Amos Christopher I, Dennis Joe, Wang Zhaoming, Byun Jinyoung, Schumacher Fredrick R, Gayther Simon A, Casey Graham, Hunter David J, Sellers Thomas A, Gruber Stephen B, Dunning Alison M, Michailidou Kyriaki, Fachal Laura, Doheny Kimberly, Spurdle Amanda B, 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, Vincent Daniel, Bacot François, Van Den Berg David J, Nelson Stefanie, Demetriades Stephen, Goldgar David E, Couch Fergus J, Forman Judith L, Giles Graham G, Conti David V, Bickeböller Heike, Risch Angela, Waldenberger Melanie, Brüske-Hohlfeld Irene, Hicks Belynda D, Ling Hua, McGuffog Lesley, Lee Andrew, Kuchenbaecker Karoline, Soucy Penny, Manz Judith, Cunningham Julie M, Butterbach Katja, Kote-Jarai Zsofia, Kraft Peter, FitzGerald Liesel, Lindström Sara, Adams Marcia, McKay James D, Phelan Catherine M, Benlloch Sara, Kelemen Linda E, Brennan Paul, Riggan Marjorie, O'Mara Tracy A, Shen Hongbing, Shi Yongyong, Thompson Deborah J, Goodman Marc T, Nielsen Sune F, Berchuck Andrew, Laboissiere Sylvie, Schmit Stephanie L, Shelford Tameka, Edlund Christopher K, Taylor Jack A, Field John K, Park Sue K, Offit Kenneth, Thomassen Mads, Schmutzler Rita, Ottini Laura, Hung Rayjean J, Marchini Jonathan, Amin Al Olama Ali, Peters Ulrike, Eeles Rosalind A, Seldin Michael F, Gillanders Elizabeth, Seminara Daniela, Antoniou Antonis C, Pharoah Paul D P, Chenevix-Trench Georgia, Chanock Stephen J, Simard Jacques, Easton Douglas F
Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom.
Cancer Epidemiol Biomarkers Prev. 2017 Jan;26(1):126-135. doi: 10.1158/1055-9965.EPI-16-0106. Epub 2016 Oct 3.
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.
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.
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.
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.
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.
常见癌症通过一个多步骤过程发展，通常包括遗传易感性。多个机构之间的合作以及来自多个来源的资金支持，促成了一种低成本基因分型微阵列——肿瘤基因芯片（OncoArray）的开发。该阵列包括一个全基因组主干，由23万个单核苷酸多态性（SNP）组成，这些SNP标记了大多数常见的遗传变异，同时对已知的易感区域进行了密集定位，包括测序实验中的罕见变异、药物遗传学标记以及癌症相关性状。
肿瘤基因芯片可使用Illumina开发的一项新技术进行基因分型，以促进高效基因分型。该联盟开发了用于选择研究SNP、标记质量控制和血统分析的标准方法。在选定的位点对该阵列进行基因分型，并使用预先指定的重复样本，以评估各中心之间以及按种族背景的基因分型准确性。
肿瘤基因芯片联盟对447,705个样本进行了基因分型。共有494,763个SNP通过了质量控制步骤，样本成功率为97%。参与的位点使用一组共同的标记和基于主成分分析的评分算法进行血统分析。
这些分析结果将使研究人员能够识别新的易感基因座，对与单一或多种癌症相关的新的或已知基因座进行精细定位，评估癌症病因的重叠程度以及已确定的疾病特异性风险基因座的多效性影响，并联合对遗传、环境和生活方式相关暴露进行建模。
正在进行的分析将为多种癌症的病因和风险评估提供线索。《癌症流行病学、生物标志物与预防》；26(1)；126 - 135。©2016美国癌症研究协会。
