Abstract: Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.

Abstract: Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.

Abstract: Novel haplotype cluster Markov models are used to phase genomic samples. After the models are built, they rapidly and accurately phase new samples without requiring that the new samples be used to re-build the models. The models set transition probabilities such that the probability for an appearance of any allele within any haplotype is a non-zero number. Furthermore, the most unlikely pairs of haplotypes are discarded from each model at each level until ? of the likelihood mass at each level is discarded. The models are also constructed such that contributing windows of SNPs partially overlap so that phasing decisions near one of the extreme ends of any model is are not significantly determinative of the phase. Additionally, the models are configured such that two or more nodes can be merged during the building/updating procedure to consolidate haplotype clusters having similar distributions.

Abstract: System, computer program products, and methods are disclosed for estimating a degree of ancestral relatedness between two individuals. The haplotype data for a population of individuals is divided into segment windows based on genetic markers, and matched segments for the haplotype data are generated. Each matched segment having a first cM width that exceeds a threshold cM width is included in counting the matched segments in each segment window. A weight associated with each segment window is estimated based on the count of matched segments in the associated segment window. A weighted sum of per-window cM widths for each matched segment is calculated based on the first cM width and the weights associated with the segment windows of the matched segment. The weighted sum of per-window cM widths are used to estimate a degree of ancestral relatedness between two individuals.

Abstract: System, computer program products, and methods are disclosed for estimating a degree of ancestral relatedness between two individuals. The haplotype data for a population of individuals is divided into segment windows based on genetic markers, and matched segments for the haplotype data are generated. Each matched segment having a first cM width that exceeds a threshold cM width is included in counting the matched segments in each segment window. A weight associated with each segment window is estimated based on the count of matched segments in the associated segment window. A weighted sum of per-window cM widths for each matched segment is calculated based on the first cM width and the weights associated with the segment windows of the matched segment. The weighted sum of per-window cM widths are used to estimate a degree of ancestral relatedness between two individuals.

Abstract: Novel haplotype cluster Markov models are used to phase genomic samples. After the models are built, they rapidly and accurately phase new samples without requiring that the new samples be used to re-build the models. The models set transition probabilities such that the probability for an appearance of any allele within any haplotype is a non-zero number. Furthermore, the most unlikely pairs of haplotypes are discarded from each model at each level until c of the likelihood mass at each level is discarded. The models are also constructed such that contributing windows of SNPs partially overlap so that phasing decisions near one of the extreme ends of any model is are not significantly determinative of the phase. Additionally, the models are configured such that two or more nodes can be merged during the building/updating procedure to consolidate haplotype clusters having similar distributions.

Abstract: Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.

Abstract: Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.

Abstract: Described are methods for identification of likelihood of health outcomes such as the development of a medical condition using health histories from genetically related individuals. Embodiments include: receiving a first set of genetic data associated with the human subject; comparing the first set of genetic data to a plurality of sets of genetic data from a plurality of other individuals; identifying from the comparison a family network comprising individuals genetically related to the human subject as defined by identity by descent; receiving a set of health history data for each individual and each individual in the family network; analyzing the set of health history data to generate a health outcome score for the human subject, the health outcome score being a measure of risk for the human subject to develop a pre-defined health outcome that is associated with the health outcome score; and reporting the health outcome score.

Abstract: Novel haplotype cluster Markov models are used to phase genomic samples. After the models are built, they rapidly and accurately phase new samples without requiring that the new samples be used to re-build the models. The models set transition probabilities such that the probability for an appearance of any allele within any haplotype is a non-zero number. Furthermore, the most unlikely pairs of haplotypes are discarded from each model at each level until c of the likelihood mass at each level is discarded. The models are also constructed such that contributing windows of SNPs partially overlap so that phasing decisions near one of the extreme ends of any model is are not significantly determinative of the phase. Additionally, the models are configured such that two or more nodes can be merged during the building/updating procedure to consolidate haplotype clusters having similar distributions.

Abstract: System, computer program products, and methods are disclosed for estimating a degree of ancestral relatedness between two individuals. The haplotype data for a population of individuals is divided into segment windows based on genetic markers, and matched segments for the haplotype data are generated. Each matched segment having a first cM width that exceeds a threshold cM width is included in counting the matched segments in each segment window. A weight associated with each segment window is estimated based on the count of matched segments in the associated segment window. A weighted sum of per-window cM widths for each matched segment is calculated based on the first cM width and the weights associated with the segment windows of the matched segment. The weighted sum of per-window cM widths are used to estimate a degree of ancestral relatedness between two individuals.

Abstract: Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.

Abstract: Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.

Abstract: Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.