Large-scale exploration of gene-gene interactions in prostate cancer using a multistage genome-wide association study

Design and quality control of large-scale two-sample Mendelian randomization studies

Background

Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors.

Methods

We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set.

Results

We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls.

Conclusions

In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

INTRODUCTION

Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC).

METHODS

Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers.

RESULTS

With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification.

CONCLUSIONS

Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

Co-amplification of genes in chromosome 8q24: a robust prognostic marker in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a leading cause of tumor-associated death worldwide, owing to its high 5-year postoperative recurrence rate and inter-individual heterogeneity. Thus, a prognostic model is urgently needed for patients with HCC. Several researches have reported that copy number amplification of the 8q24 chromosomal region is associated with low survival in many cancers. In the present work, we set out to construct a multi-gene model for prognostic prediction in HCC.

Methods

RNA sequencing and copy number variant data of tumor tissue samples of HCC from The Cancer Genome Atlas (n=328) were used to identify differentially expressed messenger RNAs of genes located on the chromosomal 8q24 region by the Wilcox test. Univariate Cox and Lasso-Cox regression analyses were carried out for the screening and construction of a prognostic multi-gene signature in The Cancer Genome Atlas cohort (n=119). The multi-gene signature was validated in a cohort from the International Cancer Genome Consortium (n=240). A nomogram for prognostic prediction was built, and the underpinning molecular mechanisms were studied by Gene Set Enrichment Analysis.

Results

We successfully established a 7-gene prognostic signature model to predict the prognosis of patients with HCC. Using the model, we divided individuals into high-risk and low-risk sets, which showed a significant difference in overall survival in the training dataset (HR =0.17, 95% CI: 0.1-0.28; P<0.001) and in the testing dataset (HR = 0.42, 95% CI: 0.23-0.74; P=0.002). Multivariate Cox regression analysis showed the signature to be an independent prognostic factor of HCC survival. A nomogram including the prognostic signature was constructed and showed a better predictive performance in short-term (1 and 3 years) than in long-term (5 years) survival. Furthermore, Gene Set Enrichment Analysis identified several pathways of significance, which may aid in explaining the underlying molecular mechanism.

Conclusions

Our 7-gene signature is a reliable prognostic marker for HCC, which may provide meaningful information for therapeutic customization and treatment-related decision making.

Comprehensive Analysis of Germline Variants in Mexican Patients with Hereditary Breast and Ovarian Cancer Susceptibility

Hereditary breast and ovarian cancer syndrome (HBOC) represents 5⁻10% of all patients with breast cancer and is associated with high-risk pathogenic alleles in BRCA1/2 genes, but only for 25% of cases. We aimed to find new pathogenic alleles in a panel of 143 cancer-predisposing genes in 300 Mexican cancer patients with suspicion of HBOC and 27 high-risk patients with a severe family history of cancer, using massive parallel sequencing. We found pathogenic variants in 23 genes, including BRCA1/2. In the group of cancer patients 15% (46/300) had a pathogenic variant; 11% (33/300) harbored variants with unknown clinical significance (VUS) and 74% (221/300) were negative. The high-risk group had 22% (6/27) of patients with pathogenic variants, 4% (1/27) had VUS and 74% (20/27) were negative. The most recurrent mutations were the Mexican founder deletion of exons 9-12 and the variant p.G228fs in BRCA1, each found in 5 of 17 patients with alterations in this gene. Rare VUS with potential impact at the protein level were found in 21 genes. Our results show for the first time in the Mexican population a higher contribution of pathogenic alleles in other susceptibility cancer genes (54%) than in BRCA1/2 (46%), highlighting the high locus heterogeneity of HBOC and the necessity of expanding genetic tests for this disease to include broader gene panels.

Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma

Recent technological advancements have permitted high-throughput measurement of the human genome, epigenome, metabolome, transcriptome, and proteome at the population level. We hypothesized that subsets of genes identified from omic studies might have closely related biological functions and thus might interact directly at the network level. Therefore, we conducted an integrative analysis of multi-omic datasets of non-small cell lung cancer (NSCLC) to search for association patterns beyond the genome and transcriptome. A large, complex, and robust gene network containing well-known lung cancer-related genes, including EGFR and TERT, was identified from combined gene lists for lung adenocarcinoma. Members of the hypoxia-inducible factor (HIF) gene family were at the center of this network. Subsequent sequencing of network hub genes within a subset of samples from the Transdisciplinary Research in Cancer of the Lung-International Lung Cancer Consortium (TRICL-ILCCO) consortium revealed a SNP (rs12614710) in EPAS1 associated with NSCLC that reached genome-wide significance (OR = 1.50; 95% CI: 1.31-1.72; p = 7.75 × 10^-9). Using imputed data, we found that this SNP remained significant in the entire TRICL-ILCCO consortium (p = .03). Additional functional studies are warranted to better understand interrelationships among genetic polymorphisms, DNA methylation status, and EPAS1 expression.

Dual tumor suppressing and promoting function of Notch1 signaling in human prostate cancer

Adenocarcinomas of the prostate arise as multifocal heterogeneous lesions as the likely result of genetic and epigenetic alterations and deranged cell-cell communication. Notch signaling is an important form of intercellular communication with a role in growth/differentiation control and tumorigenesis. Contrasting reports exist in the literature on the role of this pathway in prostate cancer (PCa) development. We show here that i) compared to normal prostate tissue, Notch1 expression is significantly reduced in a substantial fraction of human PCas while it is unaffected or even increased in others; ii) acute Notch activation both inhibits and induces process networks associated with prostatic neoplasms; iii) down-modulation of Notch1 expression and activity in immortalized normal prostate epithelial cells increases their proliferation potential, while increased Notch1 activity in PCa cells suppresses growth and tumorigenicity through a Smad3-dependent mechanism involving p21^WAF1/CIP1; iv) prostate cancer cells resistant to Notch growth inhibitory effects retain Notch1-induced upregulation of pro-oncogenic genes, like EPAS1 and CXCL6, also overexpressed in human PCas with high Notch1 levels. Taken together, these results reconcile conflicting data on the role of Notch1 in prostate cancer.

Association of Hypoxia-Inducible Factor-2 Alpha Gene Polymorphisms with the Risk of Hepatitis B Virus-Related Liver Disease in Guangxi Chinese: A Case-Control Study

OBJECTIVE

Hypoxia-inducible factor-2 alpha (HIF-2a) plays a major role in the progression of disease, although the role of HIF-2α gene polymorphisms in hepatitis B virus (HBV)-related diseases remains elusive. The aim of this study is to determine whether HIF-2a rs13419896 and rs6715787 single-nucleotide polymorphisms (SNPs) are associated with susceptibility to chronic hepatitis B (CHB), liver cirrhosis (LC), or hepatocellular carcinoma (HCC).

METHOD

A case-control study of 107 patients with CHB, 83 patients with LC, 234 patients with HCC, and 224 healthy control subjects was carried out, and the HIF-2a rs13419896 and rs6715787 SNPs were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).

RESULTS

No significant differences were observed in the genotype or allele frequency of two HIF-2a SNPs between the cases and controls (all p>0.05). However, in subgroup analysis by gender, the HIF-2a rs13419896 GA and AA genotypes were significantly associated with a risk of CHB (odds ratio [OR] = 3.565, 95% confidence interval [CI] = 1.123-11.314, p = 0.031 and OR = 12.506, 95% CI = 1.329-117.716, p = 0.027) in females, and the A allele of rs13419896 was associated with a risk of CHB (OR = 2.624, 95% CI = 1.244-5.537, p = 0.011) and LC (OR = 2.351, 95% CI = 1.002-5.518, p = 0.050) in females. The rs6715787 CG genotype polymorphism may contribute to a reduced risk of LC in the Guangxi Zhuang Chinese population (OR = 0.152, 95% CI = 0.028-0.807, p = 0.027), as determined via subgroup analysis by ethnicity. Moreover, binary logistic regression analyses that were adjusted by drinking status indicated that the AA genotype of rs13419896 may contribute to an increased risk of LC in the non-alcohol-drinking population (OR = 3.124, 95% CI = 1.091-8.947, p = 0.034). In haplotype analysis, GG haplotype was significantly associated with a reduced risk of LC (OR = 0.601, 95% CI = 0.419-0.862, p = 0.005).

