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Deucravacitinib, a novel, selective inhibitor of TYK2 is currently under review at the FDA and EMA for treatment of moderate-to-severe plaque psoriasis. It is unclear whether recent safety concerns (ie, elevated rates of lung cancer and lymphoma) related to similar medications (ie, other JAK inhibitors) are shared with this novel TYK2 inhibitor. We used a partial loss-of-function variant in TYK2 (rs34536443), previously shown to protect against psoriasis and other autoimmune diseases, to evaluate the potential effect of therapeutic TYK2 inhibition on risk of lung cancer and non-Hodgkin lymphoma. Summary genetic association data on lung cancer risk were obtained from a GWAS meta-analysis of 29 266 cases and 56 450 controls in the Integrative Analysis of Lung Cancer Risk and Aetiology (INTEGRAL) consortium. Summary genetic association data on non-Hodgkin lymphoma risk were obtained from a GWAS meta-analysis of 8489 cases and 374 506 controls in the UK Biobank and InterLymph consortium. In the primary analysis, each copy of the minor allele of rs34536443, representing partial TYK2 inhibition, was associated with an increased risk of lung cancer (OR 1.15, 95% CI 1.09-1.23, P = 2.29 × 10 -6 ) and non-Hodgkin lymphoma (OR 1.18, 95% CI 1.05-1.33, P = 5.25 × 10 -3 ). Our analyses using an established partial loss-of-function mutation to mimic TYK2 inhibition provide genetic evidence that therapeutic TYK2 inhibition may increase risk of lung cancer and non-Hodgkin lymphoma. These findings, consistent with recent reports from postmarketing trials of similar JAK inhibitors, could have important implications for future safety assessment of deucravacitinib and other TYK2 inhibitors in development., (© 2022 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Polycyclic aromatic hydrocarbons (PAH) and tobacco-specific nitrosamines (TSNA) metabolism-related genes play an important role in the development of cancers. We assessed the associations of genetic variants in genes involved in the metabolism of PAHs and TSNA with risk of squamous cell carcinoma of the head and neck (SCCHN) in European populations using two published genome-wide association study datasets. In the single-locus analysis, we identified two SNPs (rs145533669 and rs35246205) in CYP2B6 to be associated with risk of SCCHN (P = 1.57 × 10 -4 and .004, respectively), two SNPs (EPHX1 rs117522494 and CYP2B6 rs145533669) to be associated with risk of oropharyngeal cancer (P = .001 and .004, respectively), and one SNP (rs4359199 in HSD17B12) to be associated with risk of oral cancer (P = .006). A significant interaction effect was found between rs4359199 and drinking status on risks of SCCHN and oropharyngeal cancer (P < .05). eQTL and sQTL analyzes revealed that two SNPs (CYP2B6 rs35246205 and HSD17B12 rs4359199) were correlated with alternative splicing or mRNA expression levels of the corresponding genes in liver cells (P < .05 for both). In silico functional annotation suggested that these two SNPs may regulate mRNA expression by affecting the binding of transcription factors. Results from phenome-wide association studies presented significant associations between these genes and risks of other cancers, smoking behavior and alcohol dependence (P < .05). Thus, our study provided some insight into the underlying genetic mechanism of head and neck cancer, which warrants future functional validation., (© 2022 UICC.)

At the time of cancer diagnosis, body mass index (BMI) is inversely correlated with lung cancer risk, which may reflect reverse causality and confounding due to smoking behavior. We used two-sample univariable and multivariable Mendelian randomization (MR) to estimate causal relationships of BMI and smoking behaviors on lung cancer and histological subtypes based on an aggregated genome-wide association studies (GWASs) analysis of lung cancer in 29 266 cases and 56 450 controls. We observed a positive causal effect for high BMI on occurrence of small-cell lung cancer (odds ratio (OR) = 1.60, 95% confidence interval (CI) = 1.24-2.06, P = 2.70 × 10 -4 ). After adjustment of smoking behaviors using multivariable Mendelian randomization (MVMR), a direct causal effect on small cell lung cancer (OR MVMR = 1.28, 95% CI = 1.06-1.55, P MVMR = .011), and an inverse effect on lung adenocarcinoma (OR MVMR = 0.86, 95% CI = 0.77-0.96, P MVMR = .008) were observed. A weak increased risk of lung squamous cell carcinoma was observed for higher BMI in univariable Mendelian randomization (UVMR) analysis (OR UVMR = 1.19, 95% CI = 1.01-1.40, P UVMR = .036), but this effect disappeared after adjustment of smoking (OR MVMR = 1.02, 95% CI = 0.90-1.16, P MVMR = .746). These results highlight the histology-specific impact of BMI on lung carcinogenesis and imply mediator role of smoking behaviors in the association between BMI and lung cancer., (© 2020 UICC.)

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies., (© 2020 Wiley Periodicals LLC.)

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE., (© 2020 Wiley Periodicals LLC.)

We hypothesized that human genes differ by their sensitivity to ultraviolet (UV) exposure. We used somatic mutations detected by genome-wide screens in melanoma and reported in the Catalog Of Somatic Mutations In Cancer. As a measure of UV sensitivity, we used the number of silent mutations generated by C>T transitions in pyrimidine dimers of a given transcript divided by the number of potential sites for this type of mutations in the transcript. We found that human genes varied by UV sensitivity by two orders of magnitude. We noted that the melanoma-associated tumor suppressor gene CDKN2A was among the top five most UV-sensitive genes in the human genome. Melanoma driver genes have a higher UV-sensitivity compared with other genes in the human genome. The difference was more prominent for tumor suppressors compared with oncogene. The results of this study suggest that differential sensitivity of human transcripts to UV light may explain melanoma specificity of some driver genes. Practical significance of the study relates to the fact that differences in UV sensitivity among human genes need to be taken into consideration whereas predicting melanoma-associated genes by the number of somatic mutations detected in a given gene., (© 2020 Wiley Periodicals LLC.)

