Method and apparatus for visualizing polygenic risk scores

Abstract

One embodiment of the present disclosure provides a method for visualizing polygenic risk scores. The method comprises the steps of: checking whether input genotype data matches pre-stored genetic variant risk data, and mapping genes and genetic variants on the basis of the genotype data; calculating, on the basis of a plurality of preset algorithms, polygenic risk scores and attribute values for the gene and genetic variant contribution scores for the genetic variants thereof on the basis of a plurality of preset algorithms; and visualizing and providing, on the basis of the attribute values and the genetic variant contribution scores, gene-specific gene contribution scores at the population level, gene-specific attribute values at the individual level, and gene-specific genetic variant contribution scores at the individual level.

Description

Method and device for visualizing polygenic risk scores

[0001] The present invention relates to a method and device for visualizing a polygenic risk score, and more particularly, to a method and device for mapping genetic variants to genes based on genotype data, calculating polygenic risk scores, attribute values, and polygenic risk scores for genetic variants for genes according to a plurality of preset algorithms, and providing visualization data therefor.

[0002] Disease risk prediction is an essential part of preventive medicine and guides clinical management. Risk factors typically include age, gender, family history, and lifestyle.

[0003] Many recent studies have confirmed that the predictive power of disease risk prediction models is improved when using a polygenic risk score (PRS) that aggregates genetic information.

[0004] The simplest and most common form of polygenic risk score is the sum of the number of risk genetic variants (Single Nucleotide Polymorphisms, SNPs) an individual carries and the β (weight) value of the genetic variants obtained from genome-wide association study (GWAS) summary statistics, which is expressed as a single number proportional to the disease risk.

[0005] However, the problem with multi-gene risk scores is that it is difficult to directly determine which genetic factors increase the risk of disease because they are combinations of hundreds to tens of millions of genetic variants.

[0006] In addition, existing methods for constructing multi-gene risk scores, such as LDpred, PRS-CS, and LassoSum, only attempted to increase the predictive power of multi-gene risk scores, and no research is being conducted on methods for interpreting multi-gene risk scores.

[0007] Accordingly, there is a need for a method that can provide an explanation for the multi-gene risk score.

[0008] The present invention is intended to solve the problems of the prior art described above, and relates to a method and device for mapping genetic variants to genes based on genotype data, calculating polygenic risk scores, attribute values, and polygenic risk scores for genetic variants for genes according to a plurality of preset algorithms, and providing visualization data therefor.

[0009] The technical problems to be solved by the present invention are not limited to the technical problems described above, and other technical problems of the present invention can be derived from the following description.

[0010] As a technical means for solving the above-described technical problem, an embodiment according to a first aspect of the present disclosure provides a method for visualizing a polygenic risk score. The method includes a step of confirming whether input genotype data matches pre-stored genetic variant risk data, mapping genes and genetic variants based on the genotype data, a step of calculating a polygenic risk score, an attribute value, and a genetic variant contribution score for the genetic variant for the gene based on a plurality of preset algorithms, and a step of visualizing and providing, based on the attribute value and the genetic variant contribution score, a gene contribution score for each gene at a population level, an attribute value for each gene at an individual level, and a genetic variant contribution score for each gene at an individual level.

[0011] As a technical means for solving the above-described technical problem, an embodiment according to a second aspect of the present disclosure provides a multi-gene risk score visualization device. The device includes a communication module, at least one processor, and a memory electrically connected to the processor and storing at least one code to be executed by the processor, wherein the memory stores a code that, when executed through the processor, causes the processor to check whether input genotype data matches pre-stored genetic variant risk data, map genes and genetic variants based on the genotype data, calculate a multi-gene risk score for the gene, an attribute value, and a genetic variant contribution score for the genetic variant based on a plurality of preset algorithms, and visualize and provide, based on the attribute value and the genetic variant contribution score, a gene contribution score for each gene at a population level, an attribute value for each gene at an individual level, and a genetic variant contribution score for each gene at an individual level.

[0012] According to the present invention, the method can determine whether an individual is susceptible to developing a disease due to a genetic factor.

[0013] In addition, according to the present invention, existing methods for constructing PRS can be utilized as is, thereby improving convenience.

[0014] Additionally, according to the present invention, description of genetic factors can facilitate communication between clinicians and patients.

[0015] In addition, according to the present invention, it is possible to provide personalized medical services by prescribing drugs or providing treatment that targets genetic factors.

[0016] Additionally, according to the present invention, it can be helpful in developing new drugs for preventing or treating diseases.

[0017] The effects of the present invention are not limited to the effects described above, and include all effects understood from the following description.

[0018] FIG. 1 is a drawing illustrating a server and a terminal connected to the server in communication with one embodiment of the present invention.

[0019] Figure 2 is a drawing showing the detailed configuration of the server illustrated in Figure 1.

[0020] Figure 3 is a diagram illustrating an example of preprocessing.

[0021] Figures 4 to 6 are diagrams illustrating examples for calculating a multi-gene risk score, attribute value, and genetic variant contribution score.

[0022] Figures 7 to 11 are drawings showing examples of visualization.

