PERSONALIZED WELLNESS SYSTEMS AND METHODS OF USE

1 . A system for machine learning analysis, the system comprising:

a computing device comprising at least one processor, a memory, an operating system configured to perform instructions, and a computer program including the instructions, wherein the instructions, when performed by the operating system, causes the at least one processor to perform operations comprising:

(a) receiving genetic data at a plurality of genomic loci of the non-human animal subject, wherein at least one genomic locus of the plurality of genomic loci is associated with the one or more conditions;

(b) receiving phenotypic data pertaining to a plurality of phenotypes of the non-human animal subject;

(c) processing the genetic data to determine quantitative measures of the at least one genomic locus of the plurality of genomic loci;

(d) processing the phenotypic data to determine qualitative or quantitative measures of at least one phenotype of the plurality of phenotypes;

(e) producing a genotype-phenotype profile for the non-human animal subject;

(f) analyzing the genotype-phenotype profile with a machine learning prediction model to identify the non-human animal subject as having the one or more conditions or a risk of developing the one or more conditions.

2 . The system of claim 1 , wherein the machine learning prediction model comprises a clustering algorithm, a decision tree algorithm, a statistical algorithm, a gradient boosted machine (GBM), or any combination thereof.

3 . The system of claim 2 , wherein the clustering algorithm is a centroid-based clustering algorithm.

4 . The system of claim 2 , wherein the statistical algorithm is a genomic best linear unbiased prediction (GBLUP) or a Bayesian variable selection model.

5 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising validating the machine learning prediction model using samples from a validation cohort of non-human animal subjects of the same species as the non-human animal subject and that have the one or more conditions.

6 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising training the machine learning prediction model using samples from a training cohort of non-human animal subjects of the same species.

7 . The system of claim 6 , wherein the samples from the training cohort comprises:

(i) genetic data at a plurality of genomic loci of the training cohort, wherein at least one genomic locus of the plurality of genomic loci is associated with the one or more conditions; and

(ii) phenotypic data pertaining to a plurality of phenotypes of the training cohort of non-human animal subjects.

8 . The system of claim 1 , wherein the one or more conditions comprises one or more genetic conditions, one or more nutritional conditions, one or more clinical conditions, one or more fitness conditions, one or more dermatological conditions, one or more allergy conditions, or any combination thereof.

9 . The system of claim 1 , wherein the non-human animal subject is a feline, a canine, or a farm animal.

10 . The system of claim 1 , wherein the non-human animal subject is a companion animal.

11 . The system of claim 1 , wherein the genetic data is determined by:

(i) obtaining or having obtained a biological sample from the non-human animal subject; and

(ii) performing or having performed a genotyping assay on the biological sample.

12 . The system of claim 1 , further comprising a nucleic acid sequencing device, wherein the nucleic acid sequencing device comprises a whole genome sequencer, a skim sequencer, a quantitative PCR (qPCR) device, or a DNA microarray, and wherein the nucleic acid sequence device generates the genetic data.

13 . The system of claim 1 , wherein the at least one genomic locus comprises one or more polymorphisms associated with the one or more conditions.

14 . The system of claim 1 , wherein receiving the phenotypic data comprising receiving the phenotypic data from a guardian of the non-human animal subject, a veterinarian of the non-human animal subject, or a combination thereof.

15 . The system of claim 1 , wherein the phenotypic data comprises one or more physical attributes, clinical information, one or more behavioral traits, or any combination thereof.

16 . The system of claim 15 , wherein the one or more physical attributes comprises weight, body mass index, sex, age, or breed of the non-human animal subject.

17 . The system of claim 15 , wherein the clinical information comprises a medical history of the non-human animal subject or a medical history of a biological relative of the non-human animal subject.

18 . The system of claim 15 , wherein the one or more behavioral traits comprises chewing, itching, aggression, neurosis, anxiety, energy level, or any combination thereof.

19 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising:

(g) receiving activity data of the non-human animal subject;

(h) updating the genotype-phenotype profile with the activity data to produce an updated profile; and

(i) applying the machine learning prediction model to the updated profile.