A few single-nucleotide polymorphisms (SNPs) have been identified to be associated with cutaneous melanoma (CM) survival through genome-wide association studies, but stringent multiple testing corrections required for the hypothesis-free testing may have masked some true associations. Using a hypothesis-driven analysis approach, we sought to evaluate associations between SNPs in ketone body metabolic pathway genes and CM survival. We comprehensively assessed associations between 4196 (538 genotyped and 3658 imputed) common SNPs in 44 ketone body metabolic pathway genes and CM survival, using a dataset of 858 patients of a case-control study from The University of Texas M.D. Anderson Cancer Center as the discovery set and another dataset of 409 patients from the Nurses' Health Study and the Health Professionals Follow-up Study as the replication set. There were 95/858 (11.1%) and 48/409 (11.7%) patients who died of CM, respectively. We identified two independent SNPs (ie, PDSS1 rs12254548 G>C and SLC16A6 rs71387392 G>A) that were associated with CM survival, with allelic hazards ratios of 0.58 (95% confidence interval [CI] = 0.44-0.76, P = 9.00 × 10 -5 ) and 1.98 (95% CI = 1.34-2.94, P = 6.30 × 10 -4 ), respectively. Additionally, associations between genotypes of the SNPs and messenger RNA expression levels of their corresponding genes support the biologic plausibility of a role for these two variants in CM tumor progression and survival. Once validated by other larger studies, PDSS1 rs12254548 and SLC16A6 rs71387392 may be valuable biomarkers for CM survival., (© 2020 Wiley Periodicals, Inc.)

Genome-wide association studies (GWAS) have identified 45 susceptibility loci associated with lung cancer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant genetic polymorphisms in the human genome. INDELs are highly associated with multiple human diseases, including lung cancer. However, limited studies with large-scale samples have been available to systematically evaluate the effects of INDELs on lung cancer risk. Here, we performed a large-scale meta-analysis to evaluate INDELs and their risk for lung cancer in 23,202 cases and 19,048 controls. Functional annotations were performed to further explore the potential function of lung cancer risk INDELs. Conditional analysis was used to clarify the relationship between INDELs and SNPs. Four new risk loci were identified in genome-wide INDEL analysis (1p13.2: rs5777156, Insertion, OR = 0.92, p = 9.10 × 10 -8 ; 4q28.2: rs58404727, Deletion, OR = 1.19, p = 5.25 × 10 -7 ; 12p13.31: rs71450133, Deletion, OR = 1.09, p = 8.83 × 10 -7 ; and 14q22.3: rs34057993, Deletion, OR = 0.90, p = 7.64 × 10 -8 ). The eQTL analysis and functional annotation suggested that INDELs might affect lung cancer susceptibility by regulating the expression of target genes. After conducting conditional analysis on potential causal SNPs, the INDELs in the new loci were still nominally significant. Our findings indicate that INDELs could be potentially functional genetic variants for lung cancer risk. Further functional experiments are needed to better understand INDEL mechanisms in carcinogenesis., (© 2019 UICC.)

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (p TWAS = 1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (p TWAS = 3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (p TWAS = 6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk., (© 2019 UICC.)

Glioma incidence is highest in non-Hispanic Whites, and to date, glioma genome-wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case-Control Study and GliomaSE Case-Control Study previously estimated to have <80% EA, or self-identify as non-White. We estimated global and local ancestry using fastStructure and RFMix, respectively, using 1,000 genomes project reference populations. Within groups with ≥40% AA (AFR ≥0.4 ), and ≥15% NAA (AMR ≥0.15 ), genome-wide association between local EA and glioma was evaluated using logistic regression conditioned on global EA for all gliomas. We identified two regions (7q21.11, p = 6.36 × 10 -4 ; 11p11.12, p = 7.0 × 10 -4 ) associated with increased EA, and one associated with decreased EA (20p12.13, p = 0.0026) in AFR ≥0.4 . In addition, we identified a peak at rs1620291 (p = 4.36 × 10 -6 ) in 7q21.3. Among AMR ≥0.15 , we found an association between increased EA in one region (12q24.21, p = 8.38 × 10 -4 ), and decreased EA in two regions (8q24.21, p = 0. 0010; 20q13.33, p = 6.36 × 10 -4 ). No other significant associations were identified. This analysis identified an association between glioma and two regions previously identified in EA populations (8q24.21, 20q13.33) and four novel regions (7q21.11, 11p11.12, 12q24.21 and 20p12.13). The identifications of novel association with EA suggest regions to target for future genetic association studies., (© 2019 UICC.)

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single-nucleotide polymorphisms (SNPs) in 149 genes of the fatty-acid synthesis pathway with cutaneous melanoma disease-specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses' Health and Health Professionals Follow-up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51-0.84 and p = 8.34 × 10 -4 ) and 2.29 (1.55-3.39 and p = 3.61 × 10 -5 ), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies., (© 2019 UICC.)

Glutamine dependence is a unique metabolic defect seen in cutaneous melanoma (CM), directly influencing the treatment and prognosis. Here, we investigated the associations between 6025 common single-nucleotide polymorphisms (SNPs) in 77 glutamine metabolic pathway genes with CM-specific survival (CMSS) using genotyping datasets from two published genome-wide association studies (GWASs). In the single-locus analysis, 76 SNPs were found to be significantly associated with CMSS (P < .050, false-positive report probability < 0.2 and Bayesian false discovery probability < 0.8) in the discovery dataset, of which seven SNPs were replicated in the validation dataset and three SNPs (HAL rs17676826T > C, LGSN rs12663017T > A, and NOXRED1 rs8012548A > G) independently predicted CMSS, with an effect-allele attributed adjusted hazards ratio of 1.52 (95% confidence interval = 1.19-1.93) and P < .001, 0.68 (0.54-0.87) and P = .002 and 0.62 (0.46-0.83) and P = .002, respectively. The model including the number of unfavorable genotypes (NUGs) of these three SNPs and covariates improved the five-year CMSS prediction (P = .012) than the one with other covariates only. Further expression quantitative trait loci (eQTL) analysis found that the LGSN rs12663017 A allele was significantly associated with increased messenger RNA (mRNA) expression levels (P = 8.89 × 10 -11 ) in lymphoblastoid cell lines of the 1000 Genomes Project database. In the analysis of the genotype tissue expression (GTEx) project datasets, HAL rs17676826 C and NOXRED1 rs8012548 G alleles were significantly associated with their mRNA expression levels in sun-exposed skin of the lower leg (P = 6.62 × 10 -6 and 1.37 × 10 -7 , respectively) and in sun-not-exposed suprapubic skin (P < .001 and 1.43 × 10 -8 , respectively). Taken together, these genetic variants of glutamine-metabolic pathway genes may be promising predictors of survival in patients with CM., (© 2019 Wiley Periodicals, Inc.)

