A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes

Abstract

Candidate gene and genome-wide association studies (GWAS) represent two complementary approaches to uncovering genetic contributions to common diseases. We systematically reviewed the contributions of these approaches to our knowledge of genetic associations with cancer risk by analyzing the data in the Cancer Genome-wide Association and Meta Analyses database (Cancer GAMAdb). The database catalogs studies published since January 1, 2000, by study and cancer type. In all, we found that meta-analyses and pooled analyses of candidate genes reported 349 statistically significant associations and GWAS reported 269, for a total of 577 unique associations. Only 41 (7.1%) associations were reported in both candidate gene meta-analyses and GWAS, usually with similar effect sizes. When considering only noteworthy associations (defined as those with false-positive report probabilities r0.2) and accounting for indirect overlap, we found 202 associations, with 27 of those appearing in both meta-analyses and GWAS. Our findings suggest that meta-analyses of well-conducted candidate gene studies may continue to add to our understanding of the genetic associations in the post-GWAS era.

Common genetic variation in the germline influences where and how tumors develop

“…1 However, determining the precise biologic mechanism by which these loci lead to cancer susceptibility has proven challenging. More recently, there have been reports of specific germline haplotypes that increase the probability that a tumor acquires a specific mutation.…”

A method to customize population-specific arrays for genome-wide association testing

“…1,2 Multiple successful GWAS studies have been reported ranging from body size, metabolomics, and medically relevant traits (reviewed), 3,4 to hormones, 5 personality, 6 educational attainment, 7 and lifestyle characteristics. [8][9][10] The major technology behind these successes is the relatively cheap genotyping, in comparison with full genome sequencing, of DNA samples on genotyping arrays with 300 K-5 M single-nucleotide polymorphisms (SNPs), followed by imputation of the unmeasured SNPs.…”

“…The median imputation quality R 2 for MAF thresholds of 0.001, 0.01, 0.05, and 40.05 are 0.05, 0.28, 0.80, 0.99, respectively, for the 1000G imputed SNPs, with a similar quality for the autosomes and X chromosome, showing a good genome-wide coverage for association studies after imputation. 3,4 to hormones, 5 personality, 6 educational attainment, 7 and lifestyle characteristics. [8][9][10] The major technology behind these successes is the relatively cheap genotyping, in comparison with full genome sequencing, of DNA samples on genotyping arrays with 300 K-5 M single-nucleotide polymorphisms (SNPs), followed by imputation of the unmeasured SNPs.…”

Genetic Association Studies on Prostate Cancer

“…Therefore, among these candidate genes are those encoding proteins involved in androgen signaling, cell-cycle control mechanisms, major tumor-suppressors, or proto-oncogenes, as well as those involved in cellular adhesion or communication with surrounding cellular or matrix components of prostate epithelium [62,63]. This implies the need for previous knowledge when designing case-control studies using candidate gene approach [64].…”