Abstract: Computer methods and systems for quantifying a nucleic acid sample comprising nucleic acid of one or more contributors to: receive nucleic acid sequence reads obtained from the nucleic acid sample and mapped to alleles at polymorphism loci; determine, using the nucleic acid sequence reads, allele counts for each of the alleles at the polymorphism loci; use a probabilistic mixture model that applies a probabilistic mixture model to the allele counts, and that uses probability distributions to model the allele counts at the polymorphism loci; quantify, using the probabilistic mixture model, one or more fractions of nucleic acid of the one or more contributors in the nucleic acid sample; determine a probability that a specific contributor among the one or more contributors has a specific genotype; and call, based on the posterior probability, that the nucleic acid sample includes nucleic acid from the specific contributor.

Abstract: Methods and systems are provided for quantifying and deconvolving nucleic acid mixture samples including nucleic acid of one or more contributors having known or unknown genomes. The methods and systems provided herein implement processes that use Bayesian probabilistic modeling techniques to determine the abundances and confidence intervals of genetically distinct contributors in a chimerism sample, thereby improving specificity, accuracy and sensitivity, and greatly expanded the application scope over conventional methods.