%0 Journal Article %J Lancet Neurol %D 2012 %T Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. %A Traylor, Matthew %A Farrall, Martin %A Holliday, Elizabeth G %A Sudlow, Cathie %A Hopewell, Jemma C %A Cheng, Yu-Ching %A Fornage, Myriam %A Ikram, M Arfan %A Malik, Rainer %A Bevan, Steve %A Thorsteinsdottir, Unnur %A Nalls, Mike A %A Longstreth, Wt %A Wiggins, Kerri L %A Yadav, Sunaina %A Parati, Eugenio A %A DeStefano, Anita L %A Worrall, Bradford B %A Kittner, Steven J %A Khan, Muhammad Saleem %A Reiner, Alex P %A Helgadottir, Anna %A Achterberg, Sefanja %A Fernandez-Cadenas, Israel %A Abboud, Sherine %A Schmidt, Reinhold %A Walters, Matthew %A Chen, Wei-Min %A Ringelstein, E Bernd %A O'Donnell, Martin %A Ho, Weang Kee %A Pera, Joanna %A Lemmens, Robin %A Norrving, Bo %A Higgins, Peter %A Benn, Marianne %A Sale, Michele %A Kuhlenbäumer, Gregor %A Doney, Alexander S F %A Vicente, Astrid M %A Delavaran, Hossein %A Algra, Ale %A Davies, Gail %A Oliveira, Sofia A %A Palmer, Colin N A %A Deary, Ian %A Schmidt, Helena %A Pandolfo, Massimo %A Montaner, Joan %A Carty, Cara %A de Bakker, Paul I W %A Kostulas, Konstantinos %A Ferro, Jose M %A van Zuydam, Natalie R %A Valdimarsson, Einar %A Nordestgaard, Børge G %A Lindgren, Arne %A Thijs, Vincent %A Slowik, Agnieszka %A Saleheen, Danish %A Paré, Guillaume %A Berger, Klaus %A Thorleifsson, Gudmar %A Hofman, Albert %A Mosley, Thomas H %A Mitchell, Braxton D %A Furie, Karen %A Clarke, Robert %A Levi, Christopher %A Seshadri, Sudha %A Gschwendtner, Andreas %A Boncoraglio, Giorgio B %A Sharma, Pankaj %A Bis, Joshua C %A Gretarsdottir, Solveig %A Psaty, Bruce M %A Rothwell, Peter M %A Rosand, Jonathan %A Meschia, James F %A Stefansson, Kari %A Dichgans, Martin %A Markus, Hugh S %K Brain Ischemia %K Databases, Genetic %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Risk Factors %K Stroke %X

BACKGROUND: Various genome-wide association studies (GWAS) have been done in ischaemic stroke, identifying a few loci associated with the disease, but sample sizes have been 3500 cases or less. We established the METASTROKE collaboration with the aim of validating associations from previous GWAS and identifying novel genetic associations through meta-analysis of GWAS datasets for ischaemic stroke and its subtypes.

METHODS: We meta-analysed data from 15 ischaemic stroke cohorts with a total of 12 389 individuals with ischaemic stroke and 62 004 controls, all of European ancestry. For the associations reaching genome-wide significance in METASTROKE, we did a further analysis, conditioning on the lead single nucleotide polymorphism in every associated region. Replication of novel suggestive signals was done in 13 347 cases and 29 083 controls.

FINDINGS: We verified previous associations for cardioembolic stroke near PITX2 (p=2·8×10(-16)) and ZFHX3 (p=2·28×10(-8)), and for large-vessel stroke at a 9p21 locus (p=3·32×10(-5)) and HDAC9 (p=2·03×10(-12)). Additionally, we verified that all associations were subtype specific. Conditional analysis in the three regions for which the associations reached genome-wide significance (PITX2, ZFHX3, and HDAC9) indicated that all the signal in each region could be attributed to one risk haplotype. We also identified 12 potentially novel loci at p<5×10(-6). However, we were unable to replicate any of these novel associations in the replication cohort.

