Systems and methods for genetics-based analysis of health, fitness, and sports performance - Patents.com

Abstract

The present disclosure provides systems and methods for determining a subject's performance or health risk status. The method for determining a subject's performance or health risk status may include (a) receiving genetic information of the subject obtained by analyzing a biological sample obtained from or derived from the subject, (b) receiving environmental information of the subject including contextual data, activity, or physiological measurements of the subject, (c) processing the genetic information and the environmental information to determine the subject's performance or health risk status, and (d) outputting an electronic report indicating the subject's performance or health risk status.

Description

[Technical field]

[0001] cross reference

[0001] This application claims the benefit of U.S. Application No. 63 / 223,707, filed July 20, 2021, the entire contents of which are incorporated by reference herein. [Background technology]

[0002]

[0002] A particular health or performance state in an individual can be determined by the combined effect of specific environmental conditions acting on the genetic background, which can be summarized by the equation: Genetics (G) + Change in environment (ΔE) = Health state (H). Summary of the Invention

[0003]

[0003] Genetic datasets, such as single nucleotide polymorphism (SNP) genotyping data, can be widely used to calculate polygenic risk scores for certain health conditions or performance traits. While polygenic risk scores provide good predictions of how a person's genetic risk (G) affects a certain health condition or performance trait (H), they may not take into account environmental contributions (ΔE).

[0004]

[0004] Commercial genetic reports may provide genotypes with risk or performance status coupled with academic literature-based views on numerous health and fitness traits. Although these reports may be rich in information, the impact of these plans (environmental changes on health, fitness, and performance status) may be difficult to measure and present to the user in an interesting way, making them difficult to understand and follow. Genetics may play a significant role in one's health and fitness journey (e.g., one's genetic makeup may affect one's physiological performance in different sports by more than 50%). However, health and fitness decisions may be made without fully assessing, following, or following the genetic component. Thus, the health impact that genetics should have may not be fully recognized, and commercial genetic reports are perceived as low value for what are expensive tests and technologies.

[0005]

[0005] Continuous physiological monitoring can enable real-time insight into a wide range of physiological measures, such as cardiorespiratory fitness, resting heart rate, stress levels, sleep quality, training load, blood pressure, and blood glucose levels. Wearable devices can provide an affordable solution that provides an array of trackable health and fitness performance scores. As with genetic suggestions, insights based on these measures can be complex to understand, interpret, and incorporate into daily health and fitness programs. Insights from wearable devices and applications can be retrospective and may not provide predictive capabilities. Algorithms that generate metrics and insights can be heavily based on population averages, which prevents personalization and accuracy.

[0006]

[0006] To determine a more accurate health and performance status (H) at any given time, the environmental contribution (ΔE) to a person's genetic risk (G) can be determined through contextual information and continuous physiological monitoring. Continuously measuring the impact that lifestyle choices have on a person's genetic background and expressing that impact using a personalized and easily interpretable health or performance related score can lead to improved adherence to and adoption of gene-based health and fitness recommendations.

[0007]

[0007] Physiological monitoring and contextual information can serve as inputs to baseline gene scores to estimate environmental influences on gene expression that modify health or performance phenotypes. Thus, there is a need for easy-to-use and understand technologies that seamlessly integrate genetic information with physiological and contextual information to enable real-time views and predictive metrics of human health and performance indicators that are personalized for individuals.

[0008]

[0008] The present disclosure provides a method and system that integrates genetic data, contextual data, physiological biomarkers and / or continuous physiological data (e.g., may be acquired from electronic devices such as wearable devices) to output easily interpretable health, fitness and sports performance related scores through modifier algorithm calculations. The method and system of the present disclosure may be based on the concept of modifier risk scores for individual health conditions or performance characteristics, depicted in FIG. 2. Each modifier score may include static genetic contributions (e.g., pathway scores expressed as polygenic risk scores) and dynamic time-varying behavioral contributions (measured through continuous physiological monitoring and contextual information). Behavioral contributions may represent the influence that lifestyle choices have on genetic background. The composite modifier score may be a real-time fluctuation score that indicates the subject's risk level. Risk indicates when the composite modifier score reaches a threshold. The modifier risk score may enable daily suggestions to mitigate further risk, as well as predictive capabilities to indicate favorable or unfavorable outcomes given a particular health or training plan.

[0009]

[0009] In one aspect, the present disclosure provides a computer-implemented method for determining a subject's performance or health risk status, which may include the steps of: (a) receiving genetic information of the subject, where the genetic information is obtained by analyzing a biological sample obtained from or derived from the subject; (b) receiving environmental information of the subject, where the environmental information includes contextual data, activity, or physiological measurements of the subject; (c) processing the genetic information and the environmental information to determine the subject's performance or health risk status; and (d) outputting an electronic report indicative of the subject's performance or health risk status.

[0010] In some embodiments, the performance or health risk status includes a performance or health risk score, number, or quantitative metric for the subject. In some embodiments, the electronic report further includes subject-specific health and fitness suggestions, such as ways to improve a score or reduce a risk (e.g., generated based at least in part on the subject's performance or health risk status, or performance or health risk score, number, or quantitative metric).

[0011] In some embodiments, the genetic information includes nucleic acid sequence data. In some embodiments, the nucleic acid sequence data includes deoxyribonucleic acid (DNA) sequence data, ribonucleic acid (RNA) sequence data, or a combination thereof. In some embodiments, the genetic information includes genetic variations of the subject. In some embodiments, the genetic variations include at least one of single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions or deletions (indels), fusions, and translocations.

[0012] In some embodiments, the biological sample is selected from the group consisting of saliva, oral mucosa, blood, plasma, serum, urine, and combinations thereof. In some embodiments, the analysis includes at least one of a single nucleotide polymorphism (SNP) panel assay, a pharmacogenetics assay, an ancestry genetics assay, a clinical genetics assay, a pharmacogenetics assay, a sports performance genetics assay, a human health check, a test for a specific disease risk, a migraine test, a thyroid test, an eczema test, and a cancer genetics assay. In some embodiments, the analysis includes at least two of a single nucleotide polymorphism (SNP) panel assay, a pharmacogenetics assay, an ancestry genetics assay, a clinical genetics assay, a pharmacogenetics assay, a sports performance genetics assay, a human health check, a test for a specific disease risk, a migraine test, a thyroid test, an eczema test, and a cancer genetics assay. In some embodiments, the activity includes at least one of exercising, playing a sport, walking, running, sitting, standing, lying down, and sleeping. In some embodiments, the physiological measurements include vital sign measurements of the subject. In some embodiments, the vital sign measurements include at least one of heart rate, heart rate variability, systolic blood pressure, diastolic blood pressure, respiratory rate, blood oxygen concentration (SpO2), carbon dioxide concentration in respiratory gas, hormone levels, sweat analysis, blood glucose, temperature, impedance, conductivity, capacitance, resistivity, electromyography, galvanic skin response, nerve signals, and immunological markers. In some embodiments, the physiological measurements include sports performance measurements. In some embodiments, the sports performance measurements include at least one of maximum oxygen intake, blood lactate, lactate threshold, training load, training stress score, time in aerobic and anaerobic heart rate zones, pace, power, distance, and time. In some embodiments, the physiological measurements include physiological metrics that measure the effect of external influences on the human body. In some embodiments, the activity or physiological measurements are obtained using an electronic device (e.g., a wearable device).

[0013] In some embodiments, (c) further comprises determining the subject's performance or health risk status based at least in part on one or more of the subject's disease or disorder diagnosis, disease or disorder prognosis, risk of having a disease or disorder, sports-related injury risk, disease or disorder treatment history, history of previous treatments for a disease or disorder, prescribed medication history, prescribed medical device history, age, height, weight, sex, smoking status, injury risk, training load status, fitness level, competition or match readiness, and one or more symptoms. In some embodiments, the one or more symptoms comprise chronic fatigue, weight loss, nausea, insomnia, or a combination thereof.

[0014]

[0014] In some embodiments, the method further comprises generating a health plan or training plan for the subject based at least in part on the performance or health risk status determined in (c). In some embodiments, the health plan includes suggestions for preventing the onset of a disease or disorder, delaying the onset of a disease or disorder, reversing a disease or disorder, preventing injury, or maintaining a physiological or health condition of the subject. In some embodiments, the health plan includes suggestions related to one or more of diet, exercise, sports training, supplements, functional testing, blood testing, brain management, behavioral modification, skin care, environmental exposure, stress management, and mental health. In some embodiments, the training plan includes a training program or a training rehabilitation program. In some embodiments, the electronic report is indicative of the health plan. In some embodiments, the electronic report is presented on a graphical user interface of the user's electronic device. In some embodiments, the user is the subject. In some embodiments, the electronic report is displayed through a user interface (e.g., providing a bird's-eye view of the individual's context). In some embodiments, the user interface is configured to receive user input. In some embodiments, the user interface is presented via a software application (eg, a mobile software application).

[0015]

[0015] In some embodiments, the method further includes transmitting the electronic report to a remote user. In some embodiments, the method further includes transmitting the electronic health or fitness score and / or subject-specific health and fitness suggestions to a remote user. In some embodiments, the remote user is a medical professional, a nutrigenetics counselor, a sports coach, a team manager, or an individual. In some embodiments, the method further includes storing the electronic report on a remote server.

[0016]

[0016] In some embodiments, (c) includes processing the genetic information and the environmental information using a trained algorithm to determine the subject's performance or health risk status. In some embodiments, the trained algorithm includes a supervised machine learning algorithm. In some embodiments, the supervised machine learning algorithm includes a deep learning algorithm, a support vector machine (SVM), a neural network, a Gaussian naive Bayes model, a naive Bayes model, or a random forest. In some embodiments, the trained algorithm includes an unsupervised machine learning algorithm. In some embodiments, the unsupervised machine learning algorithm includes a k-means clustering model or a principal component analysis. In some embodiments, the trained algorithm is configured to determine the subject's performance or health risk status with at least about 80% accuracy. In some embodiments, the trained algorithm is configured to determine the subject's performance or health risk score with at least about 80% accuracy.

[0017] In some embodiments, the electronic report includes a graphical representation of the subject's performance or health risk status. In some embodiments, the graphical representation includes a time series graph depicting the subject's performance or health risk score over time (e.g., daily). In some embodiments, the method further includes using the electronic report to provide a therapeutic intervention to the subject. In some embodiments, the therapeutic intervention includes a medication. In some embodiments, the subject's performance or health risk status includes a risk score. In some embodiments, the method further includes using the risk score to modify a physiological estimate or measurement of the subject, and determining a performance or health risk status based at least in part on the modified physiological estimate or measurement of the subject. In some embodiments, the method further includes generating a health or training plan for the subject to reduce risk or improve performance based at least in part on the modified physiological estimate or measurement of the subject. In some embodiments, the health plan includes suggestions for preventing the onset of a disease or disorder, delaying the onset of a disease or disorder, reversing a disease or disorder, preventing injury, or maintaining the physiological or health status of the subject. In some embodiments, the health plan includes suggestions related to one or more of diet, exercise, sports training, supplements, functional tests, blood tests, brain management, behavioral change, skin care, environmental exposure, stress management, and mental health. In some embodiments, the training plan includes a training program or a training rehabilitation program. In some embodiments, the method further includes generating an updated performance or health risk status in response to the subject following the health plan. In some embodiments, at least (b) and (c) are performed continuously in real time.

[0018]

[0018] In another aspect, the present disclosure provides a system for determining a subject's performance or health risk status, the system comprising: a database configured to store genetic information of the subject and environmental information of the subject, where the genetic information is obtained by analyzing a biological sample obtained from or derived from the subject, and the environmental information includes contextual data, activity, or physiological measurements of the subject; and one or more computer processors operably coupled to the database, where the one or more computer processors are individually or collectively programmed to: (i) process the genetic information and the environmental information to determine the subject's performance or health risk status; and (ii) electronically output a report indicative of the subject's performance or health risk status.

