<p>Abstract</p> <p>Background</p> <p>Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (<it>LSR</it>) method to estimate a region-specific p-value to provide an index for the most likely candidate region.</p> <p>Results</p> <p>An advantage of the <it>LSR </it>method relative to procedures based on genotypic data is that only p-value data are needed and hence can be applied extensively to different study designs. In this study the proposed <it>LSR </it>method was compared with commonly used methods such as Bonferroni's method and FDR controlling method. We found that while all methods provide good control over false positive rate, <it>LSR </it>has much better power and false discovery rate. In the authentic analysis on psoriasis and asthma disease data, the <it>LSR </it>method successfully identified important candidate regions and replicated the results of previous association studies.</p> <p>Conclusion</p> <p>The proposed <it>LSR </it>method provides an efficient exploratory tool for the analysis of sequences of dense genetic markers. Our results show that the <it>LSR </it>method has better power and lower false discovery rate comparing with the locus-specific multiple tests.</p
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