Imputation aware meta-analysis of genome-wide association studies.
Genome-wide association studies have recently identified many new loci associated with human complex diseases. These newly discovered variants typically have weak effects requiring studies with large numbers of individuals to achieve the statistical power necessary to identify them. Likely, there exist even more associated variants, which remain to be found if even larger association studies can be assembled. Meta-analysis provides a straightforward means of increasing study sample sizes without collecting new samples by combining existing data sets. One obstacle to combining studies is that they are often performed on platforms with different marker sets. Current studies overcome this issue by imputing genotypes missing from each of the studies and then performing standard meta-analysis techniques. We show that this approach may result in a loss of power since errors in imputation are not accounted for. We present a new method for performing meta-analysis over imputed single nucleotide polymorphisms, show that it is optimal with respect to power, and discuss practical implementation issues. Through simulation experiments, we show that our imputation aware meta-analysis approach outperforms or matches standard meta-analysis approaches.
Zaitlen
Noah
N
Bioinformatics Program, University of California, San Diego, California, USA.
Eskin
Eleazar
E
D016022:Case-Control Studies; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic