A genome-wide search for loci interacting with known prostate cancer risk-associated genetic variants

Center for Genetic Epidemiology and Prevention, Van Andel Research Institute, Grand Rapids, MI, USA.
Carcinogenesis (Impact Factor: 5.33). 01/2012; 33(3):598-603. DOI: 10.1093/carcin/bgr316
Source: PubMed


Genome-wide association studies (GWAS) have identified ∼30 single-nucleotide polymorphisms (SNPs) consistently associated with prostate cancer (PCa) risk. To test the hypothesis that other sequence variants in the genome may interact with those 32 known PCa risk-associated SNPs identified from GWAS to affect PCa risk, we performed a systematic evaluation among three existing PCa GWAS populations: CAncer of the Prostate in Sweden population, a Johns Hopkins Hospital population, and the Cancer Genetic Markers of Susceptibility population, with a total sample size of 4723 PCa cases and 4792 control subjects. Meta-analysis of the interaction term between each of those 32 SNPs and SNPs in the genome was performed in three PCa GWAS populations. The most significant interaction detected was between rs12418451 in MYEOV and rs784411 in CEP152, with a P(interaction) of 1.15 × 10(-7) in the meta-analysis. In addition, we emphasized two pairs of interactions with potential biological implication, including an interaction between rs7127900 near insulin-like growth factor-2 (IGF2)/IGF2AS and rs12628051 in TNRC6B, with a P(interaction) of 3.39 × 10(-6) and an interaction between rs7679763 near TET2 and rs290258 in SYK, with a P(interaction) of 1.49 × 10(-6). Those results show statistical evidence for novel loci interacting with known risk-associated SNPs to modify PCa risk. The interacting loci identified provide hints on the underlying molecular mechanism of the associations with PCa risk for the known risk-associated SNPs. Additional studies are warranted to further confirm the interaction effects detected in this study.

Full-text preview

Available from:
  • Source
    • "A summary of the 46 GWAS variants thus far identified and their location is outlined by Goh et al. [33]. Whilst GWAS have to date identified more than 40 disease susceptibility variants, these 40 variants only explain approximately 30% of an individual's heritable risk of developing prostate cancer [34]. Thus a significant proportion of the genetic contributors to prostate cancer remains to be discovered. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The genetic architecture underpinning prostate cancer is complex, polygenic and despite recent significant advances many questions remain. Advances in genetic technologies have greatly improved our ability to identify genetic variants associated with complex disease including prostate cancer. Genome-wide association studies (GWASs) and microarray gene expression studies have identified genetic associations with prostate cancer susceptibility and tumour development. The integrins feature prominently in both studies examining the underlying genetic susceptibility and mechanisms driving prostate tumour development. Integrins are cell adhesion molecules involved in extracellular and intracellular signalling and are imperative for tumour development, migration, and angiogenesis. Although several integrins have been implicated in tumour development, the roles of integrin α(2) and integrin α(6) are the focus of this paper as evidence is now emerging that these integrins are implicit in prostate cancer susceptibility, cancer stem cell biology, angiogenesis, cell migration, and metastases to bone and represent potential biomarkers and therapeutic targets. There currently exists an urgent need to develop tools that differentiate indolent from aggressive prostate cancers and predict how patients will respond to treatment. This paper outlines the evidence supporting the use of α(2) and α(6) integrins in clinical applications for tailored patient treatment.
    Full-text · Article · Jul 2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Centrosomes are the key regulating element of cell cycle progression. Aberrations in their functional mechanism leads to several cancer related disorders. Although genomic studies in the field of centrosome have been extensively carried out, with the lack of structural conformation, the proteomic analysis of pathological genetic mutation is still a challenging task. Several computational algorithms and high range force fields are used to design the 3D structure conformation of proteins, which has now become the leading platform for in-silico drug discovery approaches. Application of these highly efficient platforms in centrosomics studies will be a novel approach to develop an efficient drug therapy for the treatment of their dysfunction disorders.
    No preview · Article · Sep 2012 · Gene
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In order to assess whether gene expression variability could be influenced by several SNPs acting in cis, either through additive or more complex haplotype effects, a systematic genome-wide search for cis haplotype expression quantitative trait loci (eQTL) was conducted in a sample of 758 individuals, part of the Cardiogenics Transcriptomic Study, for which genome-wide monocyte expression and GWAS data were available. 19,805 RNA probes were assessed for cis haplotypic regulation through investigation of ∼2,1×10(9) haplotypic combinations. 2,650 probes demonstrated haplotypic p-values >10(4)-fold smaller than the best single SNP p-value. Replication of significant haplotype effects were tested for 412 probes for which SNPs (or proxies) that defined the detected haplotypes were available in the Gutenberg Health Study composed of 1,374 individuals. At the Bonferroni correction level of 1.2×10(-4) (∼0.05/412), 193 haplotypic signals replicated. 1000G imputation was then conducted, and 105 haplotypic signals still remained more informative than imputed SNPs. In-depth analysis of these 105 cis eQTL revealed that at 76 loci genetic associations were compatible with additive effects of several SNPs, while for the 29 remaining regions data could be compatible with a more complex haplotypic pattern. As 24 of the 105 cis eQTL have previously been reported to be disease-associated loci, this work highlights the need for conducting haplotype-based and 1000G imputed cis eQTL analysis before commencing functional studies at disease-associated loci.
    Full-text · Article · Jan 2013 · PLoS Genetics
Show more