[0019]

[0019] In another aspect, the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, when executed by one or more computer processors, implements a method for determining a subject's performance or health risk status, the method may include the steps of: (a) receiving genetic information of the subject, where the genetic information is obtained by analyzing a biological sample obtained from or derived from the subject; (b) receiving environmental information of the subject, where the environmental information includes contextual data, activity, or physiological measurements of the subject; (c) processing the genetic information and the environmental information to determine the subject's performance or health risk status; and (d) outputting an electronic report indicative of the subject's performance or health risk status.

[0020]

[0020] Another aspect of the present disclosure provides a non-transitory computer-readable medium comprising machine-executable code that, when executed by one or more computer processors, performs any of the methods described above or elsewhere herein.

[0021] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto, the computer memory comprising machine executable code that, when executed by the one or more computer processors, performs any of the methods described above or elsewhere herein.

[0022]

[0022] Additional aspects and advantages of the present disclosure shall become readily apparent to those skilled in the art from the following detailed description, in which merely exemplary embodiments of the present disclosure are shown and described. As will be understood, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. Incorporation by Reference

[0023] All publications, patents, and patent applications mentioned herein are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. In the event that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained herein, the present specification is intended to supersede and / or supersede any such conflicting material.

[0023]

[0024] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (herein referred to as "Figures"), in which: [Brief description of the drawings]

[0024] [Figure 1]

[0025] FIG. 1 illustrates an example of a method 100 for determining a subject's performance or health risk status. [Diagram 2]

[0026] FIG. 1 illustrates an example of the modifier risk score concept, including a threshold (purple line) for indication of risk (or goal depending on the application) and a purple arrow indicating where the subject is in relation to the subject's risk threshold, presented as a modified risk score. [Diagram 3]

[0027] FIG. 2 illustrates an example of components and data flow of a modifier algorithm. [Figure 4]

[0028] FIG. 1 illustrates an example of a system diagram for a modifier algorithm solution. [Figure 5A]

[0029] FIG. 1 illustrates an example of a software application user interface for login and sign-up flow. [Figure 5B] FIG. 1 illustrates an example of a software application user interface for login and sign-up flow. [Figure 5C] FIG. 1 illustrates an example of a software application user interface for login and sign-up flow. [Figure 6A]

[0030] FIG. 1 illustrates an example of a software application user interface for context information capture flow. [Figure 6B] FIG. 1 illustrates an example of a software application user interface for context information capture flow. [Figure 6C] FIG. 1 illustrates an example of a software application user interface for context information capture flow. [Figure 7A]

[0031] FIG. 1 illustrates an example of a software application user interface for genetic and wearable data linkage flow. [Figure 7B] FIG. 1 illustrates an example of a software application user interface for genetic and wearable data linkage flow. [Figure 7C]FIG. 1 illustrates an example of a software application user interface for genetic and wearable data linkage flow. [Figure 7D] FIG. 1 illustrates an example of a software application user interface for genetic and wearable data linkage flow. [Figure 7E] FIG. 1 illustrates an example of a software application user interface for genetic and wearable data linkage flow. [Figure 8A]

[0032] FIG. 13 illustrates an example software application user interface including a dashboard displaying an overview of fitness metrics calculated by the modifier algorithm along with drill-down views of today's workout, recovery time, situational recovery activity capture, and fitness progress. [Figure 8B] FIG. 13 illustrates an example software application user interface including a dashboard displaying an overview of fitness metrics calculated by the modifier algorithm along with drill-down views of today's workout, recovery time, situational recovery activity capture, and fitness progress. [Figure 8C] FIG. 13 illustrates an example software application user interface including a dashboard displaying an overview of fitness metrics calculated by the modifier algorithm along with drill-down views of today's workout, recovery time, situational recovery activity capture, and fitness progress. [Figure 8D] FIG. 13 illustrates an example software application user interface including a dashboard displaying an overview of fitness metrics calculated by the modifier algorithm along with drill-down views of today's workout, recovery time, situational recovery activity capture, and fitness progress. [Figure 8E]FIG. 13 illustrates an example software application user interface including a dashboard displaying an overview of fitness metrics calculated by the modifier algorithm along with drill-down views of today's workout, recovery time, situational recovery activity capture, and fitness progress. [Figure 9A]

[0033] FIG. 1 illustrates an example software application user interface including an injury risk feature page showing daily genetically adapted risk for individual injuries (ACL, Achilles tendon, stress fracture, rotator cuff) and genetically adapted injury risk with physiological fitness over time. [Figure 9B] FIG. 1 illustrates an example software application user interface including an injury risk feature page showing daily genetically adapted risk for individual injuries (ACL, Achilles tendon, stress fracture, rotator cuff) and genetically adapted injury risk with physiological fitness over time. [Figure 9C] FIG. 1 illustrates an example software application user interface including an injury risk feature page showing daily genetically adapted risk for individual injuries (ACL, Achilles tendon, stress fracture, rotator cuff) and genetically adapted injury risk with physiological fitness over time. [Figure 9D] FIG. 1 illustrates an example software application user interface including an injury risk feature page showing daily genetically adapted risk for individual injuries (ACL, Achilles tendon, stress fracture, rotator cuff) and genetically adapted injury risk with physiological fitness over time. [Figure 10]

[0034] FIG. 1 shows examples of individual single nucleotide polymorphisms (SNPs) used to calculate gene pathway risk scores for four common overuse injury types, along with heritability scores (shown as percentages) and overall injury risk pathways. [Figure 11]

[0035] FIG. 13 illustrates an example of a probability density function of the calculated path scores. [Figure 12]

[0036] FIG. 12 illustrates an example of a cumulative distribution function of the corresponding path scores shown in FIG. [Figure 13A]

[0037] FIG. 13 illustrates an example of injury risk transitions as a function of pathway score, including a first injury risk distribution determined without genetics (red) and a second injury risk distribution determined with genetics (green). [Figure 13B] FIG. 13 illustrates an example of injury risk transitions as a function of pathway score, including a first injury risk distribution determined without genetics (red) and a second injury risk distribution determined with genetics (green). [Figure 13C] FIG. 13 illustrates an example of injury risk transitions as a function of pathway score, including a first injury risk distribution determined without genetics (red) and a second injury risk distribution determined with genetics (green). [Figure 13D] FIG. 13 illustrates an example of injury risk transitions as a function of pathway score, including a first injury risk distribution determined without genetics (red) and a second injury risk distribution determined with genetics (green). [Figure 14]

[0038] FIG. 1 illustrates a computer system programmed or otherwise configured to perform the methods provided herein. [Figure 15]

[0039] FIG. 1 illustrates an example of a framework for modeling injury using genetic modifiers. [Figure 16]

[0040] FIG. 1 illustrates an example of a modified TQR algorithm. [Figure 17]

[0041] FIG. 1 illustrates an example of the sensitivity of the injury risk model and the standard ACWR model. [Figure 18]

[0042] FIG. 1 illustrates an example of specificity of the injury risk model and the standard ACWR model. [Figure 19]

[0043] FIG. 1 illustrates an example time series of an athlete's injury risk as determined by the injury risk model and the standard ACWR model. [Figure 20A]

[0044] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 20B] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 20C] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 20D] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 20E] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 20F] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 20G] 1A-1C are diagrams illustrating examples of various views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21A]

[0045] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21B] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21C] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21D-1] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21D-2] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21E] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21F] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 21G] 1A-1C are diagrams illustrating examples of various dashboard views of a software application user interface from a player's (e.g., subject's) user perspective. [Figure 22A]

[0046] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22B] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22C] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22D] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22E] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22F] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22G]1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Fig. 22H] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22I] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22J] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22K] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. [Figure 22L] 1A-1C illustrate examples of various views of a software application user interface from a manager's user perspective. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025]

[0047] While various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It is understood that various alternatives to the embodiments of the present invention described herein may be employed.

[0026]

[0048] As used in this specification and claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a biological sample" includes multiple biological samples, including mixtures thereof.

[0027]

[0049] As used herein, the term "subject" generally refers to an organism having a testable or detectable genetic, nutrigenetic, or other health or other physiological parameter or information. The subject may be a human. The subject may be a vertebrate, such as a mammal. Non-limiting examples of mammals include humans, monkeys, livestock, sport animals, and pets. The subject may be an organism, such as an animal, a plant, a fungus, an archaea, or a bacterium. The subject may be a human. The subject may be a non-human. The subject may have or be suspected of having a health or physiological condition, such as a disease. In some examples, the subject is a patient. Alternatively, the subject may be asymptomatic for a health or physiological condition (e.g., a disease).

[0028]

[0050] The term "biological sample" as used herein generally refers to a biological sample that may be obtained from a subject. Samples obtained from a subject may include biological samples from humans, animals, plants, fungi, or bacteria. Samples may be obtained from subjects suffering from a disease or disorder, from subjects suspected of having a disease or disorder, or from subjects not having or not suspected of having a disease or disorder. The disease or disorder may be an infectious disease, an immune disorder or disease, cancer, a genetic disease, a degenerative disease, a lifestyle-related disease, an injury, a rare disease, or a disease associated with aging. The infectious disease may be caused by bacteria, viruses, fungi, and / or parasites. Samples may be obtained before and / or after treatment of a subject suffering from a disease or disorder. Samples may be obtained during treatment or a treatment regimen. Multiple samples may be obtained from a subject to monitor the effectiveness of treatment over time. Samples may be obtained from subjects having or suspected of having a disease or disorder where a definitive positive or negative diagnosis is not available from clinical testing.

[0029]

[0051] The sample may be obtained from a subject suspected of having a disease or disorder. The subject may be experiencing unexplained symptoms, such as fatigue, nausea, weight loss, aches and pains, weakness, or memory impairment. The subject may have an explained symptom. The subject may be at risk of developing a disease or disorder due to factors such as family history, age, environmental exposure, lifestyle risk factors, or the presence of other known risk factors.

[0030]

[0052] The sample may include a biological sample from a subject (e.g., a human subject), such as saliva, oral mucosa, blood, plasma, serum, cells, tissue (e.g., normal or tumor), urine, stool (excrement), or derivatives or combinations thereof. The sample may be a tissue sample, such as a tumor sample. The sample may be an acellular sample, such as a blood (e.g., whole blood), sweat, saliva, or urine sample. The biological sample may be stored under various storage conditions before processing, such as at different temperatures (e.g., at room temperature, under refrigerated or frozen conditions, at 4°C, -18°C, -20°C, or -80°C), or with different preservatives (e.g., alcohol, formaldehyde, potassium dichromate, or EDTA).

[0031]

[0053] As used herein, the term "nucleic acid" generally refers to a polymeric form of nucleotides of any length, either deoxyribonucleotides (dNTPs) or ribonucleotides (rNTPs), or analogs thereof. Nucleic acids can have any three-dimensional structure and can perform any function, known or unknown. Non-limiting examples of nucleic acids include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), coding or non-coding regions of a gene or gene fragment, loci (locuses) defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, short interfering RNA (siRNA), short hairpin RNA (shRNA), microRNA (miRNA), ribozymes, cDNA, recombinant nucleic acids, branched nucleic acids, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes, and primers. Nucleic acids can include one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure can occur before or after assembly of the nucleic acid. The sequence of nucleotides of a nucleic acid may be interrupted by non-nucleotide components. Nucleic acids may be further modified after polymerization, such as by conjugation or conjugation with a reporter agent.

[0032]

[0054] The nucleic acid molecules may include deoxyribonucleic acid (DNA), ribonucleic acid (RNA) molecules, or a combination thereof. The DNA or RNA molecules may be extracted from the sample by various methods, such as the FastDNA kit protocol from MP Biomedicals. The extraction method may extract all DNA molecules from the sample. Alternatively, the extraction method may selectively extract a portion of the DNA molecules from the sample, for example, by targeting a specific gene within the DNA molecule. Alternatively, the extracted RNA molecules from the sample may be converted to DNA molecules by reverse transcription (RT). In some embodiments, after obtaining the sample, the sample may be processed to generate multiple genomic sequences. For example, processing the sample may include extracting multiple nucleic acid (DNA or RNA) molecules from the sample and sequencing the multiple nucleic acid (DNA or RNA) molecules to generate multiple nucleic acid (DNA or RNA) sequence reads.