INTERPRETATION: Our results show that, although genetic variants can be detected in patients with ischaemic stroke when compared with controls, all associations we were able to confirm are specific to a stroke subtype. This finding has two implications. First, to maximise success of genetic studies in ischaemic stroke, detailed stroke subtyping is required. Second, different genetic pathophysiological mechanisms seem to be associated with different stroke subtypes.

FUNDING: Wellcome Trust, UK Medical Research Council (MRC), Australian National and Medical Health Research Council, National Institutes of Health (NIH) including National Heart, Lung and Blood Institute (NHLBI), the National Institute on Aging (NIA), the National Human Genome Research Institute (NHGRI), and the National Institute of Neurological Disorders and Stroke (NINDS).

%B Lancet Neurol %V 11 %P 951-62 %8 2012 Nov %G eng %N 11 %1 http://www.ncbi.nlm.nih.gov/pubmed/23041239?dopt=Abstract %R 10.1016/S1474-4422(12)70234-X %0 Journal Article %J PLoS Med %D 2017 %T Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. %A Wheeler, Eleanor %A Leong, Aaron %A Liu, Ching-Ti %A Hivert, Marie-France %A Strawbridge, Rona J %A Podmore, Clara %A Li, Man %A Yao, Jie %A Sim, Xueling %A Hong, Jaeyoung %A Chu, Audrey Y %A Zhang, Weihua %A Wang, Xu %A Chen, Peng %A Maruthur, Nisa M %A Porneala, Bianca C %A Sharp, Stephen J %A Jia, Yucheng %A Kabagambe, Edmond K %A Chang, Li-Ching %A Chen, Wei-Min %A Elks, Cathy E %A Evans, Daniel S %A Fan, Qiao %A Giulianini, Franco %A Go, Min Jin %A Hottenga, Jouke-Jan %A Hu, Yao %A Jackson, Anne U %A Kanoni, Stavroula %A Kim, Young Jin %A Kleber, Marcus E %A Ladenvall, Claes %A Lecoeur, Cécile %A Lim, Sing-Hui %A Lu, Yingchang %A Mahajan, Anubha %A Marzi, Carola %A Nalls, Mike A %A Navarro, Pau %A Nolte, Ilja M %A Rose, Lynda M %A Rybin, Denis V %A Sanna, Serena %A Shi, Yuan %A Stram, Daniel O %A Takeuchi, Fumihiko %A Tan, Shu Pei %A van der Most, Peter J %A van Vliet-Ostaptchouk, Jana V %A Wong, Andrew %A Yengo, Loic %A Zhao, Wanting %A Goel, Anuj %A Martinez Larrad, Maria Teresa %A Radke, Dörte %A Salo, Perttu %A Tanaka, Toshiko %A van Iperen, Erik P A %A Abecasis, Goncalo %A Afaq, Saima %A Alizadeh, Behrooz Z %A Bertoni, Alain G %A Bonnefond, Amélie %A Böttcher, Yvonne %A Bottinger, Erwin P %A Campbell, Harry %A Carlson, Olga D %A Chen, Chien-Hsiun %A Cho, Yoon Shin %A Garvey, W Timothy %A Gieger, Christian %A Goodarzi, Mark O %A Grallert, Harald %A Hamsten, Anders %A Hartman, Catharina A %A Herder, Christian %A Hsiung, Chao Agnes %A Huang, Jie %A Igase, Michiya %A Isono, Masato %A Katsuya, Tomohiro %A Khor, Chiea-Chuen %A Kiess, Wieland %A Kohara, Katsuhiko %A Kovacs, Peter %A Lee, Juyoung %A Lee, Wen-Jane %A Lehne, Benjamin %A Li, Huaixing %A Liu, Jianjun %A Lobbens, Stephane %A