Traditional kinship analysis uses fewer than 20 short tandem repeat (STR) loci, which lack the resolution to establish relatedness beyond parent-offspring or full siblings, and is easily confounded by mutation or mistaken testing of a close relative of the true parent1.
More advanced analyses use pieces of DNA that are directly transmitted through the maternal (mitochondrial DNA) or paternal (Y-chromosome) lines; however, these approaches are limited to a small subset of relationships and are very low resolution. For example, ~7% of unrelated Europeans share the same mitochondrial haplotype. MtDNA and Y-STRs can only suggest that two individuals may be related but cannot say what the degree of relatedness is.
Parabon’s kinship algorithm analyzes the similarity between two genomes and uses a machine learning model to predict the degree of relatedness of the two individuals. In more than 1,000 out-of-sample predictions, this method has proven to be highly accurate while maintaining a very low false-positive rate (i.e., unrelated pairs are almost never mistakenly inferred to be related). Accuracy is 100% for parent-offspring, full siblings, and 2nd-degree relatives — i.e., grandparents, aunts and uncles, and half-siblings — and Snapshot can distinguish 6th-degree relatives (e.g., second cousins once removed) from unrelated pairs with greater than 97% accuracy.
As shown in the figure below, even when Snapshot incorrectly infers the degree of relatedness between two individuals, it is almost always correct within one degree: