Charles C. Chung

Current status of genome-wide association studies in cancer.

Genome-wide association studies in cancer have already identified over 150 regions associated with two dozen specific cancers. Already, a handful of multi-cancer susceptibility regions have been uncovered, providing new insights into perhaps common mechanisms of carcinogenesis. For each new susceptibility allele, investigators now face the arduous task of interrogating each region beginning with fine mapping prior to pursuing the biological basis for the direct association of one or more variants. It appears that there may be a significant number of common alleles that contribute to the heritability of a specific cancer. Since each region confers a small contribution to the risk for cancer, it is daunting to consider any single nucleotide polymorphism (SNP) as a clinical test. Since the complex genomic architecture of each cancer differs, additional genotyping and sequence analysis will be required to comprehensively catalog susceptibility alleles followed by the formidable task of understanding the interactions between genetic regions as well as the environment. It will be critical to assess the applicability of genetic tests in specific clinical settings, such as when to perform screening tests with calculable risks (e.g., biopsies or chemoprevention), before incorporating SNPs into clinical practice. To advance the current genomic observations to the clinical venue, new studies will need to be designed to validate the utility of known genetic variants in assessing risk for cancer as well as its outcomes.

Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants

Recent discoveries of hundreds of common susceptibility SNPs from genome-wide association studies provide a unique opportunity to examine population genetic models for complex traits. In this report, we investigate distributions of various population genetic parameters and their interrelationships using estimates of allele frequencies and effect-size parameters for about 400 susceptibility SNPs across a spectrum of qualitative and quantitative traits. We calibrate our analysis by statistical power for detection of SNPs to account for overrepresentation of variants with larger effect sizes in currently known SNPs that are expected due to statistical power for discovery. Across all qualitative disease traits, minor alleles conferred “risk” more often than “protection.” Across all traits, an inverse relationship existed between “regression effects” and allele frequencies. Both of these trends were remarkably strong for type I diabetes, a trait that is most likely to be influenced by selection, but were modest for other traits such as human height or late-onset diseases such as type II diabetes and cancers. Across all traits, the estimated effect-size distribution suggested the existence of increasingly large numbers of susceptibility SNPs with decreasingly small effects. For most traits, the set of SNPs with intermediate minor allele frequencies (5–20%) contained an unusually small number of susceptibility loci and explained a relatively small fraction of heritability compared with what would be expected from the distribution of SNPs in the general population. These trends could have several implications for future studies of common and uncommon variants.

Genome-wide association study identifies two susceptibility loci for osteosarcoma

Osteosarcoma is the most common primary bone malignancy of adolescents and young adults. To better understand the genetic etiology of osteosarcoma, we performed a multistage genome-wide association study consisting of 941 individuals with osteosarcoma (cases) and 3,291 cancer-free adult controls of European ancestry. Two loci achieved genome-wide significance: a locus in the GRM4 gene at 6p21.3 (encoding glutamate receptor metabotropic 4; rs1906953; P = 8.1 × 10−9) and a locus in the gene desert at 2p25.2 (rs7591996 and rs10208273; P = 1.0 × 10−8 and 2.9 × 10−7, respectively). These two loci warrant further exploration to uncover the biological mechanisms underlying susceptibility to osteosarcoma.

Meta-analysis identifies four new loci associated with testicular germ cell tumor

We conducted a meta-analysis to identify new susceptibility loci for testicular germ cell tumor (TGCT). In the discovery phase, we analyzed 931 affected individuals and 1,975 controls from 3 genome-wide association studies (GWAS). We conducted replication in 6 independent sample sets comprising 3,211 affected individuals and 7,591 controls. In the combined analysis, risk of TGCT was significantly associated with markers at four previously unreported loci: 4q22.2 in HPGDS (per-allele odds ratio (OR) = 1.19, 95% confidence interval (CI) = 1.12–1.26; P = 1.11 × 10−8), 7p22.3 in MAD1L1 (OR = 1.21, 95% CI = 1.14–1.29; P = 5.59 × 10−9), 16q22.3 in RFWD3 (OR = 1.26, 95% CI = 1.18–1.34; P = 5.15 × 10−12) and 17q22 (rs9905704: OR = 1.27, 95% CI = 1.18–1.33; P = 4.32 × 10−13 and rs7221274: OR = 1.20, 95% CI = 1.12–1.28; P = 4.04 × 10−9), a locus that includes TEX14, RAD51C and PPM1E. These new TGCT susceptibility loci contain biologically plausible genes encoding proteins important for male germ cell development, chromosomal segregation and the DNA damage response.

