A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms.
Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.
Gao
Xiaoyi
X
Center for Genetic Epidemiology and Statistical Genetics, Miami Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida 33136, USA. xgao@med.miami.edu
Starmer
Joshua
J
Martin
Eden R
ER
D000544:Alzheimer Disease / Q000453:epidemiology / Q000235:genetics; D001699:Biometry; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D004812:Epidemiologic Methods; D005821:Genetic Techniques / Q000706:statistics & numerical data*; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis