Meta-analysis of genome-wide association studies with overlapping subjects.
Data from multiple genome-wide association studies are often analyzed together for the purposes of combining information from several studies of the same disease or comparing results across different disorders. We provide a valid and efficient approach to such meta-analysis, allowing for overlapping study subjects. The available data may contain individual participant records or only meta-analytic summary results. Simulation studies demonstrate that failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. In addition, the proposed approach can be substantially more powerful than the simple approach of splitting the overlapping subjects among studies, especially for comparing results across different disorders. The advantages of the new approach are illustrated with empirical data from two sets of genome-wide association studies.
Lin
Dan-Yu
DY
Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. lin@bios.unc.edu
Sullivan
Patrick F
PF
D000483:Alleles; D001172:Arthritis, Rheumatoid / Q000235:genetics; D016022:Case-Control Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005787:Gene Frequency; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016017:Odds Ratio; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000235:genetics