CONCLUSIONS

The HIF-2a rs13419896 polymorphism is associated with an increased risk of CHB and LC in the Guangxi Chinese population, especially in females and in the non-alcohol-drinking population, while the HIF-2a gene rs6715787 polymorphism is associated with a decreased risk of LC in the Guangxi Zhuang population.

The A Allele at rs13419896 of EPAS1 Is Associated with Enhanced Expression and Poor Prognosis for Non-Small Cell Lung Cancer

Hypoxia-inducible factor-2α (HIF-2α, or EPAS1) is important for cancer progression, and is a putative biomarker for poor prognosis for non-small cell lung cancer (NSCLC). However, molecular mechanisms underlying the EPAS1 overexpression are not still fully understood. We explored a role of a single nucleotide polymorphism (SNP), rs13419896 located within intron 1 of the EPAS1 gene in regulation of its expression. Bioinformatic analyses suggested that a region including the rs13419896 SNP plays a role in regulation of the EPAS1 gene expression and the SNP alters the binding activity of transcription factors. In vitro analyses demonstrated that a fragment containing the SNP locus function as a regulatory region and that a fragment with A allele showed higher transactivation activity than one with G, especially in the presence of overexpressed c-Fos or c-Jun. Moreover, NSCLC patients with the A allele showed poorer prognosis than those with G at the SNP even after adjustment with various variables. In conclusion, the genetic polymorphism of the EPAS1 gene may lead to variation of its gene expression levels to drive progression of the cancer and serve as a prognostic marker for NSCLC.

Allele-specific imbalance mapping at human orthologs of mouse susceptibility to colon cancer (Scc) loci

Colorectal cancer (CRC) can be classified into different types. Chromosomal instable (CIN) colon cancers are thought to be the most common type of colon cancer. The risk of developing a CIN-related CRC is due in part to inherited risk factors. Genome-wide association studies have yielded over 40 single nucleotide polymorphisms (SNPs) associated with CRC risk, but these only account for a subset of risk alleles. Some of this missing heritability may be due to gene-gene interactions. We developed a strategy to identify interacting candidate genes/loci for CRC risk that utilizes both linkage and RNA-seq data from mouse models in combination with allele-specific imbalance (ASI) studies in human tumors. We applied our strategy to three previously identified CRC susceptibility loci in the mouse that show evidence of genetic interaction: Scc4, Scc5 and Scc13. 525 SNPs from genes showing differential expression in the mouse and/or a previous role in cancer from the literature were evaluated for allele-specific imbalance in 194 paired human normal/tumor DNAs from CIN-related CRCs. One hundred three SNPs showing suggestive evidence of ASI (31 variants with uncorrected p values < 0.05) were genotyped in a validation set of 296 paired DNAs. Two variants in SNX10 (SCC13) showed significant evidence of allelic selection after multiple comparisons testing. Future studies will evaluate the role of these variants in combination with interacting genetic partners in colon cancer risk in mouse and humans.

Integrative Analysis of Normal Long Intergenic Non-Coding RNAs in Prostate Cancer

Recently, large numbers of normal human tissues have been profiled for non-coding RNAs and more than fourteen thousand long intergenic non-coding RNAs (lincRNAs) are found expressed in normal human tissues. The functional roles of these normal lincRNAs (nlincRNAs) in the regulation of protein coding genes in normal and disease biology are yet to be established. Here, we have profiled two RNA-seq datasets including cancer and matched non-neoplastic tissues from 12 individuals from diverse demography for both coding genes and nlincRNAs. We find 130 nlincRNAs significantly regulated in cancer, with 127 regulated in the same direction in the two datasets. Interestingly, according to Illumina Body Map, significant numbers of these nlincRNAs display baseline null expression in normal prostate tissues but are specific to other tissues such as thyroid, kidney, liver and testis. A number of the regulated nlincRNAs share loci with coding genes, which are either co-regulated or oppositely regulated in all cancer samples studied here. For example, in all cancer samples i) the nlincRNA, TCONS_00029157, and a neighboring tumor suppressor factor, SIK1, are both down regulated; ii) several thyroid-specific nlincRNAs in the neighborhood of the thyroid-specific gene TPO, are both up-regulated; and iii) the TCONS_00010581, an isoform of HEIH, is down-regulated while the neighboring EZH2 gene is up-regulated in cancer. Several nlincRNAs from a prostate cancer associated chromosomal locus, 8q24, are up-regulated in cancer along with other known prostate cancer associated genes including PCAT-1, PVT1, and PCAT-92. We observe that there is significant bias towards up-regulation of nlincRNAs with as high as 118 out of 127 up-regulated in cancer, even though regulation of coding genes is skewed towards down-regulation. Considering that all reported cancer associated lincRNAs (clincRNAs) are biased towards up-regulation, we conclude that this bias may be functionally relevant.

