GRANULAR ELECTION OF PREDICTIVE POLYGENIC MODELS
Systems and methods are provided for selecting from among polygenic models that predict characteristics of individuals. One embodiment is a genetic prediction server that includes a memory that stores polygenic models which predict characteristics of individuals based on genetic variants of the individuals, including a set of polygenic models for a characteristic that each perform a different analysis of genetic variants when making a prediction. The server also includes a controller that receives an indication of genetic variants of an individual, determines that the individual belongs to a demographic, and selects, based on the demographic, a polygenic model from the set to predict the characteristic for the individual.
1 . A system comprising:
a genetic prediction server comprising:
a memory that stores polygenic models which predict characteristics of individuals based on genetic variants of the individuals, including a set of polygenic models for a characteristic that each perform a different analysis of genetic variants when making a prediction; and
a controller that receives an indication of genetic variants of an individual, determines that the individual belongs to a demographic, and selects, based on the demographic, a polygenic model from the set to predict the characteristic for the individual.
2 . The system of claim 1 wherein:
each polygenic model in the set comprises a machine learning model that has been trained using known genotypes and known characteristics for members of a different demographic, and
the controller selects a machine learning model that has been trained using known genotypes and known characteristics for members of the demographic.
3 . The system of claim 1 wherein:
the controller determines that the individual belongs to multiple demographics, and selects the polygenic model based on at least two of the multiple demographics.
4 . The system of claim 3 wherein:
the controller determines a category for each of the multiple demographics, assigns a rank to each category, determines that the polygenic model has been calibrated for the demographic, determines that the demographic is within a category having a highest rank, and selects the polygenic model in response to determining that the demographic is within the category having the highest rank.
5 . The system of claim 3 wherein:
the controller selects the polygenic model in response to determining that the polygenic model has been calibrated for members belonging to the multiple demographics.
6 . The system of claim 1 wherein:
the indication provides genetic variants for less than a whole genome of the individual, and
the controller prevents selection of polygenic models that use different genetic variants as input than were provided in the indication.
7 . The system of claim 1 wherein:
the indication reports the genetic variants of the individual in the form of a deoxyribonucleic acid (DNA) microarray, a whole exome, or a whole genome, and
each polygenic model uses genetic variants for a DNA microarray, a whole exome, or a whole genome as input.
8 . A method comprising:
identifying polygenic models which predict characteristics of individuals based on genetic variants of the individuals, including a set of polygenic models for a characteristic that each perform a different analysis of genetic variants when making a prediction;
receiving an indication of genetic variants of an individual;
determining that the individual belongs to a demographic; and
selecting, based on the demographic, a polygenic model from the set to predict the characteristic for the individual.
9 . The method of claim 8 wherein:
each polygenic model in the set comprises a machine learning model that has been trained using known genotypes and known characteristics for members of a different demographic, and the method further comprises:
selecting a machine learning model that has been trained using known genotypes and known characteristics for members of the demographic.
10 . The method of claim 8 further comprising:
determining that the individual belongs to multiple demographics, wherein
selecting the polygenic model is based on at least two of the multiple demographics.
11 . The method of claim 10 further comprising:
determining a category for each of the multiple demographics;
assigning a rank to each category;
determining that the polygenic model has been calibrated for the demographic;
determining that the demographic is within a category having a highest rank; and
selecting the polygenic model in response to determining that the demographic is within a category having the highest rank.
12 . The method of claim 10 wherein:
selecting the polygenic model is performed in response to determining that the polygenic model has been calibrated for a population belonging to the multiple demographics.
13 . The method of claim 8 wherein:
the indication provides genetic variants for less than a whole genome of the individual, and the method further comprises:
preventing selection of polygenic models that use different genetic variants as input than were provided in the indication.
14 . The method of claim 8 wherein:
the indication reports the genetic variants of the individual in the form of a deoxyribonucleic acid (DNA) microarray, a whole exome, or a whole genome, and
each polygenic model uses genetic variants for a DNA microarray, a whole exome, or a whole genome as input.
15 . A non-transitory computer readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising:
receiving an indication of genetic variants of an individual;
identifying polygenic models which predict characteristics of individuals based on genetic variants of the individuals, including a set of polygenic models for a characteristic that each perform a different analysis of genetic variants when making a prediction;
determining that the individual belongs to a demographic; and
selecting, based on the demographic, a polygenic model from the set to predict the characteristic for the individual.
16 . The medium of claim 15 wherein:
each polygenic model in the set comprises a machine learning model that has been trained using known genotypes and known characteristics for members of a different demographic, and the method further comprises:
selecting a machine learning model that has been trained using known genotypes and known characteristics for members of the demographic.
17 . The medium of claim 15 wherein:
determining that the individual belongs to multiple demographics, wherein
selecting the polygenic model is based on at least two of the multiple demographics.
18 . The medium of claim 17 wherein the method further comprises:
determining a category for each of the multiple demographics;
assigning a rank to each category;
determining that the polygenic model has been calibrated for the demographic;
determining that the demographic is within a category having a highest rank; and
selecting the polygenic model in response to determining that the demographic is within a category having the highest rank.
19 . The medium of claim 17 wherein:
selecting the polygenic model is performed in response to determining that the polygenic model has been calibrated for a population belonging to the multiple demographics.
20 . The medium of claim 15 wherein:
the indication provides genetic variants for less than a whole genome of the individual, and the method further comprises:
preventing selection of polygenic models that use different genetic variants as input than were provided in the indication.