The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers

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.

Figure 1

Twenty risk regions analyzed as part of the GAME-ON OncoArray, including 17 pleiotropic regions conferring risks to two or more common cancers (breast, colorectal, lung, ovarian or prostate cancers). Panel (a) – Circos plot illustrating the 24 different regions ordered by chromosome and cytoband. The index SNP(s) at each locus are color coded by cancer type, (b) integration of correlated risk SNPs at each locus with regional catalogues of regulatory marks for related tissue types for common cancers to identify SNPs intersecting tissue specific regulatory targets. Publically available genome wide regulatory profiling data were available for the HMEC mammary epithelial cells (specific to breast cancer), LNCap cancer cells (for prostate cancer), CaOV3 cancers (for ovarian cancer), SAEC cells (for lung cancer). The first column indicates a risk-associated SNP that intersects a regulatory mark, color coded by cancer type. For other columns, colored squares represent an intersection between a risk associated SNP and a regulatory mark, and in which tissue type, indicating which marks are common across tissues and which are tissue specific. White squares indicate the most strongly associated SNPs (index SNP) in a region and a dot within the square indicates an intersection between a regulatory mark and an index. The position of each regulatory mark is indicated relative to hg19 coordinates. In panel b, only SNPs with regulatory marks are shown, thus excluding 24 of the regional associations shown in panel a.

Figure 2

Failure rates (<95% of SNPs called) for 211,594 samples genotyped by CIDR across multiple tissue types. The overall failure rate was 2.97%.

Figure 3

Correlation between replicate Hapmap samples genotyped at Cambridge versus the Center for Inherited Disease Research. Samples 1–8 are of European origin while samples 9–14 are Asian or African. There are multiple replicates of samples 1, 4, 5, 6 and 8. Samples 1–8 are European, 9–10 are Chinese, sample 11 is Japanese and samples 12–14 are Yoruban.