Epithelial cancers in the post-genomic era: should we reconsider our lifestyle?

The age-related epithelial cancers of the breast, colorectum and prostate are the most prevalent and are increasing in our aging populations. Epithelial cells turnover rapidly and mutations naturally accumulate throughout life. Most epithelial cancers arise from this normal mutation rate. All elderly individuals will harbour many cells with the requisite mutations and most will develop occult neoplastic lesions. Although essential for initiation, these mutations are not sufficient for the progression of cancer to a life-threatening disease. This progression appears to be dependent on context: the tissue ecosystem within individuals and lifestyle exposures across populations of individuals. Together, this implies that the seeds may be plentiful but they only germinate in the right soil. The incidence of these cancers is much lower in Eastern countries but is increasing with Westernisation and increases more acutely in migrants to the West. A Western lifestyle is strongly associated with perturbed metabolism, as evidenced by the epidemics of obesity and diabetes: this may also provide the setting enabling the progression of epithelial cancers. Epidemiology has indicated that metabolic biomarkers are prospectively associated with cancer incidence and prognosis. Furthermore, within cancer research, there has been a rediscovery that a switch in cell metabolism is critical for cancer progression but this is set within the metabolic status of the host. The seed may only germinate if the soil is fertile. This perspective brings together the different avenues of investigation implicating the role that metabolism may play within the context of post-genomic concepts of cancer.

Lack of correlation between in silico projection of function and quantitative real-time PCR-determined gene expression levels in colon tissue

There are a number of in silico programs that use algorithms and external web sources to predict the effect of single nucleotide polymorphisms (SNPs). While many of these programs have been shown to predict accurately the effect of SNPs in functional areas of the gene, such as 5' upstream or coding regions, empiric research may be warranted to confirm the functional consequences of SNPs that are predicted to have little to no effect. We compared predictions from FASTSNP (Function Analysis and Selection Tool for Single Nucleotide Polymorphism) and F-SNP (Functional Single Nucleotide Polymorphism) with experimentally derived genotype-phenotype correlations to determine the accuracy of these programs in predicting SNP functionality. We used normal colon tissue to evaluate 24 TagSNPs within six genes. Two of 16 SNPs that were predicted to have no functional effect in FASTSNP were significantly associated with gene expression. Only one of the eight SNPs that were predicted to have a low to high effect was significantly associated with gene expression. While the two in silico programs that were used were similar in their results for the SNPs predicted by FASTSNP to have no effect, of SNPs with scores from low to high, there were three that received an F-SNP score below what is considered functionally significant. In silico programs can fail to identify functional SNPs, supporting a continuing role for empiric analysis of SNP function. Laboratory analysis is necessary to identify causal SNPs accurately, establish biological plausibility of the effect, and ultimately inform cancer prevention strategies.

Atypical rearrangement involving 3'-IGH@ and a breakpoint at least 400 Kb upstream of an intact MYC in a CLL patient with an apparently balanced t(8;14)(q24.1;q32) and negative MYC expression