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

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.

A Subset-Based Approach Improves Power and Interpretation for the Combined Analysis of Genetic Association Studies of Heterogeneous Traits

Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.

Genome-wide association study identifies multiple loci associated with bladder cancer risk

Candidate gene and genome-wide association studies (GWAS) have identified 11 independent susceptibility loci associated with bladder cancer risk. To discover additional risk variants, we conducted a new GWAS of 2422 bladder cancer cases and 5751 controls, followed by a meta-analysis with two independently published bladder cancer GWAS, resulting in a combined analysis of 6911 cases and 11 814 controls of European descent. TaqMan genotyping of 13 promising single nucleotide polymorphisms with P < 1 × 10(-5) was pursued in a follow-up set of 801 cases and 1307 controls. Two new loci achieved genome-wide statistical significance: rs10936599 on 3q26.2 (P = 4.53 × 10(-9)) and rs907611 on 11p15.5 (P = 4.11 × 10(-8)). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 × 10(-7)) and rs4510656 on 6p22.3 (P = 6.98 × 10(-7)); these require further studies for confirmation. In conclusion, our study has identified new susceptibility alleles for bladder cancer risk that require fine-mapping and laboratory investigation, which could further understanding into the biological underpinnings of bladder carcinogenesis.

Testicular germ cell tumor susceptibility associated with the UCK2 locus on chromosome 1q23

Genome-wide association studies (GWASs) have identified multiple common genetic variants associated with an increased risk of testicular germ cell tumors (TGCTs). A previous GWAS reported a possible TGCT susceptibility locus on chromosome 1q23 in the UCK2 gene, but failed to reach genome-wide significance following replication. We interrogated this region by conducting a meta-analysis of two independent GWASs including a total of 940 TGCT cases and 1559 controls for 122 single-nucleotide polymorphisms (SNPs) on chromosome 1q23 and followed up the most significant SNPs in an additional 2202 TGCT cases and 2386 controls from four case-control studies. We observed genome-wide significant associations for several UCK2 markers, the most significant of which was for rs3790665 (PCombined = 6.0 × 10(-9)). Additional support is provided from an independent familial study of TGCT where a significant over-transmission for rs3790665 with TGCT risk was observed (PFBAT = 2.3 × 10(-3)). Here, we provide substantial evidence for the association between UCK2 genetic variation and TGCT risk.