[0023] FIG. 12 is a flowchart illustrating the sequence of a multi-gene risk score visualization method according to another embodiment of the present invention.

[0024] Figures 13 and 14 are flowcharts illustrating the sequence of detailed steps of the multi-gene risk score visualization method illustrated in Figure 12.

[0025] Hereinafter, the present disclosure will be described in detail with reference to the attached drawings. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In addition, the attached drawings are only intended to facilitate understanding of the embodiments disclosed in the present specification, and the technical concepts disclosed in the present specification are not limited by the attached drawings. All terms, including technical and scientific terms, used herein should be interpreted as having meanings generally understood by a person of ordinary skill in the technical field to which the present disclosure pertains. Terms defined in the dictionary should be interpreted as having additional meanings consistent with the relevant technical literature and the present disclosure, and shall not be interpreted in an extremely ideal or restrictive sense unless otherwise defined.

[0026] To clearly explain the present disclosure in the drawings, parts irrelevant to the description have been omitted, and the size, shape, and appearance of each component shown in the drawings may vary. Identical / similar parts throughout the specification are designated with identical / similar drawing reference numerals.

[0027] The suffixes "module" and "part" used in the following description for components are assigned or used interchangeably solely for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. Furthermore, in describing the embodiments disclosed herein, detailed descriptions of related known technologies have been omitted if they are deemed to obscure the gist of the embodiments disclosed herein.

[0028] Throughout the specification, when a part is said to be "connected (connected, in contact with, or coupled)" to another part, this includes not only cases where it is "directly connected (connected, in contact with, or coupled)" but also cases where it is "indirectly connected (connected, in contact with, or coupled)" with another member in between. Furthermore, when a part is said to "include (have or provide)" a certain component, this does not mean that it excludes other components, but rather that it may "include (have or provide)" other components, unless otherwise specifically stated.

[0029] As used herein, ordinal terms such as "first," "second," etc., are used solely to distinguish one component from another and do not limit the order or relationship of the components. For example, the first component of the present disclosure may be referred to as the "second component," and similarly, the second component may also be referred to as the "first component." As used herein, singular forms should be construed to include plural forms, unless explicitly stated otherwise.

[0030] FIG. 1 is a drawing illustrating a server and a terminal connected to the server in communication with one embodiment of the present invention.

[0031] Referring to FIG. 1, the server (100) can be connected to a terminal (200) through a preset communication network.

[0032] The server (100) checks whether there is a match between the input genotype data and the pre-stored genetic mutation risk data, and stores a code that causes the mapping of genes and genetic mutations based on the genotype data.

[0033] Here, the genotype data may be in the form of pLink. For example, the genotype data may be in at least one of the following formats: a bed file storing genotype information in binary format, a bim file storing information on genetic variants, and a fam file storing information on an individual's family.

[0034] Additionally, the genotype data may include information about at least one of a gene, a genetic variant identifier, a location, A1, A2, a family identifier, an individual identifier, sex, and a phenotype.

[0035] The server (100) calculates a polygenic risk score for a gene, an attribute value, and a genetic variant contribution score for a genetic variant based on a plurality of preset algorithms.

[0036] Here, the polygenic risk score for a gene can be a weighted sum of the number of risk genetic variants (Single Nucleotide Polymorphisms, SNPs) that an individual carries, the attribute value can be a factor for finding out which genes increase the risk of a disease for a specific population or an individual, and the genetic variant contribution score can be a score for identifying the genetic variants that increase each gene contribution score.

[0037] The server (100) provides visualizations of gene-specific contribution scores for each gene in the population, gene-specific attribute values ​​for each gene in an individual, and gene-specific genetic variation contribution scores for each gene in an individual based on attribute values ​​and genetic variation contribution scores. For example, the server (100) can visualize gene-specific contribution scores for each gene in the population, gene-specific attribute values ​​for each gene in an individual, and gene-specific genetic variation contribution scores for each gene in an individual in the form of a graph.

[0038] The terminal (200) can transmit genotype data to the server (100) and receive visualization information from the server (100).

[0039] The terminal (200) may refer to any type of handheld-based wireless communication device such as a notebook, desktop, laptop, wireless communication device with portability and mobility, or a smartphone, tablet PC, etc. equipped with a web browser.

[0040] Figure 2 is a drawing showing the detailed configuration of the server illustrated in Figure 1.

[0041] Referring to FIG. 2, the server (100) may include a communication module (110), a processor (120), and a memory (130).

[0042] The communication module (110) may include a device including hardware and software necessary to transmit and receive signals such as control signals or data signals through wired or wireless connections with other network devices.

[0043] The communication module (110) can receive genetic data from the terminal and transmit visualization information to the terminal.

[0044] The processor (120) may include various types of devices that control and process data. The processor (120) may refer to a data processing device built into hardware that has a physically structured circuit to perform a function expressed in code or instructions included in a program.

[0045] In one example, the processor (120) may be implemented in the form of a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., but the scope of the present invention is not limited thereto.

[0046] The processor (120) performs operations according to the code stored in the memory (130).

[0047] The memory (130) can store at least one of information and data input to the communication module (110), information and data required for functions performed by the processor (120), and data generated according to the execution of the processor (120).

