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The development of precision medicine strategies requires prior knowledge of the genetic background of the target population. However, despite the availability of data from admixed Americans within large reference population databases, we cannot use these data as a surrogate for that of the Brazilian population. This lack of transferability is mainly due to differences between ancestry proportions of Brazilian and other admixed American populations. To address the issue, a coalition of research centres created the Brazilian Initiative on Precision Medicine (BIPMed). In this study, we aim to characterise two datasets obtained from 358 individuals from the BIPMed using two different platforms: whole-exome sequencing (WES) and a single nucleotide polymorphism (SNP) array. We estimated allele frequencies and variant pathogenicity values from the two datasets and compared our results using the BIPMed dataset with other public databases. Here, we show that the BIPMed WES dataset contains variants not included in dbSNP, including 6480 variants that have alternative allele frequencies (AAFs) >1%. Furthermore, after merging BIPMed WES and SNP array data, we identified 809,589 variants (47.5%) not present within the 1000 Genomes dataset. Our results demonstrate that, through the incorporation of Brazilian individuals into public genomic databases, BIPMed not only was able to provide valuable knowledge needed for the implementation of precision medicine but may also enhance our understanding of human genome variability and the relationship between genetic variation and disease predisposition.

Ultraviolet light exposure and cutaneous pigmentation are important host risk factors for cutaneous melanoma (CM), and it is well known that inherited ability to produce melanin varies in humans. The study aimed to identify single-nucleotide variants (SNVs) on pigmentation-related genes with importance in risk and clinicopathological aspects of CM. The study was conducted in two stages. In stage 1, 103 CM patients and 103 controls were analyzed using Genome-Wide Human SNV Arrays in order to identify SNVs in pigmentation-related genes, and the most important SNVs were selected for data validation in stage 2 by real-time polymerase-chain reaction in 247 CM patients and 280 controls. ADCY3 c.675+9196T>G, CREB1 c.303+373G>A, and MITF c.938-325G>A were selected for data validation among 74 SNVs. Individuals with CREB1 GA or AA genotype and allele “A” were under 1.79 and 1.47-fold increased risks of CM than others, respectively. Excesses of CREB1 AA and MITF AA genotype were seen in patients with tumors at Clark levels III to V (27.8% versus 13.7%) and at III or IV stages (46.1% versus 24.9%) compared to others, respectively. When compared to others, patients with ADCY3 TT had 1.89 more chances of presenting CM progression, and those with MITF GA or AA had 2.20 more chances of evolving to death by CM. Our data provide, for the first time, preliminary evidence that inherited abnormalities in ADCY3, CREB1, and MITF pigmentation-related genes, not only can increase the risk to CM, but also influence CM patients’ clinicopathological features.

We briefly review some principles of the Bioconductor project for statistical analysis of genome scale data and show in detail how these are deployed to analyze high-density genotyping arrays manufactured by Affymetrix, Inc. (Genome-wide SNP 6.0). Issues addressed include probe design and verification of probe address assertions, preprocessing of scanned intensities via SNPRMA, genotype calling via CRLMM, and downstream analysis of genotype-expression associations.