Methods and compositions for predicting and / or monitoring cardiovascular disease and intervention thereof

Abstract

This document describes methods and compositions for predicting cardiovascular disease (CVD). Specifically, this document describes methods and compositions for determining the methylation status of at least one CpG locus and the sequence of at least one single nucleotide polymorphism (SNP) that can predict the detection of CVD or an estimate of survival from CVD.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to methods and compositions related to predicting cardiovascular disease (CVD) in individuals. BACKGROUND

[0002] Cardiovascular disease (CVD) and, in particular, coronary heart disease (CHD) is the most common type of heart disease and caused over 360,000 deaths in the United States in 2017. To reduce this toll, many risk estimators and detection methods have been developed to better identify those with or at risk of CVD (including CHD). Starting with the Framingham Risk Score (FRS) and most recently the ASCVD pooled cohort equations (PCE), these tools capture changes in key physiological parameters (such as serum lipid levels) known to be associated with CVD (including CHD) risk. Similarly, for detection, methods such as stress echocardiography and coronary computed tomography angiography (CCTA) are used.

[0003] Despite these efforts, current risk estimators and detection tests often lack sensitivity and specificity and are often inaccessible due to cost and the need to schedule in-person clinical visits that can take weeks of time. Moreover, some current risk estimators and detection methods (such as catheterization) can have serious side effects such as stroke and heart attack. Thus, there is a need for alternative stratification, detection, and management methods for CVD that have minimal risk, are scalable, and provide actionable insights. SUMMARY

[0004] Methods and compositions for predicting the presence and / or severity (e.g., level of obstruction) of cardiovascular disease (CVD) are provided, as well as methods and compositions for managing, monitoring, and / or treating CVD. For example, methods and compositions for predicting coronary heart disease (CHD) are described herein. The general principles apply to the incidence, prevalence, or severity of CVD (including, without limitation, CHD, stroke, arrhythmia, cardiac arrest, and congestive heart failure) at a window of onset (e.g., one month, six months, two years, or ten years), as well as to survival of CVD or CVD events and management of CVD or CVD events, including, but not limited to, identifying, customizing, and optimizing lifestyle (e.g., exercise, diet) and / or therapy (e.g., a particular drug or combination thereof) and / or one or more medical interventions (e.g., stent placement, angioplasty). The same general principles also apply to monitoring of CVD or CVD events, severity of CVD or CVD events, and / or response to lifestyle, therapy, and / or one or more medical interventions. In particular, methods and compositions are described that include determining the methylation status of at least one CpG locus and / or at least one single nucleotide polymorphism (SNP).

[0005] In one aspect, a kit for determining the methylation status of at least one CpG dinucleotide and / or the genotype of at least one single nucleotide polymorphism (SNP) is provided. Such a kit typically comprises: at least one first nucleic acid primer of at least 8 nucleotides in length that is complementary to a bisulfite converted nucleic acid sequence comprising a first CpG dinucleotide at a GC locus selected from the group consisting of cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, or a second CpG dinucleotide that is in linkage disequilibrium with the first CpG dinucleotide at a GC locus selected from the group consisting of cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the at least one first nucleic acid primer detects a methylated or non-methylated CpG dinucleotide; and / or at least one second nucleic acid primer of at least 8 nucleotides in length that is complementary to a DNA sequence of a first SNP selected from the group consisting of rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433, or a second SNP that is in linkage disequilibrium with a first SNP selected from the group consisting of rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433, wherein the linkage disequilibrium has a value of R > 0.3.

[0006] In some embodiments, the at least one first nucleic acid primer detects a non-methylated CpG dinucleotide. In some embodiments, the at least one first nucleic acid primer detects a methylated CpG dinucleotide.

[0007] In some embodiments, the kits described herein further comprise at least a third nucleic acid primer of at least 8 nucleotides in length that is complementary to a nucleic acid sequence upstream of a CpG dinucleotide. In some embodiments, the kits further comprise at least a third nucleic acid primer of at least 8 nucleotides in length that is complementary to a nucleic acid sequence downstream of a CpG dinucleotide.

[0008] In some embodiments, the at least one first nucleic acid primer comprises one or more nucleotide analogs. In some embodiments, the at least one first nucleic acid primer comprises one or more synthetic or non-natural nucleotides.

[0009] In some embodiments, the kits described herein further comprise a solid substrate to which the at least one first nucleic acid primer is bound. In some embodiments, the substrate is a polymer, glass, semiconductor, paper, metal, gel, or hydrogel. In some embodiments, the solid substrate is a microarray or a microfluidic card.

[0010] In some embodiments, the kits described herein further comprise a detectable label.

[0011] In another aspect, methods of determining the presence of a biomarker associated with predicting, treating, managing, and / or monitoring CVD in a biological sample from a patient are provided. Such methods generally comprise (a) providing a first portion of the biological sample and a second portion of the biological sample, wherein nucleic acids from at least the first portion are bisulfite converted; (b) contacting the first portion of the biological sample with a first oligonucleotide primer of at least 8 nucleotides in length, the first oligonucleotide primer complementary to a sequence comprising a first CpG dinucleotide or a second CpG dinucleotide, the first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, the second CpG dinucleotide in linkage disequilibrium with the first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the first nucleic acid primer detects a methylated or non-methylated CpG dinucleotide; and (c) contacting the second portion of the biological sample with a nucleic acid primer of at least 8 nucleotides in length, the nucleic acid primer complementary to a DNA sequence or bisulfite converted DNA sequence of a first SNP or a second SNP, the first SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433, the second SNP in linkage disequilibrium with the first SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433, wherein the linkage disequilibrium has a value of R > 0.3.In some embodiments, the biological sample is blood or saliva.

[0012] In some embodiments, the biological sample is blood or saliva.

[0013] In some embodiments, the at least one first nucleic acid primer detects a non-methylated CpG dinucleotide. In some embodiments, the at least one first nucleic acid primer detects a methylated CpG dinucleotide.

[0014] In some embodiments, the at least one first nucleic acid primer comprises one or more nucleotide analogs. In some embodiments, the at least one first nucleic acid primer comprises one or more synthetic or non-natural nucleotides.

[0015] In some embodiments, the window of onset for detection, severity, management, and / or monitoring is three years, five years, or ten years.

[0016] In still further aspects, methods of determining the presence of a biomarker in a biological sample from a subject, wherein the biomarker is relevant to detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, tailoring and / or optimizing one or more interventions for CVD, managing CVD, and / or monitoring CVD. Such methods generally comprise: (a) isolating a nucleic acid sample from the patient sample, (b) performing a genotyping assay on a first portion of the nucleic acid sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or a first SNP from Appendix C and / or is a second SNP in linkage disequilibrium (R > 0.3) with a first SNP selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or from Appendix C, to obtain genotypic data; and / or (c) bisulfite converting nucleic acids in a second portion of the nucleic acid and performing a methylation assessment on the second portion of the nucleic acid sample to detect the methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or a CpG site in linkage (R > 0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, to obtain methylation data; and (d) inputting the genotypic data from step (b) and / or the methylation data from step (c) into an algorithm that interprets at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect, wherein the algorithm is a machine learning algorithm that can interpret linear and non-linear effects.

[0017] In some embodiments, the at least one interaction effect is selected from a gene- environment interaction (SNPxCpG) effect, a gene-gene interaction (SNPxSNP) effect, and an environment-environment interaction (CpGxCpG) effect. In some embodiments, the at least one interaction effect is a gene- environment interaction (SNPxCpG) between a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or a CpG site from Appendix A or in linkage disequilibrium (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or a CpG site from Appendix A and a SNP selected from rs2869675, rs4376434, rsl213978, rs7585056, rs710987, rs4639796, rs1333048, rsl2714414, rs942317, and rs1441433 or a SNP from Appendix C. In some embodiments, the at least one interaction effect is an environment- environment interaction effect (CpGxCpG) between at least two CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or a CpG site from Appendix A.

[0018] In some embodiments, one or both of the at least two CpG sites is in linkage disequilibrium (R>0.3) with one or both of at least two CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A. In some embodiments, the at least one interaction effect is a gene-gene interaction effect (SNP x SNP) between one or both of at least two SNPs selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 or from Appendix C. In some embodiments, one or both of the at least two SNPs is in linkage disequilibrium (R>0.3) with one or both of at least two SNPs selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 or from Appendix C.

[0019] In some embodiments, the biological sample is a saliva sample.

[0020] In another aspect, systems for determining the methylation status of at least one CpG dinucleotide and the genotype of at least one single nucleotide polymorphism (SNP) are provided. Such systems generally include: a nucleic acid isolation module configured to isolate a nucleic acid sample from a subject sample; a genotyping assay module configured to perform a genotyping assay on a first portion of the nucleic acid sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or a first SNP from Appendix C and / or is a second SNP in linkage disequilibrium (R > 0.3) with a first SNP selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or from Appendix C, to obtain genotype data; a methylation assay module configured to bisulfite convert nucleic acids in a second portion of the nucleic acid and perform a methylation assessment on the second portion of the nucleic acid sample to detect the methylation status of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or a CpG site in linkage (R > 0.3) with a CpG selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, to obtain methylation data; and an interpretation system configured to interpret at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect based on the genotype data and / or the methylation data.

[0021] In some embodiments, such systems further include an output module configured to provide an output based on the interpretation of the interpretation system, wherein the interpretation interprets at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect based on the genotype data and / or the methylation data.

[0022] In some embodiments, the algorithm is a machine learning algorithm capable of interpreting linear effects and / or non-linear effects.

[0023] In some embodiments, dimension reduction (e.g., principal component analysis, partial least squares regression, etc.) can be used.

[0024] In yet another aspect, a non-transitory computer readable medium storing instructions executable by a processing device performing operations is provided. Such operations generally include interpreting at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect based on genotype data and / or methylation data, wherein: (i) the genotype data is based on a genotyping assay performed on a first portion of a nucleic acid sample isolated from a subject sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or a first SNP from Appendix C and / or is a second SNP in linkage disequilibrium (R>0.3) with a first SNP selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or from Appendix C to obtain genotype data; and (ii) the methylation data is based on a methylation assay performed on bisulfite converted nucleic acids in a second portion of the nucleic acid sample to detect the methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or a CpG site in linkage (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A to obtain methylation data.

[0025] In some embodiments, the operations further include providing an output based on the interpreting. Representative outputs include, without limitation, one or more of: storing a report to another non-transitory computer readable medium based on the interpreting, modifying a display based on the interpreting, triggering an audible alarm based on the interpreting, triggering a tactile or vibratory alarm based on the interpreting, triggering a printing of a report based on the interpreting, or triggering a delivery of a therapeutic agent based on the interpreting.

[0026] In one aspect, a kit for determining the methylation status of at least one CpG dinucleotide is provided. Such kits generally include at least one first nucleic acid primer of at least 8 nucleotides in length that is complementary to a bisulfite converted nucleic acid sequence comprising a first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or a second CpG dinucleotide in linkage disequilibrium with the first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the at least one first nucleic acid primer detects a methylated or non-methylated CpG dinucleotide.

[0027] In another aspect, a method of determining the presence of a biomarker in a biological sample from a subject is provided, wherein the biomarker is relevant to detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, tailoring, and / or optimizing one or more interventions for CVD, managing CVD, and / or monitoring CVD. Such methods generally comprise (a) providing a biological sample from a subject at risk for or having CVD or a CVD event, wherein nucleic acids from at least a portion of the biological sample are bisulfite converted; and (b) contacting the bisulfite converted nucleic acids with a first oligonucleotide primer of at least 8 nucleotides in length that is complementary to a sequence comprising a first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or a second CpG dinucleotide in linkage disequilibrium with the first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the first nucleic acid primer detects a methylated or non-methylated CpG dinucleotide, wherein the percentage methylation of the CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 is correlated with estimating survival of the subject.

[0028] In another aspect, there is provided a method of determining the presence of a biomarker in a biological sample from a subject, wherein the biomarker is relevant to detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, tailoring and / or optimizing one or more interventions for CVD, managing CVD and / or monitoring CVD. Such methods generally comprise (a) isolating a nucleic acid sample from the subject sample; (b) bisulfite converting at least a portion of the nucleic acid and performing a methylation assessment on the bisulfite converted nucleic acid to determine the methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A and / or a CpG site that is co-linear (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A to obtain methylation data; and (c) inputting the methylation data from step (b) into an algorithm that interprets at least one CpG principal effect, wherein the algorithm is a machine learning algorithm that is capable of interpreting linear and non-linear effects.

[0029] In still another aspect, there is provided a system for determining the methylation state of at least one CpG dinucleotide. Such systems generally comprise a nucleic acid isolation module configured to isolate a nucleic acid sample from a subject sample; a methylation assay module configured to bisulfite convert nucleic acid in at least a portion of the nucleic acid and perform a methylation assessment on the bisulfite converted nucleic acid to determine the methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A and / or a CpG site that is co-linear (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A to obtain methylation data; and an identification system configured to interpret at least one CpG principal effect based on the methylation data.

[0030] In yet another aspect, there is provided a non-transitory computer readable medium storing instructions executable by a processing device performing operations. Such a computer readable medium typically comprises interpreting at least one CpG principal effect based on methylation data, wherein: the methylation data is based on methylation assays performed on bisulfite converted nucleic acids in at least a portion of a nucleic acid sample to detect a methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or a CpG site that is co-linear (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, to obtain the methylation data.

[0031] The integrated genetic-epigenetic model described herein provides several advantages and benefits. For example,

[0032] • Early detection: PrecisionCHD can detect molecular changes that can or can not precede clinical symptoms or disease progression. This means that patients can be identified as having coronary heart disease before or after symptoms appear, allowing for earlier intervention and better outcomes.

[0033] • Actionable Clinical Intelligence™: The test can or can not be coupled to an actionable clinical intelligence platform (see, e.g., U.S. Application No. 63 / 488,463, incorporated herein by reference) that maps each patient’s molecular markers to key drivers of coronary heart disease, allowing for customized recommendations for lifestyle changes, medical interventions, estimating the effectiveness of lifestyle changes or medical interventions, monitoring, and secondary testing.

[0034] • Personalized intervention selection: The test can be used to select one or more interventions, such as lifestyle changes, therapeutic interventions, and medical interventions, for each patient or a group of patients at one or more time points.

[0035] • Personalized intervention optimization: The test can be used to optimize one or more interventions, such as lifestyle changes, therapeutic interventions, and medical interventions, for each patient or a group of patients at one or more time points.

[0036] • Personalized intervention evaluation: The test can be used to evaluate the effectiveness of interventions, such as lifestyle changes, therapeutic interventions, and medical interventions, for each patient or a group of patients by continuously monitoring CVD, CVD events, or CVD severity.

[0037] • More comprehensive assessment: The test simultaneously evaluates and integrates robust genetic and epigenetic biomarkers, providing a more comprehensive assessment of CVD status.

[0038] • Discovery of new pathways: The method can be used to discover new, previously unknown biological pathways for risk assessment, detection, intervention (e.g., lifestyle, therapy, medical), management, and monitoring of CVD. One or more pathways and one or more biomarkers can also be used to discover, develop, and validate new biological drugs for the assessment and management of cardiovascular disease. The methods described herein can be used to identify new biomarkers (e.g., methylation, SNPs, proteins, etc.) for new drug development or the ability to target therapy (e.g., gene editing). The methods described herein can also be used to discover one or more biomarkers to select the most effective drug (e.g., statin with beta blocker), the most effective type of drug (e.g., hydrophilic statin with lipophilic statin), a change in lifestyle, a medical intervention, or a combination thereof for a particular individual. In addition, the methods described herein can be used to optimize the use of a therapeutic agent (e.g., dosing, regimen, drug combination) or to identify a lifestyle change that will have the greatest effect.

[0039] • Non-invasive: The test only requires simple biological material collection (e.g., blood or saliva sample), making it a non-invasive and convenient alternative to more invasive diagnostic tests (e.g., angiography).

[0040] • Accessibility: The test can be administered remotely via a lancet-based sample collection kit that can be sent to a patient’s home on order of a test, increasing the accessibility and democratizing of CVD diagnostic testing. Alternatively, biological material can be collected in a provider setting via a vacuum container-based sample collection. Biological material in the form of a saliva sample can be collected remotely or in a provider setting.

[0041] • Cost-effectiveness and timeliness: PrecisionCHD provides clinicians with a timely and cost-effective test for coronary heart disease. PrecisionCHD is only a fraction of the cost of other heart disease tests.

[0042] • Survival estimates: PrecisionCHD or PrecisionCHD-Epi can provide survival estimates for those individuals who have been determined to have CVD or are considered at risk for developing CVD.

[0043] 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 the methods and material compositions pertain. While similar or equivalent methods and materials may be used in the practice or testing of the methods and material compositions described herein, suitable methods and materials are described below. Furthermore, materials, methods, and examples are illustrative only and are not intended to be limited to predicting incidental CHD. All publications, patent applications, patents, and other references mentioned herein are incorporated herein by reference in their entirety. Attached Figure Description

[0044] Figure 1 This is a block diagram of an example cardiovascular disease classification and monitoring system.

[0045] Figure 2 This is a flowchart of an example method for classifying and monitoring cardiovascular diseases.

[0046] Figure 3 This is a block diagram of an example computing device.

[0047] Figure 4 This is a schematic diagram illustrating a potential treatment approach for patients with positive signals of CHD and / or related comorbidities using the methods described herein.

[0048] Figures 5A-5C This indicates survival (yes or no) during follow-up relative to marker cg04988978 ( Figure 5A ), cg21161138 ( Figure 5B ) and cg12655112 ( Figure 5C The diagram of methylation of ).

[0049] Figures 6A-6C It shows the number of days until death and the marker cg04988978 ( Figure 6A ), cg21161138 ( Figure 6B ) and cg12655112 ( Figure 6C A diagram showing the relationship between methylation and methylation. Detailed Implementation

[0050] Recent predictive strategies have capitalized on rapid advances in assessing genome-wide genetic or transcriptional variation. While each of these approaches has achieved some success, their clinical impact is limited. In particular, those methods that rely solely on genetic information have a defined upper limit to their predictive power, are potentially sensitive to racial stratification, and cannot be used to monitor changes in disease states because genotypes are static.

[0051] Recent advances in whole-genome epigenetic profiling technologies have raised the possibility that DNA methylation assessment of peripheral blood DNA can serve as a mechanism for more accurate prediction of cardiovascular disease or mortality. However, predictive models that only interpret epigenetic features fail to account for confounding genetic variation, which can influence the majority of environmentally responsive methylomes. This can result in models that lack robustness in their generalizability, particularly across different populations.

[0052] Accordingly, we have developed a highly sensitive, clinically implementable integrated genetic-epigenetic tool that is capable of identifying those at risk of developing or having a cardiovascular disease (e.g., having a heart attack or sudden cardiac death). As shown herein, the methylation status of one or more specific CpG dinucleotides in combination with a genotype at one or more specific loci (e.g., CH3 x SNP) can be used to predict cardiovascular disease (CVD), including coronary heart disease (CHD). We have also developed a highly sensitive tool that is capable of estimating the survival capacity of those at risk of developing CVD, those at risk of a CVD event, or those who have been identified as having CVD.

[0053] As described herein, biomarkers can be used for the diagnosis and prognosis of cardiovascular diseases and events. The terms "marker" and "biomarker" can be used interchangeably. As used herein, a biomarker generally refers to the presence or amount of a measurable or detectable biological moiety (e.g., a protein, a genetic (e.g., polymorphism), an epigenetic (e.g., methylation), and / or a histological component). As described in more detail below, the biomarkers used herein are generally associated with cardiovascular disease.

[0054] As used herein, "patient," "subject," and "individual" can be used interchangeably.

[0055] DNA methylation

[0056] DNA does not exist as a naked molecule in cells. For example, DNA associates with proteins called histones to form a complex substance called chromatin. Chemical modifications of DNA or histones alter the structure of chromatin without changing the nucleotide sequence of the DNA. Such modifications are described as "epigenetic" modifications of DNA. Changes to chromatin structure can have profound effects on gene expression. If chromatin is condensed, factors involved in gene expression can not have access to the DNA, and the gene will be turned off. Conversely, if chromatin is "open," the gene can be turned on. Some important forms of epigenetic modification are DNA methylation and histone deacetylation.

