Cancer is a complex somatic genetic disease that is caused mainly by environmental factors. However a few inherited mutations in some critical genes can be associated with cancer development. Breast cancer accounts for one in four of all female cancers, making it the first leading cause of cancer deaths in women in the western world. Numerous epidemiological factors affect the likelihood of developing breast, but no other predictor is as powerful as an inherited mutation in the tumour-suppressor genes BRCA1 or BRCA2. TP53 was deemed a plausible candidate as well. Hereditary breast cancer accounts for only 5–10% of all breast cancer cases and individuals carrying mutations in one of these genes have a 40–80% chance of developing breast cancer, making these mutations the strongest breast cancer predictors known. The other 90-95% of breast cancer cases are sporadic and occur in women in the absence of mutations in the referred susceptibility genes. This way the identification of a plausible cause for the remaining sporadic cases is a challenging work. Recent evidence shows that there are probably background genetic factors that contribute to the development of sporadic breast cancer, such as single nucleotide polymorphisms (SNPs). The emergence of comprehensive high density maps of SNPs and affordable genotyping platforms has allowed the accomplishment of association studies. Due to linkage disequilibrium, a panel of a few hundred thousand reporter SNPs (tSNPs) can be used as tags for the majority of the millions of common variants in the genome. Statistical approaches have been extensively used for the purpose of inferring haplotypes from diploid population data. An alternative, but not very explored, approach is called the Pure-Parsimony approach. This approach finds a solution to the haplotype inference problem that minimizes the total number of distinct haplotypes used, using the well know fact that haplotypes are, in general, much less numerous than genotypes. In order to get real data to develop satisfiability models and algorithms for the problem of haplotype inference by pure parsimony, a set of breast cancer patients and control populations was genotyped. To achieve this goal, approximately 100 breast cancer patients were recruited in Oncologic Units of several Lisbon Hospitals. Each cancer patient was matched, when possible, with two healthy control individuals, with the same age, tobacco smoking status and alcohol consumption habits. A second control population (about 50 individuals) characterized by the absence of breast cancer was also used to help in ascertaining the possible role of the gene polymorphisms under study as a control population. This population was identified in the Indian reserve of Sangradouro (Mato Grosso, Brasil) where the predominant ethnic group is Xavante. For each cancer and control populations 7 SNPs in BRCA1, 19 SNPs in BRCA2 and 6 SNPs in TP53 genes were genotyped, using real-time PCR, in particularly, TaqMan® SNP Genotyping Assays from Applied Biosystems. Since the majority of genotyped SNPs were tag of other ones, the real number of SNPs analyzed is much superior then those 32 analyzed in all three genes. These experiments are expected to give sufficient data to clarify the effects of variation in SNPs in the breast cancer susceptibility, and to explain specific characteristics of the populations under study that are of great interest to science.