sample or it is not in that sample. The ratio of the probabilities of the proband genotype under these two hypotheses is easy to formulate. If indeed the proband is in the test sample, Visscher and Hill show that the log-likelihood ratio has an expect- ed value of ½N =(NzN ) (m=2N), where m is the number of SNPs that are scored. The test sample has allele frequen- cies based on N individuals, and the reference sample has N* members. This value is multiplied by 21 if the proband is not a member of the test sample, and a test statistic can be constructed as the squared difference of the logarithms of the two probabilities divided by an estimate of the variance of the difference. In a very pretty result, Visscher and Hill show that this test statistic has an approximate expected value of m=2N. Good discrimination between the two hypotheses requires a large number of SNPs and/or a small test sample. Homer et al. also remarked on the advantage of using a large number of SNPs. The Visscher and Hill result assumes the SNPs are all independent, and they showed how linkage disequilib- rium among the markers decreases the test statistic. The relationship of a proband to a GWAS could be addressed by taking the cases as the test sample and the controls as the reference sample. Depending on whether the proband was a case, or a control, or neither, the log-likelihood ratios would be positive, negative or References 1. Homer N, Szelinger S, Redman M, Duggan D, Tembe W, et al. (2008) Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP geno- typing microarrays. PLoS Genet 4(8): e1000167. doi:10.1371/journal.pgen.1000167. 2. Braun R, Rowe W, Schaefer C, Zhang J, Buetow K (2009) Needles in the haystack: identifying individuals present in pooled genomic data. PLoS Genet 5(9): e1000668. doi:10.1371/ journal.pgen.1000668. 3. Visscher PM, Hill WG (2009) The limits of individual identification from sample allele fre- quencies: theory and statistical analysis. PLoS Genet 5(9): e1000628. doi:10.1371/journal. pgen.1000628. 4. Human Genome Project Information (2003) Policies on the release of human genomic sequence data: bermuda-quality sequence. Avail- able: www.ornl.gov/hgmis/research/bermuda. html. Accessed 1 September 2009. 5. House of Representatives (26 December 2007) H.R. 2764, SEC. 218. 6. Lunshof JE, Chadwick R, Vorhaus DB, Church GM (2008) From genetic privacy to open consent. Nat Rev Genet 9(5): 406–411. 7. Wellcome Trust Sanger Center Press Releases (14 April 2003) The finished human genome – Wellcome to the genomic age. Available: http:// www.sanger.ac.uk/Info/Press/2003/030414.shtml. Accessed 1 September 2009. 8. Wellcome Trust (2003) Sharing data from large- scale biological research projects: a system of negative. Formal statistical tests follow from the work given by Visscher and Hill. Numerical work of all the authors con- firms the wisdom of restricting access to GWAS case-control allele frequencies. With the increasing sensitivity of forensic DNA profiling, often resulting from low template amplification [21], an increasing number of forensic samples contain DNA from multiple contributors, and the inter- pretation of such samples has progressed significantly since 1995, when lawyers could argue in US courts against the use of likelihood ratios for mixed samples [22]. Two features of multiple-contributor forensic profiles suggest the theory of Visscher and Hill will need further development for that context. In the first place, the very sensitivity of forensic genotyping means that allelic dropout is common [23], and fairly sophis- ticated methods [21] are needed to calculate likelihood ratios. Secondly, it will be some time before forensic scientists abandon the use of 13–20 microsatellite markers in favor of the very large numbers of SNPs consid- ered by Braun et al. and by Visscher and Hill, largely because of the investment in very large offender databases. In June 2009, there were over seven million profiles in the US databases (http://www.fbi.gov/hq/lab/ codis/clickmap.htm). Current forensic anal- yses start by determining whether or not all the proband alleles are seen within the mixture profile (maybe taking into account drop-out) and then calculating a likelihood ratio. Uncertainty over the number of tripartite responsibility. In: a meeting organized by the Wellcome Trust; 14–15 January 2003; Fort Lauderdale, Florida, United States. Available: http://www.genome.gov/Pages/Research/Well- comeReport0303.pdf. Accessed 7 October 2008. 9. Kaye J, Heeney C, Hawkins N, de Vries J, Boddington P (2009) Data sharing in genomics – re-shaping scientific practice. 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In: Proceedings of the 3rd Joint UN/ contributors in the mixture (the test sample) will make allele frequency determination difficult for the test sample. Conclusion by Greg Gibson and Elizabeth Fisher Something we can all agree on is that there is enormous goodwill in the general public toward medical research and a strong desire on the part of most people to be willing participants. At the same time, there is genuine fear of scientific abuse in general and gene technology in particular, and great potential for irreparable harm to both research and predictive health im- plementation, if identifiability issues are not addressed sensitively. As editors of a journal committed to open access to research, we are naturally suspicious of policies that restrict data access but we also understand that freedom usually comes at a price. What is essential is to get the balance of privacy protection and open, honest, and uniform consent right, and we hope that this short article encourages greater participation in the debate and education surrounding the issues. Acknowledgments 3 Detailed information about the P G organisa- tion and its members can be found at http:// www.p3g.org. ECE-Eurostat Work Session on Statistical Data Confidentiality, March 2003; Thessaloniki, Greece. Available: http://www.iiia.csic.es/ ,vtorra/publications/unrestricted/confUNECE. 2003.143.150.pdf. Accessed 1 September 2009. 17. Greenbaum D, Du J, Gerstein M (2008) Genomic anonymity: have we already lost it? Am J Bioeth 8: 71–74. 18. Lowrance WW, Collins FS (2007) Identifiability in genomic research. Science 317: 600–602. 19. Taylor P (2008) When consent gets in the way. Nature 456: 32–33. 20. Sweeney L (2001) Information explosion. In: Confidentiality, Disclosure and Data Access: Theory and Practical Application for Statistical Agencies. Doyle P, Lane JI, Theeuwes JM, Zayatzr LM, eds. New York: Elsevier Science. pp 43–74. 21. Gill P, Brenner CH, Buckleton JS, Carracedo A, Krawczak M, et al. (2006) DNA commission of the International Society of Forensic Genetics (ISFG): recommendations on the interpretation of mixtures. Int J Legal Med 160: 90–101. 22. Weir BS (1995) DNA statistics in the Simpson matter. Nat Genet 11: 365–368. 23. Gill P, Puch-Solis R, Curran J (2009) The low- template-DNA (stochastic) threshold – its deter- mination relative to risk analysis for national DNA databases. Forensic Sci Int Genet 3: 104–111. PLoS Genetics | www.plosgenetics.org 4 October 2009 | Volume 5 | Issue 10 | e1000665