Abstract: A computing server may use one or more recommender systems to predict phenotypes of individuals based on survey responses, other phenotypes, environmental factors, and genetic data of the individuals. The computing server may retrieve survey responses of a set of individuals regarding a set of phenotypes of the individuals. The computing server may construct a matrix that includes the values of the phenotypes. The computing server may predict an undetermined phenotype of a target individual using collaborative filtering, which provides the prediction based on other phenotypes of the target individuals and based on at least another individual's phenotypes. The computing server may also predict a target phenotype based on the phenotype of other individuals who are similar to the target individual. The computing server may determine another individual is similar to the target individual based on the length of identity-by-descent (IBD) segments between the two individuals.

Abstract: A computer-implemented method for linking individuals' datasets in a database may include receiving a target individual dataset of a target individual and a plurality of additional individual datasets. A computing server may generate a plurality of sub-cluster pairs of first parental groups and second parental groups. At least one of sub-cluster pairs includes a first parental group of matched segments and a second parental group of matched segments. A computing server may link the first parental groups and the second parental groups across the plurality of sub-cluster pairs to generate at least one super-cluster of a parental side. A computing server may assign metadata to one or more additional individual datasets of the plurality of additional individual datasets. The metadata may specify that the one or more additional individual datasets are connected to the target individual dataset by the parental side of the super-cluster.

Abstract: A computing server may use one or more recommender systems to predict phenotypes of individuals based on survey responses, other phenotypes, environmental factors, and genetic data of the individuals. The computing server may retrieve survey responses of a set of individuals regarding a set of phenotypes of the individuals. The computing server may construct a matrix that includes the values of the phenotypes. The computing server may predict an undetermined phenotype of a target individual using collaborative filtering, which provides the prediction based on other phenotypes of the target individuals and based on at least another individual's phenotypes. The computing server may also predict a target phenotype based on the phenotype of other individuals who are similar to the target individual. The computing server may determine another individual is similar to the target individual based on the length of identity-by-descent (IBD) segments between the two individuals.

Abstract: A system and method predicts a phenotypic trait for an individual. The system identifies a subset of SNP loci with predictive ability of the phenotypic trait. The system calculates a PRS for the individual based on the individual's genetic dataset at the identified subset of SNP loci. The system compares the PRS to a threshold PRS. The threshold PRS is determined by calculating, for each training individual of a plurality of training individuals including some reported to have and some reported to not have the phenotypic trait, a PRS, sweeping through a domain of PRS while calculating a true positive rate and a false positive rate, and then identifying an optimal threshold PRS as the threshold PRS. The system generates a prediction whether the individual has the phenotypic trait based on the comparison.

Abstract: A computing server may use one or more recommender systems to predict phenotypes of individuals based on survey responses, other phenotypes, environmental factors, and genetic data of the individuals. The computing server may retrieve survey responses of a set of individuals regarding a set of phenotypes of the individuals. The computing server may construct a matrix that includes the values of the phenotypes. The computing server may predict an undetermined phenotype of a target individual using collaborative filtering, which provides the prediction based on other phenotypes of the target individuals and based on at least another individual's phenotypes. The computing server may also predict a target phenotype based on the phenotype of other individuals who are similar to the target individual. The computing server may determine another individual is similar to the target individual based on the length of identity-by-descent (IBD) segments between the two individuals.