Citations 3
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.
Genome-Wide Association Analysis of Ischemic Stroke in Young Adults
Cheng, Yu-Ching , O’Connell, Jeffrey R. , Dueker, Nicole , McArdle, Patrick F. , Sparks, Mary J., Shen, Jess, Laurie, Cathy C. , Doheny, Kimberly F. , Pugh, Elizabeth, Brott, Thomas G. , Brown, Robert D., Nalls, Michael A., Rich, Stephen S. , Worrall, Bradford B. , Anderson, Christopher D. , Biffi, Alessandro , Cortellini, Lynelle, Furie, Karen L., Rost, Natalia S. , Manolio, Teri A. , Kittner, Steven J. , Mitchell, Braxton D. , Cole, John W. , Stine, Oscar Colin , Nelson, Sarah C. , Ling, Hua , Meschia, James F. , Rosand, Jonathan M. , Rich, Stephen S , Mitchell, Braxton D
G3 Genes|Genomes|Genetics | December 14, 2011 | Volume: 1 | Issue: 6 | Pages: 505-514
Abstract Ischemic stroke (IS) is among the leading causes of death in Western countries. There is a significant genetic component to IS susceptibility, especially among young adults. To date, research to identify genetic loci predisposing to stroke has met only with limited success. We performed a genome-wide association (GWA) analysis of early-onset IS to identify potential stroke susceptibility loci. The GWA analysis was conducted by genotyping 1 million SNPs in a biracial population of 889 IS cases and 927 controls, ages 15–49 years. Genotypes were imputed using the HapMap3 reference panel to provide 1.4 million SNPs for analysis. Logistic regression models adjusting for age, recruitment stages, and population structure were used to determine the association of IS with individual SNPs. Although no single SNP reached genome-wide significance (P < 5 × 10−8), we identified two SNPs in chromosome 2q23.3, rs2304556 (in FMNL2; P = 1.2 × 10−7) and rs1986743 (in ARL6IP6; P = 2.7 × 10−7), strongly associated with early-onset stroke. These data suggest that a novel locus on human chromosome 2q23.3 may be associated with IS susceptibility among young adults.
Quality Control Procedures for Genome‐Wide Association Studies
Turner, Stephen , Armstrong, Loren L., Bradford, Yuki, Carlson, Christopher S. , Crawford, Dana C. , Crenshaw, Andrew, Doheny, Kimberly F. , Haines, Jonathan L. , Hayes, Geoffrey, Jarvik, Gail P. , Jiang, Lan , Li, Rongling, Manolio, Teri A. , Matsumoto, Martha, McCarty, Catherine A. , McDavid, Andrew , Mirel, Daniel B., Paschall, Justin, Pugh, Elizabeth, Rasmussen, Luke V. , Wilke, Russell A. , Zuvich, Rebecca L., Ritchie, Marylyn D. , Andrade, Mariza de , Kullo, Iftikhar J. , Ling, Hua , Jarvik, Gail P , Rasmussen, Luke V
Current Protocols in Human Genetics | January 13, 2011 | Volume: 68 | Issue: 1 | Pages: No page available
AbstractGenome‐wide association studies (GWAS) are being conducted at an unprecedented rate in population‐based cohorts and have increased our understanding of the pathophysiology of complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research. Curr. Protoc. Hum. Genet. 68:1.19.1‐1.19.18 © 2011 by John Wiley & Sons, Inc.