Search

Your search keyword '"Hinks, Anne"' showing total 12 results
Start Over Author "Hinks, Anne" Database OpenAIRE
12 results on '"Hinks, Anne"'

1. The genetic profile of RF-positive polyarticular juvenile idiopathic arthritis (JIA) resembles adult rheumatoid arthritis (RA)

Author

Hinks, Anne, Marion, Miranda C., Cobb, Joanna, Comeau, Mary E., Sudman, Marc, Ainsworth, Hannah C, Bowes, John, Becker, Mara L, Bohnsack, John F., Haas, Johannes-Peter, Lovell, Daniel J, Mellins, Elizabeth D, Nelson, J. Lee, Nordal, Ellen, Punaro, Marilynn, Reed, Ann M., Rosé, Carlos D., Rosenberg, Alan M, Rygg, Marite, Smith, Samantha L, Stevens, Anne M, Videm, Vibeke, Wallace, Carol A., Wedderburn, Lucy R, Yarwood, Annie, Yeung, Rae S M, Langefeld, Carl D., Thompson, Susan D., Thomson, Wendy, and Prahalad, Sampath

Subjects
musculoskeletal diseases, genetic structures, immune system diseases, skin and connective tissue diseases, eye diseases
Abstract

Objective: Juvenile idiopathic arthritis (JIA) is comprised of seven heterogeneous categories of chronic childhood arthritides. About 5% of children with JIA have rheumatoid factor (RF) positive arthritis, which phenotypically resembles adult rheumatoid arthritis (RA). Our objective was to compare and contrast the genetics of RF-positive polyarticular JIA with RA, and selected other JIA categories, to more fully understand the pathophysiological relationships of inflammatory arthropathies. Methods: RF-positive polyarticular JIA cases (n=340) and controls (n=14,412) were genotyped using the Immunochip array. Single nucleotide polymorphisms (SNPs) were tested for association using a logistic regression model adjusting for admixture proportions. Weighted genetic risk scores (wGRS) of published RA and JIA risk loci were calculated and their ability to predict RF-positive polyarticular JIA were compared. Results: As expected, the HLA region was strongly associated with RF-positive polyarticular JIA (p=5.51x10-31). Nineteen of 44 RA risk loci and 6 of 27 oligoarticular/RF-negative polyarticular JIA risk loci were associated (p70 years. Conclusions: RF-positive polyarticular JIA is genetically more similar to adult RA than to the most common JIA categories and thus appears to be a childhood-onset presentation of autoantibody positive RA. These findings suggest common disease mechanisms, which could lead to novel therapeutic targets and shared treatment strategies.

Published
2018
Full Text
View/download PDF

2. Brief Report: The Genetic Profile of Rheumatoid Factor-Positive Polyarticular Juvenile Idiopathic Arthritis Resembles That of Adult Rheumatoid Arthritis

Author

Hinks, Anne, Marion, Miranda C., Cobb, Joanna, Comeau, Mary E., Sudman, Marc, Ainsworth, Hannah C., Bowes, John, Becker, Mara L., Bohnsack, John F., Haas, Johannes-Peter, Lovell, Daniel J., Mellins, Elizabeth D., Nelson, J. Lee, Nordal, Ellen Berit, Punaro, Marilynn, Reed, Ann M., Rose, Carlos D., Rosenberg, Alan M., Rygg, Marite, Smith, Samantha L., Stevens, Anne M., Videm, Vibeke, Wallace, Carol A., Wedderburn, Lucy R., Yarwood, Annie, Yeung, Rae S.M., Langefeld, Carl D., Thompson, Susan D., Thomson, Wendy, and Prahalad, Sampath

Subjects
musculoskeletal diseases, Adult, Male, genetic structures, Adolescent, Genotype, VDP::Medical disciplines: 700::Clinical medical disciplines: 750::Rheumatology: 759, Genetic Profile, Polymorphism, Single Nucleotide, Arthritis, Juvenile, VDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Reumatologi: 759, Arthritis, Rheumatoid, Logistic Models, Phenotype, immune system diseases, Rheumatoid Factor, Humans, Female, skin and connective tissue diseases, Child, Autoantibodies
Abstract