Vitamin B supplementation can have side effects for human health, including cancer risk. We aimed to elucidate the role of vitamin B12 in lung cancer etiology via direct measurements of pre-diagnostic circulating vitamin B12 concentrations in a nested case-control study, complemented with a Mendelian randomization (MR) approach in an independent case-control sample. We used pre-diagnostic biomarker data from 5183 case-control pairs nested within 20 prospective cohorts, and genetic data from 29,266 cases and 56,450 controls. Exposures included directly measured circulating vitamin B12 in pre-diagnostic blood samples from the nested case-control study, and 8 single nucleotide polymorphisms associated with vitamin B12 concentrations in the MR study. Our main outcome of interest was increased risk for lung cancer, overall and by histological subtype, per increase in circulating vitamin B12 concentrations. We found circulating vitamin B12 to be positively associated with overall lung cancer risk in a dose response fashion (odds ratio for a doubling in B12 [OR log2B12 ] = 1.15, 95% confidence interval (95%CI) = 1.06-1.25). The MR analysis based on 8 genetic variants also indicated that genetically determined higher vitamin B12 concentrations were positively associated with overall lung cancer risk (OR per 150 pmol/L standard deviation increase in B12 [OR SD ] = 1.08, 95%CI = 1.00-1.16). Considering the consistency of these two independent and complementary analyses, these findings support the hypothesis that high vitamin B12 status increases the risk of lung cancer., (© 2018 International Agency for Research on Cancer (IARC/WHO); licensed by UICC.)

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits., (© 2018 Wiley Periodicals, Inc.)

Glioblastoma (GBM) is the most common malignant brain tumor in the United States. Incidence of GBM increases with age, and younger age-at-diagnosis is significantly associated with improved prognosis. While the relationship between candidate GBM risk SNPs and age-at-diagnosis has been explored, genome-wide association studies (GWAS) have not previously been stratified by age. Potential age-specific genetic effects were assessed in autosomal SNPs for GBM patients using data from four previous GWAS. Using age distribution tertiles (18-53, 54-64, 65+) datasets were analyzed using age-stratified logistic regression to generate p values, odds ratios (OR), and 95% confidence intervals (95%CI), and then combined using meta-analysis. There were 4,512 total GBM cases, and 10,582 controls used for analysis. Significant associations were detected at two previously identified SNPs in 7p11.2 (rs723527 [p 54-63 = 1.50x10 -9 , OR 54-63 = 1.28, 95%CI 54-63 = 1.18-1.39; p 64+ = 2.14x10 -11 , OR 64+ = 1.32, 95%CI 64+ = 1.21-1.43] and rs11979158 [p 54-63 = 6.13x10 -8 , OR 54-63 = 1.35, 95%CI 54-63 = 1.21-1.50; p 64+ = 2.18x10 -10 , OR 64+ = 1.42, 95%CI 64+ = 1.27-1.58]) but only in persons >54. There was also a significant association at the previously identified lower grade glioma (LGG) risk locus at 8q24.21 (rs55705857) in persons ages 18-53 (p 18-53 = 9.30 × 10 -11 , OR 18-53 = 1.76, 95%CI 18-53 = 1.49-2.10). Within The Cancer Genome Atlas (TCGA) there was higher prevalence of 'LGG'-like tumor characteristics in GBM samples in those 18-53, with IDH1/2 mutation frequency of 15%, as compared to 2.1% [54-63] and 0.8% [64+] (p = 0.0005). Age-specific differences in cancer susceptibility can provide important clues to etiology. The association of a SNP known to confer risk for IDH1/2 mutant glioma and higher prevalence of IDH1/2 mutation within younger individuals 18-53 suggests that more younger individuals may present initially with 'secondary glioblastoma.', (© 2018 UICC.)

Cutaneous melanoma (CM) is considered as a steroid hormone-related malignancy. However, few studies have evaluated the roles of genetic variants encoding steroid hormone receptor genes and their related regulators (SHR-related genes) in CM-specific survival (CMSS). Here, we performed a pathway-based analysis to evaluate genetic variants of 191 SHR-related genes in 858 CMSS patients using a dataset from a genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC), and then validated the results in an additional dataset of 409 patients from the Harvard GWAS. Using multivariate Cox proportional hazards regression analysis, we identified three-independent SNPs (RORA rs782917 G > A, RORA rs17204952 C > T and DNMT1 rs7253062 G > A) as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) and 95% confidence interval of 1.62 (1.25-2.09), 1.60 (1.20-2.13) and 1.52 (1.20-1.94), respectively. Combined analysis of risk genotypes of these three SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (p trend  < 0.001); however, no improvement in the prediction model was observed (area under the curve [AUC] = 79.6-80.8%, p = 0.656), when these risk genotypes were added to the model containing clinical variables. Our findings suggest that genetic variants of RORA and DNMT1 may be promising biomarkers for CMSS, but these results needed to be validated in future larger studies., (© 2018 UICC.)

The P38MAPK pathway participates in regulating cell cycle, inflammation, development, cell death, cell differentiation, and tumorigenesis. Genetic variants of some genes in the P38MAPK pathway are reportedly associated with lung cancer risk. To substantiate this finding, we used six genome-wide association studies (GWASs) to comprehensively investigate the associations of 14 904 single nucleotide polymorphisms (SNPs) in 108 genes of this pathway with lung cancer risk. We identified six significant lung cancer risk-associated SNPs in two genes (CSNK2B and ZAK) after correction for multiple comparisons by a false discovery rate (FDR) <0.20. After removal of three CSNK2B SNPs that are located in the same locus previously reported by GWAS, we performed the LD analysis and found that rs3769201 and rs7604288 were in high LD. We then chose two independent representative SNPs of rs3769201 and rs722864 in ZAK for further analysis. We also expanded the analysis by including these two SNPs from additional GWAS datasets of Harvard University (984 cases and 970 controls) and deCODE (1319 cases and 26 380 controls). The overall effects of these two SNPs were assessed using all eight GWAS datasets (OR = 0.92, 95%CI = 0.89-0.95, and P = 1.03 × 10 -5 for rs3769201; OR = 0.91, 95%CI = 0.88-0.95, and P = 2.03 × 10 -6 for rs722864). Finally, we performed an expression quantitative trait loci (eQTL) analysis and found that these two SNPs were significantly associated with ZAK mRNA expression levels in lymphoblastoid cell lines. In conclusion, the ZAK rs3769201 and rs722864 may be functional susceptibility loci for lung cancer risk., (© 2017 Wiley Periodicals, Inc.)

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (P trend  < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS., (© 2017 Wiley Periodicals, Inc.)