[0048] Memory (130) should be interpreted as a general term for a non-volatile storage device that maintains stored information even when no power is supplied and a volatile storage device that requires power to maintain stored information. In addition to a volatile storage device that requires power to maintain stored information, memory (130) may include cloud storage, SSD, magnetic storage media, or flash storage media, but the scope of the present invention is not limited thereto.

[0049] The memory (130) is electrically connected to the processor (120) and stores at least one code to be executed by the processor (120). The memory (130) stores code that, when executed by the processor (120), causes the processor (120) to perform the following functions and procedures.

[0050] In the memory (130), a code is stored that checks whether there is a match between the input genotype data and the pre-stored genetic mutation risk data, and causes the mapping of genes and genetic mutations based on the genotype data.

[0051] The memory (130) may store a code that causes the sign of the effect size vector of the genetic variant to be changed depending on whether the input genotype data matches the previously stored genetic variant risk data. Here, the genetic variant risk data may be a file of a multi-gene risk score including the effect size vector of the genetic variant. The genetic variant risk data may be in the form of a file obtained from a PRS construction method such as lassosum or PRS-CS, and may include information on genes, genetic variant unique numbers, positions, A1, A2, and effect sizes. The effect size may be equal to the weight value of the genetic variant.

[0052] For example, the memory (130) may store a code that causes the genotype data and the genetic variant risk data to compare A1 and A2 respectively to determine whether they match. The memory (130) may store a code that causes the following results: if the results match, the current state is maintained; if the results do not match, the corresponding genetic variant is excluded; and if the results match but the values ​​do not correspond, the sign of the effect size vector is changed. Details regarding this will be described later with reference to FIG. 3.

[0053] The memory (130) may store a code that causes a genetic variation to be mapped to a gene based on the first to third methods.

[0054] The memory (130) may store code that causes primary mapping to be performed on genes and genetic variants based on the distance between the genes and genetic variants.

[0055] For example, genetic variants located close to gene A may affect gene A, so genetic variants located within a predetermined distance from gene A may be mapped to gene A.

[0056] The memory (130) may store code that causes secondary mapping of genes and genetic variants based on relationships between genes and genetic variants for which primary mapping has not been performed.

[0057] For example, the memory (130) may store a code that causes genes and genetic variants to be mapped based on the Combining SNP-to-Gene (CS2G) mapping method. The Combining SNP-to-Gene (CS2G) mapping method may be a technique for identifying relationships between genetic variants and genes based on the results of Genome-Wide Association Studies (GWAS), genetic expression information, and interaction networks.

[0058] The memory (130) may store a code that extracts a genetic variant of a mapping target based on the connection between a genetic variant and a disease for which secondary mapping has not been performed, and causes tertiary mapping to be performed by setting a corresponding area within a preset data size based on the genetic variant of the mapping target.

[0059] For example, in the memory (130), a code may be stored that causes a region to be mapped to a preset data size based on the genetic variants in the file in the order of the genetic variants with the lowest p-value when the summary statistics of GWAS are given based on the GWAS p-value / SNP heritability based mapping method.

[0060] Here, the p-value indicates the degree of statistical association, and a very low p-value may indicate that the genetic variant and a specific trait are significantly associated.

[0061] For example, memory (130) may store code that triggers analysis based on significant genetic variants with low p-values ​​to determine which gene or genetic region each genetic variant belongs to. This allows for the inference of genes strongly associated with specific traits or diseases.

[0062] As another example, the memory (130) may store a code that calculates heritability from genotype data based on the GWAS p-value / SNP heritability based mapping method and causes the region to be mapped to a preset data size based on the genetic variant in the order of the genetic variant with high heritability.

[0063] For example, the memory (130) may store code that causes the contribution of individual genetic variants to a trait to be evaluated and the total genetic influence of multiple genetic variants on the trait to be calculated.

[0064] The memory (130) may store a code that causes genes with matching genetic variations that exceed a preset probability through mapping to be grouped into one group. For example, if the genetic variations mapped to gene A are a, b, and c, and the genetic variations mapped to gene B are b, c, and d, and the preset probability is 2 / 3, the A and B genes may be grouped into one group based on the code stored in the memory (130).

[0065] The memory (130) stores a code that causes a polygenic risk score for a gene, an attribute value, and a genetic variant contribution score for a genetic variant to be calculated based on a plurality of preset algorithms.

[0066] The memory (130) may store a code that causes a polygenic risk score for a gene to be calculated based on a preset first algorithm.

[0067] The first preset algorithm can be as shown in the mathematical expression 1 below.

[0068]

[0069] Here, β is the effect size vector of genetic variants, and X can be a genotype matrix.

[0070] The memory (130) may store code that causes a genetic contribution score to be calculated based on a polygenic risk score for a gene. Here, the genetic contribution score may be a weighted sum of genetic variants mapped to a gene and the number of genetic variants carried by an individual.

[0071] Gene contribution scores can be calculated based on the mathematical formula 2 below.

[0072]

[0073] Here, is the weight vector of genetic variants contained in gene i, can be a genotype matrix of genetic variants contained in gene i.

