¹Department of Animal Science, Faculty of Agriculture, Akdeniz University, TR-07059 Antalya - TURKEY DOI : 10.9775/kvfd.2016.15883 Genome-wide association studies (GWAS) have been used to detect single nucleotide polymorphisms (SNPs) related to various animal traits. The outcome of GWAS is based on quality of the both phenotypic and genotypic datasets. False positive (or negative) associations can be obtained due to multiple hypothesis testing procedures, quality control measures, or an undetected population structure. The objectives of this study were to 1) investigate different multiple hypothesis testing procedures with different quality measures and 2) to detect and correct ancestral stratification using different single SNPs models of the bovine tuberculosis GWA data set. Based on a regression model, SNPs from chromosomes 2, 7, 8 and 13 were detected at a significance level of P<0.001 without correction for multiple hypothesis testing. However, after Bonferroni correction, Hochberg"s method and permutation test for multiple hypothesis correction genomic signals, it became non-significant. Only a false discovery rate approach detected weak signals (at level of 0.54) from chromosomes 2, 8, and 13. We used a model that took into account the effect of linkage disequilibrium to the multiple hypothesis testing procedures by combining adjacent SNPs test statistics with windows sizes of 2, 4 and 6. We detected strong genomic signals from chromosomes 13, 8, 6 and 2 at windows size 6. The results of this study showed that multiple hypothesis testing procedures are related to false positive genomic signals. It is difficult to suggest universally acceptable multiple hypothesis testing and QC measures and their thresholds due to sources of variations between species and within populations. However, additional analytical approaches and studies are needed to evaluate the effects of linkage disequilibrium on the multiple hypothesis testing procedures and QC measures (especially for minor allele frequencies) to GWAS under various scenarios including, but not limited to, level of heritability, linkage disequilibrium, population structure, and population size. Keywords : Genome wide association analyses, Multiple hypothesis testing, Quality control procedures