Circadian disruption has been linked to carcinogenesis in animal models, but the evidence in humans is inconclusive. Genetic variation in circadian rhythm genes provides a tool to investigate such associations. We examined associations of genetic variation in nine core circadian rhythm genes and six melatonin pathway genes with risk of colorectal, lung, ovarian and prostate cancers using data from the Genetic Associations and Mechanisms in Oncology (GAME-ON) network. The major results for prostate cancer were replicated in the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial, and for colorectal cancer in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). The total number of cancer cases and controls was 15,838/18,159 for colorectal, 14,818/14,227 for prostate, 12,537/17,285 for lung and 4,369/9,123 for ovary. For each cancer site, we conducted gene-based and pathway-based analyses by applying the summary-based Adaptive Rank Truncated Product method (sARTP) on the summary association statistics for each SNP within the candidate gene regions. Aggregate genetic variation in circadian rhythm and melatonin pathways were significantly associated with the risk of prostate cancer in data combining GAME-ON and PLCO, after Bonferroni correction (p pathway  < 0.00625). The two most significant genes were NPAS2 (p gene  = 0.0062) and AANAT (p gene  = 0.00078); the latter being significant after Bonferroni correction. For colorectal cancer, we observed a suggestive association with the circadian rhythm pathway in GAME-ON (p pathway  = 0.021); this association was not confirmed in GECCO (p pathway  = 0.76) or the combined data (p pathway  = 0.17). No significant association was observed for ovarian and lung cancer. These findings support a potential role for circadian rhythm and melatonin pathways in prostate carcinogenesis. Further functional studies are needed to better understand the underlying biologic mechanisms., (© 2017 UICC.)

Rho GTPases control cell division, motility, adhesion, vesicular trafficking and phagocytosis, which may affect progression and/or prognosis of cancers. Here, we investigated associations between genetic variants of Rho GTPases-related genes and cutaneous melanoma-specific survival (CMSS) by re-analyzing a published melanoma genome-wide association study (GWAS) and validating the results in another melanoma GWAS. In the single-locus analysis of 36,018 SNPs in 129 Rho-related genes, 427 SNPs were significantly associated with CMSS (p < 0.050 and false-positive report probability <0.2) in the discovery dataset, and five SNPs were replicated in the validation dataset. Among these, four SNPs (i.e., RHOU rs10916352 G > C, ARHGAP22 rs3851552 T > C, ARHGAP44 rs72635537 C > T and ARHGEF10 rs7826362 A > T) were independently predictive of CMSS (a meta-analysis derived p = 9.04 × 10 -4 , 9.58 × 10 -4 , 1.21 × 10 -4 and 8.47 × 10 -4 , respectively). Additionally, patients with an increasing number of unfavorable genotypes (NUGs) of these loci had markedly reduced CMSS in both discovery dataset and validation dataset (p trend =1.47 × 10 -7 and 3.12 × 10 -5 ). The model including the NUGs and clinical variables demonstrated a significant improvement in predicting the five-year CMSS. Moreover, rs10916352C and rs3851552C alleles were significantly associated with an increased mRNA expression levels of RHOU (p = 1.8 × 10 -6 ) and ARHGAP22 (p = 5.0 × 10 -6 ), respectively. These results may provide promising prognostic biomarkers for CM personalized management and treatment., (© 2017 UICC.)

The fatty acids (FAs) metabolism is suggested to play a pivotal role in the development of lung cancer, and we explored that by conducting a pathway-based analysis. We performed a meta-analysis of published datasets of six genome wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium, which included 12 160 cases with lung cancer and 16 838 cancer-free controls. A total of 30 722 single-nucleotide polymorphisms (SNPs) from 317 genes relevant to FA metabolic pathways were identified. An additional dataset from the Harvard Lung Cancer Study with 984 cases and 970 healthy controls was also added to the final meta-analysis. In the initial meta-analysis, 26 of 28 SNPs that passed false discovery rate multiple tests were mapped to the CYP4F3 gene. Among the 26 top ranked hits was a proxy SNP, CYP4F3 rs4646904 (P = 8.65 × 10 -6 , FDR = 0.018), which is suggested to change splicing pattern/efficiency and to be associated with gene expression levels. However, after adding data of rs4646904 from the Harvard GWAS, the significance in the combined analysis was reduced to P = 3.52 × 10 -3 [odds ratio (OR) = 1.07, 95% confidence interval (95%CI) = 1.03-1.12]. Interestingly, the small Harvard dataset also pointed to the same direction of the association in subgroups of smokers (OR = 1.07) and contributed to a combined OR of 1.13 (95% CI = 1.06-1.20, P = 6.70 × 10 -5 ). The results suggest that a potentially functional SNP in CYP4F3 (rs4646904) may contribute to the etiology of lung cancer, especially in smokers. Additional mechanistic studies are warranted to unravel the potential biological significance of the finding., (© 2017 Wiley Periodicals, Inc.)

Purpose: mRNA degradation is an important regulatory step for controlling gene expression and cell functions. Genetic abnormalities involved in mRNA degradation genes were found to be associated with cancer risks. Therefore, we systematically investigated the roles of genetic variants in the general mRNA degradation pathway in lung cancer risk., Experimental Design: Meta-analyses were conducted using summary data from six lung cancer genome-wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung and additional two GWASs from Harvard University and deCODE in the International Lung Cancer Consortium. Expression quantitative trait loci analysis (eQTL) was used for in silico functional validation of the identified significant susceptibility loci., Results: This pathway-based analysis included 6816 single nucleotide polymorphisms (SNP) in 68 genes in 14 463 lung cancer cases and 44 188 controls. In the single-locus analysis, we found that 20 SNPs were associated with lung cancer risk with a false discovery rate threshold of <0.05. Among the 11 newly identified SNPs in CNOT6, which were in high linkage disequilibrium, the rs2453176 with a RegulomDB score "1f" was chosen as the tagSNP for further analysis. We found that the rs2453176 T allele was significantly associated with lung cancer risk (odds ratio = 1.11, 95% confidence interval = 1.04-1.18) in the eight GWASs. In the eQTL analysis, we found that levels of CNOT6 mRNA expression were significantly correlated with the rs2453176 T allele, which provided additional biological basis for the observed positive association., Conclusion: The CNOT6 rs2453176 SNP may be a new functional susceptible locus for lung cancer risk. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

