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Research Publication

The Brazilian Initiative on Precision Medicine (BIPMed): fostering genomic data-sharing of underrepresented populations.

Cristiane S Rocha, Rodrigo Secolin, Maíra R Rodrigues et al.

33083011 PubMed ID
5 Authors
2020-10-02 Published
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

CS
Cristiane S Rocha
RS
Rodrigo Secolin
MR
Maíra R Rodrigues
BS
Benilton S Carvalho
IL
Iscia Lopes-Cendes
Chapter II

Abstract

Summary of the research findings

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.

Chapter III

Analysis

Comprehensive review of ancestry and genetic findings

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Summary

Key Findings

Ancestry Insights

Traits Analysis

Historical Context

Scientific Assessment