[0057] DNA methylation is a chemical modification of the DNA molecule itself and is carried out by enzymes called DNA methyltransferases. Methylation can directly turn off gene expression by preventing transcription factors from binding to the promoter. A more general effect is to attract methyl binding domain (MBD) proteins. These are associated with additional enzymes called histone deacetylases (HDACs) whose function is to chemically modify histones and change chromatin structure. Chromatin containing acetylated histones is open and transcription factors can access it and genes are potentially active. Histone deacetylation causes chromatin condensation so that transcription factors cannot access it and leads to gene silencing.

[0058] A CpG island is a short stretch of DNA in which the frequency of CpG sequences is higher than in other regions. The "p" in the term CpG indicates that the cysteine ("C") and guanine ("G") are linked by a phosphodiester bond. CpG islands are usually located around the promoters of housekeeping genes and many regulatory genes. In these locations, CG sequences in active genes are usually unmethylated. In contrast, CG sequences in inactive genes are usually methylated to suppress their expression.

[0059] As used herein, the term "methylation status" means determining whether a certain target DNA, such as a CpG dinucleotide, is methylated or unmethylated. As used herein, the term "CpG dinucleotide repeat motif" means a series of two or more CpG dinucleotides located in a DNA sequence.

[0060] About 56% of human genes and 47% of mouse genes are associated with CpG islands. Typically, CpG islands overlap with promoters and extend about 1000 base pairs downstream into the transcriptional unit. Identifying potential CpG islands during sequence analysis helps to define the 5' most end of a gene, which is very difficult in cDNA-based approaches. The skilled artisan can determine the methylation of a CpG island using any method suitable for determining such methylation. For example, the skilled artisan can use a bisulfite reaction-based method to determine such methylation.

[0061] The present disclosure provides methods of determining nucleic acid methylation at one or more loci in a subject to identify subjects having CVD.

[0062] Linkage refers to the phenomenon that DNA sequences that are close together in the genome have a tendency to be inherited together. Two sequences can be linked due to some selective advantage of co-inheritance. More typically, however, two sequences are co-inherited because meiotic recombination events occur relatively infrequently within the region between the two sequences. Co-inherited sequences are said to be "in linkage disequilibrium" with one another because, in a given population, they tend to occur together or not at all in any particular member of the population. In effect, where multiple sequences within a given chromosomal region are found to be in linkage disequilibrium with one another, they define a quasi-stable "haplotype." Conversely, recombination events occurring between two loci cause them to segregate onto different homologous chromosomes. If meiotic recombination between two physically linked sequences occurs frequently enough, the two sequences appear to segregate independently and are said to be in linkage equilibrium.

[0063] It will be appreciated that linkage disequilibrium can be quantified (using, for example, Pearson's correlation coefficient (R) or allelic co-inheritance (D')). For example, a correlation (e.g., R value) of about 0.1 or less reflects a low level of linkage, an R value of about 0.3 reflects a moderate level of linkage, and an R value of 0.5 or greater reflects a high level of linkage. It will also be appreciated that when referring to methylation (i.e., CpG sites), collinearity (with R values) is used to determine the strength of linear association between two CpGs (e.g., a low level of collinearity can be reflected by an R value of about 0.1 or less; a moderate level of collinearity can be reflected by an R value of about 0.3; and a high level of collinearity can be reflected by an R value of about 0.5 or greater).

[0064] In particular, in certain embodiments of the present disclosure, the method can be carried out as follows. A sample (such as a blood sample) is obtained from a subject. In certain embodiments, a single cell type (e.g., lymphocytes, basophils, or monocytes) can be isolated from the blood for further testing. DNA is harvested from the sample and examined to determine methylation at one or more loci. For example, the target DNA can be treated with bisulfite to remove the amino group of non-methylated cytosine residues, resulting in uracil. Because uracil bases pair with adenosine, thymidine is incorporated into the subsequent DNA strand instead of non-methylated cytosine residues during subsequent PCR amplification. Next, the target sequence is amplified by PCR and probed with locus-specific probes. Depending on the specific sequence of the probe used, only methylated or non-methylated DNA will bind to the probe.

[0065] Methods of determining the nucleic acid profile of a subject are well known to the skilled artisan and include any well known detection method. Various PCR methods are described in, for example, PCR Primer: A Laboratory Manual, Dieffenbach & Veksler eds., Cold Spring Harbor Laboratory Press, 1995. Other methods include, but are not limited to, nucleic acid quantification, restriction enzyme digestion, DNA sequencing, hybridization techniques (such as Southern blotting), amplification methods (such as ligase chain reaction (LCR), nucleic acid sequence-based amplification (NASBA), self-sustained sequence replication (SSR or 3SR), strand displacement amplification (SDA), and transcription-mediated amplification (TMA)), quantitative PCR (qPCR), digital PCR (dPCR) (e.g., digital droplet CR (ddPCR)) or other DNA analysis, and RT-PCR, in vitro translation, Northern blotting, and other RNA analysis. In another embodiment, microarray hybridization is used.

[0066] Single nucleotide polymorphisms (SNPs)

[0067] Traditional approaches to screening for genetic diseases rely on identifying abnormal gene products (e.g., sickle cell anemia) or abnormal phenotypes (e.g., mental retardation). With the development of simple and inexpensive genetic screening methods, it is now possible to identify polymorphisms that are predictive of a predisposition to a disease, even when the disease is of polygenic origin.

[0068] Single nucleotide polymorphisms (SNPs) genotype measures genetic variation in SNPs between members of a species. SNPs are single base pair changes at specific loci, usually consisting of two alleles (with rare allele frequencies > 1%). SNPs are very common. Because SNPs are conserved during evolution, they have been proposed as markers for quantitative trait locus (QTL) analysis and in place of microsatellites in association studies. Many different SNP genotyping methods are known, including hybridization-based methods (such as dynamic allele-specific hybridization, molecular beacons, and SNP microarrays), enzyme-based methods (including restriction fragment length polymorphisms, PCR-based methods, flap endonucleases, primer extension, 5'-nucleases, and oligonucleotide ligation assays), other post-amplification methods based on DNA physical properties (such as single-strand conformation polymorphism, temperature gradient gel electrophoresis, denaturing high-performance liquid chromatography, high-resolution melting of entire amplicons, use of DNA mismatch-binding proteins, SNPlex, and Surveyor nuclease assays), and sequencing (such as "next-generation" sequencing). See, e.g., U.S. Patent No. 7,972,779.

[0069] The multiple alleles at a locus can result from one or more polymorphisms in the region of the gene encoding the polypeptide or in regulatory control sequences (such as promoters or polyadenylation sequences) that affect expression of the polypeptide. Alternatively, the alleles can result from one or more polymorphisms at loci distal to the gene encoding the polypeptide or in regulatory control sequences. The polymorphisms can affect the polypeptide at the transcriptional or translational level (e.g., polypeptide transcription rate, translation rate, degradation rate, and / or activity). Allelic differences can be characterized in samples from a single subject or from multiple subjects using methods known to the skilled artisan. Such methods can include, but are not limited to, measuring the potential of a polynucleotide sequence to be expressed and / or measuring the amount of a polypeptide encoded. Methods are available to detect proteins or nucleic acids directly or indirectly, and determinations of particular interest include screening for the presence of a specific sequence or structure of a nucleic acid or polypeptide using, for example, any of a variety of known microarray technologies.

[0070] The skilled artisan will fully appreciate that the alleles need not have been previously shown to have any connection or association with a disorder phenotype. Rather, the alleles and the causative environmental risk factors can interact to predict a predisposition to a disorder phenotype even when neither the alleles nor the risk factors have any direct relationship to the disorder phenotype.

[0071] Genetic screening (also known as genotyping or molecular screening) can be broadly defined as a test to determine whether a subject has a mutation (or allele or polymorphism) that causes a disease state or is "associated" with a mutation that causes a disease state. Linkage refers to the phenomenon that DNA sequences that are close together in the genome have a tendency to be inherited together. Two sequences can be linked due to some selective advantage of co-inheritance. However, more typically, two polymorphic sequences are co-inherited because meiotic recombination events occur relatively infrequently in the region between the two polymorphisms. Co-inherited polymorphic alleles are said to be in "linkage disequilibrium" with one another because, in a given population, they tend to occur together in any particular member of the population or not at all. In effect, where multiple polymorphisms within a given chromosomal region are found to be in linkage disequilibrium with one another, they define a quasi-stable genetic "haplotype." Conversely, recombination events occurring between two polymorphic loci cause them to segregate onto different homologous chromosomes. If meiotic recombination between two physically linked polymorphisms occurs frequently enough, the two polymorphisms appear to segregate independently and are said to be in linkage equilibrium.

[0072] It will be appreciated that linkage disequilibrium can be quantified (using, for example, Pearson's correlation coefficient (R) or co-inheritance of alleles (D')). For example, a correlation (e.g., R value) of about 0.1 or less reflects a low level of linkage, an R value of about 0.3 reflects a moderate level of linkage, and an R value of 0.5 or greater reflects a high level of linkage.

[0073] While the frequency of meiotic recombination between two markers is generally proportional to their physical distance on the chromosome, the occurrence of "hot spots" and regions of suppressed chromosomal recombination can result in a discrepancy between the physical and recombination distance between two markers. Thus, in certain chromosomal regions, multiple polymorphic loci spanning a wide chromosomal domain can be in linkage disequilibrium with one another, thereby defining a wide-spanning genetic haplotype. In the event that a disease-causing mutation is found within or in linkage disequilibrium with such a haplotype, one or more of the polymorphic alleles of the haplotype can serve as a diagnostic or prognostic indicator of the likelihood of developing the disease. If the disease mutation is recent, such an association between other benign polymorphisms and the disease-causing polymorphism occurs, and thus equilibrium by recombination events has not had sufficient time to occur. Thus, the identification of a haplotype spanning or associated with a disease-causing mutation serves as a predictive measure of the likelihood that an individual has inherited the disease-causing mutation. Such a prognostic or diagnostic procedure can be utilized without the need to identify and isolate the actual disease-causing lesion. This is important because the precise determination of the molecular defect involved in a disease process can be difficult and laborious, especially in the case of multifactorial diseases.

[0074] A statistical association between a disorder and a polymorphism does not necessarily indicate that the polymorphism directly causes the disorder. Rather, the associated polymorphism can be a benign allelic variant that is associated with (i.e., in linkage disequilibrium with) a disorder-causing mutation that occurred in the recent evolutionary history, and thus equilibrium by recombination events in the intervening chromosomal segment has not had sufficient time to occur. Thus, the detection of a polymorphic allele associated with a particular disease can be utilized for diagnostic and prognostic determination purposes with respect to that disease, without regard to whether the polymorphism is directly involved in the etiology of the disease. Moreover, where a given benign polymorphic locus is in linkage disequilibrium with an apparently disease-causing polymorphic locus, other polymorphic loci in linkage disequilibrium with the benign polymorphic locus can also be in linkage disequilibrium with the disease-causing polymorphic locus. Thus, these other polymorphic loci will also be prognostic or diagnostic of the likelihood of having inherited the disease-causing polymorphic locus. Once an association is established between a particular disease or condition and a corresponding haplotype, a wide-spanning haplotype (describing a typical pattern of co-inheritance of alleles of a set of linked polymorphic markers) can be directed to diagnostic purposes. Thus, the likelihood of an individual developing a particular disease or condition can be determined by characterizing one or more disease-associated polymorphic alleles (or even one or more disease-associated haplotypes), without necessarily determining or characterizing the causative genetic variation.

[0075] A number of methods are available for detecting a particular allele at a polymorphic locus. Certain methods for detecting a particular polymorphic allele will depend, in part, on the molecular nature of the polymorphism. For example, the various allelic forms of a polymorphic locus can differ by a single base pair of DNA. Such single nucleotide polymorphisms (or SNPs) are the major contributors to genetic variation, comprising about 80% of all known polymorphisms, and are estimated to have an average density of one per 1,000 base pairs in the genome. SNPs are most commonly di-allelic, or exist in only two different forms (although theoretically there can be up to four different forms of an SNP, corresponding to the four different nucleotide bases present in DNA). However, SNPs are more stable to mutation than other polymorphisms, making them useful in association studies, where linkage disequilibrium between a marker and an unknown variant is used to map mutations that cause disease. Moreover, because SNPs typically have only two alleles, they can be genotyped by simple addition / subtraction assays rather than length measurements, making them more amenable to automation.

[0076] In one embodiment, the allelic analysis can be accomplished using a nucleic acid microarray. The field of genetic testing is rapidly evolving, and thus, one of skill will appreciate that a wide range of analytical tests exist and will be developed to determine the allelic profile of an individual in accordance with the present disclosure.

[0077] Nucleic acids and polypeptides

[0078] As described herein, the methods provided in the present disclosure rely on features contained within the nucleic acids of an individual, subject, or patient. The term "nucleic acid" refers to deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form, consisting of monomers (nucleotides) containing a sugar, a phosphate, and a base either purine or pyrimidine. Unless specifically limited, the term encompasses nucleic acids containing known analogues of natural nucleotides, as well as those with analogical binding or metabolic properties. Unless otherwise indicated, a particular nucleic acid sequence also

[0079] The term "nucleotide sequence" refers to a polymer of DNA or RNA, which can be single-stranded or double-stranded, optionally containing synthetic, non-natural, or altered nucleotide bases capable of incorporation into a DNA or RNA polymer. A DNA molecule or polynucleotide is a polymer of deoxyribonucleotides (A, G, C, and T), and an RNA molecule or polynucleotide is a polymer of ribonucleotides (A, G, C, and U).

[0080] For purposes of the present disclosure, a "gene" includes a DNA region encoding a gene product, as well as all DNA regions which regulate the production of the gene product, whether or not such regulatory sequences are adjacent to coding sequences and / or to transcribed sequences. The term "gene" is used broadly to refer to any segment of nucleic acid associated with a biological function. Genes include coding sequences and / or regulatory sequences required for their expression. Thus, genes include, but are not necessarily limited to, promoter sequences, terminators, translational regulatory sequences such as ribosome binding sites and internal ribosome entry sites, enhancers, silencers, insulators, boundary elements, replication origins, matrix attachment regions, and locus control regions. For example, a "gene" refers to a nucleic acid segment that expresses mRNA, functional RNA, or a specific protein, including regulatory sequences. "Functional RNA" refers to sense RNA, antisense RNA, ribozyme RNA, siRNA, or other RNA that can not be translated but has an effect on at least one cellular process. A "gene" also includes non-expressed segments of DNA, such as recognition sequences for other proteins, for example. A "gene" can be obtained from a variety of sources, including cloned from a source of interest or synthesized from known or predicted sequence information, and can include sequences designed to have desired parameters.

[0081] "Gene expression" refers to the conversion of the information contained in a gene into a gene product. It refers to the transcription and / or translation of an endogenous or heterologous gene, or nucleic acid segment, or transgene, in a cell. Additionally, expression refers to the transcription and stable accumulation of sense (mRNA) or functional RNA. Expression can also refer to production of a protein. The term "altered expression level" refers to an expression level in a transgenic cell or organism that is different from the expression level in a normal or untransformed cell or organism.

[0082] A gene product can be a transcription product of a gene (e.g., mRNA, tRNA, rRNA, antisense RNA, ribozyme, structural RNA, or any other type of RNA) or a protein produced by translation of an mRNA. Gene products also include RNAs modified by processes such as capping, polyadenylation, methylation, and editing, as well as proteins modified, e.g., by methylation, acetylation, phosphorylation, ubiquitination, ADP ribosylation, myristilation, and glycosylation. The term "RNA transcript" refers to the product resulting from the catalytic activity of an RNA polymerase when acting on a DNA sequence. When the RNA transcript is a complementary copy of a DNA sequence, it is referred to as a primary transcript; RNA sequences derived from post-transcriptional processing of a primary transcript are referred to as mature RNA. "Messenger RNA" (mRNA) refers to RNA that is devoid of introns and can be translated into a protein by a cell. "cDNA" refers to single- or double-stranded DNA that is complementary to, and derived from, an mRNA. "Functional RNA" refers to sense RNA, antisense RNA, ribozyme RNA, siRNA, or other RNA that can not be translated but has an effect on at least one cellular process.

[0083] A "coding sequence" or a sequence that "encodes" a polypeptide is a nucleic acid molecule that is transcribed (in the case of DNA) and / or translated (in the case of mRNA) into a polypeptide in vivo when placed under the control of appropriate regulatory sequences. The boundaries of the coding sequence are determined by a start codon at the 5' (amino) terminus and a translation stop codon at the 3' (carboxyl) terminus. A coding sequence can include, but is not limited to, cDNA from viral, procaryotic, or eucaryotic mRNA, genomic DNA sequences from viral (e.g., DNA viruses and retroviruses) or procaryotic DNA, as well as synthetic DNA sequences. A transcription termination sequence can be located 3' to the coding sequence.

[0084] "Regulatory sequence" and "suitable regulatory sequence" each refer to a nucleotide sequence located upstream (5' non-coding sequences), within, or downstream (3' non-coding sequences) of a coding sequence, and which affects the transcription, RNA processing or stability, or translation of the associated coding sequence. Regulatory sequences include enhancers, promoters, translation leader sequences, introns, and polyadenylation signal sequences. They include natural and synthetic sequences, as well as sequences that are combinations of natural and synthetic sequences.

[0085] Certain embodiments of the disclosure encompass isolated or substantially purified nucleic acid compositions. In the context of the present disclosure, an "isolated" or "purified" DNA molecule or RNA molecule is one which exists apart from its natural environment and thus is not a natural product. An isolated DNA molecule or RNA molecule can exist in a purified form or can exist in a non-natural environment, such as, for example, a transgenic host cell. For example, an "isolated" or "purified" nucleic acid molecule is substantially free of other cellular material, or culture medium when produced by recombinant techniques, or substantially free from chemical precursors or other chemicals when chemically synthesized. In one embodiment, an "isolated" nucleic acid is free of sequences that naturally flank the nucleic acid (i.e., sequences located at the 5' and 3' ends of the nucleic acid) in the genomic DNA of the organism from which the nucleic acid is derived.

[0086] By "fragment" is meant a polypeptide that consists only of a portion of a complete full-length polypeptide sequence and structure. Fragments can include C-terminal deletions, N-terminal deletions, and / or internal deletions of a native polypeptide. Protein fragments typically will include at least about 5-100 contiguous amino acid residues of a full-length molecule (e.g., at least about 15-25 contiguous amino acid residues of a full-length molecule, at least about 20-50 or more contiguous amino acid residues of a full-length molecule, or any integer between 5 amino acids and the full-length sequence).

[0087] "naturally occurring" is used to describe compositions that can be found in nature that are not produced by man. For example, a nucleotide sequence that exists in an organism that can be isolated from a source in nature and has not been intentionally modified by the hand of man is naturally occurring.

[0088] "5' non-coding sequences" refer to nucleotide sequences located 5' (upstream) to a coding sequence. 5' non-coding sequences are present in the fully-processed mRNA upstream of the translation initiation codon and can facilitate processing of a primary transcript to an mRNA, mRNA stability, or translation efficiency. "3' non-coding sequences" refer to nucleotide sequences located 3' (downstream) to a coding sequence and can include polyadenylation signals and other sequences that can affect mRNA processing or stability.

[0089] A "promoter" refers to a nucleotide sequence, usually upstream (5') of its coding sequence, that directs and / or controls the expression of a coding sequence by providing recognition for RNA polymerase and other factors required for proper transcription. A "promoter" can include a minimal promoter, which is a short DNA sequence consisting of a TATA box and other sequences for specifying the transcription initiation site, to which regulatory elements are added to control expression. A "promoter" can also refer to a nucleotide sequence that includes a minimal promoter plus one or more regulatory elements (e.g., enhancers) capable of controlling the expression of a coding sequence or functional RNA. A promoter can be derived entirely from a natural sequence, or be composed of different elements derived from different promoters found in nature, or even be composed of synthetic DNA sequences. A promoter can also contain DNA sequences involved in the binding of protein factors that control the effectiveness of transcription initiation in response to physiological or developmental conditions. "Constitutive expression" refers to expression using a constitutive promoter. "Conditional" and "regulated expression" refer to expression controlled by a regulated promoter.

