Impact of the MICA-129Met/Val Dimorphism on NKG2D-Mediated Biological Functions and Disease Risks

The major histocompatibility complex (MHC) class I chain-related A (MICA) is the most polymorphic non-classical MHC class I gene in humans. It encodes a ligand for NKG2D (NK group 2, member D), an activating natural killer (NK) receptor that is expressed mainly on NK cells and CD8⁺ T cells. The single-nucleotide polymorphism (SNP) rs1051792 causing a valine (Val) to methionine (Met) exchange at position 129 of the MICA protein is of specific interest. It separates MICA into isoforms that bind NKG2D with high (Met) and low affinities (Val). Therefore, this SNP has been investigated for associations with infections, autoimmune diseases, and cancer. Here, we systematically review these studies and analyze them in view of new data on the functional consequences of this polymorphism. It has been shown recently that the MICA-129Met variant elicits a stronger NKG2D signaling, resulting in more degranulation and IFN-γ production in NK cells and in a faster costimulation of CD8⁺ T cells than the MICA-129Val variant. However, the MICA-129Met isoform also downregulates NKG2D more efficiently than the MICA-129Val isoform. This downregulation impairs NKG2D-mediated functions at high expression intensities of the MICA-Met variant. These features of the MICA-129Met/Val dimorphism need to be considered when interpreting disease association studies. Particularly, in the field of hematopoietic stem cell transplantation, they help to explain the associations of the SNP with outcome including graft-versus-host disease and relapse of malignancy. Implications for future disease association studies of the MICA-129Met/Val dimorphism are discussed.

Comparing strategies for combined testing of rare and common variants in whole sequence and genome-wide genotype data

We used our extension of the kernel score test to family data to analyze real and simulated baseline systolic blood pressure in extended pedigrees. We compared the power for different kernels and for different weightings of genetic markers. Moreover, we compared the power of rare and common markers with 3 strategies for joint testing and on marker panels with different densities. Marker weights had much greater influence on power than the kernel chosen. Inverse minor allele frequency weights often increased power on common markers but could decrease power on rare markers. Furthermore, defining the gene region based on linkage disequilibrium blocks often yielded robust power of joint tests of rare and common markers.

Novel Association of Genetic Markers Affecting CYP2A6 Activity and Lung Cancer Risk

Metabolism of nicotine by cytochrome P450 2A6 (CYP2A6) is a suspected determinant of smoking dose and, consequently, lung cancer risk. We conducted a genome-wide association study (GWAS) of CYP2A6 activity, as measured by the urinary ratio of trans-3'-hydroxycotinine and its glucuronide conjugate over cotinine (total 3HCOT/COT), among 2,239 smokers in the Multiethnic Cohort (MEC) study. We identified 248 CYP2A6 variants associated with CYP2A6 activity (P < 5 × 10-8). CYP2A6 activity was correlated (r = 0.32; P < 0.0001) with total nicotine equivalents (a measure of nicotine uptake). When we examined the effect of these variants on lung cancer risk in the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium GWAS dataset (13,479 cases and 43,218 controls), we found that the vast majority of these individual effects were directionally consistent and associated with an increased lung cancer risk. Two hundred and twenty-six of the 248 variants associated with CYP2A6 activity in the MEC were available in TRICL. Of them, 81% had directionally consistent risk estimates, and six were globally significantly associated with lung cancer. When conditioning on nine known functional variants and two deletions, the top two SNPs (rs56113850 in MEC and rs35755165 in TRICL) remained significantly associated with CYP2A6 activity in MEC and lung cancer in TRICL. The present data support the hypothesis that a greater CYP2A6 activity causes smokers to smoke more extensively and be exposed to higher levels of carcinogens, resulting in an increased risk for lung cancer. Although the variants identified in these studies may be used as risk prediction markers, the exact causal variants remain to be identified. Cancer Res; 76(19); 5768-76. ©2016 AACR.

Association between Adult Height and Risk of Colorectal, Lung, and Prostate Cancer: Results from Meta-analyses of Prospective Studies and Mendelian Randomization Analyses

BACKGROUND

Observational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers.

