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      Race and ancestry in biomedical research: exploring the challenges

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          The use of race in biomedical research has, for decades, been a source of social controversy. However, recent events, such as the adoption of racially targeted pharmaceuticals, have raised the profile of the race issue. In addition, we are entering an era in which genomic research is increasingly focused on the nature and extent of human genetic variation, often examined by population, which leads to heightened potential for misunderstandings or misuse of terms concerning genetic variation and race. Here, we draw together the perspectives of participants in a recent interdisciplinary workshop on ancestry and health in medicine in order to explore the use of race in research issue from the vantage point of a variety of disciplines. We review the nature of the race controversy in the context of biomedical research and highlight several challenges to policy action, including restrictions resulting from commercial or regulatory considerations, the difficulty in presenting precise terminology in the media, and drifting or ambiguous definitions of key terms.

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          Most cited references 70

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          A vision for the future of genomics research.

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            Genotype, haplotype and copy-number variation in worldwide human populations.

            Genome-wide patterns of variation across individuals provide a powerful source of data for uncovering the history of migration, range expansion, and adaptation of the human species. However, high-resolution surveys of variation in genotype, haplotype and copy number have generally focused on a small number of population groups. Here we report the analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms (SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations. Analysis of SNP genotypes yields strongly supported fine-scale inferences about population structure. Increasing linkage disequilibrium is observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. New approaches for haplotype analysis produce inferences about population structure that complement results based on unphased SNPs. Despite a difference from SNPs in the frequency spectrum of the copy-number variants (CNVs) detected--including a comparatively large number of CNVs in previously unexamined populations from Oceania and the Americas--the global distribution of CNVs largely accords with population structure analyses for SNP data sets of similar size. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations.
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              Control of confounding of genetic associations in stratified populations.

              To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations--African American, African Caribbean, and Hispanic American--in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is 70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.

                Author and article information

                Genome Med
                Genome Medicine
                BioMed Central
                21 January 2009
                : 1
                : 1
                : 8
                [1 ]Faculty of Law and School of Public Health Research, Health Law Institute, University of Alberta, 89 Ave and 111 St., T6G 2H5, Canada.
                [2 ]Department of Medical History and Ethics and Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
                [3 ]Program on Life Sciences Ethics and Policy, McLaughlin-Rotman Centre for Global Health, University Health Network, University of Toronto, MaRS Centre, 101 College St, Toronto, Ontario, M5G 1L7, Canada.
                [4 ]Faculty of Medicine, Island Medical Program, University of British Columbia, 3800 Finnerty Rd, Victoria, British Columbia, V8P 5C2, Canada.
                [5 ]Department of Biopharmaceutical Sciences and Department of Medicine, Divisions of Pharmaceutical Sciences and Pharmacogenetics, Pulmonary & Critical Care Medicine, and Clinical Pharmacology, University of California, San Francisco, CA 94143-2911, USA.
                [6 ]Department of Epidemiology & Preventive Medicine, Stritch School of Medicine, Loyola University, 2160 South First Avenue, Maywood, IL 60153, USA.
                [7 ]Genome Alberta, 3553-31 St NW, Calgary, Alberta, T2L 2K7, Canada.
                [8 ]Hamline University School of Law, 1536 Hewitt Avenue, St. Paul, MN 55104, USA.
                [9 ]Department of Medicine, Section of Genetic Medicine, Department of Human Genetics, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.
                [10 ]Program in Professionalism & Bioethics, Mayo College of Medicine, 200 First St SW, Rochester, MN 55905, USA.
                [11 ]Stanford Center for Biomedical Ethics, Stanford University Medical School, 701 Welch Rd, Palo Alto, CA 94304, USA.
                [12 ]Paul M Hebert Law Center, Louisiana State University, 1 East Campus Drive, Baton Rouge, LA 70803, USA.
                [13 ]Department of Medical Ethics and Center for Bioethics, University of Pennsylvania, 3401 Market St, Philadelphia, PA 19104, USA.
                [14 ]The Centre for Applied Genomics, The Hospital for Sick Children, and Department of Molecular Genetics, University of Toronto, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada.
                [15 ]Leslie Dan School of Pharmacy, University of Toronto, 144 College St, Toronto, Ontario, M5S 3M2, Canada.
                [16 ]Leeds Institute of Health Sciences, University of Leeds, 101 Clarendon Rd, Leeds, LS2 9LJ, UK.
                [17 ]Pharmacology Division, Instituto Nacional de Câncer, Rua André Cavalcanti 37, Rio de Janeiro 20231-050, Brazil.
                [18 ]Department of Public Health Sciences and of Surgery, University of Toronto, 155 College St, Toronto, Ontario, M5T 3M7, Canada.
                [19 ]McLaughlin Centre for Molecular Medicine, University of Toronto, MaRS Centre, 101 College St, Toronto, Ontario, M5G 1L7, Canada.
                [20 ]Department of Medicine, University of Toronto and University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada.
                Copyright ©2009 BioMed Central Ltd

                Molecular medicine


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