[0090] An "enhancer" is a DNA sequence that can stimulate promoter activity. An enhancer can be an intrinsic component of the promoter or a heterologous element inserted to increase the level or tissue specificity of a promoter. Enhancers are usually capable of acting in both orientations and at distances of thousands of base pairs from the promoter. Both enhancers and other regulatory elements within promoters bind sequence-specific DNA-binding proteins that mediate their effects.

[0091] "Operably linked" means the association of nucleic acid sequences in a single nucleic acid fragment such that the function of one is affected by another. For example, a regulatory DNA sequence is said to be "operably linked to" or "in association with" a DNA sequence encoding a RNA or polypeptide if the two sequences are situated such that the regulatory DNA sequence affects the expression of the coding DNA sequence (i.e., such that the coding sequence or functional RNA is under the transcriptional control of the promoter). A coding sequence can be operably linked to a regulatory sequence in either the sense or antisense orientation.

[0092] "Expression" refers to the transcription and / or translation of an endogenous gene, heterologous gene, or nucleic acid segment or transgene in a cell. Additionally, expression refers to the transcription and stable accumulation of sense (mRNA) or functional RNA. Expression can also refer to the production of a protein. The term "altered expression level" refers to an expression level in a cell or organism that is different from the expression level of a normal cell or organism.

[0093] For sequence comparison, typically one sequence is used as the reference sequence, to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence alignment program, and sequence algorithm program is specified. The sequence comparison algorithm then calculates the percent sequence identity for the test sequence(s) relative to the reference sequence, based on the specified algorithm program parameters.

[0094] The following terms are used to describe the sequence relationship between two or more nucleic acids or polynucleotides: (a) "reference sequence," (b) "comparison window," (c) "sequence identity," (d) "percentage of sequence identity," and (e) "as used in sequence comparison." As used herein, "reference sequence" is the defined sequence used as the basis for a sequence comparison; e.g., a segment of a full-length cDNA or gene sequence, or the complete cDNA or gene sequence. As used herein, "comparison window" refers to a contiguous segment of a polynucleotide sequence, wherein the polynucleotide sequence in the comparison window can include additions or deletions (i.e., gaps) compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. Generally, the comparison window will be at least 20 contiguous nucleotides in length, and optionally can be 30, 40, 50, 100 or longer. Those skilled in the art understand that, to avoid an artificially inflated similarity to the reference sequence due to inclusion of gaps in the polynucleotide sequence, a gap penalty is typically introduced and subtracted from the number of matches.

[0095] Methods for sequence comparison are well-known in the art. Thus, the determination of percent identity between any two sequences can be accomplished using a mathematical algorithm. Non-limiting examples of such mathematical algorithms are the algorithm of Myers and Miller (Myers and Miller, CABIOS, 4, 11 (1988)); the local homology algorithm of Smith et al. (Smith et al., Adv. Appl. Math., 2, 482 (1981)); the homology alignment algorithm of Needleman and Wunsch (Needleman and Wunsch, JMB, 48, 443 (1970)); the search for similarity method of Pearson and Lipman (Pearson and Lipman, Proc. Natl. Acad. Sci. USA, 85, 2444 (1988)); the algorithm of Karlin and Altschul (Karlin and Altschul, Proc. Natl. Acad. Sci. USA, 87, 2264 (1990), as modified in Karlin and Altschul (Karlin and Altschul, Proc. Natl. Acad. Sci. USA 90, 5873 (1993)).

[0096] Computer implementations of these mathematical algorithms can be utilized for comparison of sequences to determine sequence identity. Such implementations include, but are not limited to: CLUSTAL in the PC / Gene program (available from Intelligenetics, Mountain View, California); the ALIGN program (Version 2.0); and GAP, BESTFIT, BLAST, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Version 8 (available from Genetics Computer Group (GCG), 575 Science Dr., Madison, Wisconsin). Alignment using these programs can be performed using the default parameters. The CLUSTAL program is well described in Higgins et al. (Higgins et al., CABIOS, 5, 151 (1989)); Corpet et al. (Corpet et al., Nucl. Acids Res., 16, 10881 (1988)); Huang et al. (Huang et al., CABIOS, 8, 155 (1992)); and Pearson et al. (Pearson et al., Meth. Mol. Biol., 24, 307 (1994)). The ALIGN program is based on the algorithm of Myers and Miller, supra. The BLAST programs of Altschul et al. (Altschul et al., J. Mol. Biol., 215, 403 (1990)) are based on the algorithm of Karlin and Altschul, supra.

[0097] Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information. This algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence that either match or closely align with sub-segments of the database sequence. These initial neighborhood word hits act as seeds for initiating searches to find longer HSPs containing them. The word hits are then extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Cumulative scores are calculated using, for nucleotide sequences, the parameters "M" (reward score for a pair of matching residues; always > 0) and "N" (penalty score for mismatching residues; always < 0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLAST program can be used, as described in Altschul et al. (Altschul et al., J. Mol. Biol., 215, 403 (1990)).

[0098] In addition to calculating percent sequence identity, the BLAST algorithm also performs a statistical analysis of the similarity between two sequences. One measure of similarity provided by the BLAST algorithm is the smallest sum probability (P(N)), which provides an indication of the probability by which a match between two nucleotide or amino acid sequences would occur by chance. For example, a test nucleic acid sequence is considered similar to a reference sequence if the smallest sum probability in a comparison of the test nucleic acid sequence to the reference nucleic acid sequence is less than about 0.1, less than about 0.01, or even less than about 0.001.

[0099] To obtain gapped alignments for comparison purposes, Gapped BLAST (in BLAST 2.0) can be utilized. Alternatively, PSI-BLAST (in BLAST 2.0) can be used to perform an iterated search that detects distant relationships between molecules. In utilizing BLAST, Gapped BLAST, PSI-BLAST, the default parameters of the respective programs (e.g., BLASTN for nucleotide sequences, BLASTX for proteins) can be used. The BLASTN program (for nucleotide sequences) uses as default parameters a wordlength of 11, an expectation (E) of 10, a cutoff of 100, M = 5, N = -4, and a comparison to both strands of the query. For amino acid sequences, the BLASTP program uses as default parameters a wordlength of 3, an expectation (E) of 10, and the BLOSUM62 scoring matrix. Alignment can also be performed manually by inspection.

[0100] For purposes herein, nucleic acid sequence comparisons for determining percent sequence identity can be performed using the BlastN program (version 1.4.7 or newer) with its default parameters, or any equivalent program, and its default parameters, for determining percent sequence identity with the promoter sequences disclosed herein. By "equivalent program" is meant any sequence comparison program that, for any two sequences in question, generates an alignment having identical nucleotide or amino acid residue matches and an identical percent sequence identity when compared to the alignment generated by the program described above.

[0101] As used herein, "sequence identity" or "identity" in the context of two nucleic acid or polypeptide sequences, relates to the specified percentage of residues in the two sequences that are the same when aligned for maximum correspondence over a specified comparison window, as measured using a sequence comparison algorithm or by visual inspection. When reference is made to a percentage sequence identity, it will be recognized that residues which are not identical often differ by conservative amino acid substitutions, where an amino acid residue is substituted for another with similar chemical properties (e.g., charge or hydrophobicity) and therefore does not significantly change the functional properties of the molecule. When sequences differ in conservative substitutions, the percent sequence identity can be adjusted upwards to correct for the conservative nature of the substitution. Sequences which differ by such conservative substitutions are said to have "sequence similarity" or "similarity." Means for making this adjustment are well known in the art. Typically, this involves scoring a conservative substitution as a partial rather than complete mismatch, thereby increasing the percentage sequence identity. Thus, for example, where an identical amino acid is given a score of 1 and a non-conservative substitution is given a score of zero, a conservative substitution is given a score between zero and one. The scoring of conservative substitutions is calculated, e.g., as implemented in the program PC / GENE (Intelligenetics, Mountain View, Calif.).

[0102] As used herein, "percent sequence identity" means the value determined by comparing two optimally aligned sequences over a comparison window, wherein the portion of the polynucleotide sequence in the comparison window can comprise additions or deletions (i.e., gaps) as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. The percentage is calculated by determining the number of positions at which the identical nucleic acid base or amino acid residue occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the comparison window, and multiplying the result by 100 to yield the percent sequence identity.

[0103] The term "substantial identity" of a polynucleotide sequence means that the polynucleotide comprises a sequence that, when compared to a reference sequence using one of the comparison programs described herein, using standard parameters, has at least 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, or 94%, or even at least 95%, 96%, 97%, 98%, 99%, or 100% sequence identity. Those skilled in the art will recognize that these values can be adjusted to determine corresponding identity of proteins encoded by two nucleotide sequences by taking into account codon degeneracy, amino acid similarity, reading frame positioning, and the like. Substantial identity of amino acid sequences for these purposes normally means at least 70% (e.g., 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%), at least 80% (e.g., 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%), at least 90% (e.g., 91%, 92%, 93%, or 94%), or even at least 95% (e.g., 96%, 97%, 98%, 99%, or 100%) sequence identity.

[0104] The term "substantial identity" in the context of a peptide indicates that the peptide comprises a sequence that has at least 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, or 94%, or even 95%, 96%, 97%, 98%, or 99% sequence identity to a reference sequence over a specified comparison window. In certain embodiments, optimal alignment is conducted using the homology alignment algorithm of Needleman and Wunsch (Needleman and Wunsch, J. Mol. Biol., 48, 443 (1970)). Indication that two peptide sequences are substantially identical is that one peptide is immunologically reactive with an antibody raised against the second peptide. Thus, for example, a peptide is substantially identical to a second peptide where the two peptides differ only by conservative substitutions. Accordingly, the present disclosure also provides nucleic acid molecules and peptides that are substantially identical to the nucleic acid molecules and peptides presented herein.

[0105] Another indication that nucleotide sequences are substantially identical is if two molecules hybridize to each other under stringent conditions. Hybridization of nucleic acids is discussed in more detail below.

[0106] Oligonucleotide primers and probes

[0107] As described herein, the methods provided by the present disclosure rely on oligonucleotides (sometimes referred to as primers or probes) to identify or detect a signature contained within a nucleic acid obtained from an individual, subject, or patient. The term "nucleic acid probe" or "probe specific for" a nucleic acid refers to a nucleic acid sequence that has at least about 80% (e.g., at least about 90%, e.g., at least about 95%) contiguous sequence identity or homology to a nucleic acid sequence encoding a target sequence of interest. The probes (or oligonucleotides or primers) of the present disclosure are at least about 8 nucleotides in length (e.g., at least about 8-50 nucleotides in length, e.g., at least about 10-40, e.g., at least about 15-35 nucleotides in length). The oligonucleotide probes or primers of the present disclosure can comprise at least about eight nucleotides at the 3' of the oligonucleotide that have at least about 80% (e.g., at least about 85%, e.g., at least about 90%, e.g., at least about 95%) contiguous identity to the target sequence of interest.

[0108] Primer pairs can be used to determine the nucleotide sequence of a particular SNP using PCR. Single-stranded DNA primer pairs can anneal to sequences within or around the SNP to prime amplification DNA synthesis of the SNP itself. The first step of the process involves contacting a biological sample obtained from a subject (the sample containing nucleic acids) with at least one primer to form hybridized DNA. The oligonucleotide primers that can be used in the methods of the present disclosure can be any primer consisting of from about 8 bases up to about 80 or 100 or more bases. In one embodiment of the present disclosure, the primers have between about 10 and about 20 bases.

[0109] The primers themselves can be synthesized using techniques well known in the art. Typically, the primers can be prepared using a commercially available oligonucleotide synthesizer.

[0110] The primers or probes of the present disclosure can be labeled using techniques known to those of skill in the art. For example, the labels used in the assays of the present disclosure can be primary labels (where the label comprises an element that is directly detected) or secondary labels (where a detected label binds to a primary label, e.g., as is common in immunological labeling). An introduction to labels (also referred to as "tags"), tagging or labeling procedures, and label detection can be found in Polak and Van Noorden (1997) Introduction to Immunocytochemistry, Second Edition, Springer Verlag, New York; and Haugland (1996) Handbook of Fluorescent Probes and Research Chemicals, a comprehensive handbook and catalog published by Molecular Probes, Inc. of Eugene, OR. Primary and secondary labels can include non-detectable elements as well as detectable elements. Useful primary and secondary labels in the present disclosure can include spectroscopic labels such as fluorescent dyes (e.g., fluorescein and derivatives such as fluorescein isothiocyanate (FITC) and Oregon Green™, rhodamine and derivatives (e.g., Texas Red, tetramethylrhodamine isothiocyanate (TRITC), etc.), digoxigenin, biotin, phycoerythrin, AMCA, CyDyes™, etc.), radioactive labels (e.g., 3 H、 125 I、 35 S、 14 C、 32 P、 33 P), enzymes (e.g., horseradish peroxidase, alkaline phosphatase) spectroscopic colorimetric labels such as colloidal gold or colored glass or plastic (e.g., polystyrene, polypropylene, latex) beads. Labels can be coupled directly or indirectly to components of the detection assay (e.g., labeled nucleic acids) according to methods well known in the art. As indicated above, numerous labels can be used, with the choice of label depending on the required sensitivity, ease of conjugation with the compound, stability requirements, available instrumentation, and disposal provisions.

[0111] In general, the detector for monitoring the hybridization of the probe-substrate nucleic acids is adjusted for the particular label used. Typical detectors include spectrophotometers, phototubes and photodiodes, microscopes, scintillation counters, photographic cameras, film, and the like, and combinations thereof. Examples of suitable detectors are widely available from a number of commercial sources known to the skilled artisan. In general, the optical image of the substrate containing the bound labeled nucleic acids is digitized for subsequent computer analysis.

[0112] Labels include those using (1) chemiluminescence (using horseradish peroxidase and / or alkaline phosphatase and substrates that produce light photons as a breakdown product) and kits available from, for example, Molecular Probes, Amersham, Boehringer-Mannheim, and Life Technologies / Gibco BRL; (2) color generation (using both horseradish peroxidase and / or alkaline phosphatase and substrates that produce colored precipitates) (kits available from Life Technologies / Gibco BRL and Boehringer-Mannheim); (3) semi-fluorescence, using, for example, alkaline phosphatase and the substrate AttoPhos (Amersham) or other substrates that produce fluorescent products; (4) fluorescence (for example, using Cy-5 (Amersham), fluorescein, and other fluorescent labels); (5) radioactivity, using kinase or other end-labeling methods, nick translation, random priming, or PCR to incorporate radioactive molecules into the labeled nucleic acids. Other labels and detection methods will be readily apparent to those skilled in the art.

[0113] Fluorescent labels can be used and are advantageous in that less protection is required during handling and are suitable for high throughput visualization techniques (including optical analysis of image digitization for analysis in integrated systems including computers). Preferred labels are generally characterized by one or more of the following: high sensitivity in the label, high stability, low background, low environmental sensitivity, and high specificity. Fluorescent moieties that can be incorporated into labels are generally known and include Texas Red, digoxigenin, biotin, 1- and 2-aminonaphthalene, p,p'-diaminostilbene, pyrene, tetraphenylphosphonium salt, 9-aminoacridine, p,p'-diaminobenzophenone imine, anthracene, oxacarbocyanine, merocyanine, 3-aminoformazan, perylene, bis-benzoxazolylbenzene, 1,2-benzophenazine, retinol, bis-3- aminopyridinium salt, hellebrigenin, tetracycline, sterophenol, benzimidazolylphenylamine, 2-oxo-3-chromene, indole, xanthene, 7-hydroxycoumarin, phenoxazine, salicylate, strychnine, porphyrin, triarylmethane, flavin, and the like. Many fluorescent labels are commercially available from SIGMA Chemical Company (St. Louis, MO), Molecular Probes, R&D systems (Minneapolis, MN), Pharmacia LKB Biotechnology (Piscataway, NJ), CLONTECH Laboratories, Inc. (Palo Alto, CA), Chem Genes Corp., Aldrich Chemical Company (Milwaukee, WI), Glen Research, Inc., GIBCO BRL Life Technologies, Inc. (Gaithersburg, MD), Fluka Chemica Biochemika Analytika (Fluka Chemie AG, Buchs, Switzerland), and Applied Biosystems™ (Foster City, CA), as well as numerous other commercial suppliers known to the skilled artisan.

[0114] Methods for detecting and quantifying labels are well known to those of skill in the art. Thus, for example, when the label is a radioactive label, methods for detection include scintillation counters or photographic film as in autoradiography; and when the label is optically detectable, typical detectors include microscopes, cameras, phototubes, photodiodes, and many other widely available detection systems.

[0115] Oligonucleotide primers or probes having any of a number of base sequences can be prepared according to techniques well known in the art. Suitable bases for use in preparing oligonucleotide primers or probes can be selected from naturally occurring nucleotide bases, such as adenine, cytosine, guanine, uracil, and thymine; and non-naturally occurring or "synthetic" nucleotide bases, such as 7-deaza-adenine, 8-oxo-adenine, 6- mercaptopurine, 4-acetylcytidine, 5-(carboxyhydroxyethyl)uridine, 2'-O-methylcytidine, 5- carboxymethylamino-methyl-2-thiouridine, 5-carboxymethylaminomethyluridine, dihydrouridine, 2'-O-methylpseudouridine, β, D-galactosylqueosine, 2'-O-methylguanosine, inosine, N6- isopentenyladenosine, 1-methyladenosine, 1-methylpseudouridine, 1-methylguanosine, 1- methylinosine, 2,2-dimethylguanosine, 2-methyladenosine, 2-methylguanosine, 3- methylcytidine, 5-methylcytidine, N6-methyladenosine, 7-methylguanosine, 5- methylaminomethyluridine, 5-methoxyaminomethyl-2-thiouridine, β, D-mannosylqueosine, 5- methoxy carbonylmethyluridine, 5-methoxyuridine, 2-methylthio-N6-isopentenyladenosine, N- ((9-β-D-riobofuranosyl-2-methylthio-purine-6-yl)carbamoyl)threonine, N-((9-β-D- ribofuranosylpurine-6-yl)N-methyl-carbamoyl)threonine, uridine-5-oxyacetic acid methyl ester, uridine-5-oxyacetic acid, wyosine, pseudouridine, queosine, 2-thiocytidine, 5-methyl-2- thiouridine, 2-thiouridine, 2-thiouridine, 5-methyluridine, N-((9-β-D-riobofuranosylpurine-6- yl)carbamoyl)threonine, 2'-O-methyl-5-methyluridine, 2'-O-methyluridine, wyosine, and 3-(3- amino-3-carboxypropyl)uridine. Any oligonucleotide backbone can be employed, including DNA, RNA (but RNA is less preferred than DNA), modified sugars (such as carbocyclic) and sugars containing 2' substitutions (such as fluoro and methoxy). The oligonucleotide can be one in which at least one or all of the internucleotide phosphate residues are modified phosphates, such as methylphosphonates, methylthiophosphonates, phosphoroinorpholidates, phosphoropiperazidates, and phosphoramidates (e.g., every other internucleotide phosphate residue can be modified as described). The oligonucleotide can be a "peptide nucleic acid," as described in Nielsen et al., Science, 254: 1497-1500 (1991).

[0116] As used herein, a "single base extension probe" is a nucleic acid that selectively recognizes a single nucleotide polymorphism (i.e., the A or G of an A / G polymorphism). Typically, these probes take the form of a DNA primer (e.g., as in a PCR primer) that is modified such that incorporation of the primer releases a fluorophore. One example of this format is Taqman ® A probe that uses the 5' exonuclease activity of the enzyme Taq polymerase to measure the amount of target sequence in a sample. The TaqMan® probe consists of an 18-22 bp oligonucleotide probe labeled with a reporter fluorophore at the 5' end and a quencher fluorophore at the 3' end. Incorporation of the probe molecule into a PCR strand (which occurs because the probe set is included in the PCR primer mix) releases the reporter fluorophore from the influence of the quencher. The primers must be able to recognize the target binding site. Some primer extension probes can be "activated" directly by the DNA polymerase without a full PCR extension cycle.

[0117] The only requirement is that the oligonucleotide probe should have a sequence of which at least a portion is capable of binding to a known portion of the sequence of the DNA sample. The nucleic acid probes provided by the present disclosure can be used for a variety of purposes.

