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      North African Influences and Potential Bias in Case-Control Association Studies in the Spanish Population

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          Abstract

          Background

          Despite the limited genetic heterogeneity of Spanish populations, substantial evidences support that historical African influences have not affected them uniformly. Accounting for such population differences might be essential to reduce spurious results in association studies of genetic factors with disease. Using ancestry informative markers (AIMs), we aimed to measure the African influences in Spanish populations and to explore whether these might introduce statistical bias in population-based association studies.

          Methodology/Principal Findings

          We genotyped 93 AIMs in Spanish (from the Canary Islands and the Iberian Peninsula) and Northwest Africans, and conducted population and individual-based clustering analyses along with reference data from the HapMap, HGDP-CEPH, and other sources. We found significant differences for the Northwest African influence among Spanish populations from as low as ≈5% in Spanish from the Iberian Peninsula to as much as ≈17% in Canary Islanders, whereas the sub-Saharan African influence was negligible. Strikingly, the Northwest African ancestry showed a wide inter-individual variation in Canary Islanders ranging from 0% to 96%, reflecting the violent way the Islands were conquered and colonized by the Spanish in the XV century. As a consequence, a comparison of allele frequencies between Spanish samples from the Iberian Peninsula and the Canary Islands evidenced an excess of markers with significant differences. However, the inflation of p-values for the differences was adequately controlled by correcting for genetic ancestry estimates derived from a reduced number of AIMs.

          Conclusions/Significance

          Although the African influences estimated might be biased due to marker ascertainment, these results confirm that Northwest African genetic footprints are recognizable nowadays in the Spanish populations, particularly in Canary Islanders, and that the uneven African influences existing in these populations might increase the risk for false positives in association studies. Adjusting for population stratification assessed with a few dozen AIMs would be sufficient to control this effect.

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          Most cited references 40

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          The genetic structure and history of Africans and African Americans.

          Africa is the source of all modern humans, but characterization of genetic variation and of relationships among populations across the continent has been enigmatic. We studied 121 African populations, four African American populations, and 60 non-African populations for patterns of variation at 1327 nuclear microsatellite and insertion/deletion markers. We identified 14 ancestral population clusters in Africa that correlate with self-described ethnicity and shared cultural and/or linguistic properties. We observed high levels of mixed ancestry in most populations, reflecting historical migration events across the continent. Our data also provide evidence for shared ancestry among geographically diverse hunter-gatherer populations (Khoesan speakers and Pygmies). The ancestry of African Americans is predominantly from Niger-Kordofanian (approximately 71%), European (approximately 13%), and other African (approximately 8%) populations, although admixture levels varied considerably among individuals. This study helps tease apart the complex evolutionary history of Africans and African Americans, aiding both anthropological and genetic epidemiologic studies.
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            A note on exact tests of Hardy-Weinberg equilibrium.

            Deviations from Hardy-Weinberg equilibrium (HWE) can indicate inbreeding, population stratification, and even problems in genotyping. In samples of affected individuals, these deviations can also provide evidence for association. Tests of HWE are commonly performed using a simple chi2 goodness-of-fit test. We show that this chi2 test can have inflated type I error rates, even in relatively large samples (e.g., samples of 1,000 individuals that include approximately 100 copies of the minor allele). On the basis of previous work, we describe exact tests of HWE together with efficient computational methods for their implementation. Our methods adequately control type I error in large and small samples and are computationally efficient. They have been implemented in freely available code that will be useful for quality assessment of genotype data and for the detection of genetic association or population stratification in very large data sets.
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              Use of unlinked genetic markers to detect population stratification in association studies.

              We examine the issue of population stratification in association-mapping studies. In case-control studies of association, population subdivision or recent admixture of populations can lead to spurious associations between a phenotype and unlinked candidate loci. Using a model of sampling from a structured population, we show that if population stratification exists, it can be detected by use of unlinked marker loci. We show that the case-control-study design, using unrelated control individuals, is a valid approach for association mapping, provided that marker loci unlinked to the candidate locus are included in the study, to test for stratification. We suggest guidelines as to the number of unlinked marker loci to use.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                30 March 2011
                : 6
                : 3
                Affiliations
                [1 ]CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
                [2 ]Research Unit, Hospital Universitario N.S. de Candelaria, Tenerife, Spain
                [3 ]Instituto Nacional de Toxicología y Ciencias Forenses, Delegación de Canarias, Tenerife, Spain
                [4 ]Hematology Service, Hospital Universitario Dr. Negrín, Las Palmas de Gran Canaria, Spain
                [5 ]Multidisciplinary Organ Dysfunction Evaluation Research Network, Research Unit, Hospital Universitario Dr. Negrín, Las Palmas de Gran Canaria, Spain
                [6 ]Keenan Research Center, St. Michael's Hospital, Toronto, Canada
                University of Utah, United States of America
                Author notes

                Conceived and designed the experiments: CF. Performed the experiments: AC MP-Y AH. Analyzed the data: MP-Y CF. Contributed reagents/materials/analysis tools: SB AH LG JV. Wrote the paper: MP-Y CF.

                Article
                PONE-D-10-04048
                10.1371/journal.pone.0018389
                3068190
                21479138
                Pino-Yanes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 8
                Categories
                Research Article
                Biology
                Evolutionary Biology
                Evolutionary Processes
                Hybridization
                Population Genetics
                Gene Flow
                Genetics
                Heredity
                Gene Flow
                Human Genetics
                Genetic Association Studies
                Population Genetics
                Gene Flow
                Population Biology
                Population Genetics
                Gene Flow
                Medicine
                Epidemiology
                Disease Mapping
                Genetic Epidemiology
                Social and Behavioral Sciences
                Anthropology
                Cultural Anthropology
                Ethnic Groups
                Ethnobiology
                Geography
                Biogeography

                Uncategorized

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