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Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

ABSTRACT: Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

SUBMITTER: Scott RA

PROVIDER: S-EPMC3433394 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

Scott Robert A RA Lagou Vasiliki V Welch Ryan P RP Wheeler Eleanor E Montasser May E ME Luan Jian'an J Mägi Reedik R Strawbridge Rona J RJ Rehnberg Emil E Gustafsson Stefan S Kanoni Stavroula S Rasmussen-Torvik Laura J LJ Yengo Loïc L Lecoeur Cecile C Shungin Dmitry D Sanna Serena S Sidore Carlo C Johnson Paul C D PC Jukema J Wouter JW Johnson Toby T Mahajan Anubha A Verweij Niek N Thorleifsson Gudmar G Hottenga Jouke-Jan JJ Shah Sonia S Smith Albert V AV Sennblad Bengt B Gieger Christian C Salo Perttu P Perola Markus M Timpson Nicholas J NJ Evans David M DM Pourcain Beate St BS Wu Ying Y Andrews Jeanette S JS Hui Jennie J Bielak Lawrence F LF Zhao Wei W Horikoshi Momoko M Navarro Pau P Isaacs Aaron A O'Connell Jeffrey R JR Stirrups Kathleen K Vitart Veronique V Hayward Caroline C Esko Tõnu T Mihailov Evelin E Fraser Ross M RM Fall Tove T Voight Benjamin F BF Raychaudhuri Soumya S Chen Han H Lindgren Cecilia M CM Morris Andrew P AP Rayner Nigel W NW Robertson Neil N Rybin Denis D Liu Ching-Ti CT Beckmann Jacques S JS Willems Sara M SM Chines Peter S PS Jackson Anne U AU Kang Hyun Min HM Stringham Heather M HM Song Kijoung K Tanaka Toshiko T Peden John F JF Goel Anuj A Hicks Andrew A AA An Ping P Müller-Nurasyid Martina M Franco-Cereceda Anders A Folkersen Lasse L Marullo Letizia L Jansen Hanneke H Oldehinkel Albertine J AJ Bruinenberg Marcel M Pankow James S JS North Kari E KE Forouhi Nita G NG Loos Ruth J F RJ Edkins Sarah S Varga Tibor V TV Hallmans Göran G Oksa Heikki H Antonella Mulas M Nagaraja Ramaiah R Trompet Stella S Ford Ian I Bakker Stephan J L SJ Kong Augustine A Kumari Meena M Gigante Bruna B Herder Christian C Munroe Patricia B PB Caulfield Mark M Antti Jula J Mangino Massimo M Small Kerrin K Miljkovic Iva I Liu Yongmei Y Atalay Mustafa M Kiess Wieland W James Alan L AL Rivadeneira Fernando F Uitterlinden Andre G AG Palmer Colin N A CN Doney Alex S F AS Willemsen Gonneke G Smit Johannes H JH Campbell Susan S Polasek Ozren O Bonnycastle Lori L LL Hercberg Serge S Dimitriou Maria M Bolton Jennifer L JL Fowkes Gerard R GR Kovacs Peter P Lindström Jaana J Zemunik Tatijana T Bandinelli Stefania S Wild Sarah H SH Basart Hanneke V HV Rathmann Wolfgang W Grallert Harald H Maerz Winfried W Kleber Marcus E ME Boehm Bernhard O BO Peters Annette A Pramstaller Peter P PP Province Michael A MA Borecki Ingrid B IB Hastie Nicholas D ND Rudan Igor I Campbell Harry H Watkins Hugh H Farrall Martin M Stumvoll Michael M Ferrucci Luigi L Waterworth Dawn M DM Bergman Richard N RN Collins Francis S FS Tuomilehto Jaakko J Watanabe Richard M RM de Geus Eco J C EJ Penninx Brenda W BW Hofman Albert A Oostra Ben A BA Psaty Bruce M BM Vollenweider Peter P Wilson James F JF Wright Alan F AF Hovingh G Kees GK Metspalu Andres A Uusitupa Matti M Magnusson Patrik K E PK Kyvik Kirsten O KO Kaprio Jaakko J Price Jackie F JF Dedoussis George V GV Deloukas Panos P Meneton Pierre P Lind Lars L Boehnke Michael M Shuldiner Alan R AR van Duijn Cornelia M CM Morris Andrew D AD Toenjes Anke A Peyser Patricia A PA Beilby John P JP Körner Antje A Kuusisto Johanna J Laakso Markku M Bornstein Stefan R SR Schwarz Peter E H PE Lakka Timo A TA Rauramaa Rainer R Adair Linda S LS Smith George Davey GD Spector Tim D TD Illig Thomas T de Faire Ulf U Hamsten Anders A Gudnason Vilmundur V Kivimaki Mika M Hingorani Aroon A Keinanen-Kiukaanniemi Sirkka M SM Saaristo Timo E TE Boomsma Dorret I DI Stefansson Kari K van der Harst Pim P Dupuis Josée J Pedersen Nancy L NL Sattar Naveed N Harris Tamara B TB Cucca Francesco F Ripatti Samuli S Salomaa Veikko V Mohlke Karen L KL Balkau Beverley B Froguel Philippe P Pouta Anneli A Jarvelin Marjo-Riitta MR Wareham Nicholas J NJ Bouatia-Naji Nabila N McCarthy Mark I MI Franks Paul W PW Meigs James B JB Teslovich Tanya M TM Florez Jose C JC Langenberg Claudia C Ingelsson Erik E Prokopenko Inga I Barroso Inês I