This is the peer reviewed version of the following article: Hinks, A., Marion, M.C., Cobb, J., Comeau, M.E., Sudman, M., Ainsworth, H.C., ... Prahalad, S. (2018). Brief Report: The Genetic Profile of Rheumatoid Factor? Positive Polyarticular Juvenile Idiopathic Arthritis Resembles That of Adult Rheumatoid Arthritis. Arthritis & Rheumatology, 70(6), 957-962, which has been published in final form at https://doi.org/10.1002/art.40443. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Objective: Juvenile idiopathic arthritis (JIA) comprises 7 heterogeneous categories of chronic childhood arthritides. Approximately 5% of children with JIA have rheumatoid factor (RF)–positive arthritis, which phenotypically resembles adult rheumatoid arthritis (RA). Our objective was to compare and contrast the genetics of RF‐positive polyarticular JIA with those of RA and selected other JIA categories, to more fully understand the pathophysiologic relationships of inflammatory arthropathies. Methods: Patients with RF‐positive polyarticular JIA (n = 340) and controls (n = 14,412) were genotyped using the Immunochip array. Single‐nucleotide polymorphisms were tested for association using a logistic regression model adjusting for admixture proportions. We calculated weighted genetic risk scores (wGRS) of reported RA and JIA risk loci, and we compared the ability of these wGRS to predict RF‐positive polyarticular JIA. Results: As expected, the HLA region was strongly associated with RF‐positive polyarticular JIA (P = 5.51 × 10−31). Nineteen of 44 RA risk loci and 6 of 27 oligoarticular/RF‐negative polyarticular JIA risk loci were associated with RF‐positive polyarticular JIA (P < 0.05). The RA wGRS predicted RF‐positive polyarticular JIA (area under the curve [AUC] 0.71) better than did the oligoarticular/RF‐negative polyarticular JIA wGRS (AUC 0.59). The genetic profile of patients with RF‐positive polyarticular JIA was more similar to that of RA patients with age at onset 16–29 years than to that of RA patients with age at onset ≥70 years. Conclusion: RF‐positive polyarticular JIA is genetically more similar to adult RA than to the most common JIA categories and thus appears to be a childhood‐onset presentation of autoantibody‐positive RA. These findings suggest common disease mechanisms, which could lead to novel therapeutic targets and shared treatment strategies.

Published
2017

3. Fine-mapping the MHC locus in juvenile idiopathic arthritis (JIA) reveals genetic heterogeneity corresponding to distinct adult inflammatory arthritic diseases

Author

Hinks, Anne, Bowes, John, Cobb, Joanna, Ainsworth, H. C., Marion, M. C., Comeau, M. E., Han, B., Becker, M. L., Bohnsack, J. F., de Bakker, P. I. W., Haas, J. P., Hazen, M., Lovell, D. J., Nigrovic, P. A., Nordal, E., Punnaro, M., Rosenberg, A. M., Rygg, M., Smith, Samantha, Wise, C. A., Videm, V., Wedderburn, L. R., Yarwood, Annie, Yeung, R. S. M., Prahalad, S., Langefeld, C. D., Raychaudhuri, Soumya, Thompson, S. D., and Thomson, Wendy

Subjects
musculoskeletal diseases, Juvenile idiopathic arthritis (JIA), Autoimmune disease, Rheumatoid arthritis, Gene polymorphism, genetic structures, skin and connective tissue diseases
Abstract

Objectives:Juvenile idiopathic arthritis (JIA) is a heterogeneous group of diseases, comprising seven categories. Genetic data could potentially be used to help redefine JIA categories and improve the current classification system. The human leukocyte antigen (HLA) region is strongly associated with JIA. Fine-mapping of the region was performed to look for similarities and differences in HLA associations between the JIA categories and define correspondences with adult inflammatory arthritides. Methods:Dense genotype data from the HLA region, from the Immunochip array for 5,043 JIA cases and 14,390 controls, was used to impute SNPs, HLA classical alleles and amino acids. Bivariate analysis was performed to investigate genetic correlation between the JIA categories. Conditional analysis was used to identify additional effects within the region. Comparison of the findings with those in adult inflammatory arthritic diseases was performed.ResultsWe identified category-specific associations and have demonstrated for the first time that rheumatoid factor negative polyarticular JIA and oligoarticular JIA are genetically similar in their HLA associations. We also observe that each JIA category potentially has an adult counterpart. The RF-positive polyarthritis association at HLA-DRB1 amino acid at position 13 mirrors the association in adult seropositive rheumatoid arthritis (RA). Interestingly, the combined oligoarthritis and RF-negative polyarthritis dataset shares the same association with adult seronegative RA. ConclusionsThe findings suggest the value of using genetic data in helping to classify the categories of this heterogeneous disease. Mapping JIA categories to adult counterparts could enable shared knowledge of disease pathogenesis and etiology and facilitate transition from paediatric to adult services.