To identify genetic variants involved in prognosis of cutaneous melanoma (CM), we investigated associations of single nucleotide polymorphisms (SNPs) of genes in the integrin signaling pathway with CM survival by re-analyzing a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center (MDACC) and then validated significant SNPs in another GWAS from Harvard University. In the MDACC study, 1,148 SNPs were significantly associated with CM-specific survival (CMSS) (p ≤ 0.050 and false-positive report probability ≤ 0.20), and nine SNPs were validated in the Harvard study (p ≤ 0.050). Among these, three independent SNPs (i.e., DOCK1 rs11018104 T > A, rs35748949 C > T and PAK2 rs1718404 C > T) showed a predictive role in CMSS, with an effect-allele attributed adjusted hazards ratio [adjHR of 1.50 (95% confidence interval (CI) = 1.18-1.90, p = 7.46E-04), 1.53 (1.18-1.97, 1.18E-03) and 0.58 (0.45-0.76, 5.60E-05), respectively]. Haplotype analysis revealed that a haplotype carrying two risk alleles A-T in DOCK1 was associated with the poorest survival in both MDACC (adjHR = 1.73, 95% CI = 1.19-2.50, p = 0.004) and Harvard (adjHR = 1.95, 95% CI = 1.14-3.33, p = 0.010) studies. In addition, patients with an increasing number of unfavorable genotypes (NUGs) for these three SNPs had a poorer survival. Incorporating NUGs with clinical variables showed a significantly improved ability to classify CMSS (AUC increased from 86.8% to 88.6%, p = 0.031). Genetic variants in the integrin signaling pathway may independently or jointly modulate the survival of CM patients. Further large, prospective studies are needed to validate these findings., Competing Interests: The authors state no conflict of interest., (© 2016 UICC.)

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods., (Published 2016. This article is a U.S. Government work and is in the public domain in the USA.)

The Piwi-piRNA pathway is important for germ cell maintenance, genome integrity, DNA methylation and retrotransposon control and thus may be involved in cancer development. In this study, we comprehensively analyzed prognostic roles of 3,116 common SNPs in PIWI-piRNA pathway genes in melanoma disease-specific survival. A published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used to identify associated SNPs, which were later validated by another GWAS from the Harvard Nurses' Health Study and Health Professionals Follow-up Study. After multiple testing correction, we found that there were 27 common SNPs in two genes (PIWIL4 and DCP1A) with false discovery rate < 0.2 in the discovery dataset. Three tagSNPs (i.e., rs7933369 and rs508485 in PIWIL4; rs11551405 in DCP1A) were replicated. The rs11551405 A allele, located at the 3' UTR microRNA binding site of DCP1A, was associated with an increased risk of melanoma disease-specific death in both discovery dataset [adjusted Hazards ratio (HR) = 1.66, 95% confidence interval (CI) = 1.21-2.27, p =1.50 × 10 -3 ] and validation dataset (HR = 1.55, 95% CI = 1.03-2.34, p = 0.038), compared with the C allele, and their meta-analysis showed an HR of 1.62 (95% CI, 1.26-2.08, p =1.55 × 10 -4 ). Using RNA-seq data from the 1000 Genomes Project, we found that DCP1A mRNA expression levels increased significantly with the A allele number of rs11551405. Additional large, prospective studies are needed to validate these findings., Competing Interests: of Potential Conflicts of Interest No potential conflicts of interest were disclosed., (© 2016 UICC.)

Telomeres cap chromosome ends, protecting them from degradation, double-strand breaks, and end-to-end fusions. Telomeres are maintained by telomerase, a reverse transcriptase encoded by TERT, and an RNA template encoded by TERC. Loci in the TERT and adjoining CLPTM1L region are associated with risk of multiple cancers. We therefore investigated associations between variants in 22 telomere structure and maintenance gene regions and colorectal, breast, prostate, ovarian, and lung cancer risk. We performed subset-based meta-analyses of 204,993 directly-measured and imputed SNPs among 61,851 cancer cases and 74,457 controls of European descent. Independent associations for SNP minor alleles were identified using sequential conditional analysis (with gene-level p value cutoffs ≤3.08 × 10 -5 ). Of the thirteen independent SNPs observed to be associated with cancer risk, novel findings were observed for seven loci. Across the DCLRE1B region, rs974494 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Across the TERC region, rs75316749 was positively associated with colorectal, breast, ovarian, and lung cancers. Across the DCLRE1B region, rs974404 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Near POT1, rs116895242 was inversely associated with colorectal, ovarian, and lung cancers, and RTEL1 rs34978822 was inversely associated with prostate and lung cancers. The complex association patterns in telomere-related genes across cancer types may provide insight into mechanisms through which telomere dysfunction in different tissues influences cancer risk., Competing Interests: Author’s disclosures of potential conflicts of interest: non for all authors., (© 2016 UICC.)

In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT-O, and SKAT-C, (ii) traditional fixed-effect additive models, and (iii) fixed-effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed-effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed-effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT-O/SKAT-C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed-effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages., (Published 2016. This article is a U.S. Government work and is in the public domain in the USA.)

Statistical interactions between markers of genetic variation, or gene-gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene-gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene-gene interactions. In these cases, a global test for gene-gene interactions may be the best option. Global tests have much greater power relative to multiple individual interaction tests and can be used on subsets of the markers as an initial filter prior to testing for specific interactions. In this paper, we describe a novel global test for gene-gene interactions, the global epistasis test (GET), that is based on results from random matrix theory. As we show via simulation studies based on previously proposed models for common diseases including rheumatoid arthritis, type 2 diabetes, and breast cancer, our proposed GET method has superior performance characteristics relative to existing global gene-gene interaction tests. A glaucoma GWAS data set is used to demonstrate the practical utility of the GET method., (© 2016 The Authors. Genetic Epidemiology published by Wiley Periodicals, Inc.)

Breslow thickness (BT) is a major prognostic factor of cutaneous melanoma (CM), the most fatal skin cancer. The genetic component of BT has only been explored by candidate gene studies with inconsistent results. Our objective was to uncover the genetic factors underlying BT using an hypothesis-free genome-wide approach. Our analysis strategy integrated a genome-wide association study (GWAS) of single nucleotide polymorphisms (SNPs) for BT followed by pathway analysis of GWAS outcomes using the gene-set enrichment analysis (GSEA) method and epistasis analysis within BT-associated pathways. This strategy was applied to two large CM datasets with Hapmap3-imputed SNP data: the French MELARISK study for discovery (966 cases) and the MD Anderson Cancer Center study (1,546 cases) for replication. While no marginal effect of individual SNPs was revealed through GWAS, three pathways, defined by gene ontology (GO) categories were significantly enriched in genes associated with BT (false discovery rate ≤5% in both studies): hormone activity, cytokine activity and myeloid cell differentiation. Epistasis analysis, within each significant GO, identified a statistically significant interaction between CDC42 and SCIN SNPs (pmeta-int =2.2 × 10(-6) , which met the overall multiple-testing corrected threshold of 2.5 × 10(-6) ). These two SNPs (and proxies) are strongly associated with CDC42 and SCIN gene expression levels and map to regulatory elements in skin cells. This interaction has important biological relevance since CDC42 and SCIN proteins have opposite effects in actin cytoskeleton organization and dynamics, a key mechanism underlying melanoma cell migration and invasion., Competing Interests: The authors declare that they have no conflict of interest., (© 2016 UICC.)

