Needles in the Haystack: Identifying Individuals Present in Pooled Genomic Data

In this report, we evaluate a recently-published method for resolving whether individuals are present in a complex genomic DNA mixture. Based on the intuition that an individual will be genetically “closer” to a sample containing him than to a sample not, the method investigated here uses a distance metric to quantify the similarity of an individual relative to two population samples. Although initial applications of this approach showed a promising false-negative rate, the accuracy of the assumed null distribution (and hence the true false-positive rate) remained uninvestigated; here, we explore this question analytically and describe tests of this method to assess the likelihood that an individual who is not in the mixture is mistakenly classified as being a member. Our results show that the method has a high false-positive rate in practice due to its sensitivity to underlying assumptions, limiting its utility for inferring the presence of an individual in a population. By revealing both the strengths and limitations of the proposed method, we elucidate situations in which this distance metric may be used in an appropriate manner in forensics and medical privacy policy.