[0033]

[0055] Sequencing can be performed by any suitable sequencing method, such as massively parallel sequencing (MPS), paired-end sequencing, high-throughput sequencing, next-generation sequencing (NGS), shotgun sequencing, single molecule sequencing (e.g., Pacific Biosciences of California), nanopore sequencing (e.g., Oxford Nanopore), semiconductor sequencing, pyrosequencing (e.g., 454 sequencing), sequencing-by-synthesis (SBS), sequencing by ligation, and sequencing by hybridization, or RNA-Seq (Illumina). Sequence identification can be performed using genotyping techniques, such as arrays. As an example, the array can be a microarray (e.g., Affymetrix or Illumina).

[0034]

[0056] Sequencing may include nucleic acid amplification (e.g., of DNA or RNA molecules). In some embodiments, the nucleic acid amplification is a polymerase chain reaction (PCR). A suitable number of PCRs (e.g., PCR, qPCR, reverse transcription PCR, digital PCR, etc.) may be performed to sufficiently amplify the initial amount of nucleic acid (e.g., DNA) to a desired input amount for subsequent sequencing or genotyping. In some cases, PCR may be used for global amplification of nucleic acids. This may include using adapter sequences that may first be ligated to different molecules, followed by PCR amplification using universal primers. PCR may be performed using any of several commercially available kits provided, for example, by Life Technologies, Affymetrix, Promega, Qiagen, etc. In other cases, only specific target nucleic acids within a population of nucleic acids may be amplified. Specific primers, possibly in conjunction with adapter ligation, may be used to selectively amplify specific targets for downstream sequencing or genotyping. PCR may involve targeted amplification of one or more genomic loci, such as genomic loci corresponding to one or more diseases or disorders, such as cancer markers (e.g., BRCA1 and 2). Sequencing or genotyping may involve the use of simultaneous reverse transcription (RT) and polymerase chain reaction (PCR), such as the OneStep RT-PCR kit protocols provided by Qiagen, NEB, Thermo Fisher Scientific, or Bio-Rad.

[0035]

[0057] As used herein, the terms "amplifying" and "amplification" are used interchangeably and generally refer to producing one or more copies of a nucleic acid or "amplification product". The term "DNA amplification" generally refers to producing one or more copies of a DNA molecule or "amplified DNA product". The term "reverse transcription amplification" generally refers to the production of deoxyribonucleic acid (DNA) from a ribonucleic acid (RNA) template by the action of reverse transcriptase. For example, sequencing or genotyping of DNA molecules can be performed with or without amplification of DNA molecules.

[0036]

[0058] DNA or RNA molecules can be tagged with, for example, identifiable tags to allow multiplexing of multiple samples. Any number of DNA or RNA samples can be multiplexed. For example, a multiplexing reaction can include DNA or RNA from at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, or more than 100 initial samples. For example, multiple samples can be tagged with sample barcodes so that each DNA or RNA molecule can be traced back to the sample (and environment or subject) from which the DNA or RNA molecule originates. Such tags can be attached to DNA or RNA molecules by ligation or by PCR amplification using primers.

[0037]

[0059] After sequencing nucleic acid molecules, suitable bioinformatics processes can be performed on sequence reads to generate multiple genome sequences.For example, sequence reads can be filtered for quality, trimmed to remove low quality, or aligned to one or more reference genomes (e.g., human genome).

[0038]

[0060] In some embodiments, after obtaining a biological sample, the biological sample may be processed to generate a proteome, a metabolome, or any combination thereof. For example, processing the biological sample may include extracting a plurality of proteins from the biological sample and analyzing the plurality of proteins to identify and / or quantify the plurality of proteins, thereby generating a proteome of the biological sample. As another example, processing the biological sample may include extracting a plurality of metabolites from the biological sample and analyzing the plurality of metabolites to identify and / or quantify the plurality of metabolites, thereby generating a metabolome of the biological sample. The extraction method may extract all proteins and / or metabolites from the biological sample. Alternatively, the extraction method may selectively extract a portion of proteins and / or metabolites from the biological sample, for example, by use of binding reagents such as probes or antibodies to target specific proteins and / or metabolites.

[0039]

[0061] As used herein, a user may be an end consumer, a company with at least one product that may analyze human genetic data to generate health-related recommendations and other information for an end consumer, an organization that does not have any product but may utilize human genetic data for other purposes such as research, a subject from whom a biological sample and / or nutrigenetics data is obtained, or a doctor, nurse, nutrigenetics counselor, or other medical professional. Genetic data may include nutrigenetics data that may include nutrigenetics anomalies. Genetic data may include sports performance, energy metabolism, and sports nutrition data.

[0040]

[0062] The terms "nutrigenetics" and "nutrigenomics", as used herein, generally refer to nutritional gene or nutritional genome information, such as the relationship between genome, nutrition, and health of a subject. For example, nutrigenetics analysis may relate to identifying or predicting a subject's heterogeneous or differential response to diet and nutrients based on the analysis of nucleic acid sequences with genetic variations, while nutrigenomics analysis may relate to the effect of diet and nutrients on a subject's gene expression.

[0041]

[0063] The term "nutrigenetic abnormality" as used herein generally refers to a nutrition-related abnormality in a subject's genome, such as, for example, a nutrigenetic mutation. Nutrigenetic abnormalities are abnormalities that are related (e.g., causal or highly correlated, e.g., R) to the nutrition and health of a subject, such as, for example, single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions or deletions (indels), fusions, or translocations. 2 The nutrigenetics variant can be, for example, a genetic variant associated with a differential response to nutrients (e.g., differential gene expression or DNA methylation).

[0042]

[0064] Whenever the terms "at least," "more than," or "greater than or equal to" precede the first number in a series of two or more numbers, the terms "at least," "more than," or "greater than or equal to" apply to each and every number in the series. For example, 1, 2, or 3 or more is equivalent to 1 or more, 2 or more, or 3 or more.

[0043]

[0065] Whenever the terms "not greater than," "less than," or "equal to or less than" precede the first number in a series of two or more numbers, the terms "not greater than," "less than," or "equal to or less than" apply to each and every number in the series. For example, 3, 2, or equal to or less than is equivalent to 3 or less, 2 or less, or 1 or less.

[0044]

[0066] A particular health or performance state in an individual may be determined by the combined effect of specific environmental conditions acting on the genetic background, which can be summarized by the equation: Genetics (G) + Change in environment (ΔE) = Health state (H).

[0045]

[0067] Genetic datasets, such as single nucleotide polymorphism (SNP) genotyping data, can be widely used to calculate polygenic risk scores for certain health conditions or performance traits. While polygenic risk scores provide a good estimate of how a person's genetic risk (G) affects a particular health condition or performance trait (H), they may not take into account environmental contributions (ΔE).

[0046]

[0068] Commercial genetic reports may provide genotyping results along with risk or performance status coupled with academic literature-based opinions on numerous health and fitness traits. Although these reports may be rich in information, the impact of these plans (changes in health, fitness, and performance status due to changes in the environment) may be difficult to measure and present to the user in an interesting way, making them difficult to understand and follow. Genetics may play a significant role in one's health and fitness journey (e.g., one's genetic makeup may affect one's physiological performance in different sports by more than 50%). However, health and fitness decisions may be made without fully assessing, following, or following the genetic component. Thus, the health impact that genetics should have may not be fully recognized, and commercial genetic reports are perceived as low value for what are expensive tests and technologies.

[0047]

[0069] Continuous physiological monitoring may enable real-time insight into a wide range of physiological measures, such as cardiorespiratory fitness, resting heart rate, stress levels, sleep quality, training load, blood pressure, and blood glucose levels. Wearable devices may provide an affordable solution that provides an array of trackable health and fitness performance scores. As with genetic suggestions, insights based on these measures may be complex to understand, interpret, and incorporate into daily health and fitness programs. Insights from wearable devices and applications may be retrospective and may not provide predictive capabilities. The algorithms that generate metrics and insights may be heavily based on population averages, which prevents personalization and accuracy.

[0048]

[0070] To determine a more accurate health and performance state (H) at any given time, the environmental contribution (ΔE) to a person's genetic risk (G) can be determined through contextual information and continuous physiological monitoring. Continuously measuring the impact that lifestyle choices have on a person's genetic background and expressing that impact using a personalized and easily interpretable health or performance related score can lead to improved adherence to and adoption of gene-based health and fitness recommendations.

[0049]

[0071] Physiological monitoring and contextual information can serve as inputs to baseline gene scores to estimate environmental influences on gene expression that modify health or performance phenotypes.Therefore, there is a need for easy-to-use and understand technologies that seamlessly integrate genetic information with physiological and contextual information to enable real-time views and predictive metrics of human health and performance indicators that are personalized for individuals.

[0050]

[0072] The present disclosure provides a method and system that integrates genetic data, contextual data, physiological biomarkers acquired from wearable devices, and / or continuous physiological data to output easily interpretable health, fitness, and sports performance related scores through modifier algorithm calculations. The method and system of the present disclosure may be based on the concept of modifier risk scores for an individual's health status or performance characteristics, as depicted in FIG. 2. Each modifier score may include static genetic contributions (e.g., pathway scores expressed as polygenic risk scores) and dynamic time-varying behavioral contributions (measured through continuous physiological monitoring and contextual information). Behavioral contributions may represent the influence that lifestyle choices have on genetic background. The composite modifier score may be a real-time fluctuation score that indicates the subject's risk level. Risk indicates when the composite modifier score reaches a threshold. The modifier risk score may enable daily suggestions for further risk mitigation, as well as predictive capabilities indicating favorable or unfavorable outcomes given a particular health or training plan.

[0051]

[0073] FIG. 1 illustrates an example of a method 100 for determining a subject's performance or health risk status. The method 100 may include receiving (e.g., at operation 102) genetic information of the subject. The genetic information may be obtained by analyzing a biological sample obtained from or derived from the subject. The method 100 may include receiving (e.g., at operation 104) environmental information of the subject. The environmental information may include contextual data, activity, biochemical, or physiological measurements of the subject. The method 100 may include processing (e.g., at operation 106) the genetic information and the environmental information to determine the subject's performance or health risk status. The performance or health risk status may include a performance or health risk score, a number, or a quantitative metric of the subject. The method 100 may include outputting (e.g., at operation 108) an electronic report indicative of the subject's performance or health risk status. The electronic report may further include subject-specific health and fitness suggestions, such as ways to improve scores or reduce risks (e.g., generated based at least in part on the subject's performance or health risk status, or performance or health risk scores, numbers, or quantitative metrics). The electronic report may include a graphical representation of the subject's performance or health risk status, such as a time series graph depicting the subject's performance or health risk score over time (e.g., daily). The electronic report may be displayed through a user interface (e.g., providing a panoramic view of the individual's context). The user interface may be configured to receive user input. The user interface may be presented via a software application (e.g., a mobile software application).

[0052]

[0074] Figure 2 shows an example of the modifier risk score concept, including a threshold (purple line) for indication of risk (or goal depending on the application) and a purple arrow indicating where the subject is in relation to the subject's risk threshold, presented as a modified risk score.

[0053]

[0075] The modifier algorithm used to calculate the real-time varying modifier score may include the following data flow structure, as depicted in FIG. 3, which shows an example of the components and data flow of the modifier algorithm.

[0054]

[0076] Genetic data, e.g., SNP genotyping results, can be used to calculate polygenic risk scores or pathway scores for multiple biochemical pathways. These scores can serve as inputs for various and diverse physiological models. Contextual inputs, e.g., age, sex, height, weight, and acquired previous injuries, can serve as additional inputs for the physiological models. Examples of physiological models include models of sports performance, such as maximum oxygen uptake, lactate threshold, training load, and acute:chronic workload ratio (ACWR). Continuous physiological data acquired from the wearable device, e.g., heart rate derived data, can serve as real-time inputs to the modifier algorithm, allowing the calculation of a daily modifier score, which is a combined score indicating genetic involvement together with environmental (or behavioral) involvement. Additional physiological and biochemical markers and biomarkers, such as blood results (e.g., blood glucose results), can also serve as inputs for the modifier algorithm.

