Abstract 4737: Genetic determinants associated with multiple myeloma risk in three large population sets
Authors: Gareth J. Morgan, David C. Johnson, Sophie I. Berndt, Sophie L. Corthals, Brian A. Walker, Fiona M. Ross, Nicholas J. Dickens, Faith E. Davies, Brian M. Durie, Brian Van Ness, Pieter Sonneveld, Dalsu Baris
Journal: Cancer Research (2010)

Abstract Classic epidemiological approaches aimed at identifying environmental exposures associated with aetiology of Multiple Myeloma (MM) have failed to identify a common cause. The majority of the studies aimed at identifying genetic risk in MM have been underpowered and limited in coverage. We have taken a candidate gene approach by assaying 3,404 single nucleotide polymorphism (SNPs) selected in approximately 983 candidate genes on a “Bank on a Cure” (BOAC) Targeted Genotyping assay, focusing on coding SNPs and SNPs in regulatory regions. SNPs were genotyped using DNA extracted from peripheral blood. 2595 presenting MM cases were derived from clinical trials held within the UK (1228), US (697) and the Netherlands (670). Genotype data were available from large population control datasets, which were used to examine 1809 SNPs for association with MM risk. The control population sets consisted of the UK Wellcome Trust Case-Control Consortium 2 (WTCCC2) study with 3,000 individuals from the 1958 British Birth Cohort and the UK Blood Service collections, genotyped on both the Illumina 1.2M Duo (Human1-2M-DuoCustom_v1) and the Affymetrix SNP 6.0 array; 2350 US Caucasian controls from the Nurses’ Health Study (NHS), genotyped on the Illumina 550K chip, and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), genotyped on Illumina 317K plus 240K; and 5974 Dutch &amp;gt;55yrs old population controls from the Erasmus Rotterdam Health for the Elderly (ERGO) study, genotyped on the Illumina 550K array. We have also used fluorescence in situ hybridization (FISH) status for 702 of the UK cases to perform a subset analysis for hyperdiploidy and IgH translocations, two of the major myeloma pathogenic subgroups. Quality control measures were performed on the datasets to protect against artificial effects, induced by population stratification, cross platform genotyping and low genotyping quality (95% call rate and HWE (p&amp;lt;10−5)). In the uni-variant analysis across the three population sets we found a number of SNPs associated with greater susceptibility to MM with p&amp;lt;10−6. These SNPs included IL6 (−174 C/G), AURKA Phe31Ile, DDX18 Thr94Ser and MYEOV Val159Ala. We examined weaker MM associated regulatory SNPs and their relationship with expression in a subset of 254 UK cases using data derived from the Affymetrix 133U+2 expression array. We also performed a semi-exhaustive search of pair-wise interactions using epistasis analysis within PLINK. We will present the results of this integrative approach of associating Myeloma risk with inherited genetic variation, with findings from the validation of these associations across three large datasets Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4737.