Improved imputation of common and uncommon SNPs with a new reference set

6 volume 44 | number 1 | january 2012 | nature genetics Statistical imputation of genotype data is an important statistical technique that uses patterns of linkage disequilibrium observed in a reference set of haplotypes to computationally predict genetic variants in silico1. Currently, the most popular reference sets are the publicly available International HapMap2 and 1000 Genomes data sets3. Although these resources are valuable for imputing a sizeable fraction of common SNPs, they may not be optimal for imputing data for the next generation of genome-wide association studies (GWAS) and SNP arrays, which explore a fraction of uncommon variants. We have built a new resource for the imputation of SNPs for existing and future GWAS, known as the Division of Cancer Epidemiology and Genetics (DCEG) Reference Set. The data set has genotypes for cancer-free individuals, including 728 of European ancestry from three large prospectively sampled studies4–6, 98 AfricanAmerican individuals from the Prostate, Lung, Colon and Ovary Cancer Screening Trial (PLCO), 74 Chinese individuals from a clinical trial in Shanxi, China (SHNX)7 and 349 individuals from the HapMap Project (Table 1). The final harmonized data set includes 2.8 million autosomal polymorphic SNPs for 1,249 individuals after rigorous quality control metrics were applied (see Supplementary Methods and Supplementary Tables 1 and 2). We compared the imputation performance of the DCEG Reference Set to that of the International HapMap and 1000 Genomes reference sets, which are available from the IMPUTE2 website (see URLs). We assessed imputation accuracy by taking directly genotyped SNP data from the DCEG Reference Set and masking subsets to simulate data from two low-cost commercial genotyping arrays commonly used in GWAS studies (Illumina Human Hap660 and Human OmniExpress). Probabilistic genotypes were imputed using both IMPUTE2 (ref. 8) and BEAGLE9 software and compared with the masked genotyped SNPs. Accuracy was measured using the squared Pearson correlation coefficient (R2) under an allelic dosage model (see Supplementary Methods). Using the new reference set, we observed higher imputation accuracy than that achieved with the combination of 1000 Genomes and HapMap data across a spectrum of minor allele frequencies (MAFs) (Fig. 1). Accuracy in individuals of European ancestry imputed from Hap660 or OmniExpress arrays, measured by the proportion of variants imputed with R2 > 0.8, improved by 34%, 23% and 12% for variants with MAFs of 3%, 5% and 10%, respectively. We estimated the difference in power to detect associations in GWAS design between an imputed data set and one composed of directly genotyped SNPs with the DCEG Reference Set by adapting a model developed by Park et al.10. When using Hap660 data for imputation, we observed detection rates of 92.9% when imputing with the DCEG Reference Set and 84.7% with the 1000 Genomes and HapMap reference sets relative to the detection rate attained with directly genotyped SNPs; for OmniExpress data, we observed detection rates of 93.9% and 86.2% for these reference sets, respectively. Because imputation accuracy depends on the similarity of haplotypes between reference and study populations, we examined an extreme scenario in which we used a reference population from Finland (Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, ATBC) to impute genotypes using OmniExpress data from a US population of European ancestry (PLCO) (Supplementary Fig. 1). For common SNPs, there was minimal loss of imputation accuracy when using the reference population from Finland relative to the US-based Cancer Prevention Study II (CPSII) or a combined population of HapMap individuals from Utah of Northern and Western European ancestry (CEU) and from northern Italy (Toscans in Italy, TSI). This result suggests that, for common variants, a reference set of sufficient size can adequately predict common SNPs when there is a discrepancy in population ancestry, provided that comparable haplotypes are sufficiently represented. This observation should enable investigators to proceed more confidently with imputation without additional genotyping in related but not identical populations. Improved imputation of common and uncommon snps with a new reference set

Replication study of SNP associations for colorectal cancer in Hong Kong Chinese

Background:Recent genome-wide association studies of colorectal cancer (CRC) have identified common single-nucleotide polymorphisms (SNPs) mapping to 10 independent loci that confer modest increased risk. These studies have been conducted in European populations and it is unclear whether these observations generalise to populations with different ethnicities and rates of CRC.Methods:An association study was performed on 892 CRC cases and 890 controls recruited from the Hong Kong Chinese population, genotyping 32 SNPs, which were either associated with CRC in previous studies or are in close proximity to previously reported risk SNPs.Results:Twelve of the SNPs showed evidence of an association. The strongest associations were provided by rs10795668 on 10p14, rs4779584 on 15q14 and rs12953717 on 18q21.2. There was significant linear association between CRC risk and the number of independent risk variants possessed by an individual (P=2.29 × 10−5).Conclusion:These results indicate that some previously reported SNP associations also impact on CRC risk in the Chinese population. Possible reasons for failure of replication for some loci include inadequate study power, differences in allele frequency, linkage disequilibrium structure or effect size between populations. Our results suggest that many associations for CRC are likely to generalise across populations.