Abstract:Pharmacogenomics harnesses the utility of a patient's genome (n = 1) in decisions on which therapeutic drugs and in what amounts should be administered. Often, patients with shared ancestry present with comparable genetic profiles that predict drug response. However, populations are not static, thus, often, population mobility through migration, especially enmasse as is seen for refugees, changes the pharmacogenetic profiles of resultant populations and therefore observed responses to commonly used therapeutic… Show more
“…They are intended for the visualization of the complexity of ethnic categorization in pharmacogenetics literature and only reflect how these ethnic categories were classified in the literature we reviewed. A full list of ethnic categories included in this study can be found in Table S2 .…”
Introduction
Racial and ethnic categories are frequently used in pharmacogenetics literature to stratify patients; however, these categories can be inconsistent across different studies. To address the ongoing debate on the applicability of traditional concepts of race and ethnicity in the context of precision medicine, we aimed to review the application of current racial and ethnic categories in pharmacogenetics and its potential impact on clinical care.
Methods
One hundred and three total pharmacogenetics papers involving the
CYP2C9, CYP2C19
, and
CYP2D6
genes were analyzed for their country of origin, racial, and ethnic categories used, and allele frequency data. Correspondence between the major continental racial categories promulgated by National Institutes of Health (NIH) and those reported by the pharmacogenetics papers was evaluated.
Results
The racial and ethnic categories used in the papers we analyzed were highly heterogeneous. In total, we found 66 different racial and ethnic categories used which fall under the NIH race category “White”, 47 different racial and ethnic categories for “Asian”, and 62 different categories for “Black”. The number of categories used varied widely based on country of origin: Japan used the highest number of different categories for “White” with 17, Malaysia used the highest number for “Asian” with 24, and the US used the highest number for “Black” with 28. Significant variation in allele frequency between different ethnic subgroups was identified within 3 major continental racial categories.
Conclusion
Our analysis showed that racial and ethnic classification is highly inconsistent across different papers as well as between different countries. Evidence-based consensus is necessary for optimal use of self-identified race as well as geographical ancestry in pharmacogenetics. Common taxonomy of geographical ancestry which reflects specifics of particular countries and is accepted by the entire scientific community can facilitate reproducible pharmacogenetic research and clinical implementation of its results.
“…They are intended for the visualization of the complexity of ethnic categorization in pharmacogenetics literature and only reflect how these ethnic categories were classified in the literature we reviewed. A full list of ethnic categories included in this study can be found in Table S2 .…”
Introduction
Racial and ethnic categories are frequently used in pharmacogenetics literature to stratify patients; however, these categories can be inconsistent across different studies. To address the ongoing debate on the applicability of traditional concepts of race and ethnicity in the context of precision medicine, we aimed to review the application of current racial and ethnic categories in pharmacogenetics and its potential impact on clinical care.
Methods
One hundred and three total pharmacogenetics papers involving the
CYP2C9, CYP2C19
, and
CYP2D6
genes were analyzed for their country of origin, racial, and ethnic categories used, and allele frequency data. Correspondence between the major continental racial categories promulgated by National Institutes of Health (NIH) and those reported by the pharmacogenetics papers was evaluated.
Results
The racial and ethnic categories used in the papers we analyzed were highly heterogeneous. In total, we found 66 different racial and ethnic categories used which fall under the NIH race category “White”, 47 different racial and ethnic categories for “Asian”, and 62 different categories for “Black”. The number of categories used varied widely based on country of origin: Japan used the highest number of different categories for “White” with 17, Malaysia used the highest number for “Asian” with 24, and the US used the highest number for “Black” with 28. Significant variation in allele frequency between different ethnic subgroups was identified within 3 major continental racial categories.
Conclusion
Our analysis showed that racial and ethnic classification is highly inconsistent across different papers as well as between different countries. Evidence-based consensus is necessary for optimal use of self-identified race as well as geographical ancestry in pharmacogenetics. Common taxonomy of geographical ancestry which reflects specifics of particular countries and is accepted by the entire scientific community can facilitate reproducible pharmacogenetic research and clinical implementation of its results.
“…prostate cancer in men of African descent (Metcalfe et al 2008;Williams and Powell 2009;Chornokur et al 2011;Farrell et al 2013;Martin et al 2013;Powell and Bollig-Fischer 2013). The disease burden among migrant populations too are constantly evolving and the impact of Westernisation and globalisation means that there are new healthcare challenges that could be addressed through studying genomic variation (Garduño-Diaz and Khokhar 2012; Dubé et al 2015;Barlas et al 2016).…”
Clinical genetic services and genomic research are rapidly developing but, historically, those with the greatest need are the least to benefit from these advances. This encompasses low-income communities, including those from ethnic minority and indigenous backgrounds. The "Genomix" workshop at the European Society of Human Genetics (ESHG) 2016 conference offered the opportunity to consider possible solutions for these disparities from the experiences of researchers and genetic healthcare practitioners working with underserved communities in the USA, UK and Australia. Evident from the workshop and corresponding literature is that a multi-faceted approach to engaging communities is essential. This needs to be complemented by redesigning healthcare systems that improves access and raises awareness of the needs of these communities. At a more strategic level, institutions involved in funding research, commissioning and redesigning genetic health services also need to be adequately represented by underserved populations with intrinsic mechanisms to disseminate good practice and monitor participation. Further, as genomic medicine is mainstreamed, educational programmes developed for clinicians should incorporate approaches to alleviate disparities in accessing genetic services and improving study participation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.