Published
2017
Full Text
View/download PDF

4. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Author

Burton, Paul R., Clayton, David G., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Todd, John A., 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., Stevens, Helen E., Taylor, Niall C., Walters, Graham R., Walker, Neil M., 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., Ferrier, I. Nicol, 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, Mohiuddin, M. Khalid, 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, Marcano, Carolina A. Braga, 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, and Genomics (BRAGGS), The Biologics in RA Genetics, 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, Susceptibility Collaboration (UK), Breast Cancer, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Mohammed J. R., Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Widden, Claire, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingileif B., Howie, Bryan N., Su, Zhan, Teo, Yik Ying, Vukcevic, Damjan, Bentley, David, and Compston, Alistair

Subjects
Genetic Markers, Bipolar Disorder, Population, Genome-wide association study, Single-nucleotide polymorphism, Coronary Artery Disease, Disease, Biology, Population stratification, Article, Arthritis, Rheumatoid, Crohn Disease, Gene Frequency, Diabetes Mellitus, Chromosomes, Human, Humans, Genetic Predisposition to Disease, education, Allele frequency, Genetics, education.field_of_study, Multidisciplinary, Geography, Genome, Human, CDKN2BAS, United Kingdom, Human genetics, C431 Medical Genetics, Genetics, Population, Case-Control Studies, Hypertension
Abstract

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 similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 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.

Published
2007
Full Text
View/download PDF

5. HLA-DRB1*11 and variants of the MHC class II locus are strong risk factors for systemic juvenile idiopathic arthritis

Author

Ombrello, Michael J, Remmers, Elaine F, Tachmazidou, Ioanna, Grom, Alexei, Foell, Dirk, Haas, Johannes-Peter, Martini, Alberto, Gattorno, Marco, Özen, Seza, Prahalad, Sampath, Zeft, Andrew S, Bohnsack, John F, Mellins, Elizabeth D, Ilowite, Norman T, Russo, Ricardo, Len, Claudio, Hilario, Maria Odete E, Oliveira, Sheila, Yeung, Rae S M, Rosenberg, Alan, Wedderburn, Lucy R, Anton, Jordi, Schwarz, Tobias, Hinks, Anne, Bilginer, Yelda, Park, Jane, Cobb, Joanna, Satorius, Colleen L, Han, Buhm, Baskin, Elizabeth, Signa, Sara, Duerr, Richard H, Achkar, J P, Kamboh, M Ilyas, Kaufman, Kenneth M, Kottyan, Leah C, Pinto, Dalila, Scherer, Stephen W, Alarcón-Riquelme, Marta E, Docampo, Elisa, Estivill, Xavier, Gül, Ahmet, de Bakker, Paul I W, Raychaudhuri, Soumya, Langefeld, Carl D, Thompson, Susan, Zeggini, Eleftheria, Thomson, Wendy, Kastner, Daniel L, Woo, Patricia, and British Society of Pediatric and Adolescent Rheumatology (BSPAR) Study Group

Subjects
Multicenter Study, Research Support, Non-U.S. Gov't, Journal Article, Research Support, N.I.H., Intramural
Abstract

