Genome-wide association studies in cancer--current and future directions

Abstract

Genome-wide association studies (GWAS) have emerged as an important tool for discovering regions of the genome that harbor genetic variants that confer risk for different types of cancers. The success of GWAS in the last 3 years is due to the convergence of new technologies that can genotype hundreds of thousands of single-nucleotide polymorphism markers together with comprehensive annotation of genetic variation. This approach has provided the opportunity to scan across the genome in a sufficiently large set of cases and controls without a set of prior hypotheses in search of susceptibility alleles with low effect sizes. Generally, the susceptibility alleles discovered thus far are common, namely, with a frequency in one or more population of >10% and each allele confers a small contribution to the overall risk for the disease. For nearly all regions conclusively identified by GWAS, the per allele effect sizes estimated are <1.3. Consequently, the findings of GWAS underscore the complex nature of cancer and have focused attention on a subset of the genetic variants that comprise the genomic architecture of each type of cancer, which already can differ substantially by the number of regions associated with specific types of cancer. For instance, in prostate cancer, there could be >30 distinct regions harboring common susceptibility alleles identified by GWAS, whereas in lung cancer, a disease strongly driven by exposure to tobacco products, so far, only three regions have been conclusively established. To date, >85 regions have been conclusively associated in over a dozen different cancers, yet no more than five regions have been associated with more than one distinct cancer type. GWAS are an important discovery tool that require extensive follow-up to map each region, investigate the biological mechanism underpinning the association and eventually test the optimal markers for assessing risk for a disease or its outcome, such as in pharmacogenomics, the study of the effect of genetic variation on pharmacological interventions. The success of GWAS has opened new horizons for exploration and highlighted the complex genomic architecture of disease susceptibility.

Fig. 1.

Types of genetic variations in the human genome. Common types of genetic variations can be categorized into two major groups—those that involve single base changes (e.g. SNPs) and those that alter more than one base (e.g. microsatellites or structural variants).

Fig. 2.

Coverage of various genotyping platforms on HapMap II SNPs. The coverage of commercially available genotyping platforms in HapMap populations are plotted based on estimates of linkage disequilibrium using r², the correlation coefficient. A vertical bar depicts the cut off of an r² = 0.8, which is commonly used as a threshold to effectively tag monitored SNPs. The three HapMap populations of Phase II are labeled and the percentage estimated at the threshold is provided. (A): Coverage plot in Yoruban population (Ibadan, Nigeria), (B): coverage plot in Japanese (Tokyo, Japan) and Han Chinese (Bejing, China) and (C): coverage plot of US residents with northern and western European ancestry by the Centre d'Etude du Polymorphisme Humain (CEPH).

Fig. 3.

The relationship between the estimated effect size and the allele frequency of disease susceptibility locus. The majority of disease susceptibility loci identified by GWAS in different cancers have low effect size (per allele estimated effect size of 1.1–1.3).

Fig. 4.

Linkage disequilibrium pattern and cancer susceptibility loci indentified in 8q24 region. The 8q24 region harbors multiple cancer susceptibility loci identified by GWAS. The linkage disequilibrium heat map was drawn using HapMap I + II release 22 CEU data from 127 948 to 128 950 kb genomic region (reference build 36.3). The arrowheads indicate probable recombination hotspots according to the HapMap I + II. Five distinct regions have been associated with prostate cancer risk (regions 1–5). Region 3 is also conclusively associated with colorectal cancer and precancerous colorectal adenomas. Region B harbors a breast cancer susceptibility locus rs13281615, and BL indicate a bladder cancer susceptibility locus rs9642880, which is telomeric to the region 1, and ∼30 kb centromeric to the MYC oncogene.