Using Population Descriptors in Genetics and Genomics Research A New Framework for an Evolving Field (2023) / Chapter Skim
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1 Population Descriptors in Human Genetics Research: Genesis, Evolution, and Challenges
1 Population Descriptors in Human Genetics Research: Genesis, Evolution, and Challenges
Pages 21-56
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, spurring the development of a science that sought to understand how individual traits vary, how this variation is generated, and how it is transmitted to the next generation. This raises the question of how different members of a species can share individual traits, for example, a particular eye color.
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. Human genetics, since its origin in 1900 with the discovery of interindividual differences in blood transfusions by Karl Landsteiner, has been exceptional among the genetic and genomic sciences in that it focuses on existing groups of individuals to examine heredity rather than only on the offspring of controlled crosses, as is possible in other species.
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. Human genetic variation is the result of many forces -- historical, social, and biological -- and cannot be represented by any single variable.
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Both genetics and genomics studies are today common in biomedical research on humans. Researchers use human genetic information to address a wide variety of questions about history and evolution; the development and function of cells, tissues, and organs; the biology of the human genome; and the risks and mechanisms underlying rare conditions,3 common and rare diseases,4 and heritable traits (e.g., height, blood glucose)
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Moreover, without careful study design, the effects of environmental and genetic factors can often be conflated. It should be further noted that although genetic variation can be critical to identifying disease mechanisms and interindividual trait differences, human biological processes are universal.
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3. Gene discovery for complex and polygenic traits: studies aiming to identify genetic variants associated with quantitative traits or complex disease risk, as done in genome-wide association studies (GWAS)
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, the tools developed to sequence the human genome and the resulting data were already transforming how genetics research could be done and enabling unprecedented characterization of patterns of human genetic variation (Aach et al., 2001; Birney et al., 2001; Lander et al., 2001)
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. In brief, scientists' current understanding of the distribution of human genetic variation and its evolutionary origins is that • Anatomically modern humans arose somewhere in the African continent approximately 300,000 years ago (Hublin et al., 2017)
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. POPULATION CLASSIFICATION SCHEMES IN GENETICS AND GENOMICS RESEARCH The Origins of Describing Individuals and Populations in Human Genetics Human genetics research was propelled by the discovery of interindividual differences in blood transfusions by Karl Landsteiner in the early 1900s, and his subsequent demonstration that the bloods of humans can be classified into what we now call the A, B, AB, and O groups (Landsteiner, 1961)
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. This study on a very large sample of heterogeneous individuals, which also showed geographic patterns of east–west and north–south blood group allele frequency variation, became highly influential in anthropology and human genetics by suggesting widespread allele frequency differences in human populations (Hirschfeld and Hirschfeld, 1919)
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Thus, the institutional demand for biomedical research to become more inclusive has led to many U.S.-based genetics and genomics research projects collecting OMB ethnic and racial category-based information on study participants, including measurement of biological differences between these groups (Epstein, 2007)
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A complete history of the use of population descriptors in human genetics and the early and persistent use of race in science is beyond the scope of this report and outside of the committee's statement of task. The brief summary provided here is meant only to emphasize several important points.
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1998. Making Race and Nation: A Comparison of South Africa, the United States, and Brazil. Cambridge: Cambridge University Press. Molina, Natalia.
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Biobank, the South African HAALSI study, and the Brazilian BIPMed study (Table 1-1)
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The Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) study includes a community-based cohort of 5,059 men and women 40 years old or older.8 Study data were collected around the following areas: cognition and dementia, cardiometabolic disease, human immunodeficiency virus (HIV)
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36 POPULATION DESCRIPTORS IN GENETICS AND GENOMICS RESEARCH TABLE 1-1 Comparison of Classification Schemes Used in Three Studies Using Genetics from Three Distinct Global Contextsa UK Biobank HAALSI BIPMed White: Native Language: Geographic Regions in Brazil British Shangaan where participants were born: Irish English North Any other white Afrikaans Northeast background Zulu Centre West Mixed Xhosa Southeast White and black Portuguese South Caribbean Other Unknown White and black African White and Asian Any other mixed background Asian or Asian British Indian Pakistani Bangladeshi Any other Asian background Black or black British Caribbean African Any other black background Chinese Other ethnic group Do not know Prefer not to answer aA more extensive, yet still not exhaustive, list of international programs and the population descriptors they use can be found in Appendix C NOTE: BIPMed = Brazilian Initiative on Precision Medicine; HAALSI = Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa; UK = United Kingdom.