Systemic juvenile idiopathic arthritis (sJIA) is an often severe, potentially life-threatening childhood inflammatory disease, the pathophysiology of which is poorly understood. To determine whether genetic variation within the MHC locus on chromosome 6 influences sJIA susceptibility, we performed an association study of 982 children with sJIA and 8,010 healthy control subjects from nine countries. Using meta-analysis of directly observed and imputed SNP genotypes and imputed classic HLA types, we identified the MHC locus as a bona fide susceptibility locus with effects on sJIA risk that transcended geographically defined strata. The strongest sJIA-associated SNP, rs151043342 [P = 2.8 × 10(-17), odds ratio (OR) 2.6 (2.1, 3.3)], was part of a cluster of 482 sJIA-associated SNPs that spanned a 400-kb region and included the class II HLA region. Conditional analysis controlling for the effect of rs151043342 found that rs12722051 independently influenced sJIA risk [P = 1.0 × 10(-5), OR 0.7 (0.6, 0.8)]. Meta-analysis of imputed classic HLA-type associations in six study populations of Western European ancestry revealed that HLA-DRB1*11 and its defining amino acid residue, glutamate 58, were strongly associated with sJIA [P = 2.7 × 10(-16), OR 2.3 (1.9, 2.8)], as was the HLA-DRB1*11-HLA-DQA1*05-HLA-DQB1*03 haplotype [6.4 × 10(-17), OR 2.3 (1.9, 2.9)]. By examining the MHC locus in the largest collection of sJIA patients assembled to date, this study solidifies the relationship between the class II HLA region and sJIA, implicating adaptive immune molecules in the pathogenesis of sJIA.

Published
2015

6. Investigation of rheumatoid arthritis susceptibility loci in juvenile idiopathic arthritis confirms high degree of overlap

Author

Hinks, Anne, Cobb, Joanna, Sudman, Marc, Eyre, Stephen, Martin, Paul, Flynn, Edward, Packham, Jonathon, Childhood Arthritis Prospective Study (CAPS), UK RA Genetics (UKRAG) Consortium, British Society of Paediatric and Adolescent Rheumatology (BSPAR, Barton, Anne, Worthington, Jane, Langefeld, Carl D, Glass, David N, Thompson, Susan D, and Thomson, Wendy

Subjects
musculoskeletal diseases, genetic structures, immune system diseases, skin and connective tissue diseases, eye diseases
Abstract

OBJECTIVES: Rheumatoid arthritis (RA) shares some similar clinical and pathological features with juvenile idiopathic arthritis (JIA); indeed, the strategy of investigating whether RA susceptibility loci also confer susceptibility to JIA has already proved highly successful in identifying novel JIA loci. A plethora of newly validated RA loci has been reported in the past year. Therefore, the aim of this study was to investigate these single nucleotide polymorphisms (SNP) to determine if they were also associated with JIA. METHODS: Thirty-four SNP that showed validated association with RA and had not been investigated previously in the UK JIA cohort were genotyped in JIA cases (n=1242), healthy controls (n=4281), and data were extracted for approximately 5380 UK Caucasian controls from the Wellcome Trust Case-Control Consortium 2. Genotype and allele frequencies were compared between cases with JIA and controls using PLINK. A replication cohort of 813 JIA cases and 3058 controls from the USA was available for validation of any significant findings. RESULTS: Thirteen SNP showed significant association (p

Published
2012

7. Subtype specific genetic associations for juvenile idiopathic arthritis: ERAP1 with the enthesitis related arthritis subtype and IL23R with juvenile psoriatic arthritis

Author

Hinks, Anne, Martin, Paul, Flynn, Edward, Eyre, Steve, Packham, Jon, Childhood Arthritis Prospective Study-CAPS, BSPAR Study Group, Barton, Anne, Worthington, Jane, and Thomson, Wendy

Subjects
musculoskeletal diseases
Abstract

INTRODUCTION: Juvenile idiopathic arthritis (JIA) is an umbrella term for all chronic childhood arthropathies and can be divided into seven subtypes. It includes the enthesitis related arthritis (ERA) subtype which displays symptoms similar to ankylosing spondylitis (AS) and juvenile-onset psoriatic arthritis which has similarities to psoriatic arthritis (PsA) and psoriasis (Ps). We, therefore, hypothesized that two well-established susceptibility loci for AS and Ps, ERAP1 and IL23R, could also confer susceptibility to these JIA subtypes. METHODS: Single nucleotide polymorphisms (SNPs) in ERAP1 (rs30187) and IL23R (rs11209026) were genotyped in JIA cases (n = 1,054) and healthy controls (n = 5,200). Genotype frequencies were compared between all JIA cases and controls using the Cochrane-Armitage trend test implemented in PLINK. Stratified analysis by ILAR subtype was performed. RESULTS: The ERA subtype showed strong association with ERAP1 SNP (P trend = 0.005). The IL23R SNP showed significant association in the PsA subtype (P trend = 0.04). The SNPs were not associated with JIA overall or with any other subtype. CONCLUSIONS: We present evidence for subtype specific association of the ERAP1 gene with ERA JIA and the IL23R gene with juvenile-onset PsA. The findings will require validation in independent JIA datasets. These results suggest distinct pathogenic pathways in these subtypes.