The t(8;14)(q24.1;q32), the cytogenetic hallmark of Burkitt's lymphoma, is also found, but rarely, in cases of chronic lymphocytic leukemia (CLL). Such translocation typically results in a MYC-IGH@ fusion subsequently deregulating and overexpressing MYC on der 14q32. In CLL, atypical rearrangements resulting in its gain or loss, within or outside of IGH@ or MYC locus, have been reported, but their clinical significance remains uncertain. Herein, we report a 67 year-old male with complex cytogenetic findings of apparently balanced t(8;14) and unreported complex rearrangements of IGH@ and MYC loci. His clinical, morphological and immunophenotypic features were consistent with the diagnosis of CLL.Interphase FISH studies revealed deletions of 11q22.3 and 13q14.3, and an extra copy of IGH@, indicative of rearrangement. Karyotype analysis showed an apparently balanced t(8;14)(q24.1;q32). Sequential GPG-metaphase FISH studies revealed abnormal signal patterns: rearrangement of IGH break apart probe with the 5'-IGH@ on derivative 8q24.1 and the 3'-IGH@ retained on der 14q; absence of MYC break apart-specific signal on der 8q; and, the presence of unsplit 5'-MYC-3' break apart probe signals on der 14q. The breakpoint on 8q24.1 was found to be at least 400 Kb upstream of 5' of MYC. In addition, FISH studies revealed two abnormal clones; one with 13q14.3 deletion, and the other, with concurrent 11q deletion and atypical rearrangements. Chromosome microarray analysis (CMA) detected a 7.1 Mb deletion on 11q22.3-q23.3 including ATM, a finding consistent with FISH results. While no significant copy number gain or loss observed on chromosomes 8, 12 and 13, a 455 Kb microdeletion of uncertain clinical significance was detected on 14q32.33. Immunohistochemistry showed co-expression of CD19, CD5, and CD23, positive ZAP-70 expression and absence of MYC expression. Overall findings reveal an apparently balanced t(8;14) and atypical complex rearrangements involving 3'-IGH@ and a breakpoint at least 400 Kb upstream of MYC, resulting in the relocation of the intact 5'-MYC-3' from der 8q, and apposition to 3'-IGH@ at der 14q. This case report provides unique and additional cytogenetic data that may be of clinical significance in such a rare finding in CLL. It also highlights the utility of conventional and sequential metaphase FISH in understanding complex chromosome anomalies and their association with other clinical findings in patients with CLL. To the best of our knowledge, this is the first CLL reported case with such an atypical rearrangement in a patient with a negative MYC expression.

Genetic association of the KLK4 locus with risk of prostate cancer

The Kallikrein-related peptidase, KLK4, has been shown to be significantly overexpressed in prostate tumours in numerous studies and is suggested to be a potential biomarker for prostate cancer. KLK4 may also play a role in prostate cancer progression through its involvement in epithelial-mesenchymal transition, a more aggressive phenotype, and metastases to bone. It is well known that genetic variation has the potential to affect gene expression and/or various protein characteristics and hence we sought to investigate the possible role of single nucleotide polymorphisms (SNPs) in the KLK4 gene in prostate cancer. Assessment of 61 SNPs in the KLK4 locus (± 10 kb) in approximately 1300 prostate cancer cases and 1300 male controls for associations with prostate cancer risk and/or prostate tumour aggressiveness (Gleason score <7 versus ≥ 7) revealed 7 SNPs to be associated with a decreased risk of prostate cancer at the P(trend)<0.05 significance level. Three of these SNPs, rs268923, rs56112930 and the HapMap tagSNP rs7248321, are located several kb upstream of KLK4; rs1654551 encodes a non-synonymous serine to alanine substitution at position 22 of the long isoform of the KLK4 protein, and the remaining 3 risk-associated SNPs, rs1701927, rs1090649 and rs806019, are located downstream of KLK4 and are in high linkage disequilibrium with each other (r(2) ≥ 0.98). Our findings provide suggestive evidence of a role for genetic variation in the KLK4 locus in prostate cancer predisposition.

Polymorphisms on 8q24 are associated with lung cancer risk and survival in Han Chinese

Chromosome 8q24 is commonly amplified in many types of cancer, particularly lung cancer. Polymorphisms in this region are associated with risk of different cancers. To investigate the relationship between three single nucleotide polymorphisms (SNPs) (rs1447295, rs16901979 and rs6983267) on 8q24 and lung cancer risk, we conducted an association study in two Han Chinese populations: one population was from Zhejiang Province (576 case patients and 576 control subjects), whereas the other was from Fujian Province (576 case patients and 576 control subjects). We found that rs6983267 was significantly associated with an increased risk of lung cancer in both populations. Compared with the TT genotype, the GG genotype was associated with a significant 1.555-fold increased risk of lung cancer [95% confidence interval (CI) 1.218-1.986, P = 4.0×10(-4)]. This effect was more pronounced in never-smokers [odds ratio (OR) = 2.366, 95% CI 1.605-3.488, P = 1.4×10(-5)]. Analyses stratified by histology revealed that rs6983267 GG genotype was most associated with patients with other histological types (OR = 3.012, 95% CI 1.675-5.417, P = 2.3×10(-4)). The AA genotype of rs1447295 was associated with increased risk for adenocarcinoma compared with the CC genotype (OR = 2.260, 95% CI 1.174-4.353, P = 0.015). Furthermore, the GG genotype of rs6983267 was associated with worse survival in the Zhejiang population (hazard ratio (HR) = 1.646, 95% CI 1.099-2.464, P = 0.016). No association was observed for rs16901979. These results suggest that genetic variations on 8q24 may play significant roles in the development and progression of lung cancer.

Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers

PURPOSE

To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers.

METHODS

From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non-patient cases to patient cases (false-positive ratio) for various risk thresholds.

RESULTS

Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively.

CONCLUSION

Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.

Improved statistics for genome-wide interaction analysis

Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al.'s originally-proposed statistics, on account of the inflated error rate that can result.

Gene–gene interactions are a topic of great interest to geneticists carrying out studies of how genetic factors influence the development of common, complex diseases. Genes that interact may not only make important biological contributions to underlying disease processes, but also be more difficult to detect when using standard statistical methods in which we examine the effects of genetic factors one at a time. Recently a method was proposed by Wu and colleagues [1] for detecting pairwise interactions when carrying out genome-wide association studies (in which a large number of genetic variants across the genome are examined). Wu and colleagues carried out theoretical work and computer simulations that suggested their method outperformed other previously proposed approaches for detecting interactions. Here we show that, in fact, the method proposed by Wu and colleagues can result in an over-preponderence of false postive findings. We propose an adjusted version of their method that reduces the false positive rate while maintaining high power. We also propose a new method for detecting pairs of genetic effects that shows similarly high power but has some conceptual advantages over both Wu's method and also other previously proposed approaches.

Invited commentary: more surprises from a gene desert

Pleiotropy across the 8q24 region is perhaps the most intriguing of the genome-wide association findings relating to cancer. This region of chromosome 8 is a gene desert, far from any recognized genes. Guarrera et al., whose work is reported in this issue (Am J Epidemiol. 2012;175(6):479-487), took an epidemiologic approach to learn more about the 8q24 region. They capitalized on their ascertainment of other endpoints in members of the cohort at the Turin site of the European Prospective Investigation Into Cancer and Nutrition to investigate multiple outcomes for additional pleiotropic effects in the 8q24 region. Alternative design options might involve genotyping of more variants, incorporation of more cases, or use of a single control group close to the size of the most common case group. Their analytic methods reflect the uncertainty of the underlying biology. The findings sharpen the scientific question about how variation in the 8q24 region affects pathogenesis. The genome-wide association effort is possible because of the economy of scale afforded by extremely dense genotyping. Strict adherence to the hypothesis-driven approach would ignore information that is obtainable at a trivial cost. The genome-wide association strategy tests whether agnostic data-mining methods can advance knowledge alongside or even in place of the standard hypothesis-driven approach, which is the conventional scientific method children learn in kindergarten and onward, even through graduate school and beyond.

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

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.

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

Application of a novel score test for genetic association incorporating gene-gene interaction suggests functionality for prostate cancer susceptibility regions

AIMS

We introduce an innovative multilocus test for disease association. It is an extension of an existing score test that gains power over alternative methods by incorporating a parsimonious one-degree-of-freedom model for interaction. We use our method in applications designed to detect interactions that generate hypotheses about the functionality of prostate cancer (PRCA) susceptibility regions.

METHODS

Our proposed score test is designed to gain additional power through the use of a retrospective likelihood that exploits an assumption of independence between unlinked loci in the underlying population. Its performance is validated through simulation. The method is used in conditional scans with data from stage II of the Cancer Genetic Markers of Susceptibility PRCA genome-wide association study.

RESULTS

Our proposed method increases power to detect susceptibility loci in diverse settings. It identified two high-ranking, biologically interesting interactions: (1) rs748120 of NR2C2 and subregions of 8q24 that contain independent susceptibility loci specific to PRCA and (2) rs4810671 of SULF2 and both JAZF1 and HNF1B that are associated with PRCA and type 2 diabetes.

CONCLUSIONS

Our score test is a promising multilocus tool for genetic epidemiology. The results of our applications suggest functionality for poorly understood PRCA susceptibility regions. They motivate replication study.