Recent genome-wide association studies have identified variations in the recombination repair gene, RAD52, that are associated with increased lung cancer risk, and particularly with the development of lung squamous cell carcinomas (LUSC). As LUSC development is strongly associated with smoking, DNA repair is increased in the lung tissues of smokers, presumably because of ongoing DNA damage from exposure to tobacco smoke. A key player in the DNA damage response, RAD52 plays a role in DNA strand exchange and annealing during homologous recombination (HR) in mammalian cells. In this study, we discovered two cis-expression quantitative trait loci (eQTL) SNPs in the RAD52 gene that are associated with its expression and are also associated with LUSC risk. In addition, we report that amplification of the genomic region 12p13.33, which contains the RAD52 gene, is significantly associated with the development of LUSC in the TCGA database and that somatic overexpression of RAD52 was confirmed to be significant in LUSC tumors from our own patient cohort. Consistent with these genetic findings, we demonstrate that blockade of Rad52 slows cell growth and induces senescence in mouse bronchial epithelial cells. In contrast, overexpression of Rad52 leads to an increased rate of cell proliferation. We show that depletion of Rad52 in mouse lung tumor cells alters cell cycle distribution and increases DNA damage accumulation associated with increased tumor cell death. Our genetic and functional data implicate RAD52 as a significant determinant of risk in the development of LUSC., (© 2015 Wiley Periodicals, Inc.)

Genome-wide association studies (GWASs) have characterized 13 loci associated with melanoma, which only account for a small part of melanoma risk. To identify new genes with too small an effect to be detected individually but which collectively influence melanoma risk and/or show interactive effects, we used a two-step analysis strategy including pathway analysis of genome-wide SNP data, in a first step, and epistasis analysis within significant pathways, in a second step. Pathway analysis, using the gene-set enrichment analysis (GSEA) approach and the gene ontology (GO) database, was applied to the outcomes of MELARISK (3,976 subjects) and MDACC (2,827 subjects) GWASs. Cross-gene SNP-SNP interaction analysis within melanoma-associated GOs was performed using the INTERSNP software. Five GO categories were significantly enriched in genes associated with melanoma (false discovery rate ≤ 5% in both studies): response to light stimulus, regulation of mitotic cell cycle, induction of programmed cell death, cytokine activity and oxidative phosphorylation. Epistasis analysis, within each of the five significant GOs, showed significant evidence for interaction for one SNP pair at TERF1 and AFAP1L2 loci (pmeta-int  = 2.0 × 10(-7) , which met both the pathway and overall multiple-testing corrected thresholds that are equal to 9.8 × 10(-7) and 2.0 × 10(-7) , respectively) and suggestive evidence for another pair involving correlated SNPs at the same loci (pmeta-int  = 3.6 × 10(-6) ). This interaction has important biological relevance given the key role of TERF1 in telomere biology and the reported physical interaction between TERF1 and AFAP1L2 proteins. This finding brings a novel piece of evidence for the emerging role of telomere dysfunction into melanoma development., (© 2015 UICC.)

Cutaneous melanoma (CM) is the most lethal form of skin cancers. The Hippo pathway controls cell migration, development and sizes of the organs in diverse species, and deregulation of this pathway may affect CM progression and prognosis. Therefore, we hypothesized that genetic variants of Hippo pathway genes might predict survival of CM patients. We used the genotyping data of 1,115 common single nucleotide polymorphisms (SNPs) in the 12 pathway core genes (i.e., MST1, MST2, SAV1, LATS1, LATS2, MOB1A, MOB1B, YAP1, TEAD1, TEAD2, TEAD3 and TEAD4) from the dataset of our previously published CM genome-wide association study and comprehensively analyzed their associations with CM-specific survival (CSS) in 858 CM patients by using the Kaplan-Meier analyses and Cox proportional hazards regression models. We found a predictive role of YAP1 rs11225163 CC, TEAD1 rs7944031 AG+GG and TEAD4 rs1990330 CA+AA in the prognosis of CM. In addition, patients with an increasing number of unfavorable genotypes (NUG) had a markedly increased risk of death. After incorporating NUG in the model with clinical variables, the new model showed a significantly improved discriminatory ability to classify CSS (AUC increased from 82.03% to 84.56%). Our findings suggest that genetic variants of Hippo pathway genes, particularly YAP1 rs11225163, TEAD1 rs7944031 and TEAD4 rs1990330, may independently or jointly modulate survival of CM patients. Additional large, prospective studies are needed to validate these findings., (© 2015 UICC.)

Cytokines such as Interleukin (IL)-12p70 ("IL-12") and IL-23 can influence tumor progression. We tested the hypothesis that blood levels of IL-12p40, the common subunit of both cytokines, are associated with melanoma progression. Blood from 2,048 white melanoma patients were collected at a single institution between March 1998 and March 2011. Plasma levels of IL-12p40 were determined for 573 patients (discovery), 249 patients (Validation 1) and 244 patients (Validation 2). Per 10-unit change of IL-12p40 level was used to investigate associations with melanoma patient outcome among all patients or among patients with early or advanced stage. Among stage I/II melanoma patients in the pooled data set, after adjustment for sex, age, stage and blood draw time from diagnosis, elevated IL-12p40 was associated with melanoma recurrence [hazard ratio (HR) = 1.04 per 10-unit increase in IL-12p40, 95% CI 1.02-1.06, p = 8.48 × 10(-5) ]; Elevated IL-12p40 was also associated with a poorer melanoma specific survival (HR = 1.06, 95% CI 1.03-1.09, p = 3.35 × 10(-5) ) and overall survival (HR = 1.05, 95% CI 1.03-1.08, p = 8.78×10(-7) ) in multivariate analysis. Among stage III/IV melanoma patients in the pooled data set, no significant association was detected between elevated IL-12p40 and overall survival, or with melanoma specific survival, with or without adjustment for the above covariates. Early stage melanoma patients with elevated IL-12p40 levels are more likely to develop disease recurrence and have a poorer survival. Further investigation with a larger sample size will be needed to determine the role of IL-12p40 in advanced stage melanoma patients., (© 2014 UICC.)