Published
2011

8. Identification of novel RA susceptibility loci at chromosomes 10p15, 12q13 and 22q13

Author

Barton, Anne, Thomson, Wendy, Ke, Xiayi, Eyre, Steve, Hinks, Anne, Bowes, John, Plant, Darren, Gibbons, Laura J, Wilson, Anthony G, Bax, Deborah E, Morgan, Ann W, Emery, Paul, Steer, Sophia, Hocking, Lynne, Reid, David M, Wordsworth, Paul, Harrison, Pille, and Worthington, Jane

Subjects
Arthritis, Rheumatoid, Chromosomes, Human, Pair 12, Chromosomes, Human, Pair 10, Genetic Linkage, Case-Control Studies, Chromosomes, Human, Pair 22, Chromosome Mapping, Humans, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Article
Abstract

The WTCCC study identified 49 SNPs putatively associated with rheumatoid arthritis at P = 1 x 10(-4) - 1 x 10(-5) (tier 3). Here we show that three of these SNPs, mapping to chromosome 10p15 (rs4750316), 12q13 (rs1678542) and 22q13 (rs3218253), are also associated (trend P = 4 x 10(-5), P = 4 x 10(-4) and P = 4 x 10(-4), respectively) in a validation study of 4,106 individuals with rheumatoid arthritis and an expanded reference group of 11,238 subjects, confirming them as true susceptibility loci in individuals of European ancestry.

Published
2008

9. 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

10. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes

Author

Zeggini, Eleftheria, Weedon, Michael N., Lindgren, Cecilia M., Frayling, Timothy M., Elliott, Katherine S., Lango, Hana, Timpson, Nicholas J., Perry, John R B, Rayner, Nigel W., Freathy, Rachel M., Barrett, Jeffrey C., Shields, Beverley, Morris, Andrew P., Ellard, Sian, Groves, Christopher J., Harries, Lorna W., Marchini, Jonathan L., Owen, Katharine R., Knight, Beatrice, Cardon, Lon R., Walker, Mark, Hitman, Graham A., Morris, Andrew D., Doney, Alex S F, McCarthy, Mark I., Hattersley, Andrew T., 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, and Worthington, Jane

Subjects
Genetics, education.field_of_study, Multidisciplinary, SLC30A8, biology, Population, Genome-wide association study, Type 2 diabetes, medicine.disease, Genetic architecture, Diabetes mellitus, medicine, biology.protein, education, TCF7L2, CDKAL1
Abstract

The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1 , CDKN2A/CDKN2B , and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8 . Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.

