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Assessing and managing risk when sharing aggregate genetic variant data

Nature Reviews Genetics volume 12, pages 730–736 (2011)Cite this article

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An Erratum to this article was published on 27 September 2011

This article has been updated

Abstract

Access to genetic data across studies is an important aspect of identifying new genetic associations through genome-wide association studies (GWASs). Meta-analysis across multiple GWASs with combined cohort sizes of tens of thousands of individuals often uncovers many more genome-wide associated loci than the original individual studies; this emphasizes the importance of tools and mechanisms for data sharing. However, even sharing summary-level data, such as allele frequencies, inherently carries some degree of privacy risk to study participants. Here we discuss mechanisms and resources for sharing data from GWASs, particularly focusing on approaches for assessing and quantifying the privacy risks to participants that result from the sharing of summary-level data.

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Figure 1: Sharing 5,000 SNPs at different prevalence or prior probabilities.

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  • 27 September 2011

    In the above article, the incorrect link was provided for GWAS Central. The correct link should have been http://www.gwascentral.org. In the Further Information Box, the link to http://gwas.nih.gov was incorrectly described as 'GWAS Central (includes policy)'.The editors apologize for this error.

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Acknowledgements

This manuscript represents the views and opinions of its authors and does not necessarily represent the views or policies of the NIH or the US Department of Health and Human Services. This research was supported in part by the Intramural Research Program of the NIH National Library of Medicine. D.W.C. would like to acknowledge support from the US National Heart, Lung and Blood Institute (NHLBI), award U01 HL086528. The authors thank I. Marpuri, S. Buchholtz and L. Gyi for their support in coordinating the development of this work.

Author information

Authors and Affiliations

  1. David W. Craig is at the Translational Genomics Research Institute (TGen), Phoenix, Arizona 85004, USA.,

    David W. Craig

  2. Robert M. Goor, Zhenyuan Wang, Justin Paschall, Jim Ostell, Michael Feolo and Stephen T. Sherry are at the National Center for Biotechnology Information (NCBI), Bethesda, Maryland 20892, USA.,

    Robert M. Goor, Zhenyuan Wang, Justin Paschall, Jim Ostell, Michael Feolo & Stephen T. Sherry

  3. Teri A. Manolio is at the National Human Genome Research Institute (NHGRI), Bethesda, Maryland 20892, USA.,

    Teri A. Manolio

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Correspondence to David W. Craig.

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Related links

Related links

FURTHER INFORMATION

23andMe personal genomics company

Electronic Medical Records and Genomics (eMERGE) Network

Database of Genotypes and Phenotypes (dbGaP)

dbSNP

European Genome–Phenome Archive

Genome Medicine Database of Japan (GeMDBJ)

GWAS Central

HuGE Navigator

JSNP

Nature Reviews Genetics series on Genome-Wide Association Studies

NIH GWAS policy

Phenotype–Genotype Integrator (PheGenI)

Public Population Project in Genomics (P3G)

SecureGenome

SNP Health Association Resource (SHARe)

US National Human Genome Research Institute catalogue of published GWASs

Wellcome Trust Case–Control Consortium (WTCCC)

Glossary

Allele frequency

The frequency of the less-common allele of a polymorphism. It has a value between 0 and 0.5 and can vary between populations.

Bayesian

A statistical framework for evaluating a hypothesis. The Bayesian approach assesses the probability of a hypothesis being correct by incorporating the prior probability of the hypothesis.

Discrimination threshold

The significance threshold for rejecting the null hypothesis in a statistical test.

Frequentist

A statistical framework for evaluating a hypothesis. The frequentist approach tests a hypothesis as being correct given the strength of a data set.

Imputation

A method for inferring untyped variants from neighbouring variants, based on linkage disequilibrium and haplotype structure.

Linear regression

The estimation of a first-order relationship between two variables, which involves fitting a line of best fit to the data.

Missingness

The percentage of samples that do not receive a genotype call for a SNP in a genome-wide association study.

Neyman–Pearson lemma

A theorem that assures the optimality of a likelihood ratio test between simple hypotheses at a given threshold.

Prevalence

The prior probability that a person is in a data set of interest. Alternatively, the term can refer to the fraction of individuals in a data set out of the total number of individuals that could be in the data set.

Reference data set

A data set of samples from individuals who are from the same population that was sampled in the summary-level data set of interest.

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Craig, D., Goor, R., Wang, Z. et al. Assessing and managing risk when sharing aggregate genetic variant data. Nat Rev Genet 12, 730–736 (2011). https://doi.org/10.1038/nrg3067

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