[0055]

[0077] This disclosure provides physiologically driven algorithms, artificial intelligence algorithms, system architectures that capture wearable data, and software (e.g., mobile, desktop, or tablet) application solutions. All these modules can be integrated into a system that provides users with actionable insights into their health. Figure 4 shows an example of a system diagram for a modifier algorithm solution.

[0056]

[0078] The mobile application solution (referred to as the front end in the system diagram above) can be used to communicate easily interpretable health and sports performance related scores and actionable insights to the user, and creates a user profile following a standard login and sign-up process, as shown in Figures 5A-5C, which show examples of software application user interfaces for login and sign-up flows.

[0057]

[0079] Context information, e.g., biometric information such as gender, age, weight, height, previous injury history, and specific goals, can be captured through the following screens shown in Figures 6A-6C, which show examples of software application user interfaces for the context information capture flow.

[0058]

[0080] 7A-7E show an example of a software application user interface for genetic and wearable data linkage flow. A user's genetic data (SNP genotyping results in .txt format) can be linked to the user's profile in the backend. The genotyping results can serve as input for an algorithm that calculates a polygenic risk score or pathway score for an individual for various biochemical pathways, including but not limited to: endurance, VO2 max trainability, slow twitch fibers, power, fast twitch fibers, anaerobic threshold, strength, recovery, inflammation, oxidative stress, muscle damage, injury, rotator cuff injury, anterior cruciate ligament (ACL) injury, IT band injury, stress fracture, knee osteoarthritis, and Achilles tendon injury.

[0059]

[0081] A user's wearable device and data can be linked to the user's profile through third-party authentication. Wearable data files including heart rate, speed, power, duration, time, exercise type, incline, lap details, and other biometric data can be uploaded and serve as inputs for various physiological models and modifier algorithms in the backend.

[0060]

[0082] Modifier scores for health, fitness, and sports performance can be continuously updated through modifier algorithms as new wearable and contextual data is uploaded into the system. By displaying these score outputs as easily interpretable and understandable visual metrics on the mobile application user interface, users can understand the impact of their lifestyle choices on their genetic background and how it impacts their health and performance.

[0061]

[0083] 8A-8E show an example of a software application user interface including a dashboard displaying an overview of fitness metrics calculated by modifier algorithms along with drill-down views of today's workout, recovery time, situational recovery activity capture, and fitness progress. The dynamic metrics and suggestions may enable a more accurate and personalized health and fitness journey compared to using only genetic risk scores and information that may be provided by genetic testing companies through health reports, or physiological models based solely on population averages that may be provided through wearable device applications and platforms.

[0062]

[0084] The dashboard provides an overview of key fitness metrics calculated through modifier algorithms. These scores and metrics can drive machine learning algorithm suggestions and dynamic workout plans. An example is Workout Today, which provides workout programs that are tailored according to a person's genetic predisposition for certain exercise types, their training history, and physiological model outputs such as recovery time and injury risk, all captured by a combination of genetic, contextual, and wearable data that drives machine learning algorithms.

[0063]

[0085] The injury risk feature may use the SNP genotyping data to calculate biochemical pathway risk scores associated with common overuse sports injuries, such as rotator cuff injuries, anterior cruciate ligament (ACL) injuries, stress fractures, Achilles tendon injuries, muscle injuries, connective tissue injuries, and general injury pathway risk scores. Individual pathway risk scores may be transformed by modifier algorithms along with contextual information such as previous injuries (injury type and injury date) and exercise type, as well as heart rate derived data acquired from wearable data. These transformations may provide the user with a daily genetically adapted injury risk status based on genes and behavioral engagement, as shown in Figures 9A-9D, which show examples of software application user interfaces, including injury risk feature pages showing daily genetically adapted risks for individual injuries (ACL, Achilles tendon, stress fracture, rotator cuff), and genetically adapted injury risk with physiological fitness over time, as well as the exercise type selected. Injury risk varies depending on which exercise type is selected. Injuries may be manually recorded to adjust the injury risk profile. Suggestions for mitigating different injury risks are presented by clicking on the risk bar for that particular injury type.

[0064]

[0086] The genetically adapted injury risk indicates to the user when the user is approaching a risk threshold for a particular injury type depending on the combination of the user's genetics, behavior, and exercise type. The injury risk feature also indicates the genetically adapted injury risk and physiological fitness over time. The modifier algorithm depicts how injury risk decreases over time as physiological adaptations occur through the correct training stimuli over time.

[0065]

[0087] Figure 10 shows examples of individual single nucleotide polymorphisms (SNPs) used to calculate gene pathway risk scores for four common overuse injury types (rotator cuff injury, stress fracture, Achilles tendon injury, and ACL injury) along with heritability scores (shown as percentages) and overall injury risk pathway.

[0066]

[0088] The modifier algorithm for each injury type depends on the population distribution of pathway risk scores, and the factor by which the physiological measures are adjusted is determined by the cumulative distribution function (cdf) and the user's pathway risk score. Using the cdf can ensure that pathway risk scores are not given a higher / lower weighting of impact given a particular distribution of pathway risk scores.

[0067]

[0089] Figure 11 shows an example of a probability density function of the calculated path scores, and Figure 12 shows an example of a cumulative distribution function of the corresponding path scores shown in Figure 11.

[0090] To calculate the injury risk of a particular route, from the raw data to the risk measure shown on the mobile app, the following process may be followed: The first calculation may be used to estimate the effect of the training session on the individual's physiology using metrics used in the general case. This is done by calculating the load of the session using the session duration and the session intensity. From the daily load, acute and chronic load measures are determined. Acute load is calculated over a short time period, such as 7 days. Chronic load may be calculated using a longer period (e.g. 42 days), which may vary according to different sports. The acute to chronic load ratio (ACWR) may be an indicator of how much training an individual is doing in relation to how much they can do. If this ratio becomes too high, the risk of injury may increase.

[0068]

[0091] The modifier algorithm may incorporate genetics into the equation, which may be done by adapting both session load and ACWR. How much these values ​​are adapted is determined by the individual's pathway scores, the heritability of the particular injury, and the distribution of all possible pathway scores in a given population. The modifier algorithm may calculate a modifier score that is used to transition the physiological score up or down depending on the three factors mentioned earlier. This is depicted in the diagram below for four different types of injuries. A person's risk will transition either up or down in relation to the standard scale if genetics are not taken into account.

[0069]

[0092] Figures 13A-13D show examples of injury risk transitions according to pathway score, including a first injury risk distribution (red) determined without genetics and a second injury risk distribution (green) determined with genetics.

[0070]

[0093] User Portal and Platform

[0094] The system of the present disclosure may generate profiles of subjects, facilitate data exchange of the profiles between end users (e.g., using a network such as a cloud network), store the profiles in a database (e.g., a cloud network), and / or display electronic reports including the profiles to end users.

[0071]

[0095] The system may facilitate data exchange of profiles between end users (e.g., using a network such as a cloud network) and / or store the profiles in a database (e.g., a cloud network). The system may include a network interface in network communication with digital computers of different users. The network interface may include a portal or platform, such as a user portal (e.g., for end users to view profiles) or a clinician portal (e.g., for clinicians to view or annotate profiles). In some embodiments, a cloud-based method or system may be provided to users to facilitate data exchange. Users may use a web application to log in and access their data through a cloud-based computer system within the application, the data being generated from processing at least one biological sample of the user. Data exchange and / or data storage may take into account privacy laws and policies, such as Health Insurance Portability and Accountability Act of 1996 (HIPAA) compliance and protection of protected health information (PHI).

[0072]

[0096] The systems and methods provided herein may include a user portal and / or a user platform configured to perform health, fitness, and sports performance analysis, display profiles and reports to users, and / or control access to profiles, reports, and / or data. The user portal and / or the user platform may include a server including a digital processing device or processor that may execute machine code, such as a computer program or algorithm, to enable one or more method steps or operations as disclosed herein. Such computer programs or algorithms may be executed automatically or on-demand based on one or more inputs from a user. The user portal and / or the user platform may enable users to connect with each other through the portal or platform, such as for data exchange, thereby forming a network of connected users. Such data exchange may be secure and / or cloud-based. Each user may have an account to access the network and utilize the functions associated with the data exchange securely and conveniently. The portal and / or the platform may include a user interface, such as a graphical user interface (GUI). The portal and / or the platform may include a web application or a mobile application. The portal and / or platform may include a digital display for displaying information to a user and / or an input device with which the user may interact to accept input from the user.

[0073]

[0097] In some embodiments, the electronic report including the data or health plan is presented on a user interface, such as a graphical user interface (GUI), of a user's (e.g., subject's) electronic device. The electronic report may be transmitted to a remote user (e.g., a medical professional, nutrigenetics counselor, or sports coach). Additionally, the electronic report may be stored on a remote server (e.g., a cloud-based server).

[0074]

[0098] classifier

[0099] The profiling method may include processing the genetic and environmental information of a subject using a trained algorithm (e.g., a classifier) ​​to determine the performance or health risk status of the subject. The classifier may be used to classify the subject as having a given performance or health risk status. The classifier may include a supervised or unsupervised machine learning algorithm. The classifier may include a classification and regression tree (CART) algorithm. The classifier may include, for example, a support vector machine (SVM), linear regression, logistic regression, nonlinear regression, neural network, random forest, deep learning algorithm, naive Bayes classifier. Classifiers may include unsupervised machine learning algorithms, such as clustering analysis (e.g., k-means clustering, hierarchical clustering, mixture models, DBSCAN, OPTICS algorithms), principal component analysis, independent component analysis, nonnegative matrix factorization, singular value decomposition, anomaly detection (e.g., local outlier factors), neural networks (e.g., autoencoders, deep belief networks, Hebbian learning, generative adversarial networks, self-organizing maps), expectation-maximization algorithms, and methods of moments.

[0075]

[0100] The classifier may be configured to accept multiple input variables and generate one or more output values ​​based on the multiple input variables. The multiple input variables may include genetic information and environmental information. For example, the input variables may include a set of identified variants or alleles, and / or several sequences that correspond to or align with each of the set of identified variants or alleles. For example, the input variables may include a set of genes or pathways that correspond to a polygenic risk or pathway score, and / or several sequences that correspond to or align with each of the set of genes or pathways.

[0076]

[0101] A classifier may have one or more possible output values, each of which includes one of a fixed number of possible values ​​(e.g., a linear classifier, a logistic regression classifier, etc.) indicating a classification of a biological sample into a performance or health risk state (e.g., a level of influence of an allele on a pathway). A classifier may include a binary classifier, such that each of the one or more output values ​​includes one of two values ​​(e.g., {0, 1}, {positive, negative}, or {high risk, normal risk}) indicating a classification of a subject into a performance or health risk state (e.g., a level of influence). A classifier may be another type of classifier, such that each of the one or more output values ​​includes one of three or more values ​​(e.g., {0, 1, 2}, {positive, negative, or indeterminate}, or {high risk, normal risk, or indeterminate}) indicating a classification of a subject as having a certain performance or health risk state. An output value may include a descriptive label, a numerical value, or a combination thereof. Some of the output values ​​may include a descriptive label. Such descriptive labels may provide an identification or indication of the subject's performance or health risk status. Such descriptive labels may provide an identification of a suggestion for the subject's performance or health risk status, including, for example, a therapeutic intervention, duration of the therapeutic intervention, and / or suggestions related to diet, exercise, sports training, supplements, functional tests, blood tests, brain management, behavioral modification, skin care, environmental exposure, stress management, and / or mental health. Such descriptive labels may provide an identification of a secondary clinical test that may be appropriate to perform on the subject, including, for example, a biopsy, blood tests, functional tests, computed tomography (CT) scan, magnetic resonance imaging (MRI) scan, ultrasound scan, chest x-ray, positron emission tomography (PET) scan, or PET-CT scan. Such descriptive labels may provide a prognosis of the subject's disease status. Some descriptive labels may be mapped to a numerical value, for example, by mapping "positive" to 1 and "negative" to 0.

