Open Access METHODOLOGY ARTICLE

Background: The etiology of complex diseases is due to the combination of genetic and environmental factors, usually many of them, and each with a small effect. The identification of these small-effect contributing factors is still a demanding task. Clearly, there is a need for more powerful tests of genetic association, and especially for the identification of rare effects Results: We introduce a new genetic association test based on symbolic dynamics and symbolic entropy. Using a freely available software, we have applied this entropy test, and a conventional test, to simulated and real datasets, to illustrate the method and estimate type I error and power. We have also compared this new entropy test to the Fisher exact test for assessment of association with low-frequency SNPs. The entropy test is generally more powerful than the conventional test, and can be significantly more powerful when the genotypic test is applied to low allele-frequency markers. We have also shown that both the Fisher and Entropy methods are optimal to test for association with lowfrequency SNPs (MAF around 1-5%), and both are conservative for very rare SNPs (MAF<1%) Conclusions: We have developed a new, simple, consistent and powerful test to detect genetic association of biallelic/ SNP markers in case-control data, by using symbolic dynamics and symbolic entropy as a measure of gene dependence. We also provide a standard asymptotic distribution of this test statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. The entropy test is generally as good or even more powerful than the conventional and Fisher tests. Furthermore, the entropy test is more computationally efficient than the Fisher's Exact test, especially for large number of markers. Therefore, this entropy-based test has the advantage of being optimal for most SNPs, regardless of their allele frequency (Minor Allele Frequency (MAF) between 1-50%). This property is quite beneficial, since many researchers tend to discard low allele-frequency SNPs from their analysis. Now they can apply the same statistical test of association to all SNPs in a single analysis., which can be especially helpful to detect rare effects. Background The etiology of complex diseases is due to the combination of genetic and environmental factors, usually many of them, and each with a small effect. The identification of these small-effect contributing factors is still a demanding task, often requiring a large budget, thousands of individuals, and half-a-million or more genetic markers. Even so, success is not guaranteed. In the last decade, genetic association tests have become widely used, since they can detect small genetic effects. The current availability of genomewide genotyping tools, combined with large collections of affected and unaffected individuals, has allowed for association analysis of the entire genome with the intention to detect even those small genetic effects (i.e., Odds-Ratios (OR) around 1.2) that influence common complex diseases. We have seen recently a proliferation of genome-wide association (GWA) analyses, some of which are identifying even genes with only small or modest effect sizes ([1] for a review). Nonetheless, the genetic factors found so far do not explain the total heritability of these diseases. Perhaps, the genetic architecture of these diseases is more complex than previously thought, involving many more genes, each with a small effect, and interacting among them and with environmental factors in complex ways. There is also the