title: Gene size matters.
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.
authors.affiliation: Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Mirina
authors.forename: Gil
authors.initials: G
authors.lastname: Atzmon
authors.forename: Kenny
authors.initials: K
authors.lastname: Ye
authors.forename: Aviv
authors.initials: A
authors.lastname: Bergman
mesh_terms: D020029:Base Pairing / Q000235:genetics*; D015982:Bias; D003198:Computer Simulation; D005796:Genes / Q000235:genetics*; D005819:Genetic Markers; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D020641:Polymorphism, Single Nucleotide / Q000235:genetics
chemical_list: D005819:Genetic Markers