[0074] The memory (130) may store code that causes a polygenic risk score for a gene to be calculated based on a preset second algorithm. Here, the attribute value may be used to determine which genes increase the risk of a disease in a specific population or individual.

[0075] The preset second algorithm can be calculated based on the mathematical expression 3 below.

[0076]

[0077] Here, may be the average of the genetic contribution scores of the population.

[0078] Memory (130) contains the gene contribution score within the region of risk genes within a specific population. ) of the variance ( ) can be stored as the code that causes it to be selected as the largest gene.

[0079] Additionally, the memory (130) stores the risk genes of an individual as attribute values ​​within the region ( ) can be stored as the code that causes it to be selected as the largest gene.

[0080] The memory (130) may store a code that causes a genetic variation contribution score to be calculated based on a preset third algorithm.

[0081] The third algorithm can be derived based on the mathematical expression 4 below.

[0082]

[0083] Here, β is a weight vector of genetic variants, and x can be a number of risk genetic variants. For example, x can be one of 0, 1, and 2.

[0084] In the memory (130), a code is stored that causes the gene contribution score for each gene in the population, the attribute value for each gene in an individual, and the genetic variation contribution score for each gene in an individual to be visualized and provided based on the attribute value and the genetic variation contribution score.

[0085] For example, the memory (130) may store a code that causes the gene contribution score for each gene in the population, the attribute value for each gene in the individual, and the genetic variation contribution score for each gene in the individual to be visualized and provided based on the attribute value and genetic variation contribution score at the population unit and individual unit.

[0086] In memory (130), a code can be stored that causes visualization using a manhattan plot to indicate important genes at the population level and individual level, and visualization using a locuszoom-like plot to indicate important genetic variations at the individual level.

[0087] Figure 3 is a diagram illustrating an example of preprocessing.

[0088] Referring to Figure 3, the multi-gene risk score visualization device can check whether the risk genetic variants between the genotype data and the genetic variant risk data are consistent.

[0089] For the rs1234 genetic variant, if A1 of the genotype data is A, A2 of the genotype data is G, A1 of the genetic variant risk data is A, and A2 of the genetic variant risk data is G, then since A1 of the genotype data and A1 of the genetic variant risk data are consistent with each other, and A2 of the genotype data and A2 of the genetic variant risk data are consistent with each other, the multi-gene risk score visualization device can determine that the genotype data and the genetic variant risk data are consistent with each other.

[0090] For the rs5679 genetic variant, if A1 of the genotype data is T, A2 of the genotype data is C, A1 of the genetic variant risk data is C, and A2 of the genetic variant risk data is T, then A1 of the genotype data and A2 of the genetic variant risk data are consistent with each other, and since A2 of the genotype data and A1 of the genetic variant risk data are consistent with each other, the polygenic risk score visualization device can flip alleles and reverse the signs of the effect size vectors.

[0091] For the rs5679 genetic variant, if A1 of the genotype data is G, A2 of the genotype data is C, A1 of the genetic variant risk data is A, and A2 of the genetic variant risk data is T, then A1 of the genotype data and A2 of the genetic variant risk data do not match each other, so the rs5679 genetic variant can be excluded.

[0092] Figures 4 to 6 are diagrams illustrating examples for calculating polygenic risk scores, attribute values, and genetic variant contribution scores. More specifically, Figure 4 is a diagram illustrating a process for calculating polygenic risk scores, attribute values ​​of genes, and genetic variant contribution scores, Figure 5 is a diagram illustrating an example of a genotype matrix, and Figure 6 may be a diagram illustrating an example of the effect size of a genetic variant.

[0093] Referring to FIGS. 4 to 6, the polygenic risk score visualization device can calculate a polygenic risk score at the individual level.

[0094] The polygenic risk score visualization tool can calculate a polygenic risk score at the individual level and then calculate a gene contribution score.

[0095] The polygenic risk score visualization device can calculate the variance of gene contribution scores after calculating the gene contribution scores.

[0096] The polygenic risk score visualization device can calculate the variance of the gene contribution score and then calculate the attribute value of the gene.

[0097] The polygenic risk score visualization device can calculate the genetic variant contribution score (SNP Contribution Score) after calculating the attribute value of the gene.

[0098] An example of a genotype matrix required to derive a polygenic risk score and a genetic contribution score may be as shown in Figure 5.

[0099] The genotype matrix of individual 1 for each of the first genetic variant (SNP 1), the second genetic variant (SNP 2), the third genetic variant (SNP 3), the fourth genetic variant (SNP 4), and the fifth genetic variant (SNP 5) can be 0, 2, 1, 0, and 1.

[0100] The genotype matrix of individual 2 for each of the first genetic variant (SNP 1), the second genetic variant (SNP 2), the third genetic variant (SNP 3), the fourth genetic variant (SNP 4), and the fifth genetic variant (SNP 5) can be 1, 1, 2, 2, and 0.

[0101] The genotype matrix of individual 3 for each of the first genetic variant (SNP 1), the second genetic variant (SNP 2), the third genetic variant (SNP 3), the fourth genetic variant (SNP 4), and the fifth genetic variant (SNP 5) can be 2, 0, 1, 1, and 2.

[0102] An example of the effect size of genetic variants required to derive a polygenic risk score and a genetic contribution score may be as shown in Figure 6.

