Pharmacogenetics in the brazilian population

Abstract

Brazil is the fifth largest country in the world and its present population, in excess of 190;million, is highly heterogeneous, as a result of centuries of admixture between Amerindians, Europeans, and Sub-Saharan Africans. The estimated individual proportions of biogeographical ancestry vary widely and continuously among Brazilians: most individuals, irrespective of self-identification as White, Brown or Black - the major categories of the Brazilian Census "race/color" system - have significant degrees of European and African ancestry, while a sizeable number display also Amerindian ancestry. These features have important pharmacogenetic (PGx) implications: first, extrapolation of PGx data from relatively well-defined ethnic groups is clearly not applicable to the majority of Brazilians; second, the frequency distribution of polymorphisms in pharmacogenes (e.g., CYP3A5, CYP2C9, GSTM1, ABCB1, GSTM3, VKORC, etc) varies continuously among Brazilians and is not captured by race/color self-identification; third, the intrinsic heterogeneity of the Brazilian population must be acknowledged in the design and interpretation of PGx studies in order to avoid spurious conclusions based on improper matching of study cohorts. The peculiarities of PGx in Brazilians are illustrated with data for different therapeutic groups, such as anticoagulants, HIV protease inhibitors and non-steroidal antinflammatory drugs, and the challenges and advantages created by population admixture for the study and implementation of PGx are discussed. PGx data for Amerindian groups and Brazilian-born, first-generation Japanese are presented to illustrate the rich diversity of the Brazilian population. Finally, I introduce the reader to the Brazilian Pharmacogenetic Network or Refargen, a nation-wide consortium of research groups, with the mission to provide leadership in PGx research and education in Brazil, with a population health impact.

Keywords: Brazilian pharmacogenetic network; HIV protease inhibitors; biogeographical ancestry; non-steroidal anti-inflammatory drugs; population admixture; warfarin.

Figure 1

Assignment of Brazilian samples to genetic clusters inferred using a panel of ancestry-informative markers and the program STRUCTURE. Data from 300 individuals, resident in Rio de Janeiro, self-identified as White, Brown, or Black, according to the Color classification adopted by the Brazilian Census. Each individual is represented by a vertical line, which is partitioned in three colored segments corresponding to the individual's estimated membership proportion in the African (blue), Amerindian (green), and European (red) clusters. Data from Suarez-Kurtz et al. (2007a).

Figure 2

The plot on the left shows the frequency distribution of CYP2C8*1(default, blue) *2(red), *3(orange), and *4(red) alleles according to the estimated individual proportion of African ancestry in 958 healthy, unrelated Brazilians. Non-linear logistic regression was applied to describe the association between biogeographical ancestry and frequency of CYP2C8alleles. Data from Suarez-Kurtz et al. (2010b). The plot on the right shows the frequency of the CYP2C8alleles in 70 healthy, unrelated Mozambicans (Suarez-Kurtz and Damasceno, unpublished).

Figure 3

Intra-individual variability of lopinavir pharmacokinetics. The plot shows the trough (pre-dose) concentration of lopinavir in the plasma of 99 HIV-infected males, under stable HAART treatment including two daily doses of lopinavir (400;mg) and ritonavir (100;mg). Each data point corresponds to one individual. Data from Estrela et al. (2009).

Figure 4

Influence of genetic and non-genetic factors on the warfarin dose requirement for stable anticoagulation in 390 patients from the INCL, Rio de Janeiro. Multivariate regression modeling was applied to identify the factors (covariates) that associate significantly with the individual warfarin weekly dose requirement. The relative contribution of each covariate to the final model is expressed by the partial R² statistics (abscissa), which measures the degree of association between two random variables, with the effect of a set of controlling random variables removed. The higher the R² value, the greater the contribution of the covariate to the model. Data from Perini et al. (2008).

Figure 5

Relationship between warfarin weekly doses predicted by a multiple regression model (abscissa) and the stable doses actually taken by 390 patients (ordinate) from INCL, Rio de Janeiro. The covariates in the dosing algorithm were age, weight, treatment indication, co-medication with amiodarone or simvastatin, VKORC1, and CYP2C9 genotypes and the first available INR reading for each patient. The identity line is traced. Data from Suarez-Kurtz et al. (2009).