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However, heterogeneity among studies in their design, recruitment methods, population descriptors, and measurements makes it difficult to easily compare and combine the data and metadata from multiple studies. Challenges of data harmonization include how to deal with missing data or how to compare or aggregate data and metadata in which similar but nonidentical terms are used.
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. The appropriate use of population descriptors in genomics research is a global issue, not one limited to the United States (Mir et al., 2013)
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• Update OMB categories, including disaggregating South Asian from other Asian, adding categories to describe individuals from the Middle East/North Africa, adding a category for individuals native to the United States, including an option for multiracial description, adding parent and grandparent self-identified race and ethnicity, including variables to capture sociodemographic data, and updating questions that capture information related to histori cal racial narratives. • Educate the public on the purpose of, and misconceptions about, data generated from race-associated biomedical genomics research and distinguish genetic ancestry data from sociopolitical or cultur ally based racial self-identification.
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The program has over 180,000 participants, of whom 60 percent are of non-European descent.9 TOPMed researchers have recently provided recommendations on using and reporting population descriptors for race, ethnicity, and ancestry in genomics research, including ones that acknowledge the expanding global nature of genomics research and the current focus in the United States on reckoning with racism (Khan et al., 2022) : • Avoid using U.S.
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In addition to a general appreciation of the importance of genetic variation in human disease and health, and the reduction in the cost of and widespread access to genomic technologies, this growth has occurred in part by major investments in large-scale studies, many of which have genomic sequence data available. With this growth, genetics research is now conducted by a wide range of investigators -- many of whom have a limited understanding of the rationale and use of population descriptors in human genetics, particularly its history -- both exacerbating the risk of misuse of such descriptors and creating an important opportunity to implement substantive changes.
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With this growth in genetics research has come the development of more advanced methods of understanding and describing population structure and variation, as well as a growing clarity about the contribution of such methods to elucidating the relationship between genetic variation and human traits and health outcomes. Methods to assess genetic similarity and infer genetic ancestry have been developed as have nongenetic approaches, such as geospatial mapping of study participants to states/provinces, cities, and neighborhoods.
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Chapter 5 includes a somewhat technical discussion on how to select appropriate population descriptors for genetics research, and there, the primary audience is genetics and genomics researchers. Chapters 3 and 4 focus on guiding principles to support trustworthy research and requisites for change that could facilitate implementation of the recommendations in the report.
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The statement of task focuses on understanding the current use of population descriptors in genomics research; examining best practices in the use of race, ethnicity, and genetic ancestry as population descriptors; and identifying how best practices in the use of population descriptors could be widely adopted within the biomedical and scientific communities to strengthen genetics and genomics research. The statement of task identifies four areas that are beyond the scope of this consensus study: examining the use of race and ethnicity in clinical care; examining racism in science and genomics; examining the use of race and ethnicity in biomedical research generally (e.g., beyond nongenetic and genomics research)
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. The committee is mindful that the use of population descriptors including race, ethnicity, and genetic ancestry in genomics research is currently nonstandardized and is influenced by factors such as government categories and journal reporting guidelines.
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The final report should describe best practices on the use of race, ethnicity, and genetic ancestry and other population descriptors in genetics and genomics research, as formulated by the committee. Attention should be given to how these best practices could be used by biomedical and scientific communities to increase the robustness of study designs and methods for genetics and genomics research in the United States and globally.
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2015. A global reference for human genetic variation.
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American Journal of Human Genetics 97(2)
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2018. Cohort profile: Health and Ageing in Africa: A Longitudinal Study of an Indepth community in South Africa (HAALSI)
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American Journal of Human Genetics 91(4)
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W 1998. Making race and nation: A comparison of South Africa, the United States, and Brazil. Cambridge: Cambridge University Press.
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2018. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS c atalog.
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American Journal of Human Genetics 80(6)
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2002. Toward a new vocabulary of human genetic variation.
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American Journal of Physi cal Anthropology 162(2)
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2017. Genomics, health disparities, and missed opportunities for the nation's research agenda.
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