[0103] The effect size (Beta) values ​​for the first genetic variant (SNP 1), the second genetic variant (SNP 2), the third genetic variant (SNP 3), the fourth genetic variant (SNP 4), and the fifth genetic variant (SNP 5) can be 0.2, 0.5, -0.3, 0.4, and -0.2, respectively.

[0104] The polygenic risk score visualizer can generate a polygenic risk score for each individual.

[0105] The polygenic risk score of individual 1 can be calculated as 0 x 0.2 + 2 x 0.5 + 1 x (-0.3) + 0 x 0.4 + 1 x (-0.2) = 0.5.

[0106] The polygenic risk score for individual 2 can be calculated as 1 x 0.2 + 1 x 0.5 + 2 x (-0.3) + 2 x 0.4 + 0 x (-0.2) = 0.9.

[0107] The polygenic risk score for individual 3 can be calculated as 2 x 0.2 + 0 x 0.5 + 1 x (-0.3) + 1 x 0.4 + 2 x (-0.2) = 0.1.

[0108] Assuming that the first genetic variant, the second genetic variant, and the third genetic variant are mapped to the first gene through gene mapping, the polygenic risk score visualization device can calculate the genetic contribution scores for each of individual 1, individual 2, and individual 3.

[0109] The genetic contribution score of individual 1 can be calculated as 0 x 0.2 + 2 x 0.5 + 1 x (-0.3) = 0.7.

[0110] The genetic contribution score of individual 2 can be calculated as 1 x 0.2 + 1 x 0.5 + 2 x (-0.3) = 0.1.

[0111] The genetic contribution score of individual 3 can be calculated as 2 x 0.2 + 0 x 0.5 + 1 x (-0.3) = 0.1.

[0112] The polygenic risk score visualization device can calculate the attribute values ​​of genes and the variances for the attribute values ​​of genes.

[0113] The variance of the attribute values ​​of a gene can be calculated as (0.7 + 0.1 + 0.1) / 3 = 0.3.

[0114] The genetic attribute value of individual 1 can be calculated as 0.7-0.3 = 0.4.

[0115] The genetic attribute value of individual 2 can be calculated as 0.1-0.3 = -0.2.

[0116] The genetic attribute value of individual 3 can be calculated as 0.1-0.3 = -0.2.

[0117] The polygenic risk score visualization tool can calculate the variance of genetic contribution scores.

[0118] The variance of the gene contribution score can be calculated based on the mathematical formula 5 below.

[0119]

[0120] The multi-gene risk score visualization device can calculate the variance of the gene contribution score of the first gene as 0.08, the variance of the gene contribution score of the second gene as 0.04, and the variance of the gene contribution score of the third gene as 0.04 based on mathematical expression 5.

[0121] The polygenic risk score visualization device can determine that the first gene is the most dangerous risk gene based on the variance of the gene contribution scores.

[0122] The polygenic risk score visualization tool can calculate genetic variant contribution scores.

[0123] The first genetic variant contribution score can be calculated as 0 x 0.2 = 0.

[0124] The second genetic variant contribution score can be calculated as 2 x 0.5 = 1.0.

[0125] The third genetic variant contribution score can be calculated as 1 x (-0.3) = -0.3.

[0126] FIGS. 7 to 11 are diagrams illustrating examples of visualization. More specifically, FIGS. 7(a) and 7(b) are diagrams illustrating examples of visualization at the population level, FIG. 8 is a diagram illustrating an example of the density of polygenic risk scores, FIG. 9 is a diagram illustrating an example of attribute values ​​at the individual level, FIGS. 10(a) and 10(b) are diagrams illustrating upper examples of genetic variant contribution scores at the individual level, and FIGS. 11(a) and 11(b) may be diagrams illustrating lower examples of genetic variant contribution scores at the individual level.

[0127] Referring to FIGS. 7 to 11, the polygenic risk score visualization device can provide a Manhattan Plot and a Top 10 risk gene list visualizing the importance of risk genes associated with type 2 diabetes at the population level.

[0128] As shown in Figure 7(a), the x-axis of the Manhattan Plot in the population unit represents the chromosome number, indicating which chromosome each gene is located on, and the y-axis represents the distribution of contribution scores of genes, which can show the variability of the contribution of a specific gene to a disease or trait.

[0129] The red dots in the Manhattan plot at the population level can represent the top 10 risk genes. Genes highlighted in red may be those with high risk within the population.

[0130] For example, in the Manhattan Plot at the population level, the KCNQ1, KCNQ1OT1, and CD81 genes located on chromosome 11 were identified as risk genes, and it can be seen that their genetic variants have a significant effect on the corresponding trait or disease.

[0131] As shown in Figure 7(a), the list of top 10 risk genes in a population unit may include information about the chromosome number (Chromosome; Chr) on which the gene is located, the gene name (Genes), the start and end positions of the gene on the chromosome (Start / End), the number of genetic variants associated with the gene (#SNPs), and the variance of gene contribution scores (Gene_Variance).

[0132] For example, the KCNQ1, KCNQ1OT1, and CD81 genes are located on chromosome 11, and their genetic contribution variance value is the highest at 0.00611192644, so they can be considered risk genes.