METHODS AND FINDINGS

A systematic review of prospective studies was conducted using the PubMed, Embase, and Web of Science databases. Using meta-analyses, results obtained from 62 studies were summarized for the association of a 10-cm increase in height with cancer risk. Mendelian randomization analyses were conducted using summary statistics obtained for 423 genetic variants identified from a recent GWAS of adult height and from a cancer genetics consortium study of multiple cancers that included 47,800 cases and 81,353 controls. For a 10-cm increase in height, the summary relative risks derived from the meta-analyses of prospective studies were 1.12 (95% CI 1.10, 1.15), 1.07 (95% CI 1.05, 1.10), and 1.06 (95% CI 1.02, 1.11) for colorectal, prostate, and lung cancers, respectively. Mendelian randomization analyses showed increased risks of colorectal (odds ratio [OR] = 1.58, 95% CI 1.14, 2.18) and lung cancer (OR = 1.10, 95% CI 1.00, 1.22) associated with each 10-cm increase in genetically predicted height. No association was observed for prostate cancer (OR = 1.03, 95% CI 0.92, 1.15). Our meta-analysis was limited to published studies. The sample size for the Mendelian randomization analysis of colorectal cancer was relatively small, thus affecting the precision of the point estimate.

CONCLUSIONS

Our study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.

Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103-14. ©2016 AACR.

The MICA-129 dimorphism affects NKG2D signaling and outcome of hematopoietic stem cell transplantation

The MHC class I chain-related molecule A (MICA) is a highly polymorphic ligand for the activating natural killer (NK)-cell receptor NKG2D. A single nucleotide polymorphism causes a valine to methionine exchange at position 129. Presence of a MICA-129Met allele in patients (n = 452) undergoing hematopoietic stem cell transplantation (HSCT) increased the chance of overall survival (hazard ratio [HR] = 0.77, P = 0.0445) and reduced the risk to die due to acute graft-versus-host disease (aGVHD) (odds ratio [OR] = 0.57, P = 0.0400) although homozygous carriers had an increased risk to experience this complication (OR = 1.92, P = 0.0371). Overall survival of MICA-129Val/Val genotype carriers was improved when treated with anti-thymocyte globulin (HR = 0.54, P = 0.0166). Functionally, the MICA-129Met isoform was characterized by stronger NKG2D signaling, triggering more NK-cell cytotoxicity and interferon-γ release, and faster co-stimulation of CD8⁺ T cells. The MICA-129Met variant also induced a faster and stronger down-regulation of NKG2D on NK and CD8⁺ T cells than the MICA-129Val isoform. The reduced cell surface expression of NKG2D in response to engagement by MICA-129Met variants appeared to reduce the severity of aGVHD.

Abstract 2569: A genome wide association study of lung cancer identifies 11 novel susceptibility loci

Background

Genome wide association studies (GWAS) of lung cancer have identified susceptibility loci at 15q25, 5p15, 6p21, 13q13 and 22q12 that contain relevant candidate genes such as CHRNA3/5, TERT, HLA, BRCA2 and CHEK2, respectively. Many of these alleles appear more relevant to a particularly histological type of lung cancer.

With the aim of identifying novel lung cancer susceptibility loci, The Transdisciplinary Research in Cancer of the Lung (TRICL), the International Lung Cancer Consortium (ILCCO) and Lung Cancer Cohort Consortium (LC3) have undertaken a very large collaborative GWAS across 29 lung cancer studies, including analysis of the predominant lung cancer histological subtypes.

Methods

In collaboration with the Center for Disease Research (CIDR), TRICL, ILCCO and LC3 have genotyped 18,000 case-control pairs on the GAME-On Oncoarray. SNP imputation was undertaken using the 1,000 Genomes v3 reference panel, followed by logistic regression of each genetic variant with lung cancer and considering ancestry inferred by genetic profile to correct for cryptic population structure. The OncoArray and our previous GWAS results were combined using meta-analysis. This allowed for a GWAS of 10,155,682 SNPs for 25,655 lung cancer cases and 52,451 controls, as well as histology specific analysis of 6,629 squamous cell and 9,817 adenocarcinomas. Alternate genotyping techniques (Affymetrix, Taqman) were used to confirm the fidelity of the genotyping for variants of interest.

