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Commentary |

Potential for Revealing Individual-Level Information in Genome-wide Association Studies

Thomas Lumley, PhD; Kenneth Rice, PhD
JAMA. 2010;303(7):659-660. doi:10.1001/jama.2010.120.
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Genome-wide association studies1 generate large volumes of results. While the strongest signals are the focus of most reports, full online publication of thousands or millions of association results has been encouraged.2 These aggregate results may be valuable for future scientific research,3 but analysts have recently shown47 that the aggregate results actually may reveal information about participants. Specifically, if study participants' genetic information is available, large-scale reporting of population-level variant-disease associations enables easy reconstruction of individuals' disease states.

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Figure. Predicted and Observed Measurements of Left Ventricular Mass
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To construct the predictions based on a hypothetical cohort of 12 612 individuals, additive models were fitted for each of 35 000 variants. The product of each regression coefficient and number of copies of each variant gives subject × variant predictors. Averaging these for each subject gives an overall prediction score. Finally, predictions of left ventricular mass are constructed by scaling these prediction scores to match the 25% and 75% percentiles of the observed left ventricular mass measurements. Thirty-five thousand independent variants were used, each with minor allele frequency 20%. Greater accuracy could be obtained with more variants. In line with the findings of Visscher and Hill,7 these results are not sensitive to the assumed minor allele frequency. The dashed diagonal line indicates perfect prediction.



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