The present disclosure provides methods of determining one or more tissues and/or cell-types contributing to cell-free DNA (“cfDNA”) in a biological sample of a subject. In some embodiments, the present disclosure provides a method of identifying a disease or disorder in a subject as a function of one or more determined more tissues and/or cell-types contributing to cfDNA in a biological sample from the subject.

The present disclosure provides methods of determining one or more tissues and/or cell-types contributing to cell-free DNA (“cfDNA”) in a biological sample of a subject. In some embodiments, the present disclosure provides a method of identifying a disease or disorder in a subject as a function of one or more determined more tissues and/or cell-types contributing to cfDNA in a biological sample from the subject.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.

Technology provided herein relates in part to non-invasive classification of one or more genetic copy number variations (CNVs) for a test sample. Technology provided herein is useful for classifying a genetic CNV for a sample as part of non-invasive pre-natal (NIPT) testing and oncology testing, for example.

Technology provided herein relates in part to non-invasive classification of one or more genetic copy number variations (CNVs) for a test sample. Technology provided herein is useful for classifying a genetic CNV for a sample as part of non-invasive pre-natal (NIPT) testing and oncology testing, for example.

The present disclosure relates to assessment of genetic variation, and in particular to techniques for detection of chromosomal and sub-chromosomal copy number variations. In one aspect, a computer-implemented method is provided for detecting a presence or absence of copy number variation in a target sample. The method includes obtaining sequencing data for a plurality of samples, determining a first normalized coverage for each segment/element in each of the samples according to the sequencing data, determining a second normalized coverage, including a copy number, for each segment/element in each of the samples according to the first normalized coverage, classifying the copy number for each segment/element in a target set in the target sample based on rule-based approaches, machine learning based approaches, or a combination thereof, and outputting a presence or absence of a copy number variation for each segment/element in the target set in the target sample according to the classification.

The present disclosure relates to assessment of genetic variation, and in particular to techniques for detection of chromosomal and sub-chromosomal copy number variations. In one aspect, a computer-implemented method is provided for detecting a presence or absence of copy number variation in a target sample. The method includes obtaining sequencing data for a plurality of samples, determining a first normalized coverage for each segment/element in each of the samples according to the sequencing data, determining a second normalized coverage, including a copy number, for each segment/element in each of the samples according to the first normalized coverage, classifying the copy number for each segment/element in a target set in the target sample based on rule-based approaches, machine learning based approaches, or a combination thereof, and outputting a presence or absence of a copy number variation for each segment/element in the target set in the target sample according to the classification.

Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.

Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.

The present disclosure provides methods of determining the susceptibility of a dog to liver copper accumulation, comprising detecting in a biological sample obtained from the dog the presence or absence in the genome of the dog of one or more polymorphisms, and methods of treating or breeding the dog based on such determination.