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

Souhrn

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.