Lung cancer is the leading cause of cancer death worldwide. Although several genetic variants associated with lung cancer have been identified in the past, stringent selection criteria of genome-wide association studies (GWAS) can lead to missed variants. The objective of this study was to uncover missed variants by using the known association between lung cancer and first-degree family history of lung cancer to enrich the variant prioritization for lung cancer susceptibility regions. In this two-stage GWAS study, we first selected a list of variants associated with both lung cancer and family history of lung cancer in four GWAS (3,953 cases, 4,730 controls), then replicated our findings for 30 variants in a meta-analysis of four additional studies (7,510 cases, 7,476 controls). The top ranked genetic variant rs12415204 in chr10q23.33 encoding FFAR4 in the Discovery set was validated in the Replication set with an overall OR of 1.09 (95% CI=1.04, 1.14, P=1.63×10(-4)). When combining the two stages of the study, the strongest association was found in rs1158970 at Ch4p15.2 encoding KCNIP4 with an OR of 0.89 (95% CI=0.85, 0.94, P=9.64×10(-6)). We performed a stratified analysis of rs12415204 and rs1158970 across all eight studies by age, gender, smoking status, and histology, and found consistent results across strata. Four of the 30 replicated variants act as expression quantitative trait loci (eQTL) sites in 1,111 nontumor lung tissues and meet the genome-wide 10% FDR threshold., (© 2015 Wiley Periodicals, Inc.)

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation., (© 2014 WILEY PERIODICALS, INC.)

The analysis of gene-environment (G × E) interactions remains one of the greatest challenges in the postgenome-wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case-control and powerful case-only methods. Inferences of the latter are biased when the assumption of gene-environment (G-E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB-GE), which benefits from greater rank power while accounting for population-based G-E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method first obtains posterior G-E correlation estimates in controls for each marker, borrowing strength from G-E information across the genome. These posterior estimates are then subtracted from the corresponding case-only G × E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G-E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G-E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219-226) identifies markers with low G × E interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB-GE approach., (© 2013 WILEY PERIODICALS, INC.)

Recent meta-analyses of European ancestry subjects show strong evidence for association between smoking quantity and multiple genetic variants on chromosome 15q25. This meta-analysis extends the examination of association between distinct genes in the CHRNA5-CHRNA3-CHRNB4 region and smoking quantity to Asian and African American populations to confirm and refine specific reported associations. Association results for a dichotomized cigarettes smoked per day phenotype in 27 datasets (European ancestry (N = 14,786), Asian (N = 6,889), and African American (N = 10,912) for a total of 32,587 smokers) were meta-analyzed by population and results were compared across all three populations. We demonstrate association between smoking quantity and markers in the chromosome 15q25 region across all three populations, and narrow the region of association. Of the variants tested, only rs16969968 is associated with smoking (P < 0.01) in each of these three populations (odds ratio [OR] = 1.33, 95% CI = 1.25-1.42, P = 1.1 × 10(-17) in meta-analysis across all population samples). Additional variants displayed a consistent signal in both European ancestry and Asian datasets, but not in African Americans. The observed consistent association of rs16969968 with heavy smoking across multiple populations, combined with its known biological significance, suggests rs16969968 is most likely a functional variant that alters risk for heavy smoking. We interpret additional association results that differ across populations as providing evidence for additional functional variants, but we are unable to further localize the source of this association. Using the cross-population study paradigm provides valuable insights to narrow regions of interest and inform future biological experiments., (© 2012 Wiley Periodicals, Inc.)

For complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene-gene and gene-environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy-based approach of information theory. In this article, we first develop entropy-based test statistics to identify two-way and higher order gene-gene and gene-environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two-way interactions, we propose an information gain (IG) approach based on mutual information. For three-ways and higher order interactions, an interaction IG approach is used. In both cases, we develop one-dimensional test statistics to analyze sparse data. Compared to the naive chi-square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene single nucleotide polymorphisms, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy-based approaches appear as a useful tool for detecting gene-gene and gene-environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases., (© 2011 Wiley Periodicals, Inc.)

The metabolism of xenobiotics is complex and involves multiple steps and multiple enzymes. Genetic variation in the genes encoding these enzymes as well as the level of exposure to the substrates of these enzymes could alter metabolism and clearance of potential carcinogens and thus alter cancer susceptibility. This study examined interaction effect between smoking and two single nucleotide polymorphisms (SNPs)-CYP1A1 c.1384A>G (p.Ile462Val) and EPHX1 c.337T>C (p.Tyr113His)-in modulating colorectal cancer (CRC) risk. The SNPs were selected a priori based on functional significance. In a case-only analysis, unconditional logistic regression was used to examine the associations between smoking and each SNP and between the two SNPs in 786 patients with nonfamilial CRC. There was significant multiplicative interaction for CRC risk between smoking and EPHX1 c.337T>C (odds ratio [OR] = 1.37, 95% confidence interval [CI] = 1.03-1.81, P = 0.03), particularly among smokers with a history of greater than 20 pack-years of smoking (OR = 1.52, 95% CI = 1.07-2.16, P = 0.02). In addition, there was gene-gene interaction between EPHX1 c.337T>C and CYP1A1 c.1384A>G (OR = 1.61, 95% CI = 1.02-2.55, P = 0.04). Smokers with any variant allele of EPHX1 were at increased risk for CRC, as were individuals with any variant allele of CYP1A1 together with any variant allele of EPHX1. Thus, the study of gene-environment and gene-gene interactions may help to identify high-risk subgroups that can be targeted for intensive smoking cessation and CRC screening interventions., (© 2010 Wiley-Liss, Inc.)

Genome-wide association studies (GWAS) have been widely used to identify genetic effects on complex diseases or traits. Most currently used methods are based on separate single-nucleotide polymorphism (SNP) analyses. Because this approach requires correction for multiple testing to avoid excessive false-positive results, it suffers from reduced power to detect weak genetic effects under limited sample size. To increase the power to detect multiple weak genetic factors and reduce false-positive results caused by multiple tests and dependence among test statistics, a modified forward multiple regression (MFMR) approach is proposed. Simulation studies show that MFMR has higher power than the Bonferroni and false discovery rate procedures for detecting moderate and weak genetic effects, and MFMR retains an acceptable-false positive rate even if causal SNPs are correlated with many SNPs due to population stratification or other unknown reasons.

The analysis of genomewide association studies requires methods that are both computationally feasible and statistically powerful. Given the large-scale collection of single nucleotide polymorphisms (SNPs), it is desirable to explore the information contained in their interrelationships. In particular, utilizing haplotypes rather than individual SNPs and accounting for correlations of polymorphisms in adjustment for multiple testing can lead to increased power. We present a statistically powerful and numerically efficient method based on sliding windows of adjacent SNPs to detect haplotype-disease association in genomewide studies. This method consists of an efficient algorithm to calculate a proper likelihood-ratio statistic for any given window of SNPs, along with an accurate and efficient Monte Carlo procedure to adjust for multiple testing. Simulation studies using the HapMap data showed that the proposed method performs well in realistic situations. We applied the new method to a case-control study on rheumatoid arthritis and identified several loci worthy of further investigations., ((c) 2007 Wiley-Liss, Inc.)

