Abstract: The present disclosure relates to genetic copy number variation (CNV) detection. Particularly, aspects are directed to sequencing nucleic acid obtained from a biological sample obtained from a subject to generate sequencing data. The sequence reads are ordered by mapping the sequence reads to a reference genome and stored in an ordered format, A global segmentation of the target region is performed based on the stored sequence reads and a set of segments of the target region is identified and used to determine a copy number variation (CNV) metric. A first status of a genetic condition for the subject is determined based on the CNV metric, and a report of the corresponding genetic condition screening test is determined based on the CNV metric and the status.

Abstract: 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.

Abstract: 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.

Abstract: 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.