Citations 2
Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities
Armstrong, Nicola J. , Mather, Karen A. , Sargurupremraj, Muralidharan , Knol, Maria J. , Malik, Rainer, Satizábal, Claudia L. , Yanek, Lisa R., Wen, Wei , Guðnason, Vilmundur , Dueker, Nicole , Elliott, Lloyd T. , Hofer, Edith, Bis, Joshua C. , Jahanshad, Neda , Li, Shuo , Logue, Mark, Scholz, Markus , Smith, Albert V. , Trompet, Stella , Alfaro‐Almagro, Fidel , Ames, David, Amin, Najaf , Beiser, Alexa S., Brodaty, Henry , Fennema‐Notestine, Christine , Gampawar, Piyush , Gottesman, Rebecca, Griffanti, Ludovica , Kral, Brian G. , Lampe, Leonie, Liewald, David C. , Maillard, Pauline , Marchini, Jonathan, Bastin, Mark E., Pirpamer, Lukas , Roshchupkin, Gennady V. , Stott, David J., Thalamuthu, Anbupalam , Tzourio, Christophe , Grond, Jeroen van der , Vernooij, Meike W. , Witte, A. Veronica , Wright, Margaret J. , Yang, Qiong, Siggurdsson, Siggi, Psaty, Bruce M., Villringer, Arno , Schmidt, Helena, Duijn, Cornelia M. van , Jukema, J. Wouter , Dichgans, Martin , Sacco, Ralph L., Kremen, William S., Becker, Lewis C. , Thompson, Paul M., Mosley, Thomas H. , Wardlaw, Joanna M. , Ikram, M. Arfan , Adams, Hieab H.H. , Seshadri, Sudha , Sachdev, Perminder S. , Smith, Stephen M. , Launer, L. J., Longstreth, W.T. , DeCarli, Charles , Schmidt, Reinhold, Fornage, Myriam , Debette, Stéphanie , DeCarli, Charles , Dichgans, Martin , Wardlaw, Joanna Marguerite , Thalamuthu, Anbupalam , Wen, Wei , Adams, Hieab H H , Satizabal, Claudia L , Seshadri, Sudha , Brodaty, Henry , Sachdev, Perminder S , Wright, Margaret J , Tzourio, Christophe , van Duijn, Cornelia M , Debette, Stephanie , Smith, Albert V , Maillard, Pauline , Hofer, Edith , Luciano, Michelle , Vojinović, Dina , Xia, Rui , Amouyel, Philippe , Deary, Ian J. , Jack, Clifford R. , Jenkinson, Mark , Jiang, Jiyang , Kwok, John B. , Marchini, Jonathan , Mazoyer, Bernard , Romero, José R. , Schofield, Peter R. , Schroeter, Matthias L , Trollor, Julian N , Morris, Zoë , Psaty, Bruce M. , Håberg, Asta K , Wright, Clinton B. , Thompson, Paul M. , Nyquist, Paul
Stroke | June 10, 2020 | Volume: 51 | Issue: 7 | Pages: 2111-2121
Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
A Bivariate Genome-Wide Approach to Metabolic Syndrome
Kraja, Aldi T., Vaidya, Dhananjay , Pankow, James S. , Goodarzi, Mark O., Assimes, Themistocles L. , Sovio, Ulla , Mathias, Rasika A. , Sun, Yan V. , Franceschini, Nora , Absher, Devin , Zhang, Qunyuan, Feitosa, Mary F. , Glazer, Nicole L. , Hartikainen, Anna-Liisa, Knowles, Joshua W. , Iribarren, Carlos , Kral, Brian G. , Yanek, Lisa R. , O’Reilly, Paul F. , McCarthy, Mark I. , Jaquish, Cashell E. , Couper, David , Chakravarti, Aravinda , Becker, Lewis C. , Province, Michael A. , Boerwinkle, Eric , Quertermous, Thomas , Palotie, L, Järvelin, Marjo‐Riitta , Becker, Diane M. , Kardia, Sharon L. R., Rotter, Jerome I. , Chen, Yii-Der Ida , Borecki, Ingrid B. , Goodarzi, Mark O. , Kullo, Iftikhar J. , Guo, Li , Haritunians, Talin , Hartikainen, Anna‐Liisa, North, Kari E. , Psaty, Bruce M. , McCarthy, Mark I , Assimes, Themistocles L , Rotter, Jerome I , Mathias, Rasika A , Pankow, James S , Couper, David , Province, Mike
Diabetes | March 9, 2011 | Volume: 60 | Issue: 4 | Pages: 1329-1339
OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.