Abstract: Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.

Abstract: Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Abstract: Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.

Abstract: Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.

Abstract: Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.

Abstract: Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Abstract: Error correction in ancestry classification includes obtaining, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual; performing error correction on an initial ancestry classification, including detecting a phasing error in the initial ancestry classifications; and outputting a corrected ancestry classification in which the phasing error is corrected.

Abstract: In one general aspect, a method can include identifying a current user state, associating a first action and a second action with the current user state, associating a first value and a first probability with a first new user state based on a possible performing of the first action in the current user state, associating a second value and a second probability with a second new user state based on a possible performing of the second action in the current user state, calculating a first overall value for the first action based on the first value and the first probability, calculating a second overall value for the second action based on the second value and the second probability, and providing the first action to the computing device based on determining that the first overall value is greater than the second overall value.

Abstract: Error correction in ancestry classification includes obtaining, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual; performing error correction on an initial ancestry classification, including detecting a phasing error in the initial ancestry classifications; and outputting a corrected ancestry classification in which the phasing error is corrected.

Abstract: Phasing unphased genotype data of an individual includes: obtaining the unphased genotype data of the individual; processing the unphased genotype data to determine phased haplotype data, the processing comprising performing out-of-sample population-based phasing on the unphased genotype data; wherein: performing out-of-sample population-based phasing includes performing statistical analysis using predetermined reference data that is based on genotype data of a reference population; and the genotype data of the reference population does not reference the genotype data of the individual.

Abstract: Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members.

Abstract: Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.

Abstract: Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Abstract: Performing trio-based phasing includes: obtaining a set of preliminary phased haplotype data of an individual; establishing a dynamic Bayesian network based at least in part on the set of preliminary phased haplotype data of the individual and phased haplotype data of at least one parent of the individual; and determining, based on the dynamic Bayesian network, a set of refined haplotype data of the individual.

Abstract: Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.

Abstract: The invention provides human polymorphisms that are associated with Parkinson's disease (PD). Also disclosed are compositions and methods for use in diagnostics, prognostics, prevention, treatment and/or study of PD.