ESTIMATION OF PHENOTYPES USING DNA, PEDIGREE, AND HISTORICAL DATA
Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.
1 . A system comprising:
a graphical user interface configured to receive a family tree that is at least partially defined by a target individual, the family tree comprising one or more related individuals who are related to the target individual; and
a computing server in communication with the graphical user interface, the computing server comprising memory and one or more processors, the memory configured to store instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform steps comprising:
accessing a set of DNA features of a target individual;
accessing one or more non-DNA features from data associated with the family tree;
generating a target feature vector that combines the set of DNA features of the target individual and the one or more non-DNA features; and
inputting the target feature vector to a machine learning model to generate a prediction of a trait of the target individual.
2 . The system of claim 1 , wherein the steps further comprise:
identifying community members who belong to a genetic community of the target individual;
determining a community prediction based on a prevalence of the trait among the community members; and
revising or confirming the prediction of the trait based on the community prediction.
3 . The system of claim 1 , wherein the set of DNA features comprises a subset of DNA features that are disproportionately associated with one or more related individuals in a genetic community to which the target individual belongs, and the target feature vector further comprises non-DNA features that are disproportionately associated with the related individuals in the genetic community.
4 . The system of claim 1 , wherein the steps further comprise:
normalizing or standardizing the set of DNA features and/or the one or more non-DNA features.
5 . The system of claim 1 , wherein the trait is an appearance trait, a wellness trait, a health trait, a disease, a preference, behavior, or a language of the target individual.
6 . The system of claim 1 , wherein the machine learning model is a random forest classifier, a support vector machine, a neural network, or a model trained by an unsupervised approach.
7 . The system of claim 1 , wherein at least one feature of the DNA features is determined based on a length of identity-by-descent (IBD) segments shared between the target individual and one of the related individuals.
8 . The system of claim 1 , wherein the one or more non-DNA features include one or more of the following: customer data, birth year, sex, information about a sequencing array, ethnicity compositions, residency information, socioeconomic information, family tree details, lifestyle, health, covariates, historical records, medical records, survey responses, or digital photographs.
9 . The system of claim 1 , wherein the prediction of the trait of the target individual includes a probability that the target individual has the trait.
10 . A computer-implemented method, performed by one or more processors, the computer-implemented method comprising:
receiving, through a graphical user interface, a family tree that is at least partially defined by a target individual, the family tree comprising one or more related individuals who are related to the target individual;
accessing a set of DNA features of a target individual;
accessing one or more non-DNA features from data associated with the family tree;
generating a target feature vector that combines the set of DNA features of the target individual and the one or more non-DNA features; and
inputting the target feature vector to a machine learning model to generate a prediction of a trait of the target individual.
11 . The computer-implemented method of claim 10 , further comprising:
identifying community members who belong to a genetic community of the target individual;
determining a community prediction based on a prevalence of the trait among the community members; and
revising or confirming the prediction of the trait based on the community prediction.
12 . The computer-implemented method of claim 10 , wherein the set of DNA features comprises a subset of DNA features that are disproportionately associated with one or more related individuals in a genetic community to which the target individual belongs, and the target feature vector further comprises non-DNA features that are disproportionately associated with the related individuals in the genetic community.
13 . The computer-implemented method of claim 10 , further comprising:
normalizing or standardizing the set of DNA features and/or the one or more non-DNA features.
14 . The computer-implemented method of claim 10 , wherein the trait is an appearance trait, a wellness trait, a health trait, a disease, a preference, behavior, or a language of the target individual.
15 . The computer-implemented method of claim 10 , wherein the machine learning model is a random forest classifier, a support vector machine, a neural network, or a model trained by an unsupervised approach.
16 . The computer-implemented method of claim 10 , wherein at least one feature of the DNA features is determined based on a length of identity-by-descent (IBD) segments shared between the target individual and one of the related individuals.
17 . The computer-implemented method of claim 10 , wherein the one or more non-DNA features include one or more of the following: customer data, birth year, sex, information about a sequencing array, ethnicity compositions, residency information, socioeconomic information, family tree details, lifestyle, health, covariates, historical records, medical records, survey responses, or digital photographs.
18 . The computer-implemented method of claim 10 , wherein the prediction of the trait of the target individual includes a probability that the target individual has the trait.
19 . A non-transitory computer readable medium configured to store computer code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform steps comprising:\
receiving, through a graphical user interface, a family tree that is at least partially defined by a target individual, the family tree comprising one or more related individuals who are related to the target individual;
accessing a set of DNA features of a target individual;
accessing one or more non-DNA features from data associated with the family tree;
generating a target feature vector that combines the set of DNA features of the target individual and the one or more non-DNA features; and
inputting the target feature vector to a machine learning model to generate a prediction of a trait of the target individual.
20 . The non-transitory computer readable medium of claim 19 , wherein the steps further comprise:
normalizing or standardizing the set of DNA features and/or the one or more non-DNA features.