[0077]

[0102] Some of the output values ​​may include numerical values, such as binary, integer, or continuous values. Such binary output values ​​may include, for example, {0, 1}. Such integer output values ​​may include, for example, {0, 1, 2}. Such continuous output values ​​may include, for example, probability values ​​(e.g., of a subject's classification as having a performance or health risk condition) of at least 0 and less than or equal to 1. Such continuous output values ​​may include, for example, unnormalized probability values ​​of at least 0. Such continuous output values ​​may include, for example, unnormalized probability values ​​of at least 0. Such continuous output values ​​may include, for example, an indication of a predicted duration of an intervention. Such continuous output values ​​may include, for example, a hazard ratio or odds ratio for a risk (e.g., injury risk or disease risk). Some numerical values ​​may be mapped to descriptive labels, for example, by mapping 1 to "positive" and 0 to "negative."

[0078]

[0103] Some of the output values ​​may be assigned based on one or more cutoff values. For example, a binary classification may assign an output value of "positive" or 1 if the subject has at least a 50% probability of being offered an intervention. For example, a binary classification of a subject may assign an output value of "negative" or 0 if the subject has less than a 50% probability of being offered an intervention. In this case, a single cutoff value of 50% is used to classify the subject into one of two possible binary output values. Examples of single cutoff values ​​may include about 1%, about 2%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, about 98%, and about 99%.

[0079]

[0104] As another example, a subject classification may assign an output value of "positive" or 1 if the subject has at least a 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99% chance of being suggested an intervention. A classification may assign an output value of "positive" or 1 if the subject has a greater than 50%, greater than 55%, greater than 60%, greater than 65%, greater than 70%, greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 98%, or greater than 99% chance of being suggested an intervention. The classification may assign an output value of "adverse" or 0 if the subject has a probability of being suggested an intervention of less than 50%, less than 45%, less than 40%, less than 35%, less than 30%, less than 25%, less than 20%, less than 10%, less than 5%, less than 2%, or less than 1%. The classification may assign an output value of "negative" or 0 if the subject has a probability of being suggested an intervention of less than 50%, less than 45%, less than 40%, less than 35%, less than 30%, less than 25%, less than 20%, less than 10%, less than 5%, less than 2%, or less than 1%. The classification may assign an output value of "indeterminate" or 2 if the subject is not classified as "positive", "negative", 1, or 0. In this case, a set of two cutoff values ​​is used to classify the subject into one of three possible output values. Examples of sets of cutoff values ​​may include: {1%, 99%}, {2%, 98%}, {5%, 95%}, {10%, 90%}, {15%, 85%}, {20%, 80%}, {25%, 75%}, {30%, 70%}, {35%, 65%}, {40%, 60%}, and {45%, 55%}. Similarly, a set of n cutoff values ​​may be used to classify a subject into one of n+1 possible output values, where n is any positive integer.

[0080]

[0105] The classifier may be trained using multiple independent training samples. Each of the independent training samples may include a subject, associated data, and one or more known output values ​​corresponding to the subject's performance or health risk condition. The independent training samples may include data and output obtained from multiple different subjects. The independent training samples may include data and output obtained from the same subject at multiple different time points. The independent training samples may be associated with the presence of a performance or health risk condition (e.g., training samples obtained from multiple subjects known to have the performance or health risk condition). The independent training samples may be associated with the absence of a performance or health risk condition (e.g., training samples obtained from multiple subjects known not to have the performance or health risk condition).

[0081]

[0106] The classifier may be trained using at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The independent training samples may include subjects associated with the presence of a performance or health risk condition and / or subjects associated with the absence of a performance or health risk condition. The classifier may be trained using 500 or less, 450 or less, 400 or less, 350 or less, 300 or less, 250 or less, 200 or less, 150 or less, 100 or less, or 50 or less independent training samples associated with the presence of a performance or health risk condition.

[0082]

[0107] The classifier may be trained using a first number of independent training samples associated with the presence of a performance or health risk condition and a second number of independent training samples associated with the absence of a performance or health risk condition. The first number of independent training samples associated with the presence of a performance or health risk condition may be less than or equal to the second number of independent training samples associated with the absence of a performance or health risk condition. The first number of independent training samples associated with the presence of a performance or health risk condition may be equal to the second number of independent training samples associated with the absence of a performance or health risk condition. The first number of independent training samples associated with the presence of a performance or health risk condition may be greater than the second number of independent training samples associated with the absence of a performance or health risk condition.

[0083]

[0108] The classifier can be configured to identify a performance or health risk state with at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% accuracy for at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, or up to about 300 independent samples. The accuracy of identifying a performance or health risk condition by a classifier may be calculated as the percentage of independent test samples (e.g., subjects with a performance or health risk condition) that are correctly identified or classified as having or not having the performance or health risk condition, respectively.

[0084]

[0109] The classifier can be configured to identify a performance or health risk condition with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99%. The PPV of identifying a performance or health risk state by a classifier can be calculated as the percentage of biological samples that are identified or classified as having a performance or health risk state that corresponds to a subject who truly has the performance or health risk state. PPV can also be referred to as accuracy.

[0085]

[0110] The classifier can be configured to identify a performance or health risk status with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99%. The NPV of identifying a performance or health risk state by a classifier can be calculated as the percentage of biological samples that are identified or classified as not having the performance or health risk state that corresponds to subjects who truly do not have the performance or health risk state.

[0086]

[0111] The classifier can be configured to identify a performance or health risk state with a clinical sensitivity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99%. The clinical sensitivity of identifying a performance or health risk condition by a classifier can be calculated as the percentage of independent test samples associated with the presence of a performance or health risk condition (e.g., subjects known to have a performance or health risk condition) that are correctly identified or classified as having the performance or health risk condition. Clinical sensitivity can also be referred to as recall.

[0087]

[0112] The classifier can be configured to identify a performance or health risk state with a clinical specificity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99%. Clinical specificity for identifying a performance or health risk condition by a classifier may be calculated as the percentage of independent test samples associated with the absence of a performance or health risk condition (e.g., healthy-looking subjects with negative clinical test results for the performance or health risk condition) that are correctly identified or classified as not having the performance or health risk condition.

[0088]

[0113] The classifier can be configured to identify a performance or health risk state having an Area-Under-Curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or greater than about 0.99. The AUC may be calculated as the integral of a receiver operating characteristic (ROC) curve (e.g., the area under the ROC curve) associated with a classifier in classifying a biological sample as having or not having a performance or health risk condition.

[0089]

[0114] The classifier may be adjusted or tuned to improve the performance, accuracy, PPV, NPV, clinical sensitivity, clinical specificity, or AUC of identifying one or more performance or health risk conditions. The classifier may be adjusted or tuned by adjusting the parameters of the classifier (e.g., the set of cutoff values ​​used to classify the sample as described elsewhere herein, or the weights of the neural network). The classifier may be adjusted or tuned continuously during the training process or after the training process is completed.

[0090]

[0115] After the classifier is initially trained, a subset of inputs may be identified as the most influential or most important to be included to make a high-quality classification. For example, a subset of input data may be identified as the most influential or most important to be included to make a high-quality classification or identification of a performance or health risk condition. The set of input data or a subset thereof may be ranked based on a metric indicating the impact or importance of each feature toward making a high-quality classification or identification of a performance or health risk condition. Such metrics may be used to reduce, possibly significantly, the number of input variables (e.g., predictor variables) that may be used to train the classifier to a desired performance level (e.g., based on a desired minimum accuracy, PPV, NPV, clinical sensitivity, clinical specificity, or AUC).

[0091]

[0116] For example, if training a training algorithm with a plurality of input variables, including tens or hundreds, in a classifier results in a classification accuracy of greater than 99%, training the training algorithm instead with only a selected subset of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, about 10 or less, about 15 or less, about 20 or less, about 25 or less, about 30 or less, about 35 or less, about 40 or less, about 45 or less, about 50 or less, or about 100 or less of such most influential or most important input variables of the plurality of input variables results in a reduced but still acceptable classification accuracy (e.g., at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, or at least about 98%).

[0092]

[0117] In some embodiments, the subset may be selected by rank ordering across multiple input variables and selecting a predefined number (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, about 10 or less, about 15 or less, about 20 or less, about 25 or less, about 30 or less, about 35 or less, about 40 or less, about 45 or less, about 50 or less, about 100 or less, about 150 or less, or about 200 or less) of input variables with the best metric.

[0093]

[0118] Computer Systems

[0119] The present disclosure provides a computer system that is programmed to perform the method of the present disclosure. Figure 14 shows a computer system 1401 that is programmed or otherwise configured to perform one or more functions or operations of the present disclosure, such as, for example, determining a performance or health risk status or score, and facilitating a nutrigenomics report for a subject. The computer system 1401 can manage various aspects of the portal and / or platform of the present disclosure, such as, for example, receiving genetic and / or environmental information of a subject, determining a performance or health risk status of a subject, outputting an electronic report (e.g., with various daily scores related to health, fitness, and sports performance), transmitting the electronic report to a remote user, storing the electronic report on a remote server, and processing input data using a trained algorithm to identify a performance or health risk status. The computer system 1401 can be a user's electronic device, or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0094]

[0120] The computer system 1401 includes a central processing unit (CPU, herein "processor" and "computer processor") 1405, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 1401 also includes memory or memory locations 1410 (e.g., random access memory, read-only memory, flash memory), electronic storage 1415 (e.g., hard disk), communication interface 1420 (e.g., network adapter) for communication with one or more other systems, and peripheral devices 1425, such as cache, other memory, data storage devices, and / or electronic display adapters. The memory 1410, storage 1415, interface 1420, and peripheral devices 1425 are in communication with the CPU 1405 through a communication bus (solid lines), such as a motherboard. The storage 1415 may be a data storage device (or data repository) for storing data. The computer system 1401 may be operatively coupled to a computer network ("network") 1430 with the aid of the communication interface 1420. The network 1430 may be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet.

[0095]

[0121] The network 1430 is, in some cases, a telecommunications and / or data network. The network 1430 may include one or more computer servers that may enable distributed computing, such as cloud computing. For example, the one or more computer servers may enable cloud computing through the network 1430 ("cloud") to perform various aspects of the analysis, calculation, and generation of the present disclosure, such as, for example, receiving genetic and / or environmental information of a subject, determining a performance or health risk status of a subject, outputting an electronic report, transmitting the electronic report to a remote user, storing the electronic report on a remote server, and processing input data using trained algorithms to identify a performance or health risk status. Such cloud computing may be provided by a cloud computing platform, such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud platform, and IBM Cloud. The network 1430 may implement a peer-to-peer network that may enable devices coupled to the computer system 1401 to behave as clients or servers, in some cases with the help of the computer system 1401.

[0096]

[0122] CPU 1405 may execute sequences of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as memory 1410. The instructions may be directed to CPU 1405, which may then program or otherwise configure CPU 1405 to perform methods of the present disclosure. Examples of operations performed by CPU 1405 may include fetch, decode, execute, and writeback.

[0097]

[0123] The CPU 1405 may be part of a circuit, such as an integrated circuit. One or more other components of the system 1401 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC). The storage device 1415 may store files, such as drivers, libraries, and saved programs. The storage device 1415 may store user data, such as user preferences and user programs. The computer system 1401 may include one or more additional data storage devices, possibly external to the computer system 1401, such as located on a remote server in communication with the computer system 1401 through an intranet or the Internet.

[0098]

[0124] The computer system 1401 may communicate with one or more remote computer systems through the network 1430. For example, the computer system 1401 may communicate with a user's remote computer system (e.g., a user's mobile device). Examples of remote computer systems include a personal computer (e.g., a portable PC), a slate or tablet PC (e.g., an Apple® iPad®, a Samsung® Galaxy Tab), a phone, a smartphone (e.g., an Apple® iPhone®, an Android-enabled device, a Blackberry®), or a personal digital assistant. A user may access the computer system 1401 through the network 1430.