[0118] Methods of detecting nucleic acids

[0119] A. Amplification

[0120] According to the methods of the present disclosure, amplification of DNA present in a biological sample can be performed by any means known in the art. Examples of suitable amplification techniques include, but are not limited to, polymerase chain reaction (including reverse transcriptase polymerase chain reaction for RNA amplification), ligase chain reaction, strand displacement amplification, transcription-based amplification, self-sustained sequence replication (or "3SR"), Qβ replicase system, nucleic acid sequence-based amplification (or "NASBA"), repair chain reaction (or "RCR"), and boomerang DNA amplification (or "BDA").

[0121] The bases incorporated into the amplification product can be natural or modified bases (modified either before or after amplification), and the bases can be selected to optimize subsequent detection steps (e.g., electrochemical detection steps).

[0122] Polymerase chain reaction (PCR) can be performed according to known techniques. See, e.g., U.S. Patent Nos. 4,683,195; 4,683,202; 4,800,159; and 4,965,188. Generally, PCR involves, first, treating a nucleic acid sample with an oligonucleotide primer for each strand of the particular sequence to be detected under hybridization conditions (e.g., in the presence of a thermostable DNA polymerase) such that an extension product of each primer complementary to each nucleic acid strand is synthesized and the primer is sufficiently complementary to each strand of the particular sequence to hybridize thereto such that the extension product synthesized from each primer, upon separation from its complement, can serve as a template for synthesis of an extension product of the other primer; and, if one or more sequences to be detected are present, subsequently treating the sample under denaturing conditions to separate the primer extension products from their templates. These steps are repeated periodically until the desired degree of amplification is obtained. Detection of the amplified sequence can be performed by adding to the reaction product an oligonucleotide probe (e.g., an oligonucleotide primer or probe of the present disclosure) capable of hybridizing to the reaction product, the probe carrying a detectable label; and thereafter detecting the label according to known techniques. Various labels that can be incorporated into or operably linked to nucleic acids are well known in the art, such as radioactive labels, enzymatic labels, and fluorescent labels. Where the nucleic acid to be amplified is RNA, amplification can be performed by first converting to DNA using reverse transcriptase according to known techniques.

[0123] Strand displacement amplification (SDA) can be performed according to known techniques. For example, SDA can be performed with a single amplification primer or a pair of amplification primers, and exponential amplification is achieved with a pair of amplification primers. Generally, SDA amplification primers comprise, in the 5' to 3' direction, a flanking sequence (the DNA sequence of which is immaterial), a restriction site for a restriction enzyme used in the reaction, and an oligonucleotide sequence that hybridizes to a target sequence to be amplified and / or detected (e.g., an oligonucleotide primer or probe as described herein). The length of the flanking sequence to facilitate binding of the restriction enzyme to the recognition site and to provide a DNA polymerase priming site after the restriction site is nicked out following the SDA reaction can be about 15 to 20 nucleotides. The restriction site functions in the SDA reaction. For example, the length of the oligonucleotide primer or probe portion can be about 13 to 15 nucleotides.

[0124] A ligase chain reaction (LCR) can also be performed according to known techniques. Generally, the reaction is performed with two pairs of oligonucleotide probes: one pair binds to one strand of the sequence to be detected; the other pair binds to the other strand of the sequence to be detected. Each pair overlaps completely with its corresponding strand. The reaction is performed by first denaturing (e.g., separating) the strands of the sequence to be detected, then reacting the strands with the two pairs of oligonucleotide probes in the presence of a thermostable ligase, such that each pair of oligonucleotide probes ligates together, then separating the reaction products, then periodically repeating the process until the sequence is amplified to the desired degree. Detection can then be performed in a manner analogous to that described above for PCR.

[0125] According to the methods described herein, specific SNPs at specific loci can be detected. Techniques that can be used in the methods described herein include, but are not limited to, direct DNA sequencing, PFGE analysis, allele-specific oligonucleotide (ASO), dot blot analysis, and denaturing gradient gel electrophoresis, and are well known to the skilled artisan.

[0126] There are several methods that can be used to detect DNA sequence variations. Direct DNA sequencing (i.e., manual sequencing or automated fluorescent sequencing) can detect sequence variations. Another method is single-strand conformation polymorphism assay (SSCA). This method does not detect all sequence changes, especially if the DNA fragment size is greater than 200 bp, but can be optimized to detect most DNA sequence variations. The decreased sensitivity of detection is a drawback, but the increased throughput possible with SSCA makes it an attractive, viable alternative to direct sequencing for mutation detection on a research basis. Fragments with altered mobility on the SSCA gel can then be sequenced to determine the exact nature of the DNA sequence variation. Other methods based on detection of mismatches between two complementary DNA strands include clamped denaturing gel electrophoresis (CDGE), heteroduplex analysis (HA), and chemical mismatch cleavage (CMC). Once a sequence change is identified, a large number of additional samples can be rapidly screened for the same sequence change (e.g., mutation, polymorphism) using an allele-specific detection method such as allele-specific oligonucleotide (ASO) hybridization. This technique can utilize probes labeled with gold nanoparticles to produce a visual color result.

[0127] Detection of SNPs can be accomplished by sequencing the desired target region using techniques well known in the art. Alternatively, the sequence can be amplified directly from a genomic DNA preparation from the subject's tissue using known techniques. The DNA sequence of the amplified sequence can then be determined.

[0128] There are several well-known methods for more complete but still indirect testing to confirm the presence of a mutant allele: 1) single strand conformation analysis (SSCA); 2) denaturing gradient gel electrophoresis (DGGE); 3) ribonuclease protection assay; 4) allele-specific oligonucleotide (ASO); 5) use of proteins that recognize nucleotide mismatches, such as the E. coli mutS protein; and / or 6) allele-specific PCR. For allele-specific PCR, primers are used that hybridize at their 3' end to a specific allele. If the specific mutation is not present, no amplification product is observed. The amplification refractory mutation system (ARMS) can also be used. Insertions and deletions of genes can also be detected by cloning, sequencing, and amplification. In addition, restriction fragment length polymorphism (RFLP) probes for genes or surrounding marker genes can be used to score for insertions in altered or polymorphic fragments of alleles. Other techniques for detecting insertions and deletions can be used as known in the art.

[0129] In the first three methods (SSCA, DGGE, and ribonuclease protection assay), a new electrophoretic band appears. SSCA detects a band that migrates differently because the sequence change causes a difference in base pairing within the single-stranded molecule. Ribonuclease protection involves cleaving the mutant polynucleotide into two or more smaller fragments. DGGE uses a denaturing gradient gel to detect a difference in mobility of the mutant sequence compared to the wild-type sequence. In the allele-specific oligonucleotide assay, an oligonucleotide is designed to detect a specific sequence, and the assay is performed by detecting the presence or absence of hybridization signal. In the mutS assay, the protein binds only to sequences that contain a nucleotide mismatch in a heteroduplex between the mutant sequence and the wild-type sequence.

[0130] According to the present disclosure, a mismatch is a duplex of nucleic acids that hybridize where the two strands are not 100% complementary. The lack of perfect homology can be due to a deletion, insertion, inversion, or substitution. Mismatch detection can be used to detect point mutations in genes or their mRNA products. While these techniques are not as sensitive as sequencing, they are easier to perform on large numbers of samples. An example of a mismatch cleavage technique is the ribonuclease protection method. A ribonucleic acid probe is annealed (hybridized) to mRNA or DNA isolated from tumor tissue and then digested with ribonuclease A to detect some mismatches in the duplex RNA structure. If a mismatch is detected by ribonuclease A, it is cleaved at the mismatch site. Thus, when the annealed RNA preparation is separated on an electrophoretic gel matrix, if a mismatch has been detected and cleaved by ribonuclease A, a smaller RNA product than the full-length duplex RNA of the ribonucleic acid probe and mRNA or DNA will be observed. The ribonucleic acid probe need not be of the full length of the mRNA or gene, but can be a segment of either. If the ribonucleic acid probe includes only a segment of the mRNA or gene, it will be desirable to use multiple such probes to screen the entire mRNA sequence for mismatches.

[0131] In a similar manner, DNA probes can be used to detect mismatches by enzymatic or chemical cleavage. Alternatively, mismatches can be detected by a shift in the electrophoretic mobility of mismatched duplexes relative to matched duplexes. Using either ribonucleic acid or DNA probes, PCR can be used to amplify cellular mRNA or DNA that can contain mutations prior to hybridization.

[0132] B. Sequencing

[0133] Because of its sensitivity and relative simplicity in terms of workflow and technique, Sanger sequencing is used in a variety of applications from targeted sequencing to confirmation of variants identified using orthogonal methods. Sanger sequencing utilizes a chain termination method to provide the identity and order of nucleotide bases in a given DNA strand. This method utilizes chemical analogs of the four nucleotide bases, i.e., ddNTPs, which lack the hydroxyl group needed to extend the polynucleotide chain forming the DNA molecule. By mixing radioactive and later fluorescently labeled ddNTPs with the template DNA, when the ddNTPs are incorporated randomly, it produces chains of every possible length, terminating the chain. In contrast to Sanger sequencing, Maxam and Gilbert used a chemical cleavage technique. The main advantage of the Maxam-Gilbert technique compared to the Sanger method is that sequencing can be performed from the original DNA fragment rather than from an enzymatic copy, PCR is not needed, and this method is less susceptible to secondary structure errors or enzyme errors. The products produced in the sequencing reaction can be separated by ionophoresis on an acrylamide gel or using capillary electrophoresis.

[0134] Another sequencing technique, called pyrosequencing, was developed that uses a two-enzyme process in which an adenosine triphosphate (ATP) sulfurylase is used to convert pyrophosphate to ATP, which is then used as a substrate for luciferase, which produces light in proportion to the amount of pyrophosphate. Additional methods for sequencing nucleic acids include emulsion polymerase chain reaction (PCR), reversible terminators, and sequencing by oligonucleotide ligation and detection. Capillary electrophoresis (CE) instruments can also be used for sequencing.

[0135] High-throughput sequencing techniques, called next-generation sequencing (NGS), were also developed. NGS is massively parallel, with millions of fragments sequenced simultaneously per run. This high-throughput process translates to sequencing hundreds to thousands of genes at a time. NGS also provides greater discovery power to detect new or rare variants with deep sequencing. The analytical range of NGS can extend from a small number of genes to the entire genome. Whole genome sequencing (WGS) and whole exome sequencing (WES) provide the sequence of DNA bases across the genome and exome, respectively. Whole transcriptome sequencing provides sequence information on the coding and various non-coding forms of RNA to assess variants and gene expression levels across the entire transcriptome. Targeted sequencing covers a relatively small set of genes or targeted regions of interest (e.g., to determine the presence or absence of SNPs). Real-time sequencing and single molecule sequencing (SMS) were also developed, which enable accurate sequencing of long strands of nucleic acids without intermediaries and without prior transcription or amplification.

[0136] Nanopore-based sequencing technology detects unique electrical signals as different molecules pass through a nanopore using a semiconductor-based electronic detection system. This technology provides a high-throughput, cost-effective sequencing solution. At the heart of the technology is the biological nanopore, a protein pore that is embedded in a membrane, while the brain of the technology lies in the electronics of a semiconductor integrated circuit and proprietary chemistry. The embedded electronic sensor technology in the chip enables automated membrane assembly and nanopore insertion while allowing active control over individual sensors on the circuit. See, e.g., Oxford Nanopore and Pacific Biosciences. Nanopore and electronic sensor sequencing technology can be used to determine DNA methylation directly using native non-bisulfite DNA.

[0137] C. Hybridization

[0138] The phrase "specifically hybridizes to" means that a molecule binds, duplexes, or hybridizes to only a particular nucleotide sequence in a complex mixture (e.g., total cellular) DNA or RNA under stringent conditions. "Substantially binds" means complementary hybridization between a primer or probe nucleic acid and a target nucleic acid, and includes a small number of mismatches that can be modulated by reducing the stringency of the hybridization medium to achieve the desired detection of the target nucleic acid sequence.

[0139] Generally, stringent conditions are selected to be about 5ºC lower than the thermal melting point (T m ) for the specific sequences at a defined ionic strength and pH. However, stringent conditions can encompass temperatures ranging from about 1ºC to about 20ºC lower than the thermal melting point, depending upon the desired degree of stringency, as is understood in the art. Nucleic acids that do not hybridize to each other under stringent conditions are still substantially identical if the polypeptides they encode are substantially identical. This can occur, e.g., when a copy of a nucleic acid is created using the maximum codon degeneracy allowed by the genetic code. One indication that nucleic acid sequences are substantially identical is that the polypeptide encoded by the nucleic acids has immunological cross-reactivity with the polypeptide encoded by the other nucleic acid.

[0140] "Stringent conditions" are conditions under which (1) the probe hybridizes to the target sequence, but not to other sequences (i.e., the probe is specific for the target sequence), and (2) the target sequence is not present in excess. Stringent conditions can be those in which the salt concentration, temperature, and osmolarity are as described in Sambrook et al. (1989, Molecular Cloning, A Laboratory Manual, 2nd Ed., Cold Spring Harbor Laboratory, New York). Non-limiting examples of stringent hybridization conditions include: (1) 50ºC in a solution consisting of: 0.015 M NaCl / 0.0015 M sodium citrate (SSC); 0.1% sodium dodecyl sulfate (SDS), and (2) 42ºC in a solution consisting of: 50% formamide, 0.1% Ficoll, 0.1% polyvinylpyrrolidone, 0.1% bovine serum albumin, 50 mM sodium phosphate (pH 6.5), 750 mM NaCl, 75 mM sodium citrate, and 0.1% sodium pyrophosphate. Other examples of stringent hybridization conditions are known to those skilled in the art. See, e.g., Ausubel et al. (1999, Short Protocols in Molecular Biology, 4th Ed., John Wiley & Sons, Inc., Hoboken, N.J.).

[0141] In the context of nucleic acid hybridization experiments (e.g., Southern and Northern hybridizations), "stringent hybridization conditions" and "stringent hybridization wash conditions" are sequence-dependent, and vary depending on the environment. Longer sequences hybridize specifically at higher temperatures. A thermodynamic melting point (Tm) is the temperature at which 50% of a target sequence hybridizes to a perfectly matched primer or probe sequence (at a defined ionic strength and pH). Specificity is typically a function of the temperature at which the hybridization is washed after hybridization. The key factors are ionic strength and temperature of the last wash solution. For DNA-DNA hybrids, Tm can be approximated from the equation of Meinkoth and Wahl (1984, Anal. Biochem., 138(2):267-84); T m81.5ºC + 16.6 (log M) + 0.41 (%GC) -0.61 (%formamide) - 500 / L; where M is the molarity of monovalent cations, %GC is the percentage of guanosine and cytosine nucleotides in the DNA, %formamide is the percentage of formamide in the hybridization solution, and L is the length of the hybrid in base pairs. Tm is lowered about 1ºC for each 1% mismatch; therefore, Tm, hybridization, and / or wash conditions can be adjusted to hybridize with sequences having the desired identity. For example, if sequences having >90% identity are sought, Tm can be lowered by 10ºC. Typically, stringent conditions are chosen to be about 5ºC lower than the Tm of the particular sequence to its complement under defined ionic strength and pH. However, extremely stringent conditions can utilize temperatures lower than 1ºC, 2ºC, 3ºC, or 4ºC below Tm; moderately stringent conditions can utilize temperatures lower than 6ºC, 7ºC, 8ºC, 9ºC, or 10ºC below Tm; and low stringent conditions can utilize temperatures lower than 11ºC, 12ºC, 13ºC, 14ºC, 15ºC, or 20ºC below Tm. Using this equation, hybridization and wash compositions, and the desired temperature, one of ordinary skill will understand that variations in stringency of the hybridization and / or wash solutions are inherently described. If the degree of mismatch is such that the temperature is below 45ºC (in aqueous solution) or 32ºC (in formamide solution), then the SSC concentration can be increased so that higher temperatures can be used. Typically, high stringency hybridization and wash conditions are chosen to be about 5ºC lower than Tm of the particular sequence.

[0142] An example of highly stringent wash conditions is 0.15 M NaCl at 72ºC for approximately 15 minutes. An example of stringent wash conditions is 0.2 x SSC wash at 65ºC for 15 minutes. Typically, a low-stringent wash is performed before a highly stringent wash to remove background signals. An example of a moderately stringent wash for, for example, duplexes of more than 100 nucleotides is 1 x SSC at 45ºC for 15 minutes. For short nucleotide sequences (e.g., about 10 to 50 nucleotides), stringent conditions typically involve a salt concentration of less than about 1.5 M, a Na ion concentration (or other salt) of less than about 0.01 to 1.0 M, at pH 7.0 to 8.3, and a temperature typically of at least about 30ºC, and for long oligonucleotides (e.g., > 50 nucleotides) at at least about 60ºC. Stringent conditions can also be achieved by adding a destabilizing agent such as formamide. Generally, in a specific hybridization assay, a signal-to-noise ratio of 2x (or higher) compared to that observed for irrelevant oligonucleotides indicates that a specific hybridization has been detected. If nucleic acids that do not hybridize to each other under stringent conditions encode substantially identical proteins, then the nucleic acids remain substantially identical. This can occur, for example, when generating nucleic acid copies using the maximum codon degeneracy allowed by the genetic codon.

[0143] Very stringent conditions can be applied to the T of specific oligonucleotides. m Equal. An example of stringent conditions for hybridization of complementary nucleic acids with more than 100 complementary residues on the filter in DNA or RNA blotting is 50% formamide, for example, hybridization at 37ºC in 50% formamide, 1 M NaCl, and 1% SDS, followed by washing at 60ºC to 65ºC in 0.1 x SSC. Exemplary low-stringency conditions include hybridization at 37ºC with a buffer of 30% to 35% formamide, 1 M NaCl, and 1% SDS (sodium dodecyl sulfate), followed by washing at 50ºC to 55ºC in 1x to 2x SSC (20x SSC = 3.0 M NaCl / 0.3 M trisodium citrate). Exemplary moderate-stringency conditions include hybridization at 37ºC in 40% to 45% formamide, 1.0 M NaCl, and 1% SDS, followed by washing at 55ºC to 60ºC in 0.5x to 1x SSC.

[0144] Northern blotting, or RNA blotting, is a method used to identify RNA sequences that hybridize with known probes, such as oligonucleotides, DNA fragments, cDNA or fragments thereof, or RNA fragments. Probes can be used in various ways, such as... 32P isotope labels, by biotinylation, or with enzymes. RNA to be analyzed can generally be separated electrophoretically on agarose or polyacrylamide gels, transferred to nitrocellulose, nylon, or other suitable membranes, and hybridized with probes using standard techniques well known in the art.

[0145] The nucleic acid sample can be contacted with the oligonucleotide in any suitable manner known to one of skill in the art. For example, the DNA sample can be dissolved in solution and contacted with the oligonucleotide by dissolving the oligonucleotide in the solution containing the DNA sample under conditions that allow hybridization. Suitable conditions are well known to one of skill in the art. Alternatively, the DNA sample can be dissolved in a solution containing the oligonucleotide immobilized on a solid support, whereby the DNA sample can be contacted with the oligonucleotide by immersing the solid support having the oligonucleotide immobilized thereon in a solution containing the DNA sample.

[0146] The term "substrate" refers to any solid support to which an oligonucleotide can be attached. The substrate material can be covalently or otherwise modified with a coating or functional group to facilitate binding of the oligonucleotide. Suitable substrate materials include, among others, polymers, glass, semiconductors, paper, metals, gels, and hydrogels. The substrate can have any physical shape or size, such as a disc, a strip, or a microparticle. The term "spot" refers to a unique location on a substrate to which an oligonucleotide of known sequence is attached. A spot can be an area on a planar substrate, or it can be a microparticle, for example, distinguishable from other microparticles. The term "bound" means attached to a solid substrate. For the purposes of a screening assay, a spot is "bound" to a solid substrate when it is attached at a specific location on the substrate.

[0147] In certain embodiments of the disclosure, the substrate is a polymer, glass, semiconductor, paper, metal, gel, or hydrogel. In certain embodiments of the disclosure, the kit can further comprise a solid substrate and at least one control oligonucleotide, wherein the at least one control oligonucleotide is bound to a unique spot on the substrate.

[0148] In certain embodiments of the disclosure, the solid substrate is a microarray. "Array" or "microarray" are used synonymously herein to refer to a plurality of primers or probes attached to one or more distinguishable spots on a substrate. A microarray can comprise a single substrate or multiple substrates, such as multiple beads or microspheres. A "copy" of a microarray contains the same types and arrangements of primers or probes.

