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6 results on '"Nikolov, Ivan"'

1. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.

Author

Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM, Perlis RH, Mowry BJ, Thapar A, Goddard ME, Witte JS, Absher D, Agartz I, Akil H, Amin F, Andreassen OA, Anjorin A, Anney R, Anttila V, Arking DE, Asherson P, Azevedo MH, Backlund L, Badner JA, Bailey AJ, Banaschewski T, Barchas JD, Barnes MR, Barrett TB, Bass N, Battaglia A, Bauer M, Bayés M, Bellivier F, Bergen SE, Berrettini W, Betancur C, Bettecken T, Biederman J, Binder EB, Black DW, Blackwood DH, Bloss CS, Boehnke M, Boomsma DI, Breen G, Breuer R, Bruggeman R, Cormican P, Buccola NG, Buitelaar JK, Bunney WE, Buxbaum JD, Byerley WF, Byrne EM, Caesar S, Cahn W, Cantor RM, Casas M, Chakravarti A, Chambert K, Choudhury K, Cichon S, Cloninger CR, Collier DA, Cook EH, Coon H, Cormand B, Corvin A, Coryell WH, Craig DW, Craig IW, Crosbie J, Cuccaro ML, Curtis D, Czamara D, Datta S, Dawson G, Day R, De Geus EJ, Degenhardt F, Djurovic S, Donohoe GJ, Doyle AE, Duan J, Dudbridge F, Duketis E, Ebstein RP, Edenberg HJ, Elia J, Ennis S, Etain B, Fanous A, Farmer AE, Ferrier IN, Flickinger M, Fombonne E, Foroud T, Frank J, Franke B, Fraser C, Freedman R, Freimer NB, Freitag CM, Friedl M, Frisén L, Gallagher L, Gejman PV, Georgieva L, Gershon ES, Geschwind DH, Giegling I, Gill M, Gordon SD, Gordon-Smith K, Green EK, Greenwood TA, Grice DE, Gross M, Grozeva D, Guan W, Gurling H, De Haan L, Haines JL, Hakonarson H, Hallmayer J, Hamilton SP, Hamshere ML, Hansen TF, Hartmann AM, Hautzinger M, Heath AC, Henders AK, Herms S, Hickie IB, Hipolito M, Hoefels S, Holmans PA, Holsboer F, Hoogendijk WJ, Hottenga JJ, Hultman CM, Hus V, Ingason A, Ising M, Jamain S, Jones EG, Jones I, Jones L, Tzeng JY, Kähler AK, Kahn RS, Kandaswamy R, Keller MC, Kennedy JL, Kenny E, Kent L, Kim Y, Kirov GK, Klauck SM, Klei L, Knowles JA, Kohli MA, Koller DL, Konte B, Korszun A, Krabbendam L, Krasucki R, Kuntsi J, Kwan P, Landén M, Långström N, Lathrop M, Lawrence J, Lawson WB, Leboyer M, Ledbetter DH, Lee PH, Lencz T, Lesch KP, Levinson DF, Lewis CM, Li J, Lichtenstein P, Lieberman JA, Lin DY, Linszen DH, Liu C, Lohoff FW, Loo SK, Lord C, Lowe JK, Lucae S, MacIntyre DJ, Madden PA, Maestrini E, Magnusson PK, Mahon PB, Maier W, Malhotra AK, Mane SM, Martin CL, Martin NG, Mattheisen M, Matthews K, Mattingsdal M, McCarroll SA, McGhee KA, McGough JJ, McGrath PJ, McGuffin P, McInnis MG, McIntosh A, McKinney R, McLean AW, McMahon FJ, McMahon WM, McQuillin A, Medeiros H, Medland SE, Meier S, Melle I, Meng F, Meyer J, Middeldorp CM, Middleton L, Milanova V, Miranda A, Monaco AP, Montgomery GW, Moran JL, Moreno-De-Luca D, Morken G, Morris DW, Morrow EM, Moskvina V, Muglia P, Mühleisen TW, Muir WJ, Müller-Myhsok B, Murtha M, Myers RM, Myin-Germeys I, Neale MC, Nelson SF, Nievergelt CM, Nikolov I, Nimgaonkar V, Nolen WA, Nöthen MM, Nurnberger JI, Nwulia EA, Nyholt DR, O'Dushlaine C, Oades RD, Olincy A, Oliveira G, Olsen L, Ophoff RA, Osby U, Owen MJ, Palotie A, Parr JR, Paterson AD, Pato CN, Pato MT, Penninx BW, Pergadia ML, Pericak-Vance MA, Pickard BS, Pimm J, Piven J, Posthuma D, Potash JB, Poustka F, Propping P, Puri V, Quested DJ, Quinn EM, Ramos-Quiroga JA, Rasmussen HB, Raychaudhuri S, Rehnström K, Reif A, Ribasés M, Rice JP, Rietschel M, Roeder K, Roeyers H, Rossin L, Rothenberger A, Rouleau G, Ruderfer D, Rujescu D, Sanders AR, Sanders SJ, Santangelo SL, Sergeant JA, Schachar R, Schalling M, Schatzberg AF, Scheftner WA, Schellenberg GD, Scherer SW, Schork NJ, Schulze TG, Schumacher J, Schwarz M, Scolnick E, Scott LJ, Shi J, Shilling PD, Shyn SI, Silverman JM, Slager SL, Smalley SL, Smit JH, Smith EN, Sonuga-Barke EJ, St Clair D, State M, Steffens M, Steinhausen HC, Strauss JS, Strohmaier J, Stroup TS, Sutcliffe JS, Szatmari P, Szelinger S, Thirumalai S, Thompson RC, Todorov AA, Tozzi F, Treutlein J, Uhr M, van den Oord EJ, Van Grootheest G, Van Os J, Vicente AM, Vieland VJ, Vincent JB, Visscher PM, Walsh CA, Wassink TH, Watson SJ, Weissman MM, Werge T, Wienker TF, Wijsman EM, Willemsen G, Williams N, Willsey AJ, Witt SH, Xu W, Young AH, Yu TW, Zammit S, Zandi PP, Zhang P, Zitman FG, Zöllner S, Devlin B, Kelsoe JR, Sklar P, Daly MJ, O'Donovan MC, Craddock N, Sullivan PF, Smoller JW, Kendler KS, and Wray NR