[0133] The polygenic risk score visualization device can visualize the genetic contribution and polygenic risk score (PRS) of a specific individual (ID: HG00464) at the individual level, as illustrated in Figure 8.

[0134] A density graph can be provided. A density graph can be a graph of polygenic risk scores across the entire population.

[0135] The red arrows in the density plot can indicate where the polygenic risk score of a particular individual (ID: HG00464) falls within the population.

[0136] For example, a density graph might show that a particular individual's polygenic risk score is 0.718082020126648, indicating they are in the top 2%. The polygenic risk score visualization tool can determine that the individual has a relatively higher genetic risk for the disease compared to others in the population.

[0137] The polygenic risk score visualization device can provide a gene-based Manhattan plot, as illustrated in Figure 9.

[0138] The x-axis of a gene-based Manhattan Plot can represent the chromosome on which each gene is located, and the y-axis can represent the Attributed Value of Gene, which can indicate the extent to which a particular gene contributes to an individual's disease risk.

[0139] The red dots in the genetic Manhattan plot may represent top-risk genes, those for which individuals are at high risk. For example, the CDKAL1 gene, located on chromosome 6, is shown as a top-risk gene, with a high attribute value of 0.105060896911179.

[0140] The blue dots in the genetic Manhattan Plot may represent bottom risk genes, which are genes that carry a relatively low risk to the individual.

[0141] The polygenic risk score visualization device can provide a list of the top 10 high-risk genes of an individual, as illustrated in Fig. 10(a). The list of the top 10 high-risk genes of an individual can include information about the chromosome number (Chromosome; Chr) on which the gene is located, the gene name (Genes), the start and end positions of the gene on the chromosome (Start / End), the number of genetic variants associated with the gene (#SNPs), and the attribute value of the gene (Attributed Value).

[0142] Here, the attribute value of a gene can indicate how much that gene contributes to an individual's disease risk. For example, the CDKAL1 gene has a value of 0.105060896911179, indicating that it has a significant impact on that individual.

[0143] The multi-gene risk score visualization device can visualize the genetic variant contribution scores of the top 10 risk genes using a genetic variant-based locuszoom-like plot, as shown in Fig. 10(b).

[0144] A genetic variant-based locuszoom-like plot can show how individual genetic variants contribute to a trait or disease. The graph can display the SNP contribution score and the recombination rate.

[0145] The left x-axis of the Locuszoom-Like Plot based on genetic variation of the top 10 risk genes represents the megabase (Mb) location of the gene on chromosome 6, the right x-axis represents the recombination rate in the genetic variation region, and a higher value indicates a region where recombination occurs frequently, and the y-axis can represent the degree to which each genetic variation contributes to a trait or disease.

[0146] The blue dots in the Locuszoom-like plot based on genetic variants of the top 10 risk genes can be used to visualize the contribution of each genetic variant. For example, the genetic variant with the largest contribution in the graph is 20:6963697_AT / A, and the AT allele can be interpreted as the major risk variant.

[0147] The polygenic risk score visualization device may provide a list of the top 10 low-risk genes of an individual, as illustrated in Fig. 11(a). The list of the top 10 low-risk genes of an individual may include information about the chromosome number (Chromosome; Chr) on which the gene is located, the gene name (Genes), the start and end positions of the gene on the chromosome (Start / End), the number of genetic variants associated with the gene (#SNPs), and the attribute value of the gene (Attributed Value).

[0148] The multi-gene risk score visualization device can visualize the genetic variant contribution scores of the bottom 10 risk genes using a genetic variant-based locuszoom-like plot, as shown in Fig. 11(b).

[0149] The left x-axis of the Locuszoom-Like Plot based on genetic variants of the top 10 risk genes represents the megabase (Mb) location of the gene on chromosome 7, the right x-axis represents the recombination rate in the genetic variant region, and the y-axis can represent the degree to which each genetic variant contributes to the trait or disease.

[0150] Figures 8 through 11 described above are samples from the 1000 Genomes Project for individual HG00464. The figure above may be a density plot of the individual's polygenic risk score within the population. It can be confirmed that the individual has a polygenic risk score that falls within the top 2% of individuals at high genetic risk for type 2 diabetes.

[0151] The polygenic risk score visualization tool provides a Manhattan plot and a list of the individual's top 10 risk genes, allowing you to identify which specific genes increase or decrease the polygenic risk score. For example, you can see that the CDKAL1 gene, associated with type 2 diabetes, has the highest gene attribute value. If the individual had average attribute values, the polygenic risk score would be lowered by 0.112.

[0152] Additionally, the polygenic risk score visualization tool can visualize the contribution of polygenic risk scores within genes in detail through a LocusZoom-like plot. For example, it can be confirmed that HG00464 has 263 genetic variants in the CDKAL1 gene, which increases its polygenic risk score. The polygenic risk score visualization tool can explain an individual's genetic risk at the gene and variant level through these visualizations, facilitating effective communication.

[0153] FIG. 12 is a flowchart illustrating the sequence of a multi-gene risk score visualization method according to another embodiment of the present invention.

