Personalized wellness systems and methods of use
A health and wellness system for a non-human subject comprising analyzing genetic data and phenotypic data of the non-human subject with a machine learning algorithm and making a recommendation or recommendation for products or activities for the non-human subject.
1. A method for identifying a nutritional product recommended for 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 a first condition, wherein the first condition is not a breed type;
(b) receiving phenotypic data pertaining to a plurality of phenotypes of the non-human animal subject, wherein at least one phenotype of the plurality of phenotypes is associated with a second condition;
(c) producing a genotype-phenotype profile for the non-human animal subject by processing a data set comprising the genetic data and the phenotypic data to determine qualitative or quantitative measures of the at least one genomic locus of the plurality of genomic loci, and qualitative or quantitative measures of the at least one phenotype of the plurality of phenotypes;
(d) applying a machine learning prediction model to the genotype-phenotype profile of the non-human animal subject to identify the nutritional product recommended for the non-human subject, based at least in part, on a likelihood that the non-human animal subject has:
(i) the first condition or a risk of developing the first condition; and
(ii) the second condition or a risk of developing the second condition;
(e) ranking the first condition and the second condition based, at least in part, on severity of the first condition and the second condition to identify a highest ranking condition of the first condition and the second condition; and
(f) administering the nutritional product to the non-human animal subject, wherein the nutritional product:
(i) comprises a food; and
(ii) improves, ameliorates, or prevents at least the highest ranking condition or the risk of developing at least the highest ranking condition in the non-human animal subject.
2. The method of claim 1 , wherein the first condition or the second condition 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.
3. The method of claim 1 , wherein the non-human animal subject is a feline, a canine, or a farm animal.
4. The method of claim 3 , wherein the non-human animal subject is a companion animal.
5. The method 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.
6. The method of claim 1 , further comprising receiving the genetic data from 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.
7. The method of claim 1 , wherein the at least one genomic locus comprises one or more polymorphisms associated with the first condition.
8. The method of claim 1 , further 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.
9. The method of claim 1 , wherein the plurality of phenotypes comprises any combination of weight, body mass index, sex, age, or breed of the non-human animal subject.
10. The method of claim 1 , further comprising:
receiving activity data of the non-human animal subject;
updating the genotype-phenotype profile with the activity data to produce an updated profile; and
applying the machine learning prediction model to the updated profile.
11. The method of claim 10 , wherein the activity data comprises activity level, activity type, calories burned, time asleep, or any combination thereof.
12. The method of claim 10 , wherein the activity data comprises information obtained from an activity tracking device.
13. The method of claim 1 , further comprising:
receiving environmental data of the non-human animal subject;
updating the genotype-phenotype profile with the environmental data to produce an updated profile; and
applying the machine learning prediction model to the updated profile.
14. The method of claim 13 , wherein the environmental data comprise 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.
15. The method of claim 1 , further comprising:
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;
updating the genotype-phenotype profile with the biomarker data to produce an updated profile; and
applying the machine learning prediction model to the updated profile.
16. The method of claim 1 , wherein the machine learning prediction model comprises a clustering algorithm, a decision tree algorithm, a statistical algorithm, gradient boosted machine (GBM), or any combination thereof.
17. The method of claim 16 , wherein the clustering algorithm is a centroid-based clustering algorithm.
18. The method of claim 16 , wherein the machine learning prediction model is a genomic best linear unbiased prediction (GBLUP) or a Bayesian variable selection model.
19. The method of claim 1 , further comprising validating the machine learning prediction model using samples from a validation cohort of non-human animal subjects of the same species that have the first condition or the second condition.
20. The method of claim 1 , further comprising training the machine learning prediction model using samples from a training cohort of non-human animal subjects of the same species.
21. The method of claim 20 , wherein the training data set 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 first condition; and
(ii) phenotypic data pertaining to a plurality of phenotypes of the training cohort of non-human animal subjects, wherein at least one phenotype of the plurality of phenotypes is associated with the second condition.
22. The method of claim 1 , further 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 first condition or a risk of developing the first condition and the second condition or the risk of developing the second condition in the non-human animal subject;
(ii) the genotype-phenotype profile of the non-human animal subject;
(iii) the nutritional product recommended for a non-human animal subject;
(iv) a recommendation for a behavioral modification for the non-human animal subject;
(v) a prescription of a therapeutic or prophylactic intervention for the non-human animal subject; or
(vi) any combination of (i) to (v).
23. The method of claim 22 , wherein the behavioral modification comprises increasing, reducing, or avoiding one or more activities, wherein the one or more activities (i) increases or decreases the risk that the non-human animal subject will develop the first condition or the second condition, or (ii) worsens or improves the first condition or the second condition in the non-human animal subject.
24. The method of claim 23 , wherein the one or more activities comprises:
(i) performance of a physical exercise;
(ii) ingestion of a type or quantity of the nutritional product, wherein the nutritional product further comprises feed, a supplement, a treat, or both;
(iii) exposure to a product;
(iv) usage of the product; or
(v) any combination of (i) to (iv).
25. The method of claim 1 , further comprising:
performing (a) to (b) at a plurality of time points to obtain new genotype data or new phenotype data;
updating the genotype-phenotype profile with the new genotype data, the new phenotype data, or a combination thereof, at the plurality of time points to produce an updated profile; and
applying the machine learning prediction model to the updated profile.
26. The method of claim 25 , further 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 first condition or the risk of developing the first condition and the second condition or the risk of developing the second condition in the non-human animal subject;
(ii) the updated 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).
27. The method of claim 1 , further comprising:
receiving biomarker data, activity data, environment data, behavioral data, or clinical data for the non-human animal subject;
producing an updated genotype-phenotype profile for the non-human animal subject by processing the biomarker data, the activity data, the environment data, the behavioral data, or the clinical data, or the combination thereof, to determine quantitative or qualitative measures thereof; and
applying the machine learning prediction model to the updated genotype-phenotype profile of the non-human animal subject to identify:
a new nutritional product or a new amount of the nutritional product recommended for the non-human animal subject; or
a behavioral modification for the non-human animal subject.
28. The method of claim 27 , wherein the clinical data comprises medical history of the non-human animal subject or medical history of a biological relative of the non-human animal subject.
29. The method of claim 27 , wherein the behavioral data comprises chewing, itching, aggression, neurosis, anxiety, energy level, or any combination thereof.
30. The method of claim 1 , wherein the nutritional product further comprises a food, supplement, treat, or both.