Subjects
Adult, Attention Deficit Disorder with Hyperactivity genetics, Bipolar Disorder genetics, Child, Child Development Disorders, Pervasive genetics, Crohn Disease genetics, Depressive Disorder, Major genetics, Genetic Heterogeneity, Genome, Human, Humans, Inheritance Patterns, Schizophrenia genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Mental Disorders genetics, Polymorphism, Single Nucleotide
Abstract

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

Published
2013
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2. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants.

Author

Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand WH, Samani NJ, Todd JA, Donnelly P, Barrett JC, Davison D, Easton D, Evans DM, Leung HT, Marchini JL, Morris AP, Spencer CC, Tobin MD, Attwood AP, Boorman JP, Cant B, Everson U, Hussey JM, Jolley JD, Knight AS, Koch K, Meech E, Nutland S, Prowse CV, Stevens HE, Taylor NC, Walters GR, Walker NM, Watkins NA, Winzer T, Jones RW, McArdle WL, Ring SM, Strachan DP, Pembrey M, Breen G, St Clair D, Caesar S, Gordon-Smith K, Jones L, Fraser C, Green EK, Grozeva D, Hamshere ML, Holmans PA, Jones IR, Kirov G, Moskivina V, Nikolov I, O'Donovan MC, Owen MJ, Collier DA, Elkin A, Farmer A, Williamson R, McGuffin P, Young AH, Ferrier IN, Ball SG, Balmforth AJ, Barrett JH, Bishop TD, Iles MM, Maqbool A, Yuldasheva N, Hall AS, Braund PS, Dixon RJ, Mangino M, Stevens S, Thompson JR, Bredin F, Tremelling M, Parkes M, Drummond H, Lees CW, Nimmo ER, Satsangi J, Fisher SA, Forbes A, Lewis CM, Onnie CM, Prescott NJ, Sanderson J, Matthew CG, Barbour J, Mohiuddin MK, Todhunter CE, Mansfield JC, Ahmad T, Cummings FR, Jewell DP, Webster J, Brown MJ, Lathrop MG, Connell J, Dominiczak A, Marcano CA, Burke B, Dobson R, Gungadoo J, Lee KL, Munroe PB, Newhouse SJ, Onipinla A, Wallace C, Xue M, Caulfield M, Farrall M, Barton A, Bruce IN, Donovan H, Eyre S, Gilbert PD, Hilder SL, Hinks AM, John SL, Potter C, Silman AJ, Symmons DP, Thomson W, Worthington J, Dunger DB, Widmer B, Frayling TM, Freathy RM, Lango H, Perry JR, Shields BM, Weedon MN, Hattersley AT, Hitman GA, Walker M, Elliott KS, Groves CJ, Lindgren CM, Rayner NW, Timpson NJ, Zeggini E, Newport M, Sirugo G, Lyons E, Vannberg F, Hill AV, Bradbury LA, Farrar C, Pointon JJ, Wordsworth P, Brown MA, Franklyn JA, Heward JM, Simmonds MJ, Gough SC, Seal S, Stratton MR, Rahman N, Ban M, Goris A, Sawcer SJ, Compston A, Conway D, Jallow M, Newport M, Sirugo G, Rockett KA, Bumpstead SJ, Chaney A, Downes K, Ghori MJ, Gwilliam R, Hunt SE, Inouye M, Keniry A, King E, McGinnis R, Potter S, Ravindrarajah R, Whittaker P, Widden C, Withers D, Cardin NJ, Davison D, Ferreira T, Pereira-Gale J, Hallgrimsdo'ttir IB, Howie BN, Su Z, Teo YY, Vukcevic D, Bentley D, Brown MA, Compston A, Farrall M, Hall AS, Hattersley AT, Hill AV, Parkes M, Pembrey M, Stratton MR, Mitchell SL, Newby PR, Brand OJ, Carr-Smith J, Pearce SH, McGinnis R, Keniry A, Deloukas P, Reveille JD, Zhou X, Sims AM, Dowling A, Taylor J, Doan T, Davis JC, Savage L, Ward MM, Learch TL, Weisman MH, and Brown M