[0099]

[0125] The methods provided herein may be implemented by machine (e.g., computer processor) executable instructions stored in electronic storage locations of the computer system 1401, such as, for example, on memory 1410 or electronic storage 1415. The machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by the processor 1405. In some cases, the code may be retrieved from storage 1415 and stored in memory 1410 for immediate use by the processor 1405. In some circumstances, the electronic storage 1415 may be excluded and the machine executable instructions are stored on memory 1410.

[0100]

[0126] The code may be precompiled and configured for use with a machine having a processor adapted to execute the code, or may be compiled during run-time. The code may be supplied in a programming language that may be selected to allow the code to execute in a precompiled or as compiled manner.

[0101]

[0127] Aspects of the systems and methods provided herein, such as the computer system 1401, may be embodied in programming. Various aspects of the technology may be considered as "products" or "articles of manufacture" that are typically in the form of machine (or processor) executable code and / or associated data that are held or embodied in some type of machine-readable medium. The machine executable code may be stored in electronic storage such as memory (e.g., read-only memory, random access memory, flash memory) or hard disk. A "storage" type medium may include any or all of a computer, processor, or similar tangible memory, or their associated modules, such as various semiconductor memories, tape drives, disk drives, and the like, that may provide non-transitory storage at any time for software programming. All or portions of the software may sometimes be communicated over the Internet or various other telecommunications networks. Such communication may enable, for example, loading of the software from one computer or processor to another, for example, from a management server or host computer to a computer platform of an application server. Thus, other types of media that may store software elements include light waves, radio waves, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical telephone networks, and over various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, or the like, may also be considered media that store software. As used herein, unless limited to non-transitory tangible "storage" media, the term computer or machine "readable medium" refers to any medium that participates in providing instructions to a processor for execution.

[0102]

[0128] Thus, a machine-readable medium such as a computer executable code may take many forms, including, but not limited to, a tangible storage medium, a carrier wave medium, or a physical transmission medium. Non-volatile storage media include optical or magnetic disks, such as any of the storage devices in any computer or the like, such as may be used to implement databases, etc., as shown in the drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise a bus within a computer system. Carrier wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD, or a DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with a pattern of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier wave carrying data or instructions, a cable or link transporting a carrier wave or the like, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0103]

[0129] The computer system 1401 may include or be in communication with an electronic display 1435 with a user interface (UI) 1440 for providing, for example, genomic or other data management. Examples of user interfaces include, without limitation, graphical user interfaces (GUIs) and web-based user interfaces. The user interface may be provided via an application programming interface (API).

[0104]

[0130] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software upon execution by the central processing unit 1405. The algorithms may, for example, receive genetic and / or environmental information of a subject, determine a performance or health risk status of the subject, output an electronic report, transmit the electronic report to a remote user, store the electronic report on a remote server, and process the input data using the trained algorithm to identify the performance or health risk status. EXAMPLES

[0105]

[0131] A genetic modifier algorithm for calculating real-time dynamic scores related to fitness and sports performance and athlete health.

[0132] Using the disclosed system and method, a genetic modifier algorithm was developed to calculate real-time varying scores related to fitness and sports performance and athlete health. The polygene pathway scores were used as input data to build and modify physiological models of sports performance, fitness status, and athlete health, together with physiological and performance data, contextual information, and subjective feedback obtained from wearable devices. This results in more accurate personalized data, scores, estimates, and predictions of health, fitness, and sports performance metrics. The scores are output digitally and in an easy-to-interpret manner (e.g., displayed to the user via a software user interface) in combination with personalized suggestions for the user to improve the user's physical performance and health, and to reduce the risk of injury.

[0106]

[0133] An injury and load balance modifier algorithm and machine learning model were developed as follows. The modifier algorithm generates daily scores for connective tissue injuries, muscle injuries, stress fractures, rotator cuff injuries, anterior cruciate ligament (ACL) injuries, Achilles injuries, knee osteoarthritis, combined injuries, and training load balance. Figure 15 shows an example of a framework for modeling injuries using genetic modifiers.

[0107]

[0134] From the SNP genotyping data, polygenic pathway scores were calculated for each of the injury types listed. Pathway scores were used to modify the Acute to Chronic Workload Ratio (ACWR) model to account for the athletes' genetic predisposition for different injury types. Features were constructed using the composite genetic modifier scores and the ACWR model to serve as evaluation inputs for the statistical models. A time-to-event model was implemented in the form of a multi-state model (MSM) to incorporate the athletes' time-varying training load exposure prior to non-contact soft tissue injury. The implemented MSM was evaluated using hazard ratios to demonstrate that injury features indicate an increased risk of an athlete sustaining a non-contact injury. Input data points to this model were acquired from wearable devices, with the exception of injury features. These devices provided athletes' (i) internal training load measurements (e.g., heart rate), and (ii) external training load measurements (e.g., Global Positioning System (GPS) data) during training and competition. Machine learning models were also developed to determine the likelihood of injury risk using time series data with specific features. The input features used to develop the machine learning model include stress, sleep, resting heart rate, pathway score, heritability, CTL, and ATL.

[0108]

[0135] The load balance modifier algorithm used ACWR as fitted by the power and endurance pathways, which was interpreted in the context of the digitally provided injury score. Load balance was normalized to a value between 0 and 1, using the range associated with optimizing training load and performance.

[0109]

[0136] A team score was calculated by combining the health and performance scores of each individual player. The health and performance scores were calculated from a combination of wearable data, contextual data, and genetic input. The combined team score assists management staff towards tactical and strategic decision making.

[0110]

[0137] A recovery modifier algorithm was developed as follows. The recovery modifier algorithm generates a daily recovery score along with actionable nutrition, lifestyle, and supplementation suggestions based on a modified Total Quality Recovery (TQR) model. Polygenic pathway scores for inflammation, oxidative stress, and muscle damage risk, as well as a composite recovery pathway score, were calculated to enable daily personalized gene-based recovery suggestions that are combined with a dynamic (e.g., real-time dynamic) recovery score. The suggestions were combined with electronic user feedback for contextual information gathering, which serves as input to the recovery modifier algorithm. Figure 16 shows an example of the modified TQR algorithm.

[0111]

[0138] An endurance and power modifier algorithm was developed as follows. Polygenic pathway scores for endurance pathways (e.g., VO2 max trainability and slow-twitch fibers) and power pathways (e.g., fast-twitch fibers, strength, and muscle power) were used as input features to determine dynamic fitness suggestions using training load data as input data. The suggestions were constructed as personalized training plans delivered automatically through a mobile application, providing individualized aerobic vs. anaerobic training to improve fitness levels in an efficient and safe manner. A machine learning algorithm was constructed to determine which type of exercise may be more efficient to gain fitness according to the genetics of the individual. The machine learning algorithm also informs the training time period according to the individual's training response.

[0112]

[0139] Based on the use cases for which the modifier algorithm was implemented, we built two systems: The first system was a mobile application for individual athletes that provided daily fitness, sports performance, and health suggestions determined based on a daily score calculated from a specific combination of genetic, wearable, and contextual data.

[0113]

[0140] The second system was a sports team focused athlete optimization platform that included 1) desktop applications for coaches, medical staff, sports nutritionists, and physical conditioning staff, and 2) mobile applications for each team member. The desktop application included a database of all team members, all team members' genetic data, contextual information, injury history, and real-time data integration with health, fitness, and sports performance hardware such as wearable devices. Athlete health, fitness, and sports performance related modifier scores were calculated for each team member on a daily basis, along with coaching, health, and nutrition suggestions that were generated and provided to staff members. Composite team scores were also calculated to provide insight into team selection health and performance status. Team member genetic-only data was not viewable by any of the coaching staff and was stored on the back-end in a secure, HIPAA and POPI compliant format.

[0114]

[0141] Team members received their performance and health data through a mobile application, with functionality for capturing contextual information and subjective feedback, as well as providing personalized recommendations specific to the team member.

[0115]

[0142] Athlete scores and time series information were presented in an easy to interpret visual format using a red-yellow-green status to indicate risk and / or needed intervention.

[0116]

[0143] Tables 1-4 illustrate various pathway genes and variants, including injury pathway genes and variants (Table 1), recovery pathway genes and variants (Table 2), endurance pathway genes and variants (Table 3), and power pathway gene variants (Table 4).

[0117]

[0144]

[0118] [Table 1-1]

[0119] [Table 1-2]

[0120]

[0145]

[0121] [Table 2-1]

[0122] [Table 2-2]

[0123]

[0146]

[0124] [Table 3]

[0125]

[0147]

[0126] [Table 4-1]

[0127] [Table 4-2] EXAMPLES

[0128]

[0148] Integrating genetic predisposition into injury risk prevention tools for athletes

[0149] Using the system and method of the present disclosure, genetic predisposition is integrated into injury risk prevention tools for athletes. Genetics plays an important role in injury susceptibility, but current approaches may not dynamically apply individual genetic predisposition to athletes' training programs. By continually modifying workload management based on genetic predisposition to injury, training can become more personalized for each athlete to help them reach their training goals without compromising their safety.

[0129]

[0150] An injury risk model was developed that evolved from and addressed certain limitations of the ACWR model of workload and injury management due to evidence showing that the injury risk model achieves 17% greater sensitivity for identifying injury risk.

[0130]

[0151] The injury risk model, in combination with the athlete's current fitness level and training history, can be provided as a tool to guide the athlete to train in a sustainable manner according to the athlete's genetics. It discourages overtraining and encourages the athlete to reach their goals in a way that avoids workload spikes that may increase the chances of the athlete suffering from genetically predisposed injuries. It supports athletes to reach their fitness goals and increase their resilience to the demands of their athletics.

[0131]

[0152] The significance of the Injury Risk Tool underscores a simple message - workload management and injury prevention cannot have a one-size-fits-all approach. The Injury Risk Tool ensures that an athlete's goals are reached in a manner that is optimal for the athlete's unique genetic predisposition to injury, enabling athletes and coaches to make informed training decisions that are most appropriate for each individual.

[0132]

[0153] Workload management is important as injuries have a significant negative impact on athletes, reducing their performance [1] and affecting their mental health [2]. For sports teams, injuries have a knock-on effect on overall team performance. A significant relationship between reduced injuries and improved performance can be observed in football teams, where a lower injury burden was linked to success in the UEFA Champions League [3]. Improving injury prevention strategies is necessarily a high priority for athletes, as well as for coaches, sports scientists, and teams whose primary objective is to support athletes as they strive for peak performance.

[0133]

[0154] In such endeavors, athletes may tend to push themselves to their limits in order to further hone their skills, improve their fitness, and perform at their best in competition. However, when the body is not given enough time to recover and adapt between periods of exercise, athletes are more likely to suffer from overuse injuries.[4] Overuse injuries are virtually ubiquitous in the world of sports, accounting for up to 50% of injuries.[5] To prevent overuse injuries, careful monitoring and adjustment of athletes' workloads is required to achieve an appropriate balance between exercise and adaptation.[6]

[0134]

[0155] One tool currently utilized to help determine when an athlete may be at risk of crossing the line from effective training to injury risk is the Acute-Chronic Workload Ratio (ACWR). ACWR can be used to monitor an athlete's fitness and fatigue, providing a snapshot view of injury risk at any given time point. ACWR can be valid in a variety of sports, including soccer, rugby, Australian rules football, and cricket,[7] and is suggested by the International Olympic Committee for workload management and injury prevention.[8] However, the utility of ACWR for injury prevention has its limitations by not constituting a moderator of workload that increases or decreases injury risk.

[0135]

[0156] Genetic variation may be a significant factor in injury. A strong moderator of the workload-injury relationship is an athlete's genetic predisposition [9], as this is a significant intrinsic risk factor for injury [10, 11]. Genetic variation has an impact on how individuals adapt to training [12, 13], moderates the association between a sudden increase in workload and subsequent injury

[14] . Genetic variation has an impact on biochemical pathways that play a role in musculoskeletal function and adaptation to load [12, 13]. Family and twin studies have yielded injury heritability estimates (how much a trait can be attributed to genetic disposition) ranging from 40 to 69% for different types of injury [13, 15, 16], indicating a conclusive link between genetic predisposition and injury.