[0149] Methods for detecting or predicting cardiovascular disease or estimating survival

[0150] Better cardiovascular disease risk assessment or earlier detection of cardiovascular disease is the first step to more effective prevention. Those identified as being at higher risk of CHD, CHD events (e.g., 69% PPV for CHD) or having CHD can be immediately followed up for further testing (such as coronary artery calcification testing or angiography) and more aggressive intervention. They can be periodically tested or retested to determine severity, identify, tailor, optimize one or more interventions (e.g., lifestyle, medical, therapeutic), manage and monitor. Conversely, those at lower risk (e.g., 99% NPV for CHD) or without CHD can be periodically retested due to the dynamic nature of DNA methylation to determine severity, identify, tailor, optimize one or more interventions (e.g., lifestyle, medical, therapeutic), manage and monitor them for continued prevention. In addition, those with CVD (e.g., CHD) can be evaluated and their survivability estimated based on their genetic and / or epigenetic profile. It will be appreciated that in some cases (e.g., for monitoring or follow-up purposes; for evaluating survivability of individuals at risk of developing or who have been determined to have CVD), the step involving detection of one or more SNPs can not be necessary as this information can already be available (e.g., from a prior determination) or can not be necessary to predict CVD or survivability or severity in CVD or to identify, tailor and optimize one or more interventions or to manage individuals.

[0151] Overall, calculators based on conventional risk factors and / or other detection tests (such as stress tests) are significantly less sensitive, less generalizable, and also depict a gender gap in performance compared to the integrated genetic-epigenetic model. In contrast, the integrated genetic-epigenetic model described herein has the ability to capture and better understand the complex nature of CVD through three angles: genetics (static genetic risk), DNA methylation (dynamic acquired risk), and genetic confounding of methylation signatures (i.e., G + M + GxM).

[0152] The present disclosure provides methods of determining whether a subject is likely to have a CVD by determining the methylation status of a CpG dinucleotide repeat sequence or a CpG dinucleotide repeat motif region, wherein the methylation status of the CpG dinucleotide is associated with CVD. However, the same principles apply to assessing the prevalence and / or incidence of many different types of CVD, including without limitation coronary heart disease (CHD) (e.g., obstructive CHD), stroke, arrhythmia, cardiac arrest, congestive heart failure, atherosclerotic cardiovascular disease (ASCVD) and its related cardiovascular events (CVEs), including, for example, obstructive coronary artery disease (CAD), ischemia with non-obstructive coronary artery (INOCA), myocardial infarction (MI), stroke (e.g., TIA, hemorrhagic), and cardiovascular death. The present disclosure also provides methods for determining the severity of a subject having or at risk of having a CVD and / or estimating the survival of the subject. In certain embodiments, the methods determine the methylation status and / or SNPs of a plurality (e.g., any integer between 1 and 10,000, such as at least 100) of CpG dinucleotides.

[0153] As used herein, "biological sample" encompasses, in essence, any sample type that can be obtained from a subject that can be used in the methods described herein. The biological sample can be any bodily fluid, tissue, or any other sample in which a clinically relevant biomarker level can be determined. "Biological sample" can also encompass cells in culture, cell supernatant, cell lysate, blood, serum, plasma, urine, cerebrospinal fluid, biological fluids, and tissue samples. A variety of techniques and reagents can be used in the methods of the present disclosure. In one embodiment of the present disclosure, the presence of one or more SNPs and / or the methylation status of one or more CpG dinucleotides in a blood sample or a sample derived from blood (e.g., plasma, circulating, peripheral, lymphocytes, etc.) is determined. The biological sample can also be saliva. Typically, a biological sample containing nucleic acid is provided and tested. The biological sample can be obtained from a subject using well-known techniques, such as finger prick, venipuncture, lumbar puncture, fluid sample (such as saliva or urine), or tissue biopsy, etc.

[0154] As used herein, the term "healthy" means that the subject does not exhibit a particular condition and is not more likely than randomly to be susceptible to the particular condition.

[0155] The American Psychological Association (APA) defines prevalence as "the total number or percentage of cases (e.g., of a disease or disorder) present in a population" (APA Dictionary of Psychology, (American Psychological Association, Washington, DC, 2007)). In some contexts, point prevalence is used to describe the prevalence of cases at a discrete point in time, while period prevalence is used to describe the number of cases present over a period of time (e.g., one month, one year). Prevalence is often expressed as a rate per unit of population (e.g., number of cases per 100,000 people), rather than an absolute number or percentage.

[0156] Similarly, the APA defines incidence as "the rate of new cases of a given event or condition (e.g., disorder, disease, symptom, or injury) in a specified population over a given period of time" (APA Dictionary of Psychology, (American Psychological Association, Washington, DC, 2007)). As used herein, the term "incidence" is defined as the propensity or susceptibility of a subject to exhibit a condition (in this case, CVD (e.g., CHD)). In some contexts, the period of time can be one year or less; in some contexts, the period of time can be longer than one year (e.g., two years, five years, ten years).

[0157] The APA defines diagnosis as "the process by which the nature of a disease or disorder is identified and determined through its signs and symptoms, using assessment techniques (e.g., tests and examinations) and other available evidence" (APA Dictionary of Psychology, (American Psychological Association, Washington, DC, 2007)). Diagnosis can refer to a present period of time, or to a past or future period of time.

[0158] Likewise, the APA defines prognosis as "a prediction of the course, duration, severity, and outcome of a condition, disease, or disorder" (APA Dictionary of Psychology, (American Psychological Association, Washington, DC, 2007)). For example, a prognosis can be made for a period of one month, six months, one year, five years, ten years, or longer.

[0159] Risk assessment is defined as "a study performed on a subject in an attempt to determine the likelihood that the person will develop a particular disease, or, if the disease is already present, the likelihood that the person will suffer a worsening of it or die as a result of it" (Youngson, 2005, Collins Dictionary of Medicine). In some contexts, risk assessment is based on a condition or event, rather than a disease. In some contexts, risk assessment is determined over a period of time (e.g., months, years).

[0160] As used herein, survival or survivability generally refers to the number of days an individual is expected to survive, measured from the point in time at which a biological sample (e.g., a saliva sample, a blood draw, etc.) is obtained and used to make this determination.

[0161] The skilled artisan will appreciate that in certain instances, for example, the "detection" of a biomarker (e.g., the presence or absence of a biomarker) can result in a "diagnosis." Similarly, the skilled artisan will appreciate that in certain instances, "detection" and "diagnosis" are used interchangeably.

[0162] Described herein are biomarkers that can be used in a method of detecting CVD in a subject or estimating the survivability (e.g., predictive or prognostic) of a subject having or at risk of having CVD. Such methods generally include providing a biological sample from a subject; contacting DNA from the biological sample with bisulfite under alkaline conditions; contacting the bisulfite treated DNA with at least one first oligonucleotide primer of at least 8 nucleotides in length, the at least one first oligonucleotide primer complementary to a sequence comprising a CpG dinucleotide (e.g., at GC locus called cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or another biomarker from Appendix A); and determining the methylation status of the CpG dinucleotide. It will be appreciated that the at least one first oligonucleotide probe can detect a non-methylated CpG dinucleotide or a methylated CpG dinucleotide. Such a method can further include determining the genotype of a single nucleotide polymorphism (SNP) (e.g., rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433, or another biomarker from Appendix C) or a second SNP in linkage disequilibrium with the first SNP. As described herein, the methylation of one or more particular CpG dinucleotides and the presence of one or more particular SNPs can be used to predict CVD in the subject. As also described herein, the methylation of one or more particular CpG dinucleotides and the presence of one or more particular SNPs can be used to estimate the survivability of CVD.

[0163] In some embodiments, the method further includes contacting the bisulfite treated DNA with at least one second oligonucleotide probe of at least 8 nucleotides in length, the at least one second oligonucleotide probe complementary to a sequence comprising a CpG dinucleotide, wherein the at least one second oligonucleotide probe detects a non-methylated CpG dinucleotide or a methylated CpG dinucleotide not detected by the at least one first oligonucleotide probe.

[0164] In some embodiments, the ratio of methylated CpG dinucleotides to non-methylated CpG dinucleotides in the biological sample can be determined as part of the methods described herein. Determining the ratio of methylated CpG dinucleotides to non-methylated CpG dinucleotides can allow for estimation or determination of risk or outcome.

[0165] It will be appreciated that determining the methylation status of the one or more CpG dinucleotides and determining the presence (or absence) of a SNP can utilize any number of techniques, such as amplification and / or sequencing steps. Amplification and sequencing are well known techniques in the art and are routinely used to determine the methylation status of a particular sequence and the presence / absence of a SNP.

[0166] Methods of determining the presence of a biomarker associated with CHD in a biological sample from a subject are provided. Similar methods can also be used for any other form of CVD. Such methods generally include providing a first portion of the biological sample and contacting DNA from the first portion with bisulfite under alkaline conditions. The bisulfite treated first portion can be contacted with a first oligonucleotide probe that is at least 8 nucleotides in length and complementary to a sequence comprising a CpG dinucleotide (e.g., detected at the CG loci known as cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or another biomarker from Appendix A), and if necessary or desired, a second portion of the biological sample can be contacted with a nucleic acid probe that is at least 8 nucleotides in length and complementary to a SNP (e.g., rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 or another biomarker from Appendix C).

[0167] As described herein, the percent methylation at one or more of the GC loci named cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 (or at CpG dinucleotides in linkage disequilibrium with one or more such CpG dinucleotides) and the identity of the nucleotide at one or more of the SNPs named rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 (or at SNPs in linkage disequilibrium with one or more such SNPs) are biomarkers associated with CVD and can be used to predict the likelihood that an individual will develop CVD and / or to predict the severity of the disease or outcome (e.g., survival) of the individual.

[0168] In addition to the SNPs and / or CpG biomarkers identified herein, one or more clinical indicators can be used in either or both of aiding diagnosis and prognosis, intervention (e.g., lifestyle, treatment, medical) selection or customization or optimization, management or monitoring. Such clinical indicators can include, without limitation, demographics (e.g., age, sex, race); vital signs (e.g., heart rate (beats / min), systolic blood pressure (mm Hg), diastolic blood pressure (mm Hg)); medical history (e.g., smoking, atrial fibrillation / flutter, hypertension, coronary artery disease, myocardial infarction, heart failure, peripheral arterial disease, COPD, diabetes (type 1 or type 2), CVA / TIA, chronic kidney disease, hemodialysis, angioplasty (peripheral or coronary), stent (peripheral or coronary), CABG, percutaneous coronary intervention); medications (ACE-I / ARB, beta blocker, aldosterone antagonist, loop diuretic, nitrate, CCB, statin, aspirin, warfarin, clopidogrel); coronary computed tomography angiography (e.g., aortic stenosis, FFR-CT, plaque type, total plaque); echocardiogram results (e.g., LVEF (%), RSVP (mm Hg)); stress test results (e.g., ischemia on scan, ischemia on ECG); angiography results (e.g., > 2-vessel coronary stenosis > 70%, > 3-vessel coronary stenosis > 70%); and / or laboratory measurements (e.g., sodium, blood urea nitrogen (mg / dL), creatinine (mg / dL), eGFR (median, CKD-EPI), total cholesterol (mg / dL), LDL cholesterol (mg / dL), ribitol, hemoglobin, hematocrit, triglycerides, alkaline phosphatase, HbAlc, HDL-C, non-HDL-C, ApoB, LDL-P 1HDL-P 1 sdLDL-C, VLDL-C, Lp(a), hs-CRP, LpPLA2 activity, homocysteine, B-type natriuretic peptide, glycosylated hemoglobin (%), glucose (mg / dL), HGB (mg / dL), C-reactive protein (mg / L), NT-proBNP, KIM-1, osteopontin, TIMP-1, kidney injury molecule 1, N-terminal pro-B-type natriuretic peptide, osteopontin, tissue inhibitor of metalloproteinase-1, uridine, carotenoid 3, ribitol, 1-stearoyl-2- adrenoyl-GPC, N-acetyl-isoputreanine, lysophosphatidylcholine, vanillyl lactate, 3- ureidopropionic acid, serum paraoxonase, bone morphogenetic protein 1, carboxypeptidase B2, albumin, histone H2B type 1-K, Versican core protein, insulin-like growth factor binding protein 2, matrix remodeling associated protein 5, etc.).

[0169] Kit for detecting cardiovascular disease (CVD)

[0170] In another embodiment of the disclosure, articles of manufacture and kits containing probes, oligonucleotides, and / or antibodies are provided. Such articles of manufacture can be used in the methods described herein. The articles of manufacture can include one or more containers having, for example, labels. Suitable containers include, for example, bottles, vials, and test tubes. The containers can be formed from a variety of materials such as glass or plastic. The containers can hold compositions which include one or more reagents useful in practicing the methods described herein. Labels on the containers indicate that the compositions can be used for specific applications. Kits of the disclosure will typically include the above containers and one or more other containers containing materials desirable from a commercial and user standpoint, including buffers, diluents, filters, and package inserts with instructions for use.

[0171] In certain embodiments, the disclosure provides kits for determining the methylation status of at least one CpG dinucleotide and, in some cases, also the presence of at least one single nucleotide polymorphism (SNP). In certain embodiments, the kits as described herein can contain any integer between 1 and 10,000 (such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,..., 9997, 9998, 9999, 10,000) number of primers. As used herein, the term “nucleic acid primer” or “nucleic acid probe” or “oligonucleotide” encompasses both DNA and RNA sequences. In certain embodiments, the primers or probes can be physically located on a single solid matrix or on multiple matrices.

[0172] A kit as described herein can include at least one first nucleic acid primer (e.g., at least 8 nucleotides in length) complementary to a bisulfite converted nucleic acid sequence comprising a CpG dinucleotide (e.g., detected at GC loci referred to as cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584) and, in some cases, at least one second nucleic acid primer (e.g., at least 8 nucleotides in length) complementary to a SNP (e.g., rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433). The at least one first nucleic acid primer can detect a methylated or non-methylated CpG dinucleotide.

[0173] It should be appreciated that any of the nucleic acid primers, probes, or oligonucleotides described herein can include one or more nucleotide analogs and / or one or more synthetic or non-natural nucleotides.

[0174] It should also be appreciated that a kit described herein can include a solid substrate. In some embodiments, one or more nucleic acid primers can be bound to a solid support. Examples of solid supports include, without limitation, polymers, glass, semiconductors, paper, metals, gels, or hydrogels. Additional examples of solid supports include, without limitation, microarrays or microfluidic cards.

[0175] It should also be appreciated that any of the kits described herein can include one or more detectable labels. In some embodiments, one or more nucleic acid primers can be labeled with the one or more detectable labels. Representative detectable labels include, without limitation, enzymatic labels, fluorescent labels, and colorimetric labels.

[0176] Algorithms for predicting cardiovascular disease (CVD) or estimating survivability from CVD

[0177] Any number of algorithms can be used, including without limitation statistical algorithms (e.g., linear regression, logistic regression, proportional hazards model, etc.), machine learning algorithms (e.g., random forest, gradient boosting, support vector machines, neural networks (e.g., deep neural networks, extreme learning machines (ELM)), Bayesian classifiers, hidden Markov models, etc.), deep learning algorithms (e.g., convolutional neural networks, recurrent neural networks, autoencoders, large language models, etc.), time series algorithms (e.g., ARIMA, etc.), Bayesian model algorithms (e.g., Bayesian networks, etc.), and / or financial algorithms (e.g., decision trees, discrete event simulation, budget impact, etc.). See, e.g., McKinney et al., 2011, Appl. Bioinform., 5(2):77-88; Gunther et al., 2012, BMC Genet., 13:37; and Ogutu et al., 2011, BMC Proceedings, 5(Suppl 3):S11. Any type of machine learning algorithm or deep learning neural network algorithm (tuned or untuned) can be used that is capable of capturing linear and / or non-linear contributions of traits for prediction. In some cases, a combination of algorithms is used (e.g., a combination or fusion of multiple algorithms that capture linear and / or non-linear contributions of traits).

[0178] Further, the one or more algorithms can implement any one or more of: a regression algorithm; an instance-based method (e.g., k-nearest neighbors, learning vector quantization, self-organizing map, etc.); a regularization method; a decision tree learning method (e.g., classification and regression tree, chi-squared method, random forest method, multivariate adaptive regression splines, gradient boosting machine method, etc.); a Bayesian method (e.g., Naive Bayes, Bayesian belief network, etc.); a kernel method (e.g., support vector machine, linear discriminant analysis, etc.); a clustering method (e.g., k-means clustering); an association rule learning algorithm (e.g., a priori algorithm); an artificial neural network model (e.g., back-propagation method, Hopfield network method, learning vector quantization method, etc.); a deep learning algorithm (e.g., Boltzmann machine, convolutional network method, stacked auto-encoder method, etc.); a dimensionality reduction method (e.g., principal component analysis, partial least squares regression, etc.); an ensemble method (e.g., boosting, bootstrap aggregating, gradient boosting machine method, etc.); and any other suitable algorithm.

[0179] By way of example only, Random Forest™ is a popular machine learning algorithm created by Breiman and Cutler for generating "classification trees" (see, e.g., "stat.berkeley.edu / ~breiman / RandomForests / cc_home.htm" on the World Wide Web). Using standard machine learning and predictive modeling techniques, a diagnostic classifier algorithm is written to execute in the R and Python programming languages according to the guidelines fully described by Breiman and Cutler (but it can be executed in a variety of other programming languages). The diagnostic classifier algorithm is generated using data from at least two traits (T) and the target diagnosis from the population. To determine the output (e.g., diagnosis) for a new individual, only the values of the at least two traits (T) are determined and this information is input into an algorithm that can capture the linear and non-linear contributions of the traits (e.g., the diagnostic classifier algorithm described herein or another algorithm discussed above).

[0180] Prior to fitting one or more models, input data can be conditioned or otherwise pre-processed such that the conditioned data elements (e.g., genomic reads related to a target locus, functional data, sensor data, other lifestyle data, etc.) are suitable for further processing. As described herein, conditioning can include filtering data (e.g., sensor data outputs having a confidence value below a threshold, etc.). One or more pre-processing steps can be taken on one or more model inputs prior to training one or more models, such as dimensionality reduction (e.g., principal component analysis, linear discriminant analysis, autoencoders, uniform manifold approximation and projection, partial least squares regression, etc.).

[0181] One or more model predictions (e.g., risk scores, diagnoses, etc.) can be computed with uncertainty. Methods for estimation can include bootstrap methods, Bayesian methods, Monte Carlo dropout, ensemble methods, sensitivity analysis, etc. The uncertainty of the methods and / or models described herein can be aggregated to provide an overall system uncertainty. Uncertainty quantification can encompass all aspects of marker measurements. For example, in epigenetic measurements, uncertainty quantification can include, but is not limited to, sampling error, reagent quality, instrument and human error.

[0182] The uncertainties referred to herein, which can be used for quality control. For example, one or more known reference samples and / or one or more measurement results can be compared to one or more new measurement results. The difference between one or more known measurement results and one or more new measurement results and their uncertainties can be used to evaluate one or more errors and / or one or more uncertainties and how they compare to defined acceptable thresholds. This process helps to identify sources of errors and / or uncertainties and to minimize one or more such errors and / or one or more uncertainties to ensure the reliability of one or more measurement results. For example, it can be used to determine whether one or more samples need to be re-measured or re-collected one or more times to meet defined acceptable thresholds for one or more measurement results and / or one or more markers.

[0183] The returned classification regression and / or other outputs of one or more models can include confidence-related parameters returned in such classifications. In particular, the confidence-related parameters can have scores (e.g., percentiles, other scores) indicative of confidence in the returned outputs. Confidence can be estimated by aggregating measurement results and / or modeling uncertainty. Transforming output data can also be used to enhance the scalability of the previously described models. For example, Shapley Additive explanations (SHAP), Local Interpretable Model-agnostics Explanations, Integrated Gradients, Partial Dependence plots, Global Surrogate Models, and the like can be used to enhance the scalability of user outputs. One or more of these methods can be used simultaneously. In a specific example, SHAP can be utilized to identify one or more markers that have the most significant contribution to a condition or indication.

[0184] Additionally or alternatively, dynamic aspects of features derived from the sample (e.g., changes in markers over time, changes in frequency between instances of the corresponding feature, other temporal aspects, other frequency-related aspects, and the like) can be used to predict or otherwise anticipate the health condition status to generate a personalized intervention plan.