Subjects
Aminopeptidases genetics, Breast Neoplasms epidemiology, Case-Control Studies, Chromosome Mapping, Genetics, Population, Genotype, Haplotypes genetics, Humans, Linkage Disequilibrium, Minor Histocompatibility Antigens, Multiple Sclerosis epidemiology, North America epidemiology, Polymerase Chain Reaction, Receptors, Immunologic genetics, Receptors, Interleukin genetics, Spondylitis, Ankylosing epidemiology, Thyroiditis, Autoimmune epidemiology, Autoimmunity genetics, Breast Neoplasms genetics, Multiple Sclerosis genetics, Polymorphism, Single Nucleotide genetics, Spondylitis, Ankylosing genetics, Thyroiditis, Autoimmune genetics
Abstract

We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

Published
2007
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3. Pooled DNA genotyping on Affymetrix SNP genotyping arrays.

Author

Kirov G, Nikolov I, Georgieva L, Moskvina V, Owen MJ, and O'Donovan MC

Subjects
Alleles, Computational Biology, Female, Gene Frequency, Genome, Human, Genotype, Humans, Male, Oligonucleotide Array Sequence Analysis statistics & numerical data, Reproducibility of Results, DNA genetics, DNA isolation & purification, Oligonucleotide Array Sequence Analysis methods, Polymorphism, Single Nucleotide
Abstract

Background: Genotyping technology has advanced such that genome-wide association studies of complex diseases based upon dense marker maps are now technically feasible. However, the cost of such projects remains high. Pooled DNA genotyping offers the possibility of applying the same technologies at a fraction of the cost, and there is some evidence that certain ultra-high throughput platforms also perform with an acceptable accuracy. However, thus far, this conclusion is based upon published data concerning only a small number of SNPs., Results: In the current study we prepared DNA pools from the parents and from the offspring of 30 parent-child trios that have been extensively genotyped by the HapMap project. We analysed the two pools with Affymetrix 10 K Xba 142 2.0 Arrays. The availability of the HapMap data allowed us to validate the performance of 6843 SNPs for which we had both complete individual and pooled genotyping data. Pooled analyses averaged over 5-6 microarrays resulted in highly reproducible results. Moreover, the accuracy of estimating differences in allele frequency between pools using this ultra-high throughput system was comparable with previous reports of pooling based upon lower throughput platforms, with an average error for the predicted allelic frequencies differences between the two pools of 1.37% and with 95% of SNPs showing an error of < 3.2%., Conclusion: Genotyping thousands of SNPs with DNA pooling using Affymetrix microarrays produces highly accurate results and can be used for genome-wide association studies.

Published
2006
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4. Bipolar disorder and polymorphisms in the dysbindin gene (DTNBP1).