Luan, Jian'an %A Lyssenko, Valeriya %A Meitinger, Thomas %A Miki, Tetsuro %A Miljkovic, Iva %A Moon, Sanghoon %A Mulas, Antonella %A Müller, Gabriele %A Müller-Nurasyid, Martina %A Nagaraja, Ramaiah %A Nauck, Matthias %A Pankow, James S %A Polasek, Ozren %A Prokopenko, Inga %A Ramos, Paula S %A Rasmussen-Torvik, Laura %A Rathmann, Wolfgang %A Rich, Stephen S %A Robertson, Neil R %A Roden, Michael %A Roussel, Ronan %A Rudan, Igor %A Scott, Robert A %A Scott, William R %A Sennblad, Bengt %A Siscovick, David S %A Strauch, Konstantin %A Sun, Liang %A Swertz, Morris %A Tajuddin, Salman M %A Taylor, Kent D %A Teo, Yik-Ying %A Tham, Yih Chung %A Tönjes, Anke %A Wareham, Nicholas J %A Willemsen, Gonneke %A Wilsgaard, Tom %A Hingorani, Aroon D %A Egan, Josephine %A Ferrucci, Luigi %A Hovingh, G Kees %A Jula, Antti %A Kivimaki, Mika %A Kumari, Meena %A Njølstad, Inger %A Palmer, Colin N A %A Serrano Ríos, Manuel %A Stumvoll, Michael %A Watkins, Hugh %A Aung, Tin %A Blüher, Matthias %A Boehnke, Michael %A Boomsma, Dorret I %A Bornstein, Stefan R %A Chambers, John C %A Chasman, Daniel I %A Chen, Yii-Der Ida %A Chen, Yduan-Tsong %A Cheng, Ching-Yu %A Cucca, Francesco %A de Geus, Eco J C %A Deloukas, Panos %A Evans, Michele K %A Fornage, Myriam %A Friedlander, Yechiel %A Froguel, Philippe %A Groop, Leif %A Gross, Myron D %A Harris, Tamara B %A Hayward, Caroline %A Heng, Chew-Kiat %A Ingelsson, Erik %A Kato, Norihiro %A Kim, Bong-Jo %A Koh, Woon-Puay %A Kooner, Jaspal S %A Körner, Antje %A Kuh, Diana %A Kuusisto, Johanna %A Laakso, Markku %A Lin, Xu %A Liu, Yongmei %A Loos, Ruth J F %A Magnusson, Patrik K E %A März, Winfried %A McCarthy, Mark I %A Oldehinkel, Albertine J %A Ong, Ken K %A Pedersen, Nancy L %A Pereira, Mark A %A Peters, Annette %A Ridker, Paul M %A Sabanayagam, Charumathi %A Sale, Michele %A Saleheen, Danish %A Saltevo, Juha %A Schwarz, Peter Eh %A Sheu, Wayne H H %A Snieder, Harold %A Spector, Timothy D %A Tabara, Yasuharu %A Tuomilehto, Jaakko %A van Dam, Rob M %A Wilson, James G %A Wilson, James F %A Wolffenbuttel, Bruce H R %A Wong, Tien Yin %A Wu, Jer-Yuarn %A Yuan, Jian-Min %A Zonderman, Alan B %A Soranzo, Nicole %A Guo, Xiuqing %A Roberts, David J %A Florez, Jose C %A Sladek, Robert %A Dupuis, Josée %A Morris, Andrew P %A Tai, E-Shyong %A Selvin, Elizabeth %A Rotter, Jerome I %A Langenberg, Claudia %A Barroso, Inês %A Meigs, James B %K Diabetes Mellitus, Type 2 %K Genetic Variation %K Genome-Wide Association Study %K Glycated Hemoglobin A %K Humans %K Phenotype %K Risk %X

BACKGROUND: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes.

METHODS & FINDINGS: Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants.

CONCLUSIONS: As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.

%B PLoS Med %V 14 %P e1002383 %8 2017 Sep %G eng %N 9 %R 10.1371/journal.pmed.1002383