[0154] The multi-gene risk score visualization method described below can be performed by the multi-gene risk score visualization device or server (100) described above with reference to FIGS. 1 to 11. Therefore, the contents of the embodiments of the present disclosure described above with reference to FIGS. 1 to 11 can be equally applied to the embodiments described below, and any overlapping contents with the above description will be omitted below. The steps described below do not necessarily have to be performed in order, and the order of the steps can be set in various ways, and the steps can be performed almost simultaneously.

[0155] Referring to FIG. 12, the method for visualizing a polygenic risk score includes a gene and genetic variant mapping step (S100), a polygenic risk score, attribute value, and genetic variant contribution score calculation step (S200), and a population- and individual-specific polygenic risk score visualization step (S300).

[0156] The gene and genetic variant mapping step (S100) is a step for verifying the consistency between the input genotype data and the stored genetic variant risk data, and for mapping genes and genetic variants based on the genotype data. The gene and genetic variant mapping step (S100) may include a step for grouping genes with a preset number or more matching genetic variants into a single group through mapping.

[0157] The step of calculating the polygenic risk score, attribute value, and genetic variant contribution score (S200) is a step of calculating the polygenic risk score, attribute value, and genetic variant contribution score for a gene based on multiple preset algorithms.

[0158] The population- and individual-specific polygenic risk score visualization step (S300) is a step that provides visualizations of gene-specific genetic contribution scores in the population, gene-specific attribute values ​​in individuals, and gene-specific genetic variation contribution scores in individuals based on attribute values ​​and genetic variation contribution scores.

[0159] Figures 13 and 14 are flowcharts illustrating the sequence of detailed steps of the multi-gene risk score visualization method illustrated in Figure 12.

[0160] Referring to FIG. 13, the step of calculating the polygenic risk score, attribute value, and genetic variant contribution score (S200) may include a step of changing the sign of the effect size vector of the genetic variant (S110), a first mapping step based on the distance between genes and genetic variants (S120), a second mapping step based on the connectivity between genes and genetic variants (S130), and a third mapping step of the genetic variants based on the data size (140).

[0161] The step of changing the sign of the effect size vector of a genetic variant (S110) may be a step of changing the sign of the effect size vector of a genetic variant depending on whether there is a match between the input genotype data and the pre-stored genetic variant risk data.

[0162] The distance-based primary mapping step (S120) between genes and genetic variants may be a step of performing primary mapping for genes and genetic variants based on the distance between genes and genetic variants.

[0163] The secondary mapping step (S130) based on connectivity between genes and genetic variants may be a step of performing secondary mapping for genes and genetic variants based on connectivity between genes and genetic variants for which primary mapping has not been performed.

[0164] The data size-based genetic variation 3rd mapping step (140) may be a step of extracting genetic variations of a mapping target based on the connection between genetic variations and diseases for which secondary mapping has not been performed, and performing 3rd mapping by setting a corresponding area within a preset data size based on the genetic variations of the mapping target.

[0165] Referring to FIG. 14, the step of calculating the polygenic risk score, attribute value, and genetic variant contribution score (S200) may include a step of calculating the polygenic risk score of a gene based on a first algorithm (S210), a step of calculating the gene contribution score (S220), a step of calculating the attribute value of a gene based on a second algorithm (S230), and a step of calculating the genetic variant contribution score based on a third algorithm (S240).

[0166] The step (S210) of calculating a polygenic risk score for a gene based on a first algorithm may be a step of calculating a polygenic risk score for a gene based on a preset first algorithm. Here, the preset first algorithm may be in the form of Equation 1 of PRS=β x, where β is a weight vector of genetic variants, and X is a genotype matrix.

[0167] The gene contribution score calculation step (S220) may be a step of calculating the gene contribution score based on the polygenic risk score for the gene. Here, the gene contribution score is can be calculated based on the formula, is the weight vector of genetic variants contained in gene i, can be a genotype matrix of genetic variants contained in gene i.

[0168] The second algorithm-based gene attribute value calculation step (S230) may be a step of calculating attribute values ​​based on a polygenic risk score for a gene based on a preset second algorithm. Here, the preset second algorithm It can be in the form of formula 3.

[0169] The third algorithm-based genetic variation contribution score calculation step (S240) may be a step of calculating the genetic variation contribution score based on a preset third algorithm. Here, the third algorithm is in the form of Equation 4, β is a weight vector of genetic variants, and x can be the number of risk genetic variants.

[0170] Those skilled in the art will appreciate that the present disclosure can be easily modified into other specific forms based on the above description without changing the technical spirit or essential characteristics of the present disclosure. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of the present disclosure is indicated by the following claims, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present disclosure. The scope of the present application is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present application.

[0171] The form for carrying out the invention is substantially the same as the best form for carrying out the invention.

[0172] The present invention can be used to predict diseases, and thus has industrial applicability.

Claims

1. In a method performed by a processor, a) A step of checking whether there is a match between the input genotype data and the pre-stored genetic variation risk data, and mapping genes and genetic variations based on the genotype data; b) a step of calculating a polygenic risk score, an attribute value, and a genetic variant contribution score for the genetic variant for the gene based on a plurality of preset algorithms; and c) A method for visualizing a multi-gene risk score, comprising a step of visualizing and providing a gene contribution score for each gene at the population level, an attribute value for each gene at the individual level, and a genetic variation contribution score for each gene at the individual level based on the attribute value and the genetic variation contribution score.

