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Pharmacogenomics, Ancestry and Clinical Decision Making for Global Populations

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Pharmacogenomics, Ancestry and Clinical Decision Making for Global Populations The Pharmacogenomics Journal (2014) 14, 217–222 & 2014 Macmillan Publishers Limited All rights reserved 1470-269X/14 www.nature.com/tpj ORIGINAL ARTICLE Pharmacogenomics, ancestry and clinical decision making for global populations E Ramos1, A Doumatey1, AG Elkahloun2, D Shriner1, H Huang1, G Chen1, J Zhou1, H McLeod3, A Adeyemo1 and CN Rotimi1 Pharmacogenomically relevant markers of drug response and adverse drug reactions are known to vary in frequency across populations. We examined minor allele frequencies (MAFs), genetic diversity (FST) and population structure of 1156 genetic variants (including 42 clinically actionable variants) in 212 genes involved in drug absorption, distribution, metabolism and excretion (ADME) in 19 populations (n ¼ 1478). There was wide population differentiation in these ADME variants, reflected in the range of mean MAF (DMAF) and FST. The largest mean DMAF was observed in African ancestry populations (0.10) and the smallest mean DMAF in East Asian ancestry populations (0.04). MAFs ranged widely, for example, from 0.93 for single-nucleotide polymorphism (SNP) rs9923231, which influences warfarin dosing to 0.01 for SNP rs3918290 associated with capecitabine metabolism. ADME genetic variants show marked variation between and within continental groupings of populations. Enlarging the scope of pharmacogenomics research to include multiple global populations can improve the evidence base for clinical translation to benefit all peoples. The Pharmacogenomics Journal (2014) 14, 217–222; doi:10.1038/tpj.2013.24; published online 9 July 2013 Keywords: ADME; clinical decision making; global populations; pharmacogenomics INTRODUCTION study, we provide a comprehensive analysis of human genetic Rapid advances in genomic science have led to identification by variation on B200 ADME genes in 19 global populations, regulatory agencies of a growing list of clinically important including the largest set of African ancestry populations studied biomarkers for drug response and toxicity. For example, the US for pharmacogenomics. We describe the range of variation Food and Drug Administration (FDA) now maintains a table of observed at multiple layers spanning from continental groups to pharmacogenomic biomarkers in drug labels,1 while the UK the individual. We discuss the impact of the observed variation on Medicines and HealthCare Products Agency and the European clinical decision making, as well as the utility of such data for Medicines Agency both have mechanisms for considering such regulatory purposes, including testing recommendations. biomarkers for targeted therapy and drug safety warnings. Clinically validated pharmacogenomic biomarkers can help physicians optimize drug selection, dose and treatment duration MATERIALS AND METHODS while averting adverse drug reactions.2 However, the drive to Study populations position pharmacogenomics as a core element in personalized A total of 1478 individuals from 19 populations with ancestry from medicine still suffers from limited data. For example, it is different parts of the world were included in this study (Supplementary estimated that over 90% of drugs currently used in clinical Table 1). Fifteen of these populations were from the 1000 Genomes Project practice lack valid and predictive biomarkers for therapeutic (http://www.1000genomes.org/) sample collection. The populations (and 3 their designated labels) were: Yoruba in Ibadan, Nigeria (YRI); Luhya in effects and/or avoiding severe side effects. Another limitation Webuye, Kenya (LWK); Maasai in Kinyawa, Kenya (MKK); African ancestry in is that our understanding of the distribution of human Southwest USA (ASW); Utah residents with Northern and Western pharmacogenomic variation remains limited due in part to the European ancestry from the Centre d’Etude du Polymorphisme Humain poor representation of ethnically diverse samples from various (CEPH) collection (CEU); Toscans in Italy (TSI); British from England and parts of the world in such studies. A more comprehensive Scotland (GBR); Finnish from Finland (FIN); Iberian populations in Spain understanding of the genetic landscape of the absorption, (IBS); Han Chinese in Beijing, China (CHB); Han Chinese South (CHS); distribution, metabolism and excretion (ADME) genes across Japanese in Tokyo, Japan (JPT); Mexican ancestry in Los Angeles, California global populations with different ancestral backgrounds can (MXL); Puerto Rican in Puerto Rico (PUR); and Columbians in Medellin (CLM; facilitate the translation of pharmacogenomics