[0136]

[0157] In a study of 289 soccer players, each of whom was tested for genetic markers associated with injuries, soccer players with genetic markers for GDF5, AMPD1, COL5A1, or IGF were more likely to suffer an injury during the season and played significantly fewer games than players who did not have these genetic markers.

[17]

[0137]

[0158] The MMP3 gene marker is also strongly associated with hamstring injuries, with each copy of the gene marker increasing the risk of hamstring injuries by two-fold in soccer players.

[18] Similar associations may exist between gene markers and ACL injuries

[19] , rotator cuff tears

[20] , and stress fractures.

[21] Thus, genetics not only plays a general role in an individual's risk of injury, but also influences the specific site of injury as well.

[0138]

[0159] Genetics can be integrated into workload management. A limitation of genetic information is that while it can be useful in identifying athletes who are injury prone, it only provides part of the picture. Injury risk models were developed to dynamically incorporate genetic predisposition, along with other stressors, into an athlete's workload, to determine how an athlete's genetics influences the athlete's daily performance, and to measure the extent to which genetics is a moderator in the workload-injury relationship.

[0139]

[0160] By modifying an athlete's daily workload and ACWR according to the athlete's genetic predisposition, the Injury Risk Tool provides athletes with a daily, personalized, actionable measure of how their current workload, tailored according to their unique genetics, is increasing or decreasing their susceptibility to injury.

[0140]

[0161] To determine the feasibility of using genetics to personalize an athlete's training load, past training data, workload data, genetic data, and injury history were collected from 120 amateur endurance athletes. Utilizing machine learning and pathway analysis methodologies, the integrated influence of multiple genetic markers on specific injury susceptibility was calculated. Achilles tendon, hamstring, knee ligament (ACL), stress fractures, and rotator cuff injuries were examined.

[0141]

[0162] Data were collected from 120 amateur endurance athletes: historical training data spanning periods from 18 months up to 120 months; survey data on injury history including injury date, injury type, and severity (which was then categorized into acute and overuse injuries, with a total of 70 overuse injuries included in the final analysis); workload data from a Global Positioning System (GPS)-enabled wearable device, often used in conjunction with an electrocardiogram (ECG) chest band; and genetic markers for the five injury types shown in Table 5.

[0142]

[0163]

[0143] [Table 5]

[0144]

[0164] Training load was calculated from participants' Training Stress Score (TSS). TSS is a measure of the workload from a training session. It may be a product of the intensity and duration of the workout, and as either of these increases, TSS also increases. TSS may be calculated using the following formula:

[0145]

[0165] TSS=IF×t min

[0166]

[0146]

number

[0147]

[0167] In the formula, t min is the duration of the workout in minutes, IF is the Intensity Factor (how hard you worked), and Threshold is the performance threshold metric (e.g., HR, pace, and / or power) at the threshold level.

[0148]

[0168] Long-term training load (CTL) may be considered a measure of an athlete's fitness. It is the running average of an athlete's daily training load (TSS) over a historical duration, such as the past 6 weeks or 42 days of data points depending on the sport. CTL may be calculated using the following formula:

[0149]

[0169]

[0150]

number

[0151]

[0170] where TC is the Time Constant (TC standard can be any duration depending on the sport, such as 42 days for CTL, but can vary according to application).

[0152]

[0171] Short-term training load (ATL) is considered a measure of fatigue regarding how an athlete's most recent training has affected their body. ATL is the running average of an athlete's TSS over the past seven days. ATL may be calculated using the following formula:

[0153]

[0172]

[0154]

number

[0155]

[0173] where TC is the time constant (TC standard is 7 days for ATL but can vary according to application).

[0174] The relationship between an athlete's fatigue (ATL) and fitness (CTL) is described by the exponentially weighted moving average (EWMA) ACWR. A peak ACWR higher than 1.5 within one week of injury or prior to injury has been shown to be associated with increased injury susceptibility [22, 23]. ACWR may be calculated using the following formula:

[0156]

[0175] ACWR=ATL:CTL

[0176] The relationship between injury and workload spikes 2 weeks, 1 week, and the week of injury was examined for both the standard ACWR model and the injury risk model. To compare the two models, a binary classification system was developed that was trained to classify athletes as either sustaining an injury or remaining injury-free.

[0157]

[0177] There are four possible outcomes using this classification system: (1) the model indicated injury risk and the athlete was injured, a true positive; (2) the model did not indicate injury risk and the athlete did not become injured, a true negative; (3) the model indicated injury risk but the athlete avoided injury, a false positive; and (4) the model did not indicate injury risk but the athlete was injured, a false negative.

[0158]

[0178] The ability of the injury risk model to discriminate the risk of sustaining an injury was compared to that of the standard ACWR model. Sensitivity and specificity were used to measure and compare model performance

[24] .

[0159]

[0179] Results showed that the injury risk model had higher sensitivity in identifying injuries in athletes compared to the standard ACWR model. The injury risk model correctly identified up to 17% more injury occurrences compared to the standard ACWR model. In addition, the injury risk model had high specificity of up to 77%, indicating that the model correctly indicated when the athlete was not at risk for injury 77% of the time.

[0160]

[0180] Sensitivity, also known as the true positive rate, represents the percentage of injured athletes correctly identified by the model as being at risk for injury. As shown in Figure 17, the injury risk model (red) had 11%, 11%, and 17% higher sensitivity to workload spikes (i) the week of injury, (ii) one week prior to injury, and (iii) two weeks prior to injury, respectively, compared to the standard ACWR model (purple).

[0161]

[0181] Specificity represents the percentage of non-injured athletes who are correctly classified as not at risk for injury, also referred to as true negatives

[24] . As shown in Figure 18, both the injury risk model and the standard ACWR model had high specificity. Compared to the standard ACWR model (purple), the injury risk model (red) had 6%, 8%, and 8% lower specificity for the ACWR spikes in the (i) week of injury, (ii) 1 week prior to injury, and (iii) 2 weeks prior to injury, respectively.

[0162]

[0182] The odds ratio (OR) for injury occurring within a week that the injury risk model indicated risk of injury was calculated to be 1.54 (95% confidence interval (CI) 0.8-2.95). This indicates that athletes identified as being at risk of injury by the injury risk model were 1.54 times more likely to sustain an injury than athletes not identified as being at risk of injury.

[0163]

[0183] The injury risk model may be applied to an athlete's workload. The time series in FIG. 19 depicts an example of how the injury risk model more accurately determined an athlete's injury risk compared to the standard ACWR model. The graph shows the athlete's injury risk (low, medium, or high) during a period in which the athlete was training for multiple Ironman races. Throughout this period, the standard ACWR model (purple line) underestimated the athlete's injury risk.

[0164]

[0184] Notably, the standard ACWR model failed to indicate that the athlete was at risk for injury in the week prior to sustaining the Achilles tendon injury. In contrast, the injury risk model (red line) accurately indicated that the athlete was at risk for injury one week prior to the injury occurring.

[0165]

[0185] Results of the injury risk model validation study highlight the critical role of genetics in how athletes respond to high training loads. Athletes with genetic markers that increase predisposition to certain injuries are less resilient to sudden spikes in training workload. This is demonstrated by the 17% increased sensitivity of the injury risk model to correctly identify when an athlete is at higher risk of sustaining an injury, compared to the standard ACWR model.

[0166]

[0186] In the real-world scenario of the Ironman athlete, if the athlete had access to the injury risk tool at the time and considered their genetic predisposition to Achilles tendon injury, the athlete may have adapted their training plan and avoided an Achilles tendon injury that would have forced them to stop training for several weeks.

[0167]

[0187] The injury risk model not only provides an indication of whether an athlete is injury prone, but also represents an extremely useful tool for personalized workload management. In its application, the injury risk tool provides an athlete with a daily indication of whether their current training load is optimal, based on the athlete's current fitness and the athlete's genetic predisposition to injury.

[0168]

[0188] A key principle in the application of the injury risk tool is that an optimal training plan can be based on a gradual progression of workload, taking into account the genetic predisposition an athlete has to certain injuries. By improving fitness in a sustainable manner and taking into account training history, the tool suggests the best actions an athlete can take to make them more resilient to the types of injuries they are genetically prone to, and to protect them from workload spikes that may induce injuries.

[0169]

[0189] Athletes who use injury risk tools to inform their training are encouraged to consider how their unique genetic makeup influences their workload strategy. This deeper layer of personalization could provide peace of mind that an athlete's injury risk is being more closely monitored, resulting in an added layer of confidence during training and competition.

[0170]

[0190] Whether applying the Injury Risk Tool for use with elite athletes, professional sports teams, or for personal fitness goals, the aim is to achieve optimized training with minimal or no injury risk for each individual.

[0171]

[0191] References

[0192] [1] G. Verrall, Y. Kalairajah, J. Slavotinek, and A. Spriggins, “Assessment of player performance following return to sport after hamstring muscle strain injury,” Journal of Science and Medicine in Sport, pp. 87-90, 2006, incorporated herein by reference in its entirety.

[0172]

[0193] [2] L. Podlog and R. Eklund, “Return to sport after serious injury: a retrospective examination of motivation and psychological outcomes,” Journal of sport rehabilitation, pp. 20-34, 2005, incorporated herein by reference in its entirety.

[0173]

[0194] [3] M. Hagglund, M. Walden, H. Magnusson, K. Kristenson, H. Bengtsson, and J. Ekstrand, “Injuries affect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study,” British journal of sports medicine, pp. 738-742, 2013, which is incorporated by reference in its entirety.

[0174]

[0195] [4] J. Yang, A. Tibbetts, T. Covassin, G. Cheng, S. Nayar, and E. Heiden, “Epidemiology of overuse and acute injuries among competitive collegiate athletes,” Journal of athletic training, pp. 198-204, 2012, incorporated herein by reference in its entirety.

[0175]

[0196] [5] M. Smucny, S. Parikh, and N. Pandya, “Consequences of single sport specialization in the pediatric and adolescent athletes,” Orthopedic Clinics, Vol. 46, No. 2, pp. 249-258, 2015, which is incorporated by reference in its entirety.

[0176]

[0197] [6] R. Morton, “Modelling training and overtraining”, Journal of sports sciences, pp. 335-340, 1997, incorporated herein by reference in its entirety.

[0177]

[0198] [7] R. Andrade, E. Wik, A. Rebelo-Marques, P. Blanch, R. Whiteley, J. Espregueira-Mendes, and T. Gabbett, “Is the acute: chronic workload ratio (ACWR) associated with risk of Time-Loss injury in professional team sports? A systematic review of methodology, variables and injury risk in practical situations,” Sports medicine, pp. 1613-1635, 2020, which is incorporated by reference in its entirety.

[0178]

[0199] [8] T. Soligard, M. Schwellnus, J. Alonso, R. Bahr, B. Clarsen, Dijkstra, GTHP, M. Gleeson, M. Hagglund, M. Hutchinson, and C. van Rensburg, “How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury”, British journal of sports medicine, pp. 1030-1041, 2016, which is incorporated by reference in its entirety.

[0179]

[0200] [9] T. Gabbett, “Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners”, British journal of sports medicine, pp. 58-66, 2020, incorporated herein by reference in its entirety.

[0180]

[0201]

[10] T. Lim, C. Santiago, H. Pareja-Galeano, T. Iturriaga, A. Sosa-Pedreschi, N. Fuku, M. Perez-Ruiz, and T. Yvert, “Genetic variations associated with non-contact muscle injuries in sport: A systematic review,” Scandinavian Journal of Medicine & Science in Sports, pp. 2014-2032, 2021, which is incorporated by reference in its entirety.

[0181]

[0202]

[11] N. Vaughn, H. Stepanyan, R. Gallo, and A. Dhawan, “Genetic factors in tendon injury: a systematic review of the literature,” Orthopaedic journal of sports medicine, pp. 2325967117724416, 2017, which is incorporated by reference in its entirety.

[0182]

[0203]

[12] R. Tashjian, A. Hollins, H. Kim, S. Teefey, W. Middleton, K. Steger-May, L. Galatz, and Y. Yamaguchi, “Factors affecting healing rates after arthroscopic double-row rotator cuff repair,” American Journal of Sports Medicine, pp. 2435-42, 2010, which is incorporated by reference in its entirety.

