Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data.
To identify genetic variants with modest effects on complex human diseases, a growing number of networks or consortia are created for sharing data from multiple genome-wide association studies on the same disease or related disorders. A central question in this enterprise is whether to obtain summary results or individual participant data from relevant studies. We show theoretically and numerically that meta-analysis of summary results is statistically as efficient as joint analysis of individual participant data (provided that both analyses are performed properly under the same modeling assumptions). We illustrate this equivalence with case-control data from the Finland-United States Investigation of NIDDM Genetics (FUSION) study. Collating only summary results will increase the number and representativeness of available studies, simplify data collection and analysis, reduce resource utilization, and accelerate discovery.
Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC 27599-7420, USA. lin@bios.unc.edu
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D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000235:genetics; D005387:Finland / Q000453:epidemiology; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide; D014481:United States / Q000453:epidemiology