Although extended pedigrees are often sampled through probands with extreme levels of a quantitative trait, Markov chain Monte Carlo (MCMC) methods for segregation and linkage analysis have not been able to perform ascertainment corrections. Further, the extent to which ascertainment of pedigrees leads to biases in the estimation of segregation and linkage parameters has not been previously studied for MCMC procedures. In this paper, we studied these issues with a Bayesian MCMC approach for joint segregation and linkage analysis, as implemented in the package Loki. We first simulated pedigrees ascertained through individuals with extreme values of a quantitative trait in spirit of the sequential sampling theory of Cannings and Thompson [Cannings and Thompson [1977] Clin. Genet. 12:208-212]. Using our simulated data, we detected no bias in estimates of the trait locus location. However, in addition to allele frequencies, when the ascertainment threshold was higher than or close to the true value of the highest genotypic mean, bias was also found in the estimation of this parameter. When there were multiple trait loci, this bias destroyed the additivity of the effects of the trait loci, and caused biases in the estimation all genotypic means when a purely additive model was used for analyzing the data. To account for pedigree ascertainment with sequential sampling, we developed a Bayesian ascertainment approach and implemented Metropolis-Hastings updates in the MCMC samplers used in Loki. Ascertainment correction greatly reduced biases in parameter estimates. Our method is designed for multiple, but a fixed number of trait loci., (Copyright (c) 2007 Wiley-Liss, Inc.)

The authors evaluated the familial aggregation of lung and other cancers in first-degree relatives of lung cancer patients self-reported to be lifetime never smokers. The data, derived from a large lung cancer case-control study, included 2,465 first-degree relatives of 316 never smoker lung cancer cases and 2,441 first-degree relatives of 318 never smoker controls, frequency matched to the cases on age, gender and ethnicity. The median age of the cases and the controls was 61 years, about 2/3 were women, and about 80% were Caucasian. Overall, there was a 25% excess risk [95% CI (1.05-1.50)] of any type of cancer among the first-degree relatives of cases, and case offspring exhibited a 2-fold excess cancer risk (1.03-4.10) compared with control offspring. There was also a 44% excess risk (1.05-1.97) of young onset cancers (before age 50) among relatives of cases. Smoking case relatives had an increased risk of any cancer [odds ratio (OR) = 1.36 (1.03-1.81)] and a 5.52-fold risk (1.19-25.51) of young onset lung cancer compared with smoking control relatives. Female case relatives had a 58% excess breast cancer risk (1.04-2.43), and case mothers a 2.57-fold breast cancer risk (1.16-4.24). A significant excess of testicular cancer was observed among case male relatives [OR = 12.32 (1.71-88.90)], although based on only 9 cases. The age at lung cancer diagnosis tended to be earlier in case relatives (61.4, SD = 12.9) compared with control relatives (66.2, SD = 11.4; p = 0.07). Our analysis provides further evidence for the importance of genetic factors for lung cancer in never smokers.

Few studies have assessed mutagen sensitivity and lung cancer (LC) risk associations in the context of multiple epidemiological risk factors. We evaluated mutagen sensitivity as a susceptibility marker and explored the interplay of the genetic marker and multiple epidemiologic risk factors in modulating LC risk. This largest case-control study included 977 newly diagnosed LC patients and 977 controls, matched by age, gender, ethnicity and smoking status. Cases exhibited significantly higher mutagen sensitivity than controls in bleomycin (0.76 vs. 0.62 breaks/cell, p < 0.001) and benzo[a]pyrene diol epoxide (BPDE) assays (0.70 vs. 0.61 breaks/cell, p < 0.001). Mutagen sensitivity also exhibited dose-response relationship with LC risk in quartile analysis (p for trend <0.001). In smokers, history of emphysema, absence of hay fever history, LC in first-degree relatives, belomycin sensitivity, and high pack-year were identified to be the top 5 significant risk factors in a stepwise logistic regression model (odds ratios (ORs) of 2.69, 1.91, 1.84, 1.73 and 1.67, respectively). Analyses of joint effects of risk factors showed that compared to the reference group, subjects with no exposure to any of the aforementioned risk factors, the ORs for exposure to 1, 2, 3, 4, 5 or more of the risk factors were 1.56, 2.39, 3.75, 6.74 and 17.39, respectively (p for trend <0.001). This study strongly supports mutagen sensitivity as a predisposition factor for LC and demonstrates the importance of assessing multiple risk factors to comprehensively assess LC risk. This new integrative approach should facilitate identification of high-risk subgroups and has important implications in LC prevention.

Aurora-A kinase is considered a potential cancer susceptibility gene that encodes a centrosome-associated, cell cycle-regulated serine/threonine kinase. We studied two single nucleotide polymorphisms (SNP) in the coding region of Aurora-A, 91T-to-A (F31I) and 169G-to-A (V57I). We studied the influence of these two polymorphisms on age of onset of hereditary nonpolyposis colorectal cancer (HNPCC). Genotyping of the Aurora-A polymorphisms was carried out on 125 Caucasian with mismatch repair (MMR) gene mutations with real-time pyrophosphate DNA sequencing. For the 91T-to-A polymorphism, we found that patients with HNPCC who were homozygous for the wild-type allele developed colorectal cancer (CRC) 7 years earlier than patients who were homozygous or heterozygous for the mutant allele. The169G-to-A polymorphism did not have a significant influence on risk for HNPCC. However, when we did haplotype analysis for these two polymorphisms, the 91A-169G haplotype was associated with protection from HNPCC at an earlier age.

The group that formed on the theme of linkage analyses of rheumatoid arthritis RA and related phenotypes (Group 10) in the Genetic Analysis Workshop 15 comprised 18 sets of investigators. Two data sets were available: one was a real set provided by the North American Rheumatoid Arthritis Consortium and collaborators in Canada, France (European Consortium Of Rheumatoid Arthritis Families) and the UK; the other was a simulated data set modelled after the real data set. Whereas a majority of the investigators analyzed the RA affection status as a binary phenotype, a few contributions considered data on correlated quantitative traits such as anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M. The different investigators applied a wide spectrum of linkage methods. As expected, most methods could identify the human leukocyfeantigen region on chromosome 6 as a major genetic factor for RA. In addition, some novel chromosomal regions provided significant evidence of linkage in multiple contributions in the group. In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect linkage., (. (c) 2007 Wiley-Liss, Inc.)