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Needles in the haystack: identifying individuals present in pooled genomic data.

Braun R, Rowe W, Schaefer C, Zhang J, Buetow K - PLoS Genet. (2009)

Bottom Line: The results reveal that the 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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America. braunr@mail.nih.gov

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 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.

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Distribution of T.Distributions of T for out-of-group samples who are                                related (red line) and unrelated (blue line) to individuals in                                    G for HapMap YRI (A) and HapMap CEPH (B)                                populations. (C) and (D) show the same distributions as (A) and (B)                                respectively, with the addition (green line) of individuals who are                                in G and unrelated to F (i.e.,                                true positives). Dashed black lines indicate the T                                significance thresholds of ±1.64 at nominal .
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pgen-1000668-g002: Distribution of T.Distributions of T for out-of-group samples who are related (red line) and unrelated (blue line) to individuals in G for HapMap YRI (A) and HapMap CEPH (B) populations. (C) and (D) show the same distributions as (A) and (B) respectively, with the addition (green line) of individuals who are in G and unrelated to F (i.e., true positives). Dashed black lines indicate the T significance thresholds of ±1.64 at nominal .

Mentions: The method as described in [1] and summarized in the Introduction was implemented using R [4]. Subsets of the data described above were used to construct pools and , using the remaining genotypes as test samples for which the hypothesis is true. A summary of the tests is provided in Table 1. In each test, SNPs which did not achieve a minor allele frequency in both and were excluded from the computation.


Needles in the haystack: identifying individuals present in pooled genomic data.

Braun R, Rowe W, Schaefer C, Zhang J, Buetow K - PLoS Genet. (2009)

Distribution of T.Distributions of T for out-of-group samples who are                                related (red line) and unrelated (blue line) to individuals in                                    G for HapMap YRI (A) and HapMap CEPH (B)                                populations. (C) and (D) show the same distributions as (A) and (B)                                respectively, with the addition (green line) of individuals who are                                in G and unrelated to F (i.e.,                                true positives). Dashed black lines indicate the T                                significance thresholds of ±1.64 at nominal .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2747273&req=5

pgen-1000668-g002: Distribution of T.Distributions of T for out-of-group samples who are related (red line) and unrelated (blue line) to individuals in G for HapMap YRI (A) and HapMap CEPH (B) populations. (C) and (D) show the same distributions as (A) and (B) respectively, with the addition (green line) of individuals who are in G and unrelated to F (i.e., true positives). Dashed black lines indicate the T significance thresholds of ±1.64 at nominal .
Mentions: The method as described in [1] and summarized in the Introduction was implemented using R [4]. Subsets of the data described above were used to construct pools and , using the remaining genotypes as test samples for which the hypothesis is true. A summary of the tests is provided in Table 1. In each test, SNPs which did not achieve a minor allele frequency in both and were excluded from the computation.

Bottom Line: The results reveal that the 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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America. braunr@mail.nih.gov

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 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.

Show MeSH