[0183]

[0204]

[13] K. Magnusson, A. Turkiewicz, R. Frobell, and M. Englund, “High genetic contribution to anterior cruciate ligament rupture: Heritability ~ 69%,” British journal of sports medicine, pp. 385-389, 2021, which is incorporated by reference in its entirety.

[0184]

[0205]

[14] J. Windt and T. Gabbett, “How do training and competition workloads relate to injury? The workload-injury aetiology model,” British Journal of Sports Medicine, Vol. 51, No. 5, pp. 428-435, 2017, incorporated herein by reference in its entirety.

[0185]

[0206]

[15] T. Andrew, L. Antioniades, K. Scurrah, A. MacGregor, and T. Spector, “Risk of wrist fracture in women is heritable and is influenced by genes that are largely independent of those influencing BMD,” Journal of bone and mineral research, pp. 67-74, 2005, which is incorporated by reference in its entirety.

[0186]

[0207]

[16] A. Hakim, L. Cherkas, T. Spector, and A. MacGregor, “Genetic associations between frozen shoulder and tennis elbow: a female twin study,” Rheumatology, pp. 739-742, 2003, which is incorporated by reference in its entirety.

[0187]

[0208]

[17] K. McCabe and C. Collins, “Can genetics predict sports injury? The association of the genes GDF5, AMPD1, COL5A1 and IGF2 on soccer player injury occurrence,” Sports, p. 21, 2018, which is incorporated by reference in its entirety.

[0188]

[0209]

[18] J. Larruskain, D. Celorrio, I. Barrio, A. Odriozola, S. Gil, J. Fernandez-Lopez, R. Nozal, I. Ortuzar, J. Lekue, and J. Aznar, “Genetic Variants and Hamstring Injury in Soccer: An Association and Validation Study,” Medicine and science in sports and exercise, pp. 361-368, 2018, which is incorporated by reference in its entirety.

[0189]

[0210]

[19] M. Rahim, A. Gibbon, H. Hobbs, W. van der Merwe, M. Posthumus, M. Collins, and A. September, “The association of genes involved in the angiogenesis‐associated signaling pathway with risk of anterior cruciate ligament rupture,” Journal of Orthopaedic Research, pp. 1612-1618, 2014, which is incorporated by reference in its entirety.

[0190]

[0211]

[20] J. Assuncao, A. Godoy-Santos, M. Dos Santos, E. Malavolta, M. Gracitelli, and A. Neto, “Matrix metalloproteases 1 and 3 promoter gene polymorphism is associated with rotator cuff tear,” Clinical Orthopaedics and Related Research, pp. 1904-1910, 2017, which is incorporated by reference in its entirety.

[0191]

[0212]

[21] I. Varley, D. Hughes, J. Greeves, T. Stellingwerff, C. Ranson, W. Fraser, and C. Sale, “RANK / RANKL / OPG pathway: genetic associations with stress fracture period prevalence in elite athletes,” Bone, pp. 131-136, 2015, which is incorporated by reference in its entirety.

[0192]

[0213]

[22] B. Hulin, T. Gabbett, D. Lawson, P. Caputi, and J. Sampson, “The acute: chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players,” British Journal of Sports Medicine, pp. 231-236, 2015, incorporated herein by reference in its entirety.

[0193]

[0214]

[23] T. Gabbett, “The training-injury prevention paradox: should athletes be training smarter and harder?” British journal of sports medicine, pp. 273-280, 2016, incorporated herein by reference in its entirety.

[0194]

[0215]

[24] J. Ruddy, S. Cormack, R. Whiteley, M. Williams, R. Timmins, and D. Opar, “Modeling the risk of team sport injuries: a narrative review of different statistical approaches,” Frontiers in physiology, Vol. 10, p. 829, 2019, incorporated by reference in its entirety. EXAMPLES

[0195]

[0216] Software Application User Interface

[0217] Using the systems and methods of the present disclosure, a software application user interface was developed for use by a variety of users, including players (eg, subjects) and managers.

[0196]

[0218] 20A-20G show examples of various views of a software application user interface from a player's (eg, subject's) user perspective.

[0219] 21A-21G show examples of various dashboard views of a software application user interface from a player's (eg, subject's) user perspective.

[0197]

[0220] 22A-22G show examples of various views of the software application user interface from a manager user perspective.

[0221] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the present invention be limited by the specific examples provided within the specification. Although the present invention has been described with reference to the foregoing specification, the description and illustration of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions may now occur to those skilled in the art without departing from the present invention. Furthermore, it should be understood that all aspects of the present invention are not limited to the specific depictions, configurations, or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention. It is therefore contemplated that the present invention will cover any such alternatives, modifications, variations, or equivalents. It is intended that the appended claims define the scope of the present invention, and that methods and structures within the scope of these claims and their equivalents are hereby covered.

Claims

**Claim 1** A computer-implemented method for determining a subject's performance or health risk status, comprising: (a) receiving genetic information of the subject, wherein the genetic information is obtained from the subject or by analyzing a biological sample obtained from or derived from the subject; (b) receiving environmental information of the subject, wherein the environmental information includes context data, activities, or physiological measurements of the subject; (c) processing the genetic information and the environmental information to determine the performance or health risk status of the subject; and (d) outputting an electronic report indicating the performance or health risk status of the subject. **Claim 2** The method according to claim 1, wherein the performance or health risk status includes a performance or health risk score, numerical value, or quantitative metric of the subject. **Claim 3** The method according to claim 1, wherein the genetic information includes nucleic acid sequence data, and the nucleic acid sequence data includes deoxyribonucleic acid (DNA) sequence data, ribonucleic acid (RNA) sequence data, or a combination thereof. **Claim 4** The method according to claim 1, wherein the genetic information includes genetic variations of the subject, and the genetic variations include at least one of single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions or deletions (indels), fusions, and translocations. **Claim 5** The method according to claim 1, wherein the biological sample is selected from the group consisting of saliva, oral mucosa, blood, plasma, serum, urine, and combinations thereof. **Claim 6** The method according to claim 1, wherein the analysis includes at least one of a single nucleotide polymorphism (SNP) panel assay, pharmacogenetic assay, ancestral genetics assay, clinical genetics assay, pharmacogenetics assay, sports performance genetics assay, human dog, test for a specific disease risk, migraine test, thyroid test, eczema test, and cancer genetics assay. **Claim 7** The method according to claim 1, wherein the activities include at least one of exercising, playing sports, walking, running, sitting, standing, lying down, and sleeping. **Claim 8** The physiological measurement values include the vital sign measurement values of the subject, and the vital sign measurement values include at least one of heart rate, heart rate variability, systolic blood pressure, diastolic blood pressure, respiratory rate, blood oxygen concentration (SpO2), carbon dioxide concentration in respiratory gas, hormone level, sweat analysis, blood glucose, body temperature, impedance, conductivity, capacitance, resistivity, electromyogram examination, electrodermal response, nerve signal, and immunological marker. The method according to claim 1.

9. The physiological measurement values include the sports performance measurement values of the subject, and the sports performance measurement values include at least one of maximum oxygen uptake, blood lactate, lactate threshold, training load, training stress score, time in aerobic and anaerobic heart rate zones, pace, power, distance, and time. The method according to claim 1.

10. The physiological measurement values include physiological metrics for measuring the effects of external influences on the human body. The method according to claim 1.

11. The activity or the physiological measurement values are obtained using an electronic device, and the electronic device includes a wearable device. The method according to claim 1.

12. (c) further includes determining the performance or health risk status of the subject based at least in part on one or more of the diagnosis of a disease or disorder, prognosis of a disease or disorder, risk of having a disease or disorder, risk of sports-related injury, treatment history of a disease or disorder, history of previous treatment for a disease or disorder, history of prescribed medications, history of prescribed medical devices, age, height, weight, gender, smoking status, injury risk, training load status, fitness level, readiness for competition or a test, and one or more symptoms of the subject. The method according to claim 1.

13. The method according to claim 1 further includes generating a health plan or a training plan for the subject based at least in part on the performance or health risk status determined in (c).

14. The health plan of claim 13 includes (i) proposals for preventing the onset of a disease or disorder, delaying the onset of a disease or disorder, reversing a disease or disorder, preventing an injury, or maintaining the physiological or health state of the subject, (ii) proposals related to one or more of diet, exercise, sports training, supplements, functional tests, blood tests, brain management, behavior modification, skin care, environmental exposure, stress management, and mental health, or (iii) a combination thereof.

15. The method of claim 1, wherein the electronic report is presented on a graphical user interface of a user's electronic device, the graphical user interface is presented via a software application, and the software application includes a mobile software application.

16. The method of claim 1, further comprising transmitting the electronic report to a remote user, wherein the remote user is a healthcare provider, a nutrigenetics counselor, a sports coach, a team manager, or an individual.

17. The method of claim 1, wherein (c) includes processing the genetic information and the environmental information using a trained algorithm to determine the performance or health risk status of the subject.

18. The trained algorithm of claim 17 includes a supervised machine learning algorithm, and the supervised machine learning algorithm includes a deep learning algorithm, a support vector machine (SVM), a neural network, a Gaussian naive Bayes model, a naive Bayes model, or a random forest.

19. The trained algorithm of claim 17 includes an unsupervised machine learning algorithm, and the unsupervised machine learning algorithm includes a k-means clustering model or a principal component analysis.

20. The method of claim 1, wherein the electronic report includes a graphical representation of the performance or health risk status of the subject, and the graphical representation includes a time series graph depicting the performance or health risk score of the subject over time.

21. The method according to claim 1, further comprising the step of using the electronic report to provide a therapeutic intervention to the subject, wherein the therapeutic intervention includes a drug.

22. The performance or health risk status of the subject includes a risk score, and the method further comprises: (i) using the risk score to modify a physiological estimate or measurement of the subject; and (ii) determining the performance or health risk status of the subject based at least in part on the modified physiological estimate or measurement of the subject. The method according to claim 1.

23. The method according to claim 22, further comprising generating a health plan or training plan for the subject to reduce risk or improve performance based at least in part on the modified physiological estimate or measurement of the subject.

24. The health plan includes: (i) proposals for preventing the onset of a disease or disorder, delaying the onset of a disease or disorder, reversing a disease or disorder, preventing an injury, or maintaining the physiological or health status of the subject; (ii) proposals related to one or more of diet, exercise, sports training, supplements, functional tests, blood tests, brain management, behavioral modification, skin care, environmental exposure, stress management, and mental health; or (iii) combinations thereof. The method according to claim 23.

25. The method according to claim 1, further comprising generating an updated performance or health risk status in response to the subject following the health plan.

26. The electronic report further includes subject-specific health and fitness proposals. The subject-specific health and fitness proposals include proposals for improving a score or reducing the risk of the subject. The method according to claim 1.

27. At least (b) and (c) are performed continuously in real time. The method according to claim 1.

28. A system for determining the performance or health risk status of a subject, comprising A database configured to store the genetic information of the subject and the environmental information of the subject, wherein the genetic information is obtained from the subject or obtained by analyzing a biological sample derived therefrom, and the environmental information includes context data, activities, or physiological measurements of the subject, a database, One or more computer processors operably coupled to the database, wherein the one or more computer processors are (i) processing the genetic information and the environmental information to determine the performance or health risk status of the subject, (ii) One or more computer processors programmed individually or collectively to electronically output a report indicating the performance or health risk status of the subject, a system.

29. A non-transitory computer-readable medium comprising machine-executable code that, when executed by one or more computer processors, implements a method for determining the performance or health risk status of a subject, the method comprising: (a) receiving the genetic information of the subject, wherein the genetic information is obtained from the subject or obtained by analyzing a biological sample derived therefrom, (b) receiving the environmental information of the subject, wherein the environmental information includes context data, activities, or physiological measurements of the subject, (c) processing the genetic information and the environmental information to determine the performance or health risk status of the subject, (d) outputting an electronic report indicating the performance or health risk status of the subject, a non-transitory computer-readable medium.

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