[0185] The sample can be collected once (e.g., at a single point in time) or at multiple points in time (e.g., at random points, at regular points, in relation to triggering events, at other frequencies, and the like).

[0186] As described herein, the inputs can be the methylation status of at least one genotype (e.g., SNP) and at least one CpG dinucleotide and / or other data, and the outcome can represent a positive probability or a negative probability of CVD, however, severity can also be evaluated. The trait (T) used to determine the outcome can represent the methylation status of at least one CpG dinucleotide or at least one genotype (e.g., genotype of a SNP), but the trait (T) can also correspond to at least one interaction (e.g., between methylation status and genotype (CpG x SNP), between methylation status of two different loci (CpG x CpG), or between two different genotypes (SNP x SNP)). It should be appreciated that any such interactions can be visualized using partial dependence plots.

[0187] The inputs can also be data or information (e.g., a data set) including, without limitation, clinical diagnoses; demographics (e.g., gender, race); lifestyle; imaging (e.g., cardiac CT scan, cardiac MRI, coronary angiogram); features derived from imaging (e.g., FFR from CT scan, percent stenosis); results of electrocardiogram or echocardiogram testing; results of stress testing; blood tests (e.g., for metabolic assays, genetics, epigenetics, proteins, etc.); blood pressure; results of carotid ultrasound; and combinations thereof.

[0188] The data set can include, without limitation, data derived from one or more of the patient’s body weight (e.g., receiving a patient’s body weight value generated from a digital body scale), body fat percentage, muscle mass, body water, height or other length measurement (e.g., via a ruler or tape measure), other body mass index (BMI) related parameters, blood chemistry and biochemistry information, inflammation markers, fasting blood glucose, high-density lipids, low-density lipids, blood interleukins, c-reactive protein, blood cell counts, electrophysiological signals (e.g., electroencephalogram signals, electromyogram signals, galvanic skin response signals, electrocardiogram signals, etc.), heart rate, body temperature, cardiovascular parameters, continuous glucose monitoring (glycemic response), respiration parameters (e.g., respiration rate, respiration depth / shallowness, etc.), blood oxygenation signals, exercise parameters, and any other suitable physiologically related parameters. Additionally or alternatively, the data set can include data derived from one or more of an individual’s electronic health record, health plan declarations, questionnaires, surveys, wearable devices, public sources (e.g., repositories), and any other direct or indirect data. Additionally or alternatively, the data set can include raw, imputed, transformed, longitudinal, cross-sectional, or temporal data.

[0189] Figure 1is a block diagram of an example CVD classification system 100. In some embodiments, the system 100 can perform monitoring and / or prediction of CVD. For example, the system 100 can be used to perform one or more of the example methods described herein.

[0190] In the illustrated example, the subject 101 provides a subject sample 102. In some embodiments, the subject sample 102 can be a blood sample, a saliva sample, a mucus sample, a urine or fecal sample, or any other appropriate biological sample from the subject 101. In some embodiments, a medical professional 103 (e.g., a doctor, a nurse, a laboratory technician, a caregiver) can assist the subject 101 in obtaining the subject sample 102. In some embodiments, the subject 101 can obtain the subject sample 102 from themselves (e.g., by using a portable blood sampling device or a home collection kit).

[0191] A nucleic acid isolation module 110 isolates a nucleic acid sample 112 from the subject sample 102. In some embodiments, the nucleic acid isolation module 110 can be a manual, semi-automated, or automated process that performs one or more of cell lysis, removal of contaminating proteins, deactivation of DNA and / or RNA enzymes, and recovery of DNA and / or RNA. For example, the nucleic acid isolation module 110 can be a portion of an automated process or an analytical device configured to isolate the nucleic acid sample 112 from the subject sample 102. In another example, the nucleic acid isolation module 110 can be a portion of one or more of the example kits described in this document for use by a human user such as the medical professional 103.

[0192] The genotyping assay module 120 receives the portion 114a of the nucleic acid sample 112. The genotyping assay module 120 is configured to perform a genotyping assay on the portion 114a of the nucleic acid sample 112 to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or a first SNP from Appendix C and / or is a second SNP in linkage disequilibrium (R > 0.3) with a first SNP selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rsl333048, rsl2714414, rs942317, and rs1441433 or from Appendix C to determine, identify, or otherwise obtain a set of genotype data 122. In some embodiments, the genotyping assay module 120 can be a manual, semi-automated, or automated process. For example, the genotyping assay module 120 can be an automated process or a portion of an analytical device configured to perform a genotyping assay on the portion 114a. In another example, the genotyping assay module 120 can be a portion of one or more example kits described in this document for use by a human user such as a medical professional 103 or a laboratory technician.

[0193] The methylation assay module 130 receives the portion 114b of the nucleic acid sample 112. The methylation assay module 130 is configured to bisulfite-convert nucleic acids in the portion 114b of the nucleic acid sample 112 and perform a methylation assessment on the portion 114b of the nucleic acid sample 112 to detect a methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or a CpG site in linkage disequilibrium (R > 0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A to determine, identify, or otherwise obtain a set of methylation data 132.

[0194] The identification system 140 is configured to receive the set of genotypic data 122 and the set of methylation data 132 and identify one or more predetermined traits or characteristics of the subject 101 based on a diagnostic classifier algorithm module 142. The diagnostic classifier algorithm module 142 is configured to account for at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect. In some embodiments, the diagnostic classifier algorithm module 142 can execute one or more of the algorithms described herein that can indicate the presence of a disease (e.g., a diagnostic indicator), or a predisposition to develop a disease (e.g., a prediction), or a severity of a disease, or a selection, customization, or optimization of one or more interventions (e.g., lifestyle, medical, therapeutic), or an effectiveness of management or monitoring of a disease, severity, or risk. For example, the identification system can be configured to identify genetic and / or environmental characteristics that determine the presence or likelihood of developing a disease (e.g., a cardiovascular disease) in a subject, even when the disease is of polygenic origin. In some implementations, the diagnostic classifier algorithm module 142 can be a machine learning algorithm that can account for linear and non-linear effects.

[0195] The identification system 840 provides an output 150 based on the diagnostic and / or prognostic indicators provided by the diagnostic classifier algorithm module 142. In some embodiments, the identification system 140 can include an output module configured to provide the output 150. In some implementations, the output 150 can be an identification of one or more diseases that the subject 101 can have already developed. For example, the output 150 can indicate that a trait indicative of the presence of a cardiovascular disease was found in the subject 101. In some implementations, the output 150 can be an indication of the likelihood that the subject 101 can develop a disease within a predetermined timeframe (e.g., a 43% chance that the subject 101 can develop a cardiovascular disease within 3 years, a 77% chance that the subject 101 can have a heart attack within 2 years). In some implementations, the output 150 can include treatment and / or prevention recommendations based on the diagnostic and / or prognostic indicators provided by the diagnostic classifier algorithm module 142. For example, in response to an identification or prediction of a diabetes or a heart condition in the subject 101, the output 150 can include a recommendation to consult with a medical professional 103, to identify possible dietary or lifestyle changes for the subject 101 to address or avoid the condition, to identify potential interventions and / or remedies for the subject 101 to consider when consulting with a medical professional 103, or a combination of these and / or any other appropriate information based on the output of one or more algorithms of the diagnostic classifier algorithm module 142. In some cases, the output 150 can be an estimate of the survivability of a subject at risk of developing or who has been determined to have CVD.

[0196] In the illustrated example, the output 150 is provided in various formats. The format of the information provided by the output 150 can be a message 160 provided to the subject 101 and / or the medical personnel 103. In some implementations, the format of the message 160 can be a report (e.g., a word processing file, a portable document format file) stored at least temporarily on a non-transitory storage medium (e.g., a hard drive, a flash memory) that can be retrieved by the subject 101 and / or the medical personnel 103 for review. In some implementations, the format of the message 160 can be an electronic message (e.g., an email, a text message, an instant message) that is transmitted to the subject 101 and / or the medical personnel 103 for review. In some implementations, the message 160 can be a printed report. For example, the output 150 can be provided to a printing system configured to generate a hard copy report based on the output 150. Subsequent automated or manual processing systems can package the report as a letter or other package that can be physically delivered to the subject 101 and / or the medical personnel 103 (e.g., the system 100 can create a paper printout and mail it through the postal mail).

[0197] The therapeutic device 170 can be configured to receive the diagnostic and / or prognostic indicators provided by the output 150 and provide an intervention (e.g., lifestyle, treatment, medication) based on the diagnostic and / or prognostic indicators. For example, the output 150 can indicate that the subject 101 has a high likelihood of suffering a cardiac arrest within the next two years, and the therapeutic device 170 can be a pharmaceutical (e.g., a tablet or capsule) or an implantable drug delivery system that reacts by identifying or receiving a configuration setting for an appropriate dosage of a statin, acetylsalicylic acid (aspirin), an anti-inflammatory drug, a blood thinner, or a combination of these and / or any other appropriate therapeutic agent and / or prophylactic substance. In some embodiments, the therapeutic device 170 can be configured to also include one or more of the nucleic acid isolation module 110, the genotyping assay module 120, the methylation assay module 130, or the identification system 140.

[0198] The storage system 180 is configured to store the output 150. For example, the information included in the output 150 can be stored temporarily, for a predetermined period of time, or substantially permanently in a database, in a file, or as any other appropriate collection of data. In some embodiments, the storage system 180 can store the output 150 in a non-transitory storage medium (e.g., a hard drive, a flash memory). For example, the output 150 can include some or all of the set of genotype data 122, the set of methylation data 132, and / or the personal health record of the subject 101 that the subject 101 can store or carry with them. In some embodiments, the storage system 180 can store the output 150 as a physical medium, for example, the storage system 180 can include a printer that can generate a paper report based on the output 150, and / or store the report as a hard copy that can be physically archived for later retrieval.

[0199] The input / output device 182 is a physical device configured to display or otherwise present an output that is perceptible to a person (e.g., the subject 101, the medical staff 103). For example, the input / output device 182 can be an electronic display device in a doctor’s office. The system 100 can process the subject sample 102 and then change the configuration of pixels on a screen to modify the information displayed by the input / output device 182 based on the output 150 (e.g., the screen can be updated to display the identified diagnosis and / or prognosis of the subject 101 to the medical staff). In another example, the input / output device 182 can be configured to provide audible (e.g., spoken output) and / or tactile (e.g., Braille, haptic, vibratory) output that modifies or otherwise translates the output 150 into a physical and / or tangible output (e.g., to convey the diagnostic and / or prognostic indicators in a manner perceptible to a user with a visual impairment). In another example, the input / output device 182 can be configured to change, translate, or modify a physical characteristic of a physical structure or medium based on the output 150.

[0200] A user device 184 (e.g., a computer, a smartphone, a tablet, a computerized terminal) is configured to display, emit, or otherwise present one or more outputs that are perceivable by a human user (e.g., the subject 101 and / or the medical personnel 103). For example, the user device 184 can receive the output 150 (e.g., as data, as the message 160) and provide an alert and / or provide the output to the user based on the output 150 (e.g., display a report, read a report aloud). In some embodiments, the user device 184 can include one or more of the storage device 180 or the input / output device 182. In some embodiments, the user device 182 can be part of the therapeutic device 170. In some embodiments, the user device 184 can be configured to include one or more of the nucleic acid isolation module 110, the genotyping assay module 120, the methylation assay module 130, or the identification system 140.

[0201] In some implementations, some or all of the system 100 can be reused to provide additional information. For example, the system 100 can be used to collect an initial set of health information for the subject 101 and / or to identify information that can assist the medical personnel 103 in an initial diagnosis / prognosis. Subsequently, the system 100 can be used to recheck the subject 101, e.g., to determine the effectiveness of prescribed medical and / or lifestyle strategies over time. Since the set of genotypic data 122 for an individual does not change over time, the system 100 can avoid performing the functions of the genotyping assay module 120 again. In such examples, the methylation assay module 130 can be used to generate an updated version of the set of methylation data 132, and the updated set of methylation data 132 can be provided to the identification system 140, along with the previously generated set of genotypic data 122, for processing. In some implementations, the subject sample 102 can be collected periodically and processed based on the existing set of genotypic data 122 and the updated set of methylation data 132 to produce an updated output 150, which can be used to provide ongoing monitoring of one or more conditions identified for the subject 101.

[0202] Figure 2 is a flowchart of an example method 200 for cardiovascular disease classification. In some implementations, the method 200 can be some or all of the example methods described above. In some implementations, the method 200 can be a method performed by some or all of the example system 100 in Figure 1

[0203] At 210, a nucleic acid sample is isolated from a subject sample. For example, the example nucleic acid isolation module 110 can be configured to isolate and / or substantially purify a nucleic acid composition from the example subject sample 102 to produce the example nucleic acid sample 112.​

[0204] At 220, a genotyping assay is performed on a first portion of the nucleic acid sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rs1333048, rsl2714414, rs942317, and rs1441433 or a first SNP from Appendix C and / or is a second SNP in linkage disequilibrium (R>0.3) with a first SNP selected from rs2869675, rs4376434, rsl2139789, rs7585056, rs710987, rs4639796, rs1333048, rsl2714414, rs942317, and rs1441433 or from Appendix C to obtain genotype data. For example, an example genotyping assay module 120 can be used to analyze an example portion 114a of the nucleic acid sample 112 to produce an example set of genotype data 122.

[0205] At 230, a second portion of the nucleic acid sample is bisulfite converted and a methylation assessment is performed on the second portion of the nucleic acid sample to detect the methylation state of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or a CpG site in linkage disequilibrium (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A to obtain methylation data. For example, an example methylation assay module 130 can be used to process a portion 114b of the nucleic acid sample 112 to produce an example set of methylation data 132.

[0206] At 240, the genotype data from step 220 and / or the methylation data from step 230 are input into an algorithm. For example, an example set of genotype data 122 and an example set of methylation data 132 are input into an example identification system 140 and processed using an example diagnostic classifier algorithm module 142.

[0207] At 250, the at least one SNP main effect and / or the at least one CpG main effect and / or the at least one interaction effect is interpreted. For example, the example diagnostic classifier algorithm module 142 can be configured to interpret the at least one SNP main effect and / or the at least one CpG main effect and / or the at least one interaction effect. In some implementations, the diagnostic classifier algorithm module 142 can be a machine learning algorithm capable of interpreting linear and non-linear effects.

[0208] At 260, an output is provided. For example, the example identification system 140 can provide the example output 150.

[0209] At 270, another nucleic acid sample is isolated from another sample from the subject. For example, the example nucleic acid isolation module 110 can be configured to isolate and / or substantially purify a nucleic acid composition from another sample to produce another example nucleic acid sample. Since the collection of genotype data 122 from the subject does not change over time, the newly produced nucleic acid sample can be used to obtain methylation data 132, which is used with the existing collection of genotype data 122 to provide an updated output (e.g., to perform a check on the subject 101 at a later point in time). In some implementations, this streamlined approach can be performed periodically or semi-periodically to provide ongoing monitoring of one or more medical conditions identified for the subject 101.

[0210] Figure 3 FIG. 3 is a block diagram of an example computing device 300, 350 that can be used to implement a system and method described in the present document, either as a client or as one server or multiple servers. The computing device 300 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The computing device 300 can also represent a portion of a larger computerized system, including, for example, a network of computers. The computing device 350 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting as to the implementations of the present document described and claimed herein.

[0211] The computing device 300 includes a processor 302, a memory 304, a storage device 306, a high-speed interface 308 connecting to the memory 304 and multiple high-speed expansion ports 310, and a low speed interface 312 connecting to a low speed bus 314 and the storage device 306. Each of the components 302, 304, 306, 308, 310, and 312 are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. The processor 302 can process instructions for execution within the computing device 300, including instructions stored in the memory 304 or on the storage device 306 to display graphical information for a GUI on an external input / output device, such as a display 316 coupled to the high-speed interface 308. In other implementations, multiple processors and / or multiple buses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 300 can be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

[0212] The memory 304 stores information within the computing device 300. In one implementation, the memory 304 is a computer-readable medium. In one implementation, the memory 304 is a volatile memory unit or units. In another implementation, the memory 304 is a non-volatile memory unit or units.

[0213] The storage device 306 can provide mass storage for the computing device 300. In one implementation, the storage device 306 is a computer-readable medium. In various implementations, the storage device 306 can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 304, the storage device 306, or memory on processor 302.

[0214] The high-speed controller 308 manages bandwidth-intensive operations for the computing device 300, while the low-speed controller 312 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 308 is coupled to memory 304, display 316 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 310, which can accept various expansion cards (not shown). The low-speed controller 312 is coupled to storage device 306 and low-speed expansion port 317 by a low-speed bus 314. The low-speed expansion port 317, which can include various communication ports (e.g., USB, Bluetooth, Low Energy Bluetooth (BLE), Ethernet, Wireless Ethernet (WiFi), High-Definition Multimedia Interface (HDMI), ZIGBEE, visible or infrared transceivers, Infrared Data Association (IrDA), fiber optics, laser, sound, or ultrasound), can be coupled to one or more input / output devices, such as a keyboard, a pointing device, a scanner, a networking device (e.g., a gateway, a modem, a switch, or a router) through a network adapter 313.

[0215] Peripheral devices can communicate with the high-speed controller 308 through one or more peripheral interfaces of the low-speed controller 312, including, but not limited to, a USB stack, an Ethernet stack, a WiFi radio, a Low Energy Bluetooth (BLE) radio, a ZIGBEE radio, an HDMI stack, and a Bluetooth radio suitable for sensor configurations. For example, a sensor that outputs readings through a USB wire can communicate through the USB stack.

[0216] The network adapter 313 can communicate with a network 315. Computer networks often have one or more gateways, modems, routers, media interfaces, media bridges, repeaters, switches, hubs, domain name servers (DNS), and dynamic host configuration protocol (DHCP) servers that allow communication between devices on the network and devices on other networks, such as the Internet. One such gateway can be a network gateway that routes network communication traffic between devices within the network and devices outside the network. One common type of network communication traffic routed through a network gateway is a domain name server (DNS) request, which is a request to a DNS to resolve a uniform resource locator (URL) or indicated uniform resource (URI) to an associated Internet Protocol (IP) address.

[0217] The network 315 can comprise one or more networks. The one or more networks can provide communication under various modes or protocols such as Global System for Mobile Communications (GSM) voice calls, Short Message Service (SMS), Enhanced Messaging Service (EMS), or Multimedia Messaging Service (MMS) messages, Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Personal Digital Cellular (PDC), Wideband Code Division Multiple Access (WCDMA), CDMA2000, General Packet Radio Service (GPRS) or one or more television or cable networks, etc. For example, communication can be via radio frequency transceiver. Also, short-range communication can be achieved using Bluetooth, BLE, ZIGBEE, WiFi, IrDA, or other such transceivers.

[0218] In some embodiments, the network 315 can have a hub-and-spoke network configuration. The hub-and-spoke network configuration can allow for a scalable network that can accommodate components that are added, removed, fail, and are replaced. For example, this can allow for more, fewer, or different devices on the network 315. For example, if a device fails or is replaced by a newer version of the device, the network 315 can be configured such that the network adapter 313 can update the replacement device.

[0219] In some embodiments, the network 315 can have a mesh network configuration (e.g., ZIGBEE). A mesh configuration can contrast with a conventional star / tree network configuration in which networked devices are only directly linked to a small number of other network devices (e.g., bridges / switches), and the links between these devices are hierarchical. A mesh network configuration can allow infrastructure nodes (e.g., bridges, switches, and other infrastructure devices) to be directly and non-hierarchically connected to other nodes. Connections can be dynamically self-organizing and self-configuring to route data. By not relying on a central coordinator, multiple nodes can participate in the relaying of information. In the event of a failure of one or more nodes or communication links between nodes, a mesh network can self-configure to dynamically redistribute workloads and provide fault tolerance and network robustness.

[0220] The computing device 300 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 320, or multiple times in a group of such servers. It can also be implemented as part of a rack server system 324. It can also be implemented in a

[0221] The computing device 350 includes a processor 352, memory 364, an input / output device such as a display 354, a communication interface 366, and a transceiver 368, among other components. The device 350 can also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the components 350, 352, 364, 354, 366, and 368 are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.

