Described herein are a secure system for sharing private data and related systems and methods for incentivizing and validating private data sharing. In some embodiments, private data providers may register to selectively share private data under controlled sharing conditions. The private data may be cryptographically secured using encryption information corresponding to one or more secure execution environments. To demonstrate to the private data providers that the secure execution environment is secure and trustworthy, attestations demonstrating the security of the secure execution environment may be stored in a distributed ledger (e.g., a public blockchain). Private data users that want access to shared private data may publish applications for operating on the private data to a secure execution environment and publish, in a distributed ledger, an indication that the application is available to receive private data. The distributed ledger may also store sharing conditions under which the private data will be shared.

Described herein are a secure system for sharing private data and related systems and methods for incentivizing and validating private data sharing. In some embodiments, private data providers may register to selectively share private data under controlled sharing conditions. The private data may be cryptographically secured using encryption information corresponding to one or more secure execution environments. To demonstrate to the private data providers that the secure execution environment is secure and trustworthy, attestations demonstrating the security of the secure execution environment may be stored in a distributed ledger (e.g., a public blockchain). Private data users that want access to shared private data may publish applications for operating on the private data to a secure execution environment and publish, in a distributed ledger, an indication that the application is available to receive private data. The distributed ledger may also store sharing conditions under which the private data will be shared.

The present disclosure provides methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father. The ploidy state is determined by using a joint distribution model to create a plurality of expected allele distributions for different possible fetal ploidy states given the parental genotypic data, and comparing the expected allelic distributions to the pattern of measured allelic distributions measured in the mixed sample, and choosing the ploidy state whose expected allelic distribution pattern most closely matches the observed allelic distribution pattern. The mixed sample of DNA may be preferentially enriched at a plurality of polymorphic loci in a way that minimizes the allelic bias, for example using massively multiplexed targeted PCR.

The present disclosure provides methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father. The ploidy state is determined by using a joint distribution model to create a plurality of expected allele distributions for different possible fetal ploidy states given the parental genotypic data, and comparing the expected allelic distributions to the pattern of measured allelic distributions measured in the mixed sample, and choosing the ploidy state whose expected allelic distribution pattern most closely matches the observed allelic distribution pattern. The mixed sample of DNA may be preferentially enriched at a plurality of polymorphic loci in a way that minimizes the allelic bias, for example using massively multiplexed targeted PCR.

Disclosed herein include systems, devices, and methods for identifying recombinant variants (e.g., gene conversion variants) of genes such as GBA gene and CYP21A2 gene, the copy numbers of recombinant variants, and gene variant status (e.g., carrier, compound heterozygous, or homozygous).

Provided are methods for determining a disease risk associated with an embryo that comprise constructing the genome of the embryo based on (i) one or more genetic variants in the embryo, (ii) a paternal haplotype, (iii) a maternal haplotype (iv) a transmission probability of the paternal haplotype, and (v) a transmission probability of the maternal haplotype; assigning a polygenic risk score to the embryo based on the constructed genome of the embryo; determining the disease risk associated with the embryo based on the polygenic risk score; and determining transmission of disease causing genetic variants and/or haplotypes from the paternal genome and/or maternal genome to the embryo. Also provided are methods of determining a range of disease risk for potential children for a mother and a potential sperm donor. Also provided are methods of determining disease risk in an individual.

Provided are methods for determining a disease risk associated with an embryo that comprise constructing the genome of the embryo based on (i) one or more genetic variants in the embryo, (ii) a paternal haplotype, (iii) a maternal haplotype (iv) a transmission probability of the paternal haplotype, and (v) a transmission probability of the maternal haplotype; assigning a polygenic risk score to the embryo based on the constructed genome of the embryo; determining the disease risk associated with the embryo based on the polygenic risk score; and determining transmission of disease causing genetic variants and/or haplotypes from the paternal genome and/or maternal genome to the embryo. Also provided are methods of determining a range of disease risk for potential children for a mother and a potential sperm donor. Also provided are methods of determining disease risk in an individual.

A method evolves generic computational building blocks. The method initializes a parent population with randomly generated programs or programs evolved by a genetic programming instance that uses randomized targets. The method also obtains a list of randomly generated test inputs. The method generates a target dataset that includes input-output pairs of randomly generated binary strings. The method also applies a fitness function to assign a fitness score to each program, based on the target dataset. The method grows a seed list by applying genetic operators, and selecting offspring that satisfy a novelty condition. The novelty condition is representative of an ability of a program to produce unique output for the list of randomly generated test inputs. The method iterates until a terminating condition has been satisfied. The terminating condition is representative of an ability of programs in the seed list to solve one or more genetic programming instances.

A method evolves generic computational building blocks. The method initializes a parent population with randomly generated programs or programs evolved by a genetic programming instance that uses randomized targets. The method also obtains a list of randomly generated test inputs. The method generates a target dataset that includes input-output pairs of randomly generated binary strings. The method also applies a fitness function to assign a fitness score to each program, based on the target dataset. The method grows a seed list by applying genetic operators, and selecting offspring that satisfy a novelty condition. The novelty condition is representative of an ability of a program to produce unique output for the list of randomly generated test inputs. The method iterates until a terminating condition has been satisfied. The terminating condition is representative of an ability of programs in the seed list to solve one or more genetic programming instances.

A method for generating a list of probabilities associated with a list of diseases for a first patient, the method including first acquiring a first set of data of the first patient including an age value, a gender value; second acquiring data describing a disease of the patient, the disease being extracted from a first database, each disease being associated with a first prevalence statistic and a first incidence statistic, and each disease being associated with a list of signs; third acquiring data describing a first sign that includes a first sensitivity statistic and a second specificity statistic for each disease of a predefined list of diseases associated with the sign; generating, from a first modelling of a Bayesian network and input data including the data of the first, second and third acquisitions, of a set of probabilities, each probability being associated with a given disease of the first list.