20 . The system of claim 19 , wherein the activity data comprises activity level, activity type, calories burned, time asleep, or any combination thereof.

21 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising:

(g) receiving environmental data of the non-human animal subject;

(h) updating the genotype-phenotype profile with the environmental data to produce an updated profile; and

(i) applying the machine learning prediction model to the updated profile.

22 . The system of claim 21 , wherein the environmental data comprises a city environment, a rural environment, geographic location of residence, presence of allergens, time spent inside/outside, frequency of stair use, or any combination thereof.

23 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising:

(g) receiving biomarker data for the non-human animal subject, wherein the biomarker data comprises a presence or a level of one or more biomarkers detected in a biological sample obtained from the non-human animal subject, wherein the one or more biomarkers comprises a protein, a sugar, a lipid, a hormone, a vitamin, a cell, a metabolite, an electrolyte, a mineral, or any combination thereof;

(h) updating the genotype-phenotype profile with the biomarker data to produce an updated profile; and

(i) applying the machine learning prediction model to the updated profile.

24 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising providing a notification to a guardian of the non-human animal subject or a veterinarian of the non-human animal subject, wherein the notification comprises:

(i) the one or more conditions or the risk of developing the one or more conditions in the non-human animal subject;

(ii) the genotype-phenotype profile of the non-human animal subject;

(iii) a recommendation for a product, a behavioral modification, or any combination thereof, for the non-human animal subject;

(iv) a prescription of a therapeutic or prophylactic intervention for the non-human animal subject; or

(v) any combination of (i) to (iv).

25 . The system of claim 24 , wherein the behavioral modification comprises increasing, reducing, or avoiding one or more activities.

26 . The system of claim 25 , wherein the one or more activities comprises:

(I) performance of a physical exercise;

(II) ingestion of a type or quantity of a food, a supplement, or a treat;

(III) exposure to the product;

(IV) usage of the product; or

(V) any combination of (I) to (IV).

27 . The system of claim 26 , wherein the food, the supplement, the treat, or any combination thereof is manufactured to improve, ameliorate, or prevent the one or more conditions in the non-human animal subject.

28 . The system of claim 1 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising:

(g) performing (a) and (b) iteratively at a plurality of time points over a lifespan of the non-human animal subject;

(h) updating the profile with biomarker data, environmental data, activity data, new genotype data, or new phenotype data at a plurality of time points over the lifespan of the non-human animal subject to produce an updated profile; and

(i) applying the machine learning prediction model to the updated profile to identify the non-human subject as having the one or more conditions or the risk of developing the one or more conditions.

29 . The system of claim 28 , wherein the instructions, when performed by the operating system, causes the at least one processor to perform further operations comprising providing a notification to a guardian of the non-human animal subject or a veterinarian of the non-human animal subject at one or more of the plurality of time points, wherein the notification comprises:

(i) the one or more conditions or the risk of developing the one or more conditions in the non-human animal subject;

(ii) the updated genotype-phenotype profile of the non-human animal subject;

(iii) a recommendation for a product, a behavioral modification, or any combination thereof, for the non-human animal subject;

(iv) a prescription of a therapeutic or prophylactic intervention for the non-human animal subject; or

(v) any combination of (i) to (iv).

30 . A method for identifying one or more conditions in a non-human animal subject, the method comprising:

(a) receiving genetic data at a plurality of genomic loci of the non-human animal subject, wherein at least one genomic locus of the plurality of genomic loci is associated with the one or more conditions;

(b) receiving phenotypic data pertaining to a plurality of phenotypes of the non-human animal subject;

(c) producing a genotype-phenotype profile for the non-human animal subject by processing the data set to determine quantitative measures of the at least one genomic locus of the plurality of genomic loci, and qualitative or quantitative measures of at least one phenotype of the plurality of phenotypes; and

(d) applying a machine learning prediction model to the genotype-phenotype profile of the non-human animal subject to identify the non-human animal subject as having the one or more conditions or a risk of developing the one or more conditions.