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

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Abstract
Recent publications have described and applied a novel metric that quantifies the genetic distance of an individual with respect to two population samples, and have suggested that the metric makes it possible to infer the presence of an individual of known genotype in a sample for which only the marginal allele frequencies are known. However, the assumptions, limitations, and utility of this metric remained incompletely characterized. Here we present empirical tests of the method using publicly accessible genotypes, as well as analytical investigations of the method's strengths and limitations. The results reveal that the null distribution is sensitive to the underlying assumptions, making it difficult to accurately calibrate thresholds for classifying an individual as a member of the population samples. As a result, the false-positive rates obtained in practice are considerably higher than previously believed. However, despite the metric's inadequacies for identifying the presence of an individual in a sample, our results suggest potential avenues for future research on tuning this method to problems of ancestry inference or disease prediction. By revealing both the strengths and limitations of the proposed method, we hope to elucidate situations in which this distance metric may be used in an appropriate manner. We also discuss the implications of our findings in forensics applications and in the protection of GWAS participant privacy. 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.