[0222] The processor 352 can execute instructions, for example, stored in the memory 364 to implement data processing applications, programs, or processes. The processor can also include one or more integrated circuits, which share bus-based or point-to-point communication with each other and / or other components within the device 350. The processor can process instructions for execution within the computing device 350, including instructions stored in the memory 364 or on another storage device or medium.

[0223] The processor 352 can communicate with a user through a control interface 358 and a display interface 356 coupled to the display 354. The display 354 can be, for example, a TFT LCD display or an OLED display, or other appropriate display technology. The display interface 356 can comprise appropriate circuitry for driving the display 354 to present graphical and other information to a user. The control interface 358 can receive commands from the user and convert them for submission to the processor 352. In addition, an external interface 362 can be provided in communication with processor 352, so as to enable near area communication of device 350 with other devices. External interface 362 can provide, for example, for wired communication (e.g., via a docking procedure) or wireless communication (e.g., via Bluetooth or other such technologies).

[0224] The memory 364 stores information within the computing device 350. In one implementation, the memory 364 is a computer-readable medium. In one implementation, the memory 364 is one or more volatile memory units. In another implementation, the memory 364 is one or more non-volatile memory units. Expansion memory 374 can also be provided and connected to device 350 through expansion interface 372, which can include, for example, a SIMM card interface. Such expansion memory 374 can be supplied in a rigorous storage card environment for use with a mobile communication device such as the computing device 350, as well as other consumer electronic devices such as the electronic device 350. For example, expansion memory 374 can be provided as a security module for the device 350, and can act as a secure storage for information related to a subscriber. In this implementation, because a subscriber's identifiable information is considered sensitive, the expansion memory 374 can act as the functionality of a file system independent from the operating system. Instead of being stored in a volatile or non-volatile memory, such as system memory 366 or flash memory 370, contraction and / or association information can be stored in the expansion memory 374, as it is considered secure from other information stored in device 350. In one implementation, information gathered during the contraction and / or association process can be stored in the expansion memory 374, and can be deleted after the process is complete. In another implementation, the information can be stored in the expansion memory 374 for a longer period of time, such as a period of time that is longer than a period of time that the device 350 is in use.

[0225] The memory can include, for example, flash memory and / or MRAM memory, as described below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 364, the expansion memory 374, or memory on processor 352.

[0226] The device 350 can communicate wirelessly through the communication interface 366, which can include the digital signal processing circuitry where necessary. The communication interface 366 can provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, GPRS, WiMAX, LTE, 5G, etc. Such communication can occur, for example, through the radio-frequency transceiver 368. In addition, short-range communication can occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown) operating under an appropriate mode or protocol, e.g., to provide for communication of data, as well as audio communication over the airways. In addition, GPS receiver module 370 can provide additional wireless data, which can be used as deemed appropriate by the applications running on the device 350.

[0227] The device 350 can also communicate audibly using an audio codec 360, which can receive spoken information from a user and convert it to usable digital information. The audio codec 360 can likewise generate audible sounds for a user, such as through a speaker, e.g., in a handset of the device 350. Such sound can include sound from voice telephone calls, can include recorded voice

[0228] The computing device 350 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a cellular telephone 380. It can also be implemented as part of a smart-phone 382, personal digital assistant, or other similar mobile device.

[0229] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0230] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms "machine-readable medium" "computer-readable medium" refers to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0231] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0232] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), and the Internet.

[0233] Some communication networks can be configured to carry power as well as information over the same physical medium. This allows a single cable to provide both a data connection and power to a device. Examples of such shared media include Power over Network configurations, in which power is provided over a medium that was primarily or previously used for communication. One specific embodiment of Power over Network is Power over Ethernet (POE), which carries power and data together over twisted-pair Ethernet cabling. Examples of such shared media also include Power over Network configurations, in which communication occurs over a medium that was primarily or previously used to provide power. One specific embodiment of Power over Network is Power Line Communication (PLC) (also known as power-line carrier, power-line digital subscriber line (PDSL), mains communication, power-line telecommunications or power-line networking (PLN), Ethernet over power lines (EOP)), in which data is carried over conductors that are also used for alternating current power transmission.

[0234] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0235] The computing system can include routers, gateways, modems, switches, hubs, bridges, and repeaters. A router is a network device that forwards data packets between computer networks and performs traffic directing functions between them. A network switch is a network device that connects networking devices together and forwards data to the destination device based on the destination MAC address information that is associated with each packet by performing packet switching. A gateway is a network device that provides services running network between discrete networks. Some gateways can be different from routers or switches in that they can communicate using more than one protocol and can operate on one or more of the seven layers of the Open Systems Interconnection model (OSI). A media bridge is a network device that translates data between transmission media so that it can be transmitted between computers. A modem is a type of media bridge that is typically used to connect a local area network to a wide area network, such as a telecommunications network. A network repeater is a network device that receives a signal and retransmits it to extend the range of transmission and allow the signal to cover longer distances or overcome communication barriers.

[0236] It will be clear that the present disclosure enables the skilled person to construct (typically using a computer) a matrix in which the methylation status of one or more CpG dinucleotides and / or one or more genotypes (e.g. SNPs; e.g. at one or more alleles) can be assessed as described herein to identify interactions and allow the prediction of the presence or incidence of CVD. Although such analysis is complex, it does not require undue experimentation as all the required information is readily available to the skilled person or can be obtained by experiments as described herein.

[0237] Methods of treating, managing, and / or monitoring cardiovascular disease

[0238] The present disclosure provides methods for determining the likelihood that a subject has CVD, methods for monitoring CVD (e.g., disease progression) in a subject, determining the severity (e.g., degree of obstruction) of CVD in a subject at risk for or determined to have CVD, and / or estimating survival of the subject. As used herein, CVD includes, without limitation, CHD, stroke, arrhythmia, cardiac arrest, and congestive heart failure. The methods and compositions described herein provide the ability to better assess a subject's risk for cardiovascular disease or monitor the presence of cardiovascular disease, which is the first step in more effective prevention. In addition, the methods and compositions described herein provide the ability to estimate survival of a subject at risk for or determined to have CVD, which can allow for therapies and / or lifestyle changes that can extend or expand the subject's survival.

[0239] Upon making a positive prognosis for a cardiac outcome (e.g., cardiovascular death, myocardial infarction (MI), stroke, all-cause death, or a composite thereof), a medical practitioner can advantageously use the prognosis information thus obtained to identify a need for an intervention or tailor or optimize an intervention performed on the subject, such as a stress test by ECG response or myocardial perfusion imaging, coronary computed tomography angiography, diagnostic cardiac catheterization, percutaneous coronary (e.g., balloon angioplasty (with or without stent placement)), coronary artery bypass graft (CABG), enrollment in a clinical trial, and administration of or monitoring of the effects of a pharmaceutical agent selected from, but not limited to, nitrates, beta blockers, ACE inhibitors, antiplatelet agents, and lipid lowering agents. In addition, a medical practitioner can advantageously use the prognosis information thus obtained to make recommendations for lifestyle changes, including, without limitation, dietary changes, exercise regimens, cessation of smoking and / or alcohol consumption, and combinations thereof. Using the information provided by the methods described herein, a medical practitioner can manage the intervention and monitor the individual to observe, for example, improvement.

[0240] Those identified as being at higher risk (e.g., 69% PPV for CVD) of developing a disease or identified as having a disease can be immediately followed up for further testing or more aggressive intervention. Conversely, those at lower risk can be periodically retested and monitored due to the dynamic nature of DNA methylation to ensure continuous prevention.

[0241] Interventions for cardiovascular disease can depend on the type of cardiovascular disease and the symptoms the individual is experiencing. Interventions for cardiovascular disease can be preventative, therapeutic, or palliative. Treatments for cardiovascular disease can include, for example, lifestyle changes (e.g., diet (e.g., low-fat diet), weight loss, exercise, reducing or stopping smoking and / or drinking); therapeutic agents (e.g., beta blockers, statins, calcium channel blockers, ACE inhibitors, vasodilators, alteplase, small molecule modulators, prebiotics / probiotics / synbiotics / postbiotics); medical interventions (e.g., angioplasty, bypass surgery, implantable devices, endarterectomy); gene therapy; gene editing; base editing; epigenetic therapy; epigenetic silencing; and / or epigenetic editing.

[0242] In accordance with the present disclosure, those of skill in the art can employ conventional molecular biology, microbiology, biochemistry, and recombinant DNA techniques. Such techniques are explained fully in the literature. The present application will be further described in the following examples which do not limit the scope of the methods and compositions of matter described in the claims.

[0243] Examples

[0244] Example 1 - Materials and Methods

[0245] This study features data and / or biological material from three sources. The first set of anonymized whole genome genetic, whole genome DNA methylation, and clinical data comes from the Framingham Heart Study (FHS) offspring cohort, the second set of anonymized clinical data and DNA comes from the Intermountain Healthcare (IM) biorepository, and the third set comes from the Iowa cohort, as described in more detail below. The procedures and protocols for analyzing the FHS data and the Iowa cohort were approved by the University of Iowa Institutional Review Board (IRB# 201503802 and IRB# 201910834), and the procedures and protocols for analyzing the IM material were approved by the Intermountain Healthcare Institutional Review Board (IRB# 1024811).

[0246] Example 2 - Framingham Heart Study (FHS) offspring cohort

[0247] Details regarding the collection and preparation of clinical and biological data for the FHS cohort have been previously described (dbGAP study accession number: phs000007). Briefly, demographic, risk factor, and clinical information were derived from offspring cohorts, including coronary heart disease (CHD) status. CHD was considered to be present if an individual was diagnosed with CHD. Conversely, CHD was considered to be absent if an individual was not diagnosed with CHD. Sources of clinical data to determine CHD events included subject reports, medical record review, and death certificates. The name and date of CHD onset used in this study were determined by a panel of three investigators from the Framingham Endpoint Review Committee, but can be similarly applied to other CVD.

[0248] Whole genome DNA methylation data analyzed using Illumina Infinium HumanMethylation450 BeadChip arrays (San Diego, CA, USA) was available from 2,567 subjects who had blood drawn. Standard sample and probe level quality control was performed as described in previous studies, which resulted in 2,560 samples and DNA methylation data from 403,192 loci remaining (see, e.g., Dogan et al., 2018, Genes, 9:641; Pidsley et al., 2013, BMC Genomics, 14:1-10; Triche, 2014, FDb.InfiniumMethylation.hg19: Annotation package for Illumina Infinium DNA methylation probes. R package version 2.2.0; Davis et al., 2018, Handle Illumina methylation data. R package version 2.22.0; and Dogan et al., 2018, PLoS One, 13:e0190549). Whole genome genotype data obtained using Affymetrix GeneChip Human Mapping 500K arrays (Santa Clara, CA, USA) was available for 2,406 remaining samples. After performing standard sample and probe level quality control procedures in PLINK as previously described, the total number of samples and SNPs remaining were 2,295 and 472,822, respectively (Dogan et al., 2018, Genes, 9:641; Dogan et al., 2018, PLoS One, 13:e0190549; and Purcell et al., 2007, Am. J. Hum. Genet., 81:559-75). The total number of subjects was 2,111 based on the number of those diagnosed with CHD (cases) and those not diagnosed with or who did not have a CHD event within the four years of examination (controls). The demographics and conventional risk factors for these individuals are summarized in Table 1.

[0249] Table 1. Summary of demographics and conventional CHD risk factors for individuals in the Framingham Heart Study Offspring Cohort

[0250]

[0251] HDL: high-density lipoprotein, HbAlc: hemoglobin Alc, SBP: systolic blood pressure, DBP: diastolic blood pressure.

[0252] Example 3 - Intermountain Healthcare cohort

[0253] The first independent validation cohort consisted of 252 subjects from the Intermountain Healthcare (IM) Cardiac Institute INSPIRE registry who underwent coronary angiography. CHD case subjects were defined as > 18 year old adults with no history of CHD or myocardial infarction (MI) prior to baseline coronary angiography but were clinically diagnosed with CHD (> 70% stenosis) at the time of angiography. Control subjects were defined as > 18 year old adults with no history of CHD or myocardial infarction (MI) prior to baseline coronary angiography, no clinical diagnosis of CHD (< 50% stenosis) at the time of baseline coronary angiography, and no clinical diagnosis of CHD (> 70% stenosis) at the time of angiography, MI, revascularization, or death from CHD within four years of baseline coronary angiography.

[0254] The demographics of these individuals are summarized in Table 2.

[0255] Table 2. Demographics summary for Intermountain Healthcare validation set

[0256]

[0257] Each of these 253 subjects was subjected to whole genome DNA methylation and genetic assessment by the University of Minnesota Genomics Center using the Illumina Infinium MethylationEpicBeadchip array and the Illumina Infinium Multi-Ethnic Global BeadChip array (Illumina, San Diego, CA), respectively. These data were then subjected to the same quality control procedures as described above for the FHS samples.

[0258] Example 4 - Iowa cohort

[0259] The second independent validation cohort consisted of 167 subjects. Demographics are shown in Table 3. The presence or absence of a clinical diagnosis of CHD was determined by medical record.

[0260] Table 3. Summary of demographics for Iowa validation set

[0261]

[0262] Example 5 - Integrating genetic-epigenetic coronary heart disease risk prediction models

[0263] Because one of the goals of this study was to translate array-based methylation loci into a clinically implementable digital PCR (dPCR) assay (which has fixed limitations on precision) prior to data mining using only data from the FHS training set, the methylation variables were reduced to include loci based on delta beta (D beta) (the absolute difference between cases and controls). All methylation locus beta values were converted to M values and subsequently scaled to have zero mean and unit variance.

[0264] All data mining, feature selection, model development, and model tuning were performed only on the FHS training set. Our data mining methods have been outlined in previous publications (Dogan et al., 2018, Genes, 9:641; Dogan et al., 2018, PLoS One, 13:e0190549). All analyses were performed in Python. Briefly, an under-sampling based approach was implemented to account for the high class imbalance and coupled to a fused machine learning algorithm that incorporates cross-validation to reveal non-linear methylation-SNP interactions and highly predictive biological features in the FHS training set (Han et al., 2011, Data Mining: Concepts and Techniques, Elsevier). Thus, a marker set consisting of six DNA methylation loci and ten SNPs was selected that had the best combined performance in terms of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The fused model consisting of these 16 biomarkers was subjected to hyperparameter tuning and finalized for testing.

[0265] Example 6 - Survival analysis and prognostic scores

[0266] Using data from the FHS, Kaplan-Meier survival curves and Cox proportional hazards can be fitted to show CHD as a function of risk groups (high vs. low) as predicted by the integrated genetic-epigenetic model. The y-axis represents the probability of not having CHD. The 95% confidence intervals (CIs) were calculated for each distribution, and the log-rank test was used to compare the distributions of the high-risk group and the low-risk group.

[0267] Example 7 - Results

[0268] The clinical and demographic characteristics of the FHS, IM, and Iowa cohorts are outlined in Tables 1, 2, and 3, respectively. All subjects from the FHS cohort were of European ancestry, but there were non-European ancestries in the IM and Iowa cohorts. The most notable difference was in gender. CHD controls (those not diagnosed with CHD) in the Iowa cohort were younger compared to the FHS and IM cohorts.

[0269] Example 8 - Integrating genetic-epigenetic coronary heart disease risk prediction models

[0270] Using integrated genome-wide SNP and methylation data from the training set, a CHD prediction model was built to identify those with CHD. All subjects had genetic (SNP) and epigenetic (DNA methylation) molecular data. All data mining, variable selection, and model development work were performed on the FHS training set. The performance of the final model developed using the FHS training set was validated using data from the FHS test set as well as the IM and Iowa independent external validation sets. Using data from the FHS training set, a machine learning (a subset of artificial intelligence) program was used to develop a model for detecting CHD. The final model was built using data from ten SNPs and six DNA methylation loci (a total of 16 biomarkers). The performance of the model was then fine-tuned, and after final completion, independently checked in the FHS test as well as the IM and Iowa independent external validation sets to better understand the generalizability of this biomarker set.

[0271] This final integrated model consisted of a total of 16 biomarkers, of which six were DNA methylation biomarkers and the remaining ten were SNPs. The six methylation loci were cg04988978 (5' promoter region of MPO), cg21161138 (gene body of AHRR), cg12655112 (gene body of EHD4), cg03725309 (body of SARS1), cg12586707 (3' intergenic region of CXCL1), and cg17901584 (gene body of DHCR24), while the ten SNPs were rs2869675 (gene body of PREX1), rs4376434 (intergenic region near LINC00972), rs12129789 (gene body of KCND3), rs7585056 (intergenic region near TMEM18), rs710987 (gene body of LINC010019), rs4639796 (gene body of ZBTB41), rs1333048 (3' intergenic region of CDKN2B), rs12714414 (intergenic region near TMEM18), rs942317 (gene body of KTN1-AS1), and rs1441433 (gene body of PPP3CA).

[0272] The overall and gender-specific performance of this model in detecting CHD in all three cohorts is shown in Table 4. As expected, PrecisionCHD had the best performance in the FHS training dataset used to develop the model. More importantly, PrecisionCHD demonstrated robust generalizability. It had a sensitivity of 75% or better in all cohorts, with the highest validation sensitivity and specificity of 88% and 77% in the external Iowa validation cohort. Overall, the model demonstrated an average AUC, sensitivity, and specificity of 81%, 80%, and 75%, respectively, in the three sets not used in model training (i.e., FHS test, IM, and Iowa). An 80% sensitivity (true positive rate) indicates that 80 out of 100 individuals with CHD were correctly identified by PrecisionCHD. Similarly, a 75% specificity (true negative rate) indicates that 75 out of 100 individuals without CHD were correctly identified. Similarly, the average sensitivity and specificity for males were 81% and 73%, respectively. For females, the average sensitivity and specificity were 76% and 75%, respectively. Overall, the model performed similarly for males and females and across cohorts, indicating minimal to no gender bias and robust generalizability.

[0273] Table 4. Performance of PrecisionCHDTM in Framingham Heart Study, Intermountain Healthcare, and Iowa cohorts.

[0274]

[0275] AUC: Area under the receiver operating characteristic curve

[0276] FHS: Framingham Heart Study cohort

[0277] IM: Intermountain Healthcare cohort

[0278] Iowa: Iowa cohort

[0279] Sensitivity: True positive rate; Specificity: True negative rate

[0280] Example 9 - Additional data

[0281] Appendix A shows the list of CpGs associated with CVD through methylation. Appendix B shows the list of genes associated with CVD through methylation. Appendix C shows the list of SNPs associated with CVD. The values provided in Appendix A, Appendix B, and Appendix C are the mean of ten-fold cross-validation scores, AUC ROC (Area under the receiver operating characteristic curve), sensitivity, and specificity, which were calculated by logistic regression. Sensitivity is the true positive rate, and specificity is the true negative rate.

[0282] Example 10 - PrecisionCHD

[0283] PrecisionCHD is a quantitative test that helps detect coronary heart disease (CHD) early. See Table 5. This non-invasive test evaluates ten single nucleotide polymorphisms (SNPs) and six DNA methylation markers in genomic DNA isolated from a person’s peripheral whole blood. Coronary heart disease is caused by factors that can be inherited (genetic) and acquired, potentially modifiable lifestyle and environmental (epigenetic) factors. PrecisionCHD early detection test measures certain complex genetic and epigenetic relationships associated with CHD and uses a machine learning model to predict CHD status.

[0284] The PrecisionCHD test is initially intended for use in adults aged 35-80 years old who are undergoing coronary heart disease evaluation. The results of the test are intended to be interpreted by a healthcare provider in conjunction with a comprehensive medical evaluation. This test is not intended for standalone coronary heart disease diagnostic purposes and is not intended to replace the diagnosis and treatment of coronary heart disease by a healthcare professional.

[0285] Table 5. Summary of PrecisionCHD

[0286]

[0287] As described herein, PrecisionCHD evaluates a total of 16 biomarkers, including ten SNP genotypes and six DNA methylation biomarkers. The PrecisionCHD test uses standard Taqman assays to analyze genotypes and proprietary methylation-sensitive digital PCR assays to analyze DNA methylation markers. The biomarkers captured by PrecisionCHD are mapped to several complex pathways related to the biology and pathogenesis of CHD, such as serine metabolism, cholesterol biosynthesis, smoking, and inflammation. Cardio Diagnostics’ Actionable Clinical Intelligence™ (ACI™) platform (see, e.g., U.S. Application No. 63 / 488,463, incorporated herein by reference) maps the biomarker information to modifiable risk factors for CHD to help guide personalized interventions.

