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      Evaluating the promise of inclusion of African ancestry populations in genomics

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          Abstract

          The lack of representation of diverse ancestral backgrounds in genomic research is well-known, and the resultant scientific and ethical limitations are becoming increasingly appreciated. The paucity of data on individuals with African ancestry is especially noteworthy as Africa is the birthplace of modern humans and harbors the greatest genetic diversity. It is expected that greater representation of those with African ancestry in genomic research will bring novel insights into human biology, and lead to improvements in clinical care and improved understanding of health disparities. Now that major efforts have been undertaken to address this failing, is there evidence of these anticipated advances? Here, we evaluate the promise of including diverse individuals in genomic research in the context of recent literature on individuals of African ancestry. In addition, we discuss progress and achievements on related technological challenges and diversity among scientists conducting genomic research.

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          The personal and clinical utility of polygenic risk scores

          Eric Topol, Nathan Wineinger, Ali Torkamani (2018)
          Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.
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            Genetic analyses of diverse populations improves discovery for complex traits

            Genevieve Wojcik, Mariaelisa Graff, Katherine K Nishimura … (2019)
            Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.
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              Topic choice contributes to the lower rate of NIH awards to African-American/black scientists

              Travis A. Hoppe, Aviva Litovitz, Kristine Willis … (2019)
              Topic choice is a previously unappreciated contributor to the lower rate of NIH awards to AA/B scientists.
              Article 0 comments Cited 205 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Charles N. Rotimi:
                ORCID: http://orcid.org/0000-0001-5759-053X
                rotimic@mail.nih.gov
                Journal
                Journal ID (nlm-ta): NPJ Genom Med
                Journal ID (iso-abbrev): NPJ Genom Med
                Title: NPJ Genomic Medicine
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2056-7944
                Publication date (Electronic): 25 February 2020
                Publication date PMC-release: 25 February 2020
                Publication date Collection: 2020
                Volume: 5
                Electronic Location Identifier: 5
                Affiliations
                [1 ]ISNI 0000 0001 2297 5165, GRID grid.94365.3d, Center for Research on Genomics and Global Health, National Human Genome Research Institute, , National Institutes of Health, ; Bethesda, MD USA
                [2 ]ISNI 0000 0004 1936 9510, GRID grid.253615.6, Department of Clinical Research and Leadership, , The George Washington University School of Medicine and Health Sciences, ; Washington, DC USA
                Author information
                http://orcid.org/0000-0002-0827-9101
                http://orcid.org/0000-0001-5759-053X
                Article
                Publisher ID: 111
                DOI: 10.1038/s41525-019-0111-x
                PMC ID: 7042246
                PubMed ID: 32140257
                SO-VID: 47d8f821-e488-4500-8020-fc2a0a95950f
                Copyright © © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 3 July 2019
                Date accepted : 16 December 2019
                Categories
                Subject: Review Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                Keywords: personalized medicine,genome-wide association studies
                Data availability:
                Keywords: personalized medicine, genome-wide association studies

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