Author

Raybould R, Green EK, MacGregor S, Gordon-Smith K, Heron J, Hyde S, Caesar S, Nikolov I, Williams N, Jones L, O'Donovan MC, Owen MJ, Jones I, Kirov G, and Craddock N

Subjects
Adult, Alleles, Case-Control Studies, Demography, Dysbindin, Dystrophin-Associated Proteins, Exons, Female, Genotype, Humans, Male, Middle Aged, United Kingdom, Bipolar Disorder genetics, Carrier Proteins genetics, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide genetics
Abstract

Background: Several studies support the dysbindin (dystrobrevin binding protein 1) gene (DTNBP1) as a susceptibility gene for schizophrenia. We previously reported that variation at a specific 3-locus haplotype influences susceptibility to schizophrenia in a large United Kingdom (UK) Caucasian case-control sample., Methods: Using similar methodology to our schizophrenia study, we have investigated this same 3-locus haplotype in a large, well-characterized bipolar sample (726 Caucasian UK DSM-IV bipolar I patients; 1407 ethnically matched controls)., Results: No significant differences were found in the distribution of the 3-locus haplotype in the full sample. Within the subset of bipolar I cases with predominantly psychotic episodes of mood disturbance (n = 133) we found nominally significant support for association at this haploptype (p < .042) and at SNP rs2619538 (p = .003), with a pattern of findings similar to that in our schizophrenia sample. This finding was not significant after correction for multiple testing., Conclusions: Our data suggest that variation at the polymorphisms examined does not make a major contribution to susceptibility to bipolar disorder in general. They are consistent with the possibility that DTNBP1 influences susceptibility to a subset of bipolar disorder cases with psychosis. However, our subset sample is small and the hypothesis requires testing in independent, adequately powered samples.

Published
2005
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5. Genetic Classification of Populations using Supervised Learning

Author

Bridges, Michael, Heron, Elizabeth A., O'Dushlaine, Colm, Segurado, Ricardo, Morris, Derek, Corvin, Aiden, Gill, Michael, Pinto, Carlos, O'Donovan, Michael Conlon, Kirov, George, Craddock, Nicholas John, Holmans, Peter Alan, Williams, Nigel Melville, Georgieva, Lyudmila, Nikolov, Ivan, Norton, Nadine, Williams, Hywel John, Toncheva, Draga, Milanova, Vihra, Owen, Michael John, and The International Schizophrenia Consortium (ISC)

Subjects
Population genetics, Genome-wide association study, computer.software_genre, Quantitative Biology - Quantitative Methods, 0302 clinical medicine, Bulgaria, Genetics and Genomics/Genetics of Disease, Quantitative Methods (q-bio.QM), Genetics, Principal Component Analysis, 0303 health sciences, education.field_of_study, Multidisciplinary, Artificial neural network, Principal component analysis, Medicine, Research Article, Science, Population, Genetic differences, Genetics and Genomics/Complex Traits, Biology, Machine learning, Polymorphism, Single Nucleotide, 03 medical and health sciences, Genetics and Genomics/Population Genetics, Humans, Learning, education, QH426, 030304 developmental biology, business.industry, Supervised learning, R1, Support vector machine, Genetics, Population, Scotland, Case-Control Studies, FOS: Biological sciences, Artificial intelligence, Computational Biology/Population Genetics, Nerve Net, Scale (map), business, computer, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies., Comment: Accepted PLOS One

Published
2010
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6. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

Author

Nejentsev, Sergey, Howson, Joanna M. M., Walker, Neil M., Szeszko, Jeffrey, Field, Sarah F., Stevens, Helen E., Reynolds, Pamela, Hardy, Matthew, King, Erna, Masters, Jennifer, Hulme, John, Maier, Lisa M., Smyth, Deborah, Bailey, Rebecca, Cooper, Jason D., Ribas, Gloria, Campbell, R. Duncan, Clayton, David G., Todd, John A., Burton, Paul R., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Taylor, Niall C., Walters, Graham R., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O’Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Nicol Ferrier, I., Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Khalid Mohiuddin, M., Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Braga, Carolina A., Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bryan, Claire, Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Jilur, Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingeleif B., Howie, Bryan N., Su, Zhan, Ying Teo, Yik, Vukcevic, Damjan, Bentley, David, and Compston, Alistair

Subjects
Genotype, Genes, MHC Class II, Genes, MHC Class I, Human leukocyte antigen, Biology, Polymorphism, Single Nucleotide, Article, White People, MHC Class II Gene, Gene Frequency, HLA-DQ Antigens, Databases, Genetic, HLA-B Antigens, HLA-DQ beta-Chains, Humans, Genetic Predisposition to Disease, Alleles, Genetics, Multidisciplinary, HLA-DQ Antigen, HLA-A Antigens, MHC Class I Gene, HLA-DR Antigens, HLA-B, Histocompatibility, HLA-A, C431 Medical Genetics, Diabetes Mellitus, Type 1, Case-Control Studies, Sample Size, HLA-DRB1 Chains
Abstract

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group.

Published
2007

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