[0288] Example 11 - PrecisionCHD workflow

[0289] Testing with PrecisionCHD is simple, fast, and convenient. The steps include:

[0290] 1. Eligibility criteria:

[0291] a. 35-80 years old

[0292] b. Ongoing coronary heart disease evaluation

[0293] i. Exclusion: Patients who have undergone bone marrow transplant are not eligible

[0294] 2. Sample collection:

[0295] a. Option 1: Upon receipt of a test order from a clinician, a home lancet-based sample collection kit is mailed directly to the patient.

[0296] b. Option 2: Blood is drawn in a provider setting.

[0297] c. Option 3: Blood is drawn in a non-provider setting (e.g., mobile clinic, community center, phlebotomy center).

[0298] 3. Samples are processed at a high-complexity CLIA laboratory to analyze genotypic and methylation biomarkers.

[0299] 4. Analysis of biomarkers is performed, and a clinical report is generated and shared with the ordering clinician.

[0300] 5. The clinician will also receive access to the actionable clinical intelligence platform (see, e.g., U.S. Application No. 63 / 488,463, incorporated herein by reference) that maps molecular information to modifiable risk factors related to patient status.

[0301] 6. Results of the clinician’s discussion with the patient and prevention / management plan are summarized. Figure 4 Several potential action items that can be implemented using the PrecisionCHD test based on positive CHD signals are summarized.

[0302] Example 12 - Customized interventions

[0303] The methods described herein can help select and / or tailor appropriate interventions. The effectiveness of an intervention (e.g., lifestyle or medication) can be evaluated by administering the test before and after the intervention to quantify changes in status or risk, if any, to guide other future interventions. This process can be repeated to identify the best intervention for an individual patient. Thus, the methods described herein can help manage interventions for individual patients and then subsequently monitor the patients to evaluate the efficacy of such interventions.

[0304] Example 13 - PrecisionCHD-Epi for assessing mortality risk in those with coronary heart disease

[0305] PrecisionCHD™ is a powerful integrated genetic-epigenetic diagnostic tool that is used to detect the presence of coronary heart disease (CHD) in a clinical cohort or health promotion programs following underwriting for life insurance. Its non-genetic version, PrecisionCHD-Epi, is a sensitive screening tool used by life insurance companies to screen individuals for the presence of CHD at the underwriting stage. However, if an individual has been determined to have CHD, we performed experiments to determine if PrecisionCHD-Epi could help further stratify the severity of CHD as it relates to mortality risk.

[0306] This is an important issue for life insurance companies as mortality risk stratification for those who have been diagnosed with CHD is one of the highest risks faced by medical underwriters. To build these assessments on a solid medical foundation, in an influential article published over two decades ago, Dr. Anthony Milano reviewed the existing literature and recommended the use of CHD classification and left ventricular function as key to mortality risk assessment (Milano, 2000, J. Insur. Med. New York, 32(3): 167-85). Since that time, other markers of CHD death, such as N-terminal pro brain natriuretic peptide (NT-ProBNP), have been added to underwriting medical devices to help with prediction (Bibbins-Domingo et al., 2007, JAMA, 297(2): 169-76). Nonetheless, the prediction of mortality for those with CHD is still not optimal. In 2009, Sijbrands and colleagues evaluated the mortality risk of 62,334 Dutch male insurance applicants (including 3,963 subjects with current cardiovascular disease (CVD)) using the underwriting procedure of Nationale-Nederlanden (Sijbrands et al., 2009, PloS One, 4(5):e5457). Despite the full availability of medical information and the clear relationship between CVD and mortality risk, Sijbrands concluded that “no individual deaths could be identified separately by the medical evaluation employed”.

[0307] PrecisionCHD-Epi can perhaps change this dynamic and help underwriters stratify the mortality risk of CHD. PrecisionCHD-Epi sensitively screens for the molecular signature of CHD using the same six methylation-sensitive digital PCR assays that drive PrecisionCHD™. The inherent understanding of this approach is that not all forms of CHD are the same and both the treatment and prognosis of patients determined to have CHD will be different.

[0308] To demonstrate this and show the ability of this technique to predict CHD severity, we analyzed the clinical and epigenetic data of 244 subjects in the Framingham Heart Study (FHS) who were diagnosed with CHD by the FHS Endpoint Committee in Wave 8 of the FHS Offspring Cohort Study (Cupples et al., 1988, “The Framingham Heart Study, Section 35. An Epidemiological Investigation of Cardiovascular Disease Survival following Cardiovascular Events: 30 Year Follow-up,” Lung and Blood Institute). Table 6 describes the characteristics of the subjects. Highlighting the deficiency of existing methods in the identification of CHD in women, two-thirds of those diagnosed with CHD were male. The rate of self-reported smoking was higher in males, but overall low. Values of other parameters such as blood pressure and lipid values were not clinically significant.

[0309] Table 6. Clinical and Demographic Characteristics

[0310]

[0311] Table 7 shows the non-numerical transformed methylation values for each of the six CpG sites targeted by the MSdPCR panel. Males had significantly lower methylation values at cg12655112 and cg12586707, with demethylation at each site correlating with CHD status.

[0312] Table 7. MSdPCR DNA methylation values for PrecisionCHD-Epi sites by gender

[0313]

[0314] * p < 0.05, ** p < 0.01

[0315] We then examined the relationship of the methylation markers to survival. During follow-up, a total of 71 FHS subjects with CHD (48 males and 23 females) were observed to die. Using their time to survival and standard proportional hazards modeling, we analyzed the relationship between methylation values at the six loci and survival using stepwise regression. A simple proportional hazards model constructed using all six MSdPCR markers predicted survival (p < 0.006). Adding age, sex, or any of the traditional predictors of survival (lipids, HbAlc, and BP) given in Table 1 did not improve the prediction. Stepwise removal of non-significant markers yielded a model of three markers, cg04988978, cg21161138, and cg12655112, whose predicted survival chi-square p-value was p < 0.002, with logWorth values (the -log of the p-value) of 2.00, 1.74, and 3.07, respectively.

[0316] Visualizing the effect of markers in an interactive multivariate survival model can be challenging. An alternative, but less powerful, approach to understanding the relationship of methylation to survival can be obtained by simply plotting the relationship of methylation to whether or not a subject died during follow-up. Figure 5A 、 Figure 5B and Figure 5C shows this relationship for each of the three markers to the state of death. Overall, those with cg04988978 methylation in the lower two deciles were four times more likely to die during the follow-up period than those in the upper two deciles (28 of 50 versus 7 of 50). A similar, but less strong, effect was observed for methylation at cg12655112 and cg21161138. In contrast, among those who died, there was no significant relationship between cg04988978 and days to survival using bivariate analysis, as shown in Figure 6A However, for both cg12655112 and cg21161138, there was a strong significant relationship between days to survival and methylation status using bivariate analysis, as shown in Figure 6B and Figure 6C In summary, these indicate a strong and complex relationship between the methylation markers in the PrecisionCHD-Epi test and survival.

[0317] In summary, using PrecisionCHD or PrecisionCHD-Epi, we can now provide survival estimates for those at risk of developing CHD or those who have been determined to have CHD.

[0318] It is to be understood that while the method and compositions of matter have been described in conjunction with a number of different aspects herein, the foregoing descriptions of various aspects are intended to illustrate and not limit the scope of the method and compositions of matter. Other aspects, advantages, and modifications are within the scope of the following claims.

[0319] Disclosed are methods and compositions that can be used in, used in conjunction with, used to prepare, or are products of the disclosed methods and compositions. These and other materials are disclosed herein, and it is understood that combinations, subsets, interactions, groups, etc. of these methods and compositions are disclosed. That is, while specific mention of each and every different individual and collective combination and permutation of these compositions and methods can not be explicitly disclosed, each and every such combination and permutation is specifically contemplated herein. For example, if a particular composition of matter or a particular method is disclosed and discussed, and a number of compositions or methods are discussed, each and every combination and permutation of the compositions and the methods is specifically contemplated unless specifically indicated to the contrary. Likewise, any subset or combination of these is also specifically contemplated and disclosed.

[0320]

Claims

1. A kit for determining the methylation status of at least one CpG dinucleotide and the genotype of at least one single nucleotide polymorphism (SNP), said kit comprising: At least one first nucleic acid primer of at least 8 nucleotides in length, complementary to a bisulfite-converted nucleic acid sequence containing a first CpG dinucleotide or a second CpG dinucleotide, wherein the first CpG dinucleotide is located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, and the second CpG dinucleotide is in linkage disequilibrium with the first CpG dinucleotide located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the at least one first nucleic acid primer detects methylated or unmethylated CpG dinucleotides, and At least one second nucleic acid primer of at least 8 nucleotides in length, complementary to the DNA sequence or bisulfite-converted DNA sequence of the first SNP or the second SNP, wherein the first SNP is selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317 and rs1441433, and the second SNP is in linkage disequilibrium with the first SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317 and rs1441433, wherein the linkage disequilibrium has a value of R>0.

3.

2. The kit according to claim 1, wherein the at least one first nucleic acid primer detects the unmethylated CpG dinucleotide.

3. The kit according to claim 1, wherein the at least one first nucleic acid primer detects the methylated CpG dinucleotide.

4. The kit according to any one of claims 1-3, wherein the at least one first nucleic acid primer comprises one or more nucleotide analogs.

5. The kit according to any one of claims 1-4, wherein the at least one first nucleic acid primer comprises one or more synthetic or non-natural nucleotides.

6. The kit according to any one of claims 1-5, wherein the kit further comprises a solid matrix bound to the at least one first nucleic acid primer.

7. The kit according to claim 6, wherein the matrix is ​​a polymer, glass, semiconductor, paper, metal, gel or hydrogel.

8. The kit according to claim 6, wherein the solid matrix is ​​a microarray or a microfluidic card.

9. The kit according to any one of claims 1-8, wherein the kit further comprises a detectable marker.

10. The kit according to any one of claims 1-9, wherein the kit further comprises at least a third nucleic acid primer of at least 8 nucleotides in length that is complementary to the nucleic acid sequence upstream of the CpG dinucleotide.

11. The kit according to any one of claims 1-9, wherein the kit further comprises at least a third nucleic acid primer of at least 8 nucleotides in length that is complementary to the nucleic acid sequence downstream of the CpG dinucleotide.

12. A method for determining the presence of a biomarker in a biological sample from a subject, wherein the biomarker is associated with detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, customizing and / or optimizing one or more interventions for CVD, managing CVD and / or monitoring CVD, the method comprising: (a) Providing a first portion and a second portion of the biological sample, wherein the nucleic acid from at least the first portion is converted from bisulfite; (b) The first portion of the biological sample is contacted with a first oligonucleotide primer of at least 8 nucleotides in length, the first oligonucleotide primer being complementary to a sequence containing a first CpG dinucleotide or a second CpG dinucleotide, the first CpG dinucleotide being located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, the second CpG dinucleotide being in linkage disequilibrium with the first CpG dinucleotide located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the first nucleic acid primer detects methylated or unmethylated CpG dinucleotides; and (c) Contact a second portion of the biological sample with a nucleic acid primer of at least 8 nucleotides in length, the nucleic acid primer being complementary to the DNA sequence of a first SNP or a second SNP or a bisulfite-transformed DNA sequence, wherein the first SNP is selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, and rs1271441.

4. rs942317 and rs1441433, the second SNP is in chain disequilibrium with the first SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433, wherein the chain disequilibrium has a value of R > 0.

3. The percentage of methylation of the CpG dinucleotide at the GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, and the identity of the nucleotide at the first SNP or the second SNP that is not in linkage equilibrium with the first SNP, selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 are biomarkers associated with the detection of CVD or the estimation of survival from CVD.

13. The method of claim 12, wherein the biological sample is selected from blood and saliva.

14. The method of claim 12, wherein the at least one first nucleic acid primer detects the unmethylated CpG dinucleotide.

15. The method of claim 12, wherein the at least one first nucleic acid primer detects the methylated CpG dinucleotide.

16. The method of claim 12, wherein the at least one first nucleic acid primer comprises one or more nucleotide analogs.

17. The method of claim 12, wherein the at least one first nucleic acid primer comprises one or more synthetic or non-natural nucleotides.

18. The method of claim 12, wherein the window of onset is three years.

19. A method for determining the presence of a biomarker in a biological sample from a subject, wherein the biomarker is associated with detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, customizing and / or optimizing one or more interventions for CVD, managing CVD and / or monitoring CVD, the method comprising: (a) Obtaining nucleic acid samples from the subject samples; (b) Genotyping is performed on a first portion of the nucleic acid sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 or a first SNP from Appendix C and / or a second SNP that is in linkage disequilibrium (R>0.3) with a first SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 or a first SNP from Appendix C, to obtain genotypic data; and / or (c) The nucleic acid in the second part of the nucleic acid is converted with bisulfite, and the second part of the nucleic acid sample is subjected to methylation assessment to detect at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A and / or CpG sites collinear (R>0.3) with CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A, to obtain methylation data; and (d) Input the genotype data from step (b) and / or the methylation data from step (c) into an algorithm that explains at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect, wherein the algorithm is a machine learning algorithm capable of explaining linear and nonlinear effects.

20. The method of claim 19, wherein the at least one interaction effect is selected from gene-environment interaction (SNPxCpG) effect, gene-gene interaction (SNPxSNP) effect, and environment-environment interaction (CpGxCpG) effect.

21. The method of claim 19, wherein the at least one interaction effect is a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, or a CpG site collinear (R>0.3) with a CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, and a CpG site selected from rs2869675, rs4376434, and rs1212. Gene-environment interaction effects (SNPxCpG) between SNPs selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs1441433 or SNPs from Appendix C and SNPs with moderate linkage disequilibrium (R>0.3).

22. The method of claim 19, wherein the at least one interaction effect is an environment-environment interaction effect (CpGxCpG) between at least two CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A.

23. The method of claim 22, wherein one or both of the at least two CpG sites are collinear (R>0.3) with one or both of the at least two CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A.

24. The method of claim 19, wherein the at least one interaction effect is a gene-gene interaction effect (SNPxSNP) between at least two SNPs selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317 and rs1441433 or from Appendix C.

25. The method of claim 24, wherein one or both of the at least two SNPs are collinear (R>0.3) with one or both of the at least two SNPs selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317 and rs1441433 or from Appendix C.

26. The method according to any one of claims 19-25, wherein the biological sample is a saliva sample.

27. A system for determining the methylation state of at least one CpG dinucleotide and the genotype of at least one single nucleotide polymorphism (SNP), said system comprising: A nucleic acid separation module, configured to separate nucleic acid samples from subject samples; A genotyping module is configured to perform genotyping on a first portion of the nucleic acid sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs14414. 33 or a first SNP from Appendix C and / or a second SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317 and rs1441433 or a first SNP from Appendix C that is in linkage disequilibrium (R>0.3) to obtain genotypic data; A methylation assay module is configured to convert the nucleic acid in the second part of the nucleic acid with bisulfite and to evaluate the methylation of the second part of the nucleic acid sample to detect at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A and / or CpG sites collinear (R>0.3) with CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A, in order to obtain methylation data; as well as An identification system configured to interpret at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect based on the genotype data and / or the methylation data.

28. The system of claim 27, wherein the algorithm is a machine learning algorithm capable of explaining linear and nonlinear effects.

29. The system of claim 27 or 28, further comprising an output module configured to provide output based on the identification of the identification system, wherein the identification is based on the genotype data from step (b) and / or the methylation data from step (c) to explain at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect.

30. A non-transitory computer-readable medium storing instructions executable by a processing means for performing operations, the operations including: Interpret at least one SNP main effect and / or at least one CpG main effect and / or at least one interaction effect based on genotype data and / or methylation data, wherein: The genotyping data is based on genotyping of a first portion of a nucleic acid sample isolated from the subject's sample to detect the presence of at least one SNP, wherein the at least one SNP is selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317, and rs144143.

3. Or a first SNP from Appendix C and / or a second SNP selected from rs2869675, rs4376434, rs12129789, rs7585056, rs710987, rs4639796, rs1333048, rs12714414, rs942317 and rs1441433 or a first SNP from Appendix C that is in linkage disequilibrium (R>0.3) to obtain the genotype data; and The methylation data is based on a methylation assay of the bisulfite-converted nucleic acid in the second part of the nucleic acid sample to detect the methylation status of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A and / or CpG sites collinear (R>0.3) with CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707 and cg17901584 or from Appendix A, in order to obtain methylation data.

31. The non-transitory computer-readable medium of claim 30, wherein the operation further comprises providing output based on the interpretation.

32. The non-transitory computer-readable medium of claim 31, wherein the output comprises one or more of the following: storing a report to another non-transitory computer-readable medium based on the interpretation, modifying a display based on the interpretation, triggering an audible alarm based on the interpretation, triggering a tactile or vibration alarm based on the interpretation, triggering printing of a report based on the interpretation, or triggering delivery of a therapeutic agent based on the interpretation.

33. A kit for determining the methylation state of at least one CpG dinucleotide, the kit comprising: At least one first nucleic acid primer of at least 8 nucleotides in length, complementary to a bisulfite-converted nucleic acid sequence containing a first CpG dinucleotide or a second CpG dinucleotide, wherein the first CpG dinucleotide is located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, and the second CpG dinucleotide is in linkage disequilibrium with the first CpG dinucleotide located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has a value of R > 0.3, wherein the at least one first nucleic acid primer detects methylated or unmethylated CpG dinucleotides.

34. A method for determining the presence of a biomarker in a biological sample from a subject, wherein the biomarker is associated with detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, customizing and / or optimizing one or more interventions for CVD, managing CVD and / or monitoring CVD, the method comprising: (a) Provide a biological sample from a subject at risk of or suffering from CVD, wherein at least a portion of the nucleic acid from the biological sample is converted from bisulfite; as well as (b) The bisulfite-converted nucleic acid is contacted with a first oligonucleotide primer of at least 8 nucleotides in length. The first oligonucleotide primer is complementary to a sequence containing either a first CpG dinucleotide or a second CpG dinucleotide. The first CpG dinucleotide is located at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584. The second CpG dinucleotide is in linkage disequilibrium with the first CpG dinucleotide at a GC locus selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584, wherein the linkage disequilibrium has an R value > 0.

3. The first nucleic acid primer detects methylated or unmethylated CpG dinucleotides. The percentage of methylation of the CpG dinucleotide at the GC loci selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 was associated with the estimated survival of the subject.

35. A method for determining the presence of a biomarker in a biological sample from a subject, wherein the biomarker is associated with detecting CVD, determining the severity of CVD, estimating survival from CVD, identifying, customizing and / or optimizing one or more interventions for CVD, managing CVD and / or monitoring CVD, the method comprising: (a) Isolating nucleic acid samples from the subject samples; (b) Transforming at least a portion of the nucleic acid with bisulfite and performing a methylation assessment on the bisulfite-transformed nucleic acid to determine the methylation status of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or CpG sites collinear (R>0.3) with CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, to obtain methylation data; and (c) Input the methylation data from step (b) into an algorithm that explains at least one main effect of CpG, wherein the algorithm is a machine learning algorithm capable of explaining linear and nonlinear effects.

36. A system for determining the methylation state of at least one CpG dinucleotide, the system comprising: A nucleic acid separation module, configured to separate nucleic acid samples from subject samples; A methylation assay module is configured to convert at least a portion of the nucleic acid in the bisulfite-converted nucleic acid and to assess the methylation of the bisulfite-converted nucleic acid to determine the methylation status of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or CpG sites collinear (R>0.3) with CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, in order to obtain methylation data; as well as An identification system configured to interpret at least one CpG main effect based on the methylation data.

37. A non-transitory computer-readable medium storing instructions executable by a processing means for performing operations, the operations including: At least one main CpG effect can be explained based on methylation data, where: The methylation data are based on a methylation assay of bisulfite-converted nucleic acids in at least a portion of a nucleic acid sample to detect the methylation status of at least one CpG site selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A and / or CpG sites collinear (R>0.3) with CpG sites selected from cg04988978, cg21161138, cg12655112, cg03725309, cg12586707, and cg17901584 or from Appendix A, in order to obtain methylation data.

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