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.
The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers
Wang, Zhaoming , Schumacher, Fredrick R. , Gayther, Simon A. , Casey, Graham , Sellers, Thomas A. , Gruber, Stephen B., Dunning, Alison M. , Michailidou, Kyriaki , Fachal, Laura , Doheny, Kimberly F. , Li, Yafang , Xiao, Xiangjun , Romm, Jane, Pugh, Elizabeth, Coetzee, Gerhard A. , Hazelett, Dennis J. , Bojesen, Stig E. , Caga-Anan, Charlisse , Haiman, Christopher A. , Kamal, Ahsan , Luccarini, Craig , Tessier, Daniel C., Vincent, Daniel , Bacot, François, Berg, David Van Den , Nelson, Stefanie A. , Demetriades, Stephen, Goldgar, David E., Forman, Judith L., Conti, David V. , Bickeböller, Heike , Risch, Angela , Waldenberger, Mélanie , Brüske‐Hohlfeld, Irene, Hicks, Belynda , McGuffog, Lesley, Kuchenbaecker, Karoline , Soucy, Penny, Manz, Judith, Cunningham, Julie M. , Butterbach, Katja, Kote‐Jarai, Zsofia , Kraft, Peter , Lindström, Sara , Adams, Marcia, McKay, James , Phelan, Catherine M., Benlloch, Sara, Kelemen, Linda E., Brennan, Paul , Riggan, Marjorie J. , O’Mara, Tracy A. , Shen, Hongbing , Shi, Yongyong , Thompson, Deborah J. , Goodman, Marc T. , Nielsen, Sune F. , LaBoissière, Sylvie, Schmit, Stephanie L. , Shelford, Tameka, Edlund, Christopher K., Taylor, Jack A. , Field, John K. , Park, Sue K. , Offit, Kenneth , Thomassen, Mads , Schmutzler, Rita K., Ottini, Laura , Hung, Rayjean J., Olama, Ali Amin Al , Peters, Ulrike , Eeles, Rosalind A. , Seldin, Michael F., Gillanders, Elizabeth M. , Seminara, Daniela, Antoniou, Antonis C. , Chenevix‐Trench, Georgia , Simard, Jacques , Easton, Douglas F. , Durocher, Francine , Hung, Rayjean J. , Amos, Christopher I. , Dennis, Joe , Byun, Jinyoung , Hunter, David J. , Spurdle, Amanda B. , Couch, Fergus J. , Giles, Graham G. , Ling, Hua , Lee, Andrew , FitzGerald, Liesel M. , Berchuck, Andrew , Marchini, Jonathan , Pharoah, Paul D.P. , Chanock, Stephen Jacob , Hazelett, Dennis J , Eeles, Rosalind A , Van Den Berg, David J , Hung, Rayjean J , Wang, Zhaoming , O'Mara, Tracy A , Field, John K , Dunning, Alison M , Michailidou, Kyriaki , Taylor, Jack A , Kraft, Peter , Simard, Jacques , Chenevix-Trench, Georgia , Ottini, Laura , Conti, David V , Bojesen, Stig E , Risch, Angela , Bickeböller, Heike , Shen, Hongbing , Goodman, Marc T , Fachal, Laura
Cancer Epidemiology, Biomarkers & Prevention | October 3, 2016 | Volume: 26 | Issue: 1 | Pages: 126-135
Abstract Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126–35. ©2016 AACR.