The identification of allelic variants of human genes is of great importance when assessing genetic susceptibility. The emerging role of genetic polymorphisms in association studies has created the need for high throughput genotyping methodologies that allow a more rapid identification of relevant polymorphisms related to individual cancer risk enabling the extension to large-scale association studies. DNA pooling methodology may be of great importance considering the cost, time and labor that are involved in large-scale genotyping analysis carried out on individual samples. Alternatively, when using pooled samples which are made up of DNA from many individuals treated as a single sample, these factors are decrease drastically. In this way, the use of DNA pooling methodology, as a pre-selection tool, allows the identification of the most relevant polymorphisms to be studied, facilitating the estimation of the allelic frequency of each SNP in different populations. The present study initially aimed to validate the DNA pooling approach for the identification of genetic polymorphisms potentially associated with individual cancer risk generating pools with known allelic frequencies and using studies ongoing in the laboratory. Finally, our main aim was to test the accuracy of the pooled DNA analysis comparing the results of the allelic frequencies determined using pooled samples with the allelic frequency previously estimated by individual genotyping and previously published. In order to analyze the possibility of establishing differences between populations, we created DNA pools using a Portuguese control population, a breast cancer population and a Xavante Indian population characterized by a total absence of breast cancer cases. The pools were firstly created with known allelic frequencies, previously determined by individual genotyping, and, latter, randomly incremented in sample size to 200 patients and controls. Our results showed that the DNA pooling approach was a useful tool for the analysis of allelic distribution in the different populations studied. Ιn conclusion, our results showed that this methodology can be applied as an effective approach to identify SNPs of importance in genetic susceptibility to disease which may be used in association studies conducted subsequently by individual genotyping.