data to clinical Figure 1). The remaining four populations were obtained from ongoing studies in West Africa and the United States as follows: three groups—Igbo practice and public health policy. Achieving this task is now more from Nigeria (IGBO); Akan from Ghana (AKAN) and Gaa-Adangbe from feasible with increasing access to genotyping and sequencing Ghana (GAA)—were obtained from participants in the Africa America technologies, as well as the availability of gene chips specifically Diabetes Mellitus study6 and the fourth group comprised African designed to assess polymorphic alleles of drug metabolizing Americans from the metropolitan Washington, DC area that participated enzymes and other genes involved in the ADME of drugs.4,5 In this in the Howard University Family Study (HUFS).7 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA; 2Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA and 3Institute for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, NC, USA. Correspondence: Dr E Ramos, Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA. E-mail: [email protected] Received 5 February 2013; revised 1 May 2013; accepted 13 May 2013; published online 9 July 2013 Pharmacogenomics and global populations E Ramos et al 218 Figure 1. Map of populations analyzed in this study. Positions of populations marked on the map indicate location of sample collection. African ancestry populations are indicated by blue markers A–H (YRI, IGBO, GAA, AKAN, LWK, MKK, ASW and HUFS, respectively), European ancestry populations by green markers I–M (CEU, TSI, GBR, FIN and IBS, respectively), East Asian ancestry populations by purple markers N–P (CHB, CHS and JPT, respectively) and Latin American populations by pink markers Q–S (MXL, PUR and CLM, respectively). AKAN, Akan from Ghana; ASW, African ancestry in Southwest USA; CEU, Centre d’Etude du Polymorphisme Humain collection; CHS, Han Chinese South; CLM, Columbians in Medellin; GAA, Gaa-Adangbe from Ghana; HUFS, Howard University Family Study; IGBO, Igbo from Nigeria; JPT, Japanese in Tokyo, Japan; LWK, Luhya in Webuye, Kenya; MKK, Maasai in Kinyawa, Kenya; MXL, Mexican ancestry in Los Angeles, California; PUR, Puerto Rican in Puerto Rico; YRI, Yoruba in Ibadan, Nigeria. Samples from five population groups (IGBO, AKAN, GAA, MKK and HUFS) the minimum MAF for a given allele within a specified group of were directly genotyped using the Affymetrix DMET Plus platform (Santa populations was calculated to define the range of difference (DMAF). Clara, CA, USA) at the National Human Genome Research Institute Microarray Density plots for comparing DMAF were drawn using the R software Core laboratory at the National Institutes of Health (Bethesda, MD, USA) as package (www.r-project.org). Pairwise FST was used as a measure of described in the Supplementary Methods. For the other 14 groups, DMET population differentiation for a given marker between populations. FST Plus markers were extracted from the 1000 Genomes as described (Supple- values range from zero to one with one meaning that the two populations mentary Methods). To facilitate continental level and other comparisons, being compared are completely separated and zero means no divergence populations were grouped as follows: continental African samples (YRI, IGBO, (that is, the populations are freely sharing genetic materials through GAA, AKAN, LWK and MKK) were designated as AFR; continental African interbreeding). More information on FST estimates is described samples plus the African-American populations (ASW and HUFS) were (Supplementary Methods). designated AFR þ AA; continental European and Centre d’Etude du Polymor- Principal components of ancestry were computed by decomposing the phisme Humain samples (CEU, TSI, GBR, FIN and IBS) were designated as EUR; centered genotype matrix of the entire data set (1478 individuals and 1156 continental East Asian samples (CHB, CHS and JPT) were designated as EAS; markers). The number of significant principal components was estimated and Latin American samples (MXL, PUR and CLM) were designated as AMR. using the minimum average partial test.8 ADME variants are a subset of Data management is further described (Supplementary Methods). overall human genetic variation and there is abundant evidence that they are under selection.9 Therefore, we evaluated the similarity between the distribution of the studied variants and a random set of non-ADME markers Data analysis across the genome. Procrustes analysis,10 a method for comparing spatial The intersection of the DMET assay markers with the 1000 Genomes phase maps of human population genetic variation, was used to conduct a 1 data set yielded a final analytic data set comprising 1156 markers from statistical
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