Computer implemented identification of treatments for predicted predispositions with clinician assistance
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
1. A computer-implemented method comprising:
obtaining, by a genetics platform, self-reported user data and genetic attributes for a particular user, wherein the genetics platform includes software executing on one or more computing devices, and wherein the self-reported user data includes behaviors or experiences of the particular user;
storing, by the genetics platform, the self-reported user data and the genetic attributes of the particular user in one or more databases;
based on the self-reported user data and the genetic attributes of the particular user, determining, by the genetics platform, a set of modifiable attributes that are exhibited by the particular user; and
providing, by the genetics platform and to a client device, an indication of the set of the modifiable attributes, wherein the modifiable attributes include one or more of interventions, lifestyle modifications, or clinical therapies selected by a clinician.
2. The computer-implemented method of claim 1 , wherein determining the set of modifiable attributes that are exhibited by the particular user is also based on phenotypic attributes of the particular user.
3. The computer-implemented method of claim 1 , further comprising:
receiving, by the genetics platform, clinical user data for the particular user from the clinician or a health care provider; and
storing, by the genetics platform, the clinical user data in the one or more databases, and wherein determining the set of modifiable attributes that are exhibited by the particular user is also based on the clinical user data.
4. The computer-implemented method of claim 3 , wherein the clinical user data relates to one or more of a blood sugar level, a blood pressure, a medical treatment, a medication, an historical treatment, a hospitalization, or a surgery.
5. The computer-implemented method of claim 1 , further comprising:
based on the genetic attributes of the particular user, predicting, by the genetics platform, that the particular user exhibits a particular phenotype.
6. The computer-implemented method of claim 5 , wherein predicting that the particular user exhibits the particular phenotype is also based on one or more of age data, sex-type data, family history, or environmental exposures of the particular user.
7. The computer-implemented method of claim 5 , wherein the modifiable attributes influence expression of the particular phenotype.
8. The computer-implemented method of claim 1 , wherein the genetics platform supports communication between the particular user and the clinician.
9. The computer-implemented method of claim 1 , wherein the modifiable attributes are behavioral attributes.
10. The computer-implemented method of claim 1 , wherein the genetic attributes are non-monogenetic.
11. The computer-implemented method of claim 1 , wherein the genetic attributes are single nucleotide polymorphisms.
12. The computer-implemented method of claim 1 , wherein the genetic attributes are selected from an entire genome.
13. The computer-implemented method of claim 1 , further comprising:
based on the genetic attributes, determining, by the genetics platform, that the modifiable attributes are statistically associated with a predisposition for a particular phenotype.
14. The computer-implemented method of claim 13 , wherein determining that the modifiable attributes are statistically associated with the predisposition for the particular phenotype comprises:
determining statistical associations between combinations of the modifiable attributes and the particular phenotype; and
selecting the combinations of the modifiable attributes having at least a minimum statistical association with the particular phenotype.
15. The computer-implemented method of claim 13 , wherein determining that the modifiable attributes are statistically associated with the particular phenotype comprises training a linear-regression-based or logistic-regression-based model on at least genetic attributes and phenotypic attributes for a population to predict predisposition for the particular phenotype, and wherein determining the set of the modifiable attributes that are exhibited by the particular user comprises applying the linear-regression-based or the logistic-regression-based model to the genetic attributes of the particular user.
16. The computer-implemented method of claim 1 , wherein determining the set of the modifiable attributes that are exhibited by the particular user is also based on timestamped historical physical attributes or timestamped historical behavioral attributes of the particular user.
17. The computer-implemented method of claim 1 , wherein attributes of the self-reported user data are timestamped, and wherein determining the set of the modifiable attributes that are exhibited by the particular user comprises predicting onset or progression of disease based on the attributes as timestamped.
18. The computer-implemented method of claim 1 , wherein determining that the set of modifiable attributes that are exhibited by the particular user comprises calculating a risk associated with the particular user exhibiting a particular phenotype.
19. A non-transitory computer-readable medium storing program instructions that, when executed by a processor, cause a computing system to perform operations comprising:
obtaining self-reported user data and genetic attributes for a particular user, wherein the self-reported user data includes behaviors or experiences of the particular user;
storing the self-reported user data and the genetic attributes of the particular user in one or more databases;
based on the self-reported user data and the genetic attributes of the particular user, determining a set of modifiable attributes that are exhibited by the particular user; and
providing, to a client device, an indication of the set of the modifiable attributes, wherein the modifiable attributes include one or more of interventions, lifestyle modifications, or clinical therapies selected by a clinician.
20. A computing system comprising:
one or more processors;
memory; and
program instructions stored in the memory that, when executed by the one or more processors, cause the computing system to perform operations comprising:
obtaining self-reported user data and genetic attributes for a particular user, wherein the self-reported user data includes behaviors or experiences of the particular user;
storing the self-reported user data and the genetic attributes of the particular user in one or more databases;
based on the self-reported user data and the genetic attributes of the particular user, determining a set of modifiable attributes that are exhibited by the particular user; and
providing, to a client device, an indication of the set of the modifiable attributes, wherein the modifiable attributes include one or more of interventions, lifestyle modifications, or clinical therapies selected by a clinician.