Abstract: Provided herein are bioinformatic tools and processes used to classify the presence or absence of genetic mosaicism for a copy number variation in one or more fetuses (e.g., predict whether one fetus or more than one fetus is affected with the copy number variation). Sample nucleic acid is subjected to a sequencing process and the resulting sequence reads are analyzed to identify a genetic copy number variation region. A genetic mosaicism for the copy number variation region is classified for one fetus or more than one fetus based on: (i) a mosaicism ratio of a fraction of nucleic acid having the copy number variation region to a fraction of fetal nucleic acid, and (ii) the chromosome having the genetic copy number variation region (e.g., the type of aneuploidy identified) or (ii) a number of fetuses being carried by the pregnant female.

Abstract: Techniques are described for identifying a genetic variant in a test sample by comparing sequences reads obtained from the test sample to unique k-mers that are representative of a target genomic region. In one particular aspect, a method is described that includes generating a dictionary of a target genomic region having a set of unique k-mers by: accessing a sequence of the target genomic region, determining a set of k-mers for the target genomic region, comparing the set of k-mers for the target genomic region with one or more sets of k-mers for non-target genomic regions, and selecting the unique k-mers that do not appear in the one or more sets of k-mers for non-target genomic regions. The dictionary can then be used to identify a genetic variant in a test sample by comparing sequences reads obtained from the test sample to the unique k-mers in the dictionary.

Abstract: Technology provided herein relates in part to non-invasive classification of one or more mosaic copy number variations (CNVs) for a test sample. Technology provided herein is useful for classifying a mosaic CNV for a sample as part of non-invasive pre-natal (NIPT) testing and oncology testing, for example. In particular, a method is provided for classifying presence or absence of genetic mosaicism for a biological sample, the method includes identifying a genetic copy number variation region in sample nucleic acid from a subject, e.g. a pregnant female, determining a fraction of nucleic acid having the copy number variation in the sample nucleic acid, determining a fraction of a minority nucleic acid, e.g. fetal nucleic acid, in the sample nucleic acid, comparing the two fractions to generate a mosaicism ratio, and classifying a presence or absence of a genetic mosaicism for the copy number variation region according to the mosaicism ratio.