2. In paragraph 1, Step a) above, a-1) a step of changing the sign of the effect size vector of the genetic variant according to whether there is a match between the input genotype data and the pre-stored genetic variant risk data; and a-2) A method for visualizing a multigenic risk score, comprising a step of mapping the genetic variant to the gene based on the first to third methods.

3. In paragraph 2, Step a-2) above, A step of performing primary mapping on the gene and the genetic variant based on the distance between the gene and the genetic variant; A step of performing secondary mapping for the gene and the genetic variant based on the relationship between the gene and the genetic variant for which the primary mapping has not been performed; and A method for visualizing a multi-gene risk score, comprising the steps of extracting a genetic variant of a mapping target based on the relationship between the genetic variant and the disease for which the second mapping has not been performed, and performing a third mapping by setting a corresponding area within a preset data size based on the genetic variant of the mapping target.

4. In paragraph 1, Step a) above, A method for visualizing a multi-gene risk score, comprising a step of grouping genes that match a genetic variant greater than a preset probability through the above mapping into one group.

5. In paragraph 1, Step b) above, A step of calculating the polygenic risk score for the gene based on a preset first algorithm, The above-described first algorithm is in the form of Equation 1 of PRS = β x, Here, β is the effect size vector of genetic variants, and X is the genotype matrix, a method for visualizing polygenic risk scores.

6. In paragraph 5, Step b) above, A step of calculating a genetic contribution score based on a polygenic risk score for the above genes; and Further comprising a step of calculating an attribute value based on the polygenic risk score for the gene based on a preset second algorithm, The above genetic contribution score is It is calculated based on formula 2, The above preset second algorithm It is in the form of formula 3, Here, is the weight vector of genetic variants contained in gene i, A method for visualizing a polygenic risk score, wherein the genotype matrix is ​​a matrix of genetic variants contained in gene i.

7. In paragraph 6, Step b) above, Further comprising a step of calculating the genetic variation contribution score based on a preset third algorithm, The third algorithm above is is in the form of formula 4, Here, β is the effect size vector of genetic variants, and x is the number of risk genetic variants, a method for visualizing polygenic risk scores.

8. Communication module; at least one processor; and A memory electrically connected to the processor and storing at least one code to be executed by the processor, The above memory, when executed through the processor, causes the processor to: A multi-gene risk score visualization device that stores code that causes the device to check whether there is a match between input genotype data and pre-stored genetic variant risk data, map genes and genetic variants based on the genotype data, calculate a multi-gene risk score for the gene, an attribute value, and a genetic variant contribution score for the genetic variant based on a plurality of preset algorithms, and visualize and provide, based on the attribute value and the genetic variant contribution score, a gene contribution score for each gene at the population level, an attribute value for each gene at the individual level, and a genetic variant contribution score for each gene at the individual level.

9. In paragraph 8, The above memory causes the processor to: A multi-gene risk score visualization device that changes the sign of the effect size vector of the genetic variant based on whether there is a match between the input genotype data and the pre-stored genetic variant risk data, and stores a code that causes the genetic variant to be mapped to the gene based on the first to third methods.

10. In paragraph 9, The above memory causes the processor to: A multi-gene risk score visualization device storing code that causes a first mapping to be performed on the gene and the genetic variant based on the distance between the gene and the genetic variant, a second mapping to be performed on the gene and the genetic variant based on the relationship between the gene and the genetic variant for which the first mapping has not been performed, a genetic variant of a mapping target to be extracted based on the relationship between the genetic variant and the disease for which the second mapping has not been performed, and a third mapping to be performed by setting a corresponding area within a preset data size based on the genetic variant of the mapping target.

11. In paragraph 8, The above memory causes the processor to: A multi-gene risk score visualization device storing a code that causes genes matching a genetic variant greater than a preset probability through the above mapping to be grouped into one group.

12. In paragraph 8, The above memory causes the processor to: Store a code that causes the polygenic risk score for the gene to be calculated based on a preset first algorithm, The above-described first algorithm is in the form of Equation 1 of PRS = β x, Here, β is the effect size vector of genetic variants and X is the genotype matrix, a multigenic risk score visualization device.

13. In paragraph 12, The above memory causes the processor to: Store a code that causes a gene contribution score to be calculated based on a polygenic risk score for the gene, and a property value to be calculated based on the polygenic risk score for the gene based on a preset second algorithm, The above genetic contribution score is It is calculated based on formula 2, The above preset second algorithm It is in the form of formula 3, Here, is the effect size vector of genetic variants included in gene i, A multigenic risk score visualization device, which is a genotype matrix of genetic variants contained in gene i.

14. In paragraph 13, The above memory causes the processor to: Store a code that causes the genetic variant contribution score to be calculated based on a preset third algorithm, The third algorithm above is is in the form of formula 4, Here, β is the effect size vector of genetic variants and x is the number of risk genetic variants, a polygenic risk score visualization device.

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