Abstract: A method for training a convolutional neural net for contamination analysis is provided. A training dataset is obtained comprising, for each respective training subject in a plurality of subjects, a variant allele frequency of each respective single nucleotide variant in a respective plurality of single nucleotide variants, and a respective contamination indication. First and second subsets of the plurality of training subjects have first and second contamination indication values, respectively. A corresponding first channel comprising a first plurality of parameters that include a respective parameter for a single nucleotide variant allele frequency of each respective single nucleotide variant in a set of single nucleotide variants in a reference genome is constructed for each respective training subject.

Abstract: Systems and methods for validating that a DNA sample is from a test subject are disclosed. The test subject reports one or more characteristics (biological sex, ethnicity, and/or age) that may be predicted from the DNA sample. The predictions are compared to the reported characteristics to validate the DNA sample. To validate according to biological sex, the system determines a Y-chromosome signal based on counts of sequence reads for a gene specific to the Y chromosome and, similarly, an X-chromosome signal using another gene specific to the X chromosome. The biological sex is predicted based on a comparison of the two signals. To validate according to ethnicity, the system predicts ethnicity based on detected allele frequencies for SNPs specific to each chromosome. To validate according to age, the system calculates the methylation densities for age-informative CpG sites. The system utilizes trained regression models to predict the age using the methylation densities.