gene size matters基因的大小事务.pdfVIP

Gene Size Matters 1 2,3 4 . 1 . Alexandra Mirina , Gil Atzmon , Kenny Ye * , Aviv Bergman * 1 Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America, 2 Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America, 3 Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States of America, 4 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America Abstract In this work we show that in genome-wide association studies (GWAS) there is a strong bias favoring of genes covered by larger numbers of SNPs. Thus, we state here that there is a need for correction for such bias when performing downstream gene-level analysis, e.g. pathway analysis and gene-set analysis. We investigate several methods of obtaining gene level statistical significance in GWAS, and compare their effectiveness in correcting such bias. We also propose a simple algorithm based on first order statistic that corrects such bias. Citation: Mirina A, Atzmon G, Ye K, Bergman A (2012) Gene Size Matters. PLoS ONE 7(11): e49093. doi:10.1371/journal.pone.0049093 Editor: Chunyu Liu, University of Illinois at Chicago, United States of America Received June 26, 2012; Accepted October 8, 2012; Published November 9, 2012 Copyright: 2012 Mirina et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by Ellison Medical Foundation Senior Scholar Award AG-SS-2235 (/) and Nationa