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      Tests for Genetic Interactions in Type 1 Diabetes : Linkage and Stratification Analyses of 4,422 Affected Sib-Pairs

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          Abstract

          OBJECTIVE

          Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1 diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions.

          RESEARCH DESIGN AND METHODS

          To address this issue, the Type 1 Diabetes Genetics Consortium recruited 3,892 families, including 4,422 affected sib-pairs. After genotyping 6,090 markers, linkage analyses of these families were performed, using a novel method and taking into account factors such as genotype at known susceptibility loci.

          RESULTS

          Evidence for linkage was robust at the HLA and INS loci, with logarithm of odds (LOD) scores of 398.6 and 5.5, respectively. There was suggestive support for five other loci. Stratification by other risk factors (including HLA and age at diagnosis) identified one convincing region on chromosome 6q14 showing linkage in male subjects (corrected LOD = 4.49; replication P = 0.0002), a locus on chromosome 19q in HLA identical siblings (replication P = 0.006), and four other suggestive loci.

          CONCLUSIONS

          This is the largest linkage study reported for any disease. Our data indicate there are no major type 1 diabetes subtypes definable by linkage analyses; susceptibility is caused by actions of HLA and an apparently random selection from a large number of modest-effect loci; and apart from HLA and INS, there is no important susceptibility factor discoverable by linkage methods.

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          Most cited references26

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          Genome-wide association study of copy number variation in 16,000 cases of eight common diseases and 3,000 shared controls

          Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to play an important role in genetic susceptibility to common disease. To address this we undertook a large direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed ~19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated ~50% of all common CNVs larger than 500bp. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell-lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease, IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis, and type 1 diabetes, and TSPAN8 for type 2 diabetes, though in each case the locus had previously been identified in SNP-based studies, reflecting our observation that the majority of common CNVs which are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs which can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.
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            Allele-sharing models: LOD scores and accurate linkage tests.

            Starting with a test statistic for linkage analysis based on allele sharing, we propose an associated one-parameter model. Under general missing-data patterns, this model allows exact calculation of likelihood ratios and LOD scores and has been implemented by a simple modification of existing software. Most important, accurate linkage tests can be performed. Using an example, we show that some previously suggested approaches to handling less than perfectly informative data can be unacceptably conservative. Situations in which this model may not perform well are discussed, and an alternative model that requires additional computations is suggested.
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              A genome-wide search for human type 1 diabetes susceptibility genes.

              We have searched the human genome for genes that predispose to type 1 (insulin-dependent) diabetes mellitus using semi-automated fluorescence-based technology and linkage analysis. In addition to IDDM1 (in the major histocompatibility complex on chromosome 6p21) and IDDM2 (in the insulin gene region on chromosome 11p15), eighteen different chromosome regions showed some positive evidence of linkage to disease. Linkages to chromosomes 11q (IDDM4) and 6q (IDDM5) were confirmed by replication, and chromosome 18 may encode a fifth disease locus. There are probably no genes with large effects aside from IDDM1. Therefore polygenic inheritance is indicated, with a major locus at the major histocompatibility complex.
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                Author and article information

                Journal
                Diabetes
                diabetes
                diabetes
                Diabetes
                Diabetes
                American Diabetes Association
                0012-1797
                1939-327X
                March 2011
                21 February 2011
                : 60
                : 3
                : 1030-1040
                Affiliations
                [1] 1Centre for Diabetes Research, Western Australian Institute for Medical Research, University of Western Australia, Crawley, Australia
                [2] 2Centre for Medical Research, University of Western Australia, Crawley, Australia
                [3] 3Centre for Clinical Immunology and Biomedical Statistics, Murdoch University, Perth, Australia
                [4] 4Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
                [5] 5Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
                [6] 6Roche Molecular Systems, Pleasanton, California
                [7] 7Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
                [8] 8INSERM UMR-S 958, Faculté de Médecine Denis-Diderot, Paris, France the
                [9] 9University Paris 7, Paris, France
                [10] 10Steno Diabetes Center, Gentofte, Denmark
                [11] 11Juvenile Diabetes Research Foundation, New York, New York
                [12] 12Science Park, University Hospital Glostrup, Glostrup, Denmark
                [13] 13Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, U.K.
                Author notes
                Corresponding author: Grant Morahan, gem@ 123456waimr.uwa.edu.au .
                Article
                1195
                10.2337/db10-1195
                3046821
                21266329
                15f2e1d9-075c-4125-b091-fadb1a75a6b1
                © 2011 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

                History
                : 23 August 2010
                : 15 December 2010
                Categories
                Genetics

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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