Published
2007
Full Text
View/download PDF

11. Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Author

Romagnoni, Alberto, Jégou, Simon, Van Steen, Kristel, Wainrib, Gilles, Hugot, Jean-Pierre, Peyrin-Biroulet, Laurent, Chamaillard, Mathias, Colombel, Jean-Frederick, Cottone, Mario, D’Amato, Mauro, D’Incà, Renata, Halfvarson, Jonas, Henderson, Paul, Karban, Amir, Kennedy, Nicholas A., Khan, Mohammed Azam, Lémann, Marc, Levine, Arie, Massey, Dunecan, Milla, Monica, Ng, Sok Meng Evelyn, Oikonomou, Ioannis, Peeters, Harald, Proctor, Deborah D., Rahier, Jean-Francois, Rutgeerts, Paul, Seibold, Frank, Stronati, Laura, Taylor, Kirstin M., Törkvist, Leif, Ublick, Kullak, Van Limbergen, Johan, Van Gossum, Andre, Vatn, Morten H., Zhang, Hu, Zhang, Wei, Andrews, Jane M., Bampton, Peter A., Barclay, Murray, Florin, Timothy H., Gearry, Richard, Krishnaprasad, Krupa, Lawrance, Ian C., Mahy, Gillian, Montgomery, Grant W., Radford-Smith, Graham, Roberts, Rebecca L., Simms, Lisa A., Hanigan, Katherine, Croft, Anthony, Amininijad, Leila, Cleynen, Isabelle, Dewit, Olivier, Franchimont, Denis, Georges, Michel, Laukens, Debby, Theatre, Emilie, Van Gossum, André, Vermeire, Severine, Aumais, Guy, Baidoo, Leonard, Barrie, Arthur M., Beck, Karen, Bernard, Edmond-Jean, Binion, David G., Bitton, Alain, Brant, Steve R., Cho, Judy H., Cohen, Albert, Croitoru, Kenneth, Daly, Mark J., Datta, Lisa W., Deslandres, Colette, Duerr, Richard H., Dutridge, Debra, Ferguson, John, Fultz, Joann, Goyette, Philippe, Greenberg, Gordon R., Haritunians, Talin, Jobin, Gilles, Katz, Seymour, Lahaie, Raymond G., McGovern, Dermot P., Nelson, Linda, Ng, Sok Meng, Ning, Kaida, Paré, Pierre, Regueiro, Miguel D., Rioux, John D., Ruggiero, Elizabeth, Schumm, L. Philip, Schwartz, Marc, Scott, Regan, Sharma, Yashoda, Silverberg, Mark S., Spears, Denise, Steinhart, A. Hillary, Stempak, Joanne M., Swoger, Jason M., Tsagarelis, Constantina, Zhang, Clarence, Zhao, Hongyu, Aerts, Jan, Ahmad, Tariq, Arbury, Hazel, Attwood, Anthony, Auton, Adam, Ball, Stephen G., Balmforth, Anthony J., Barnes, Chris, Barrett, Jeffrey C., Barroso, Inês, Barton, Anne, Bennett, Amanda J., Bhaskar, Sanjeev, Blaszczyk, Katarzyna, Bowes, John, Brand, Oliver J., Braund, Peter S., Bredin, Francesca, Breen, Gerome, Brown, Morris J., Bruce, Ian N., Bull, Jaswinder, Burren, Oliver S., Burton, John, Byrnes, Jake, Caesar, Sian, Cardin, Niall, Clee, Chris M., Coffey, Alison J., MC Connell, John, Conrad, Donald F., Cooper, Jason D., Dominiczak, Anna F., Downes, Kate, Drummond, Hazel E., Dudakia, Darshna, Dunham, Andrew, Ebbs, Bernadette, Eccles, Diana, Edkins, Sarah, Edwards, Cathryn, Elliot, Anna, Emery, Paul, Evans, David M., Evans, Gareth, Eyre, Steve, Farmer, Anne, Ferrier, I. Nicol, Flynn, Edward, Forbes, Alistair, Forty, Liz, Franklyn, Jayne A., Frayling, Timothy M., Freathy, Rachel M., Giannoulatou, Eleni, Gibbs, Polly, Gilbert, Paul, Gordon-Smith, Katherine, Gray, Emma, Green, Elaine, Groves, Chris J., Grozeva, Detelina, Gwilliam, Rhian, Hall, Anita, Hammond, Naomi, Hardy, Matt, Harrison, Pile, Hassanali, Neelam, Hebaishi, Husam, Hines, Sarah, Hinks, Anne, Hitman, Graham A., Hocking, Lynne, Holmes, Chris, Howard, Eleanor, Howard, Philip, Howson, Joanna M. M., Hughes, Debbie, Hunt, Sarah, Isaacs, John D., Jain, Mahim, Jewell, Derek P., Johnson, Toby, Jolley, Jennifer D., Jones, Ian R., Jones, Lisa A., Kirov, George, Langford, Cordelia F., Lango-Allen, Hana, Lathrop, G. Mark, Lee, James, Lee, Kate L., Lees, Charlie, Lewis, Kevin, Lindgren, Cecilia M., Maisuria-Armer, Meeta, Maller, Julian, Mansfield, John, Marchini, Jonathan L., Martin, Paul, Massey, Dunecan CO, McArdle, Wendy L., McGuffin, Peter, McLay, Kirsten E., McVean, Gil, Mentzer, Alex, Mimmack, Michael