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      People of the British Isles: preliminary analysis of genotypes and surnames in a UK-control population

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      1 , * , 1 , 1 , 2 , 1 , 3 , 1 , 1 , 4 , 1 , 5 , 1 , 6 , 6 , 6 , 7 , 7 , 8 , 8 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 2 , 1
      European Journal of Human Genetics
      Nature Publishing Group
      people of the British Isles, population structure, control population, admixture, surnames

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          Abstract

          There is a great deal of interest in a fine-scale population structure in the UK, both as a signature of historical immigration events and because of the effect population structure may have on disease association studies. Although population structure appears to have a minor impact on the current generation of genome-wide association studies, it is likely to have a significant part in the next generation of studies designed to search for rare variants. A powerful way of detecting such structure is to control and document carefully the provenance of the samples involved. In this study, we describe the collection of a cohort of rural UK samples (The People of the British Isles), aimed at providing a well-characterised UK-control population that can be used as a resource by the research community, as well as providing a fine-scale genetic information on the British population. So far, some 4000 samples have been collected, the majority of which fit the criteria of coming from a rural area and having all four grandparents from approximately the same area. Analysis of the first 3865 samples that have been geocoded indicates that 75% have a mean distance between grandparental places of birth of 37.3 km, and that about 70% of grandparental places of birth can be classed as rural. Preliminary genotyping of 1057 samples demonstrates the value of these samples for investigating a fine-scale population structure within the UK, and shows how this can be enhanced by the use of surnames.

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          Most cited references29

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          A map of human genome variation from population-scale sequencing.

          The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.
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            Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

            There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 x 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10(-5) and 5 x 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
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              Association study designs for complex diseases.

              Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.
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                Author and article information

                Journal
                Eur J Hum Genet
                European Journal of Human Genetics
                Nature Publishing Group
                1018-4813
                1476-5438
                February 2012
                10 August 2011
                1 February 2012
                : 20
                : 2
                : 203-210
                Affiliations
                [1 ]simpleDepartment of Oncology, University of Oxford, ORCRB , Headington, Oxford, UK
                [2 ]simpleThe Wellcome Trust Centre for Human Genetics , Oxford, UK
                [3 ]simpleAnthony Nolan, 2-3 Heathgate Place , London, UK
                [4 ]simpleMRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics , Oxford, UK
                [5 ]simpleClinical Trials and Research Governance, Manor House, John Radcliffe Hospital , Headington, Oxford, UK
                [6 ]simpleDepartment of Geography, University College London , London, UK
                [7 ]simpleInstitute of Human Genetics, Newcastle University, International Centre for Life, Central Parkway , Newcastle upon Tyne, UK
                [8 ]simpleSection of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, St James's University Hospital , Leeds, UK
                [9 ]simplePeninsula College of Medicine & Dentistry, St Lukes Campus, University of Exeter , Exeter, UK
                [10 ]simplePeninsula NIHR Clinical Research Facility, Peninsula Medical School (University of Exeter) , Exeter, UK
                [11 ]simpleDepartment of Medical Genetics, Haematology & Pathology, Cardiff University School of Medicine, Institute of Medical Genetics Building , Cardiff, UK
                [12 ]simpleDepartment of Genetics, University of Leicester , Leicester, UK
                [13 ]simpleSchool of Natural & Applied Sciences, Faculty of Health & Life Sciences, University of Lincoln , Lincoln, UK
                [14 ]simpleTransplant Immunology, Royal Liverpool University Hospital , Liverpool, UK
                [15 ]simpleSchool of Medicine, Health Policy and Practice, University of East Anglia , Norwich, UK
                [16 ]simpleDepartment of Statistics, University of Oxford , Oxford, UK
                Author notes
                [* ]simpleDepartment of Oncology, University of Oxford, ORCRB , Old Road Campus, Headington, Oxford OX3 7DQ, UK. Tel: +01 86 561 7007; Fax: +01 86 561 7100; E-mail: bruce.winney@ 123456clinpharm.ox.ac.uk
                Article
                ejhg2011127
                10.1038/ejhg.2011.127
                3260910
                21829225
                4d78d40e-29f1-4764-b776-ed3067b0903a
                Copyright © 2012 Macmillan Publishers Limited

                This work is licensed under the Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

                History
                : 26 January 2011
                : 09 May 2011
                : 24 May 2011
                Categories
                Article

                Genetics
                surnames,admixture,population structure,control population,people of the british isles
                Genetics
                surnames, admixture, population structure, control population, people of the british isles

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