Genome-wide association (GWA) studies are used widely for detecting gene variants' contribution to diseases and traits. Recent researches indicate to several methodological challenges in the study design for GWA, for example, sample size issues, power calculations, false positive rate adjustments, and commercial chips' coverage of the genome. Chromosomal regions can also influence the observed genetic diversity under certain conditions; mainly the regions of secondary structures and large-scale repeats may affect the fidelity in marker genotyping. This study was to find such regions that contained markers with more variability and to examine the correlation of this variability to the factors relevant in a GWA study design, such as the false positive rate. We enrolled healthy controls from eight independent GWA designs then assigned randomly into case and control status. Minor allele frequency estimates, and case-control association analyses were performed using PLINK for sets with different sample sizes. Marker numbers exhibiting high variability in the allele frequency estimates, and the average number of false positives were calculated for bins across the autosomal genome. We found that SNP variability (in allele frequency) was unrelated to the sample size. More variable regions correlated to regions of more average number of false positives, after adjusting for confounders, such as sample size. We suggested that regions with more variability might have structural characteristics that made them difficult to be scanned during the genotyping process. Our study has great public health relevance because regions with more variability could undermine the effective study of a candidate genes and disease relationship during a research, or worse leading to erroneous conclusions. We advise in studying these regions, the researchers could lower their false positive rates to avoid inaccurately significant levels