L., Morgan, Ann E., Morris, Andrew P., Mowat, Craig, Munroe, Patricia B., Myers, Simon, Newman, William, Nimmo, Elaine R., O’Donovan, Michael C., Onipinla, Abiodun, Ovington, Nigel R., Owen, Michael J., Palin, Kimmo, Palotie, Aarno, Parnell, Kirstie, Pearson, Richard, Pernet, David, Perry, John RB, Phillips, Anne, Plagnol, Vincent, Prescott, Natalie J., Prokopenko, Inga, Quail, Michael A., Rafelt, Suzanne, Rayner, Nigel W., Reid, David M., Renwick, Anthony, Ring, Susan M., Robertson, Neil, Robson, Samuel, Russell, Ellie, Clair, David St, Sambrook, Jennifer G., Sanderson, Jeremy D., Sawcer, Stephen J., Schuilenburg, Helen, Scott, Carol E., Scott, Richard, Seal, Sheila, Shaw-Hawkins, Sue, Shields, Beverley M., Simmonds, Matthew J., Smyth, Debbie J., Somaskantharajah, Elilan, Spanova, Katarina, Steer, Sophia, Stephens, Jonathan, Stevens, Helen E., Stirrups, Kathy, Stone, Millicent A., Strachan, David P., Su, Zhan, Symmons, Deborah P. M., Thompson, John R., Thomson, Wendy, Tobin, Martin D., Travers, Mary E., Turnbull, Clare, Vukcevic, Damjan, Wain, Louise V., Walker, Mark, Walker, Neil M., Wallace, Chris, Warren-Perry, Margaret, Watkins, Nicholas A., Webster, John, Weedon, Michael N., Wilson, Anthony G., Woodburn, Matthew, Wordsworth, B. Paul, Yau, Chris, Young, Allan H., Zeggini, Eleftheria, Brown, Matthew A., Burton, Paul R., Caulfield, Mark J., Compston, Alastair, Farrall, Martin, Gough, Stephen C. L., Hall, Alistair S., Hattersley, Andrew T., Hill, Adrian V. S., Mathew, Christopher G., Pembrey, Marcus, Satsangi, Jack, Stratton, Michael R., Worthington, Jane, Hurles, Matthew E., Duncanson, Audrey, Ouwehand, Willem H., Parkes, Miles, Rahman, Nazneen, Todd, John A., Samani, Nilesh J., Kwiatkowski, Dominic P., McCarthy, Mark I., Craddock, Nick, Deloukas, Panos, Donnelly, Peter, Blackwell, Jenefer M., Bramon, Elvira, Casas, Juan P., Corvin, Aiden, Jankowski, Janusz, Markus, Hugh S., Palmer, Colin NA, Plomin, Robert, Rautanen, Anna, Trembath, Richard C., Viswanathan, Ananth C., Wood, Nicholas W., Spencer, Chris C. A., Band, Gavin, Bellenguez, Céline, Freeman, Colin, Hellenthal, Garrett, Pirinen, Matti, Strange, Amy, Blackburn, Hannah, Bumpstead, Suzannah J., Dronov, Serge, Gillman, Matthew, Jayakumar, Alagurevathi, McCann, Owen T., Liddle, Jennifer, Potter, Simon C., Ravindrarajah, Radhi, Ricketts, Michelle, Waller, Matthew, Weston, Paul, Widaa, Sara, and Whittaker, Pamela

Subjects
692/4020/1503/257/1402, 45, 692/308/2056, 45/43, article, 129, 3. Good health
Abstract

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.

12. Characterisation of a Susceptibility Locus for Inflammatory Arthritis

Author

Steel, Kathryn Jean Audrey, HINKS, ANNE AM, BARTON, ANNE A, Thomson, Wendy, Hinks, Anne, and Barton, Anne

Subjects
rheumatoid arthritis, psoriatic arthritis, rheumatology, genomics, juvenile idiopathic arthritis
Abstract

Inflammatory arthritis (IA) types such as rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA) and psoriatic arthritis (PsA) have been shown to exhibit common clinical features. As complex diseases they have a known genetic component, some of which is known to be shared. The aim of this study was to assess the genetic overlap between 3 types of IA (RA, JIA and PsA) using genotype data generated on the Immunochip array and to select a biologically promising overlapping region for further genetic and functional investigation.Overlap analysis was performed using association data generated for a large cohort of inflammatory arthritis cases and shared controls (11,475 RA; 2816 JIA; 929 PsA respectively). 50 genetic regions were identified as being associated with more than 1 type of IA (p

Published
2014

Catalog

Books, media, physical & digital resources
See catalog results