Nature genetics 20120812 9

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further ...[more]

PMID: 22885924

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Project description:BackgroundGenome-wide association studies can have limited power to identify QTL, partly due to the stringent correction for multiple testing and low linkage-disequilibrium between SNPs and QTL. Regional Heritability Mapping (RHM) has been advanced as an alternative approach to capture underlying genetic effects. In this study, RHM was used to identify loci underlying variation in the 16(th) QTLMAS workshop simulated traits.MethodsThe method was implemented by fitting a mixed model where a genomic region and the overall genetic background were added as random effects. Heritabilities for the genetic regional effects were estimated, and the presence of a QTL in the region was tested using a likelihood ratio test (LRT). Several region sizes were considered (100, 50 and 20 adjacent SNPs). Bonferroni correction was used to calculate the LRT thresholds for genome-wide (p < 0.05) and suggestive (i.e., one false positive per genome scan) significance.ResultsGenomic heritabilities (0.31, 0.32 and 0.48, respectively) and genetic correlations (0.80, -0.42 and 0.19, between trait-pairs 1&2, 1&3 and 2&3) were similar to the simulated ones. RHM identified 7 QTL (4 at genome-wide and 3 at suggestive level) for Trait1; 4 (2 genome-wide and 2 suggestive) for Trait2; and 7 (6 genome-wide and 1 suggestive) for Trait3. Only one of the identified suggestive QTL was a false-positive. The position of these QTL tended to coincide with the position where the largest QTL (or several of them) were simulated. Several signals were detected for the simulated QTL with smaller effect. A combined analysis including all significant regions showed that they explain more than half of the total genetic variance of the traits. However, this might be overestimated, due to Beavis effect. All QTL affecting traits 1&2 and 2&3 had positive correlations, following the trend of the overall correlation of both trait-pairs. All but one QTL affecting traits 1&3 were negatively correlated, in agreement with the simulated situation. Moreover, RHM identified extra loci that were not found by association and linkage analysis, highlighting the improved power of this approach.ConclusionsRHM identified the largest QTL among the simulated ones, with some signals for the ones with small effect. Moreover, RHM performed better than association and linkage analysis, in terms of both power and resolution.

Dataset Information

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

Publications

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

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