anaerobic exercise reactivity prediction model

Tables 8 and 9 show the estimated accuracy of prediction and the overall results of internal verification. The prediction accuracy was estimated to be 91.1% and 82.3% for% change in _{V'O 2 max and% change in knee peak torque, respectively.} The same results were obtained using LOOCV analysis. Bootstrapping analysis (n=1,000) showed _{an average prediction accuracy of 91.0% for% change in V'O 2} max (95% confidence interval 84.8-97.4%), and average prediction accuracy for% change in knee peak torque. Is 82.3% (95% confidence interval is 75.6%-91.3%). On the other hand, in the randomization test, which tests after randomly arranging the subject's actual exercise responsiveness, it was found that the prediction accuracy of about 50% was on average when n=1,000 sets were used. This indicates that the prediction accuracy of the constructed prediction model is reliable.

<Example of predicting aerobic exercise response>

According to the predictive model established above, the exercise response to aerobic exercise of one of the clinical trial subjects is predicted as follows.

First, if the genotype of the subject is scored according to the genotype, it appears as follows (in this example, a formula that does not consider the weight of each SNP was used).

Since the total of the genotype score is 12 points, the result of predicting exercise response using the genotype score is predicted as a normal responder (according to the previously established prediction model, a score predicted as a non-responder: 10 points or less, predicted as a normal response group. A score of 11 to 14 points, a score predicted as a high responder: 15 points or more).

In addition, looking at the prediction results using the linear regression equation, in the prediction model described above, Y=a+bX was a=-34.667, b=3.632, and since the total genotype score of the subject was X=12, the value of Y = 8.917. Is obtained. According to the cutoff value set through the prediction model, a value predicted as a non-responder group: Y is 0 or less, a value predicted as a normal response group: 0<Y<18, a value predicted as a high response group: Y is 18 or more Therefore, the subject could be predicted as a normal response group.

When synthesizing the results from the two methodologies, the subject was usually predicted as a responder.

<Example of predicting anaerobic exercise response>

According to the predictive model described above, the exercise response to anaerobic exercise of one of the clinical trial subjects is predicted as follows.

First, if the genotype of the subject is scored according to the genotype, it appears as follows (in this example, a formula that does not consider the weight of each SNP was used).

Since the total of the genotype score is 6 points, the result of predicting exercise response using the genotype score is predicted as a non-responder (according to the previously established prediction model, a score predicted as a non-responder: 7 points or less, predicted as a normal response group. A score of 8 to 12 points, a score predicted as a high responder: 13 points or higher).

In addition, looking at the prediction results using the linear regression equation, in the prediction model described above, Z=e+fX was e=-46.994, f=5.823, and since the total genotype score of the subject was X=6, Z = -12.056. The value is obtained. According to the cutoff value set through the prediction model, a value predicted as a non-responder group: Z is 0 or less, a value predicted as a normal response group: 0<Z<24, a value predicted as a high response group: Since Z is 24 or more , The subject could be predicted as a non-responder group.

When combining the results of the two methodologies, the subject was predicted to be non-responder.

SEQUENCE LISTING <110> DAEWOONG PHARMACEUTICAL CO., LTD. BIO AGE CO., LTD. <120> BIOMARKER FOR PREDICTING TRAINING RESPONSE <130> P16C24C1548 <160> 19 <170> PatentIn version 3.2 <210> 1 <211> 41 <212> DNA <213> Homo sapiens <400> 1 tgtgaatgca gtagccctgc racaatagct ctgataacca a 41 <210> 2 <211> 41 <212> DNA <213> Homo sapiens <400> 2 atggctcagt cagttgatgt yagccggccc tggtcattgg c 41 <210> 3 <211> 41 <212> DNA <213> Homo sapiens <400> 3 tggagactcc agatctttca yagggcattt catcctcaga a 41 <210> 4 <211> 41 <212> DNA <213> Homo sapiens <400> 4 cctagaaatc tccaaggcct rtgttaaaac catcccttac c 41 <210> 5 <211> 41 <212> DNA <213> Homo sapiens <400> 5 tcccgaatga atcaattcac ratgattatt cgcattttga a 41 <210> 6 <211> 41 <212> DNA <213> Homo sapiens <400> 6 cacagccatg gaaccaaagc daggcatcta caagccaagg c 41 <210> 7 <211> 41 <212> DNA <213> Homo sapiens <400> 7 aatcgaaagc atatagagaa yatgaggaag catgaaaagt c 41 <210> 8 <211> 41 <212> DNA <213> Homo sapiens <400> 8 tcatgaacta cttctgagtt rtttactact gatttgtggg g 41 <210> 9 <211> 41 <212> DNA <213> Homo sapiens <400> 9 aatctcagct ctacttgtaa mtcactgtga tgccttaggt g 41 <210> 10 <211> 41 <212> DNA <213> Homo sapiens <400> 10 gggtcaaaca cagtaaatga rtgatttctg taagtattag a 41 <210> 11 <211> 41 <212> DNA <213> Homo sapiens <400> 11 atgaaaatca accatgtttt mtctcaccct ctctcttttg t 41 <210> 12 <211> 41 <212> DNA <213> Homo sapiens <400> 12 ctagtaatac aactgtttag yacctggctt atactattaa c 41 <210> 13 <211> 41 <212> DNA <213> Homo sapiens <400> 13 cttttgccca ggctgtcagc kctttggccc cagttaatga c 41 <210> 14 <211> 41 <212> DNA <213> Homo sapiens <400> 14 gaggacacca cagaggagct kagagcttgt cagctaccac c 41 <210> 15 <211> 39 <212> DNA <213> Homo sapiens <400> 15 aatagttgtt tctatttcrt agaggtgttg tgaagagta 39 <210> 16 <211> 41 <212> DNA <213> Homo sapiens <400> 16 ttacagcatc agaatgttta yatctgcaca attctttgat t 41 <210> 17 <211> 41 <212> DNA <213> Homo sapiens <400> 17 aaattatctt tggggataca yatgcaggtt cattacttag g 41 <210> 18 <211> 41 <212> DNA <213> Homo sapiens <400> 18 ctggcaagtc caaaatcagc rgggtaggct agtaggctgg a 41 <210> 19 <211> 41 <212> DNA <213> Homo sapiens <400> 19 gctgcatcct cctcgctgtg ygaaagagac cgtagatttt a 41