Results

We have identified common genetic variants exceeding genome wide significance (p&lt;5×10-8) at eleven novel susceptibility loci. This included two associated with overall lung cancer (1p31, 19q13), eight with lung adenocarcinomas (3q28, 6p25, 8p12, 9p21, 10q25, 11q23, 15q21, 20q13) and one with squamous-cell lung carcinomas (10q24). These genetic variants are located near several intriguing candidate genes, such as telomere function genes (OBFC1, RTEL1), nicotine metabolism genes (CYP2A6), genes somatically mutated in lung cancer (NRG1) and genetic susceptibility loci linked to other cancers (IRF4, CDNK2A). In additional, we noted several borderline (p&lt;10-6) associations with common variants located near nicotine addiction genes (CHRNB2, CHRNA2, CHRNA4, DBH) and other genes somatically translocated in lung cancer (ROS1). Integration of eQTL databases suggests that many of the associated genetic variants influence gene expression levels of these candidate genes.

Conclusion

We have identified eleven novel lung cancer susceptibility loci, doubling the number implicated by GWAS. These genetic variants were common (MAF 0.05-0.49) with modest to small genetic effects (OR's 1.10-1.17). Further expansion of GWAS efforts, particularly within histological subtypes of lung cancer, is likely to identify additional susceptibility loci and further increase our understanding of lung cancer aetiology.

Citation Format: James Dowling Mckay, Yafang Li, Younghun Han, Xiangjun Xioa, John Field, Xuchen Zong, Heike Bickeböller, David C. Christiani, Paul Brennan, Maria-Teresa Landi, Rayjean Hung, Christopher I. Amos, on behalf of the OncoArray Lung Cancer Group. A genome wide association study of lung cancer identifies 11 novel susceptibility loci. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2569.

Polymorphisms of the centrosomal gene (FGFR1OP) and lung cancer risk: a meta-analysis of 14,463 cases and 44,188 controls

Centrosome abnormalities are often observed in premalignant lesions and in situ tumors and have been associated with aneuploidy and tumor development. We investigated the associations of 9354 single-nucleotide polymorphisms (SNPs) in 106 centrosomal genes with lung cancer risk by first using the summary data from six published genome-wide association studies (GWASs) of the Transdisciplinary Research in Cancer of the Lung (TRICL) (12,160 cases and 16 838 controls) and then conducted in silico replication in two additional independent lung cancer GWASs of Harvard University (984 cases and 970 controls) and deCODE (1319 cases and 26,380 controls). A total of 44 significant SNPs with false discovery rate (FDR) ≤ 0.05 were mapped to one novel gene FGFR1OP and two previously reported genes (TUBB and BRCA2). After combined the results from TRICL with those from Harvard and deCODE, the most significant association (P combined = 8.032 × 10(-6)) was with rs151606 within FGFR1OP. The rs151606 T>G was associated with an increased risk of lung cancer [odds ratio (OR) = 1.10, 95% confidence interval (95% CI) = 1.05-1.14]. Another significant tagSNP rs12212247 T>C (P combined = 9.589 × 10(-6)) was associated with a decreased risk of lung cancer (OR = 0.93, 95% CI = 0.90-0.96). Further in silico functional analyzes revealed that rs151606 might affect transcriptional regulation and result in decreased FGFR1OP expression (P trend = 0.022). The findings shed some new light on the role of centrosome abnormalities in the susceptibility to lung carcinogenesis.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5×10(-8)) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6×10(-7)) and MTMR2 at 11q21 (rs10501831, P = 3.1×10(-6)) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4×10(-7)) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05×10(-4) for KCNIP4, represented by rs9799795) and AC (P = 2.16×10(-4) for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

Cross Cancer Genomic Investigation of Inflammation Pathway for Five Common Cancers: Lung, Ovary, Prostate, Breast, and Colorectal Cancer

BACKGROUND

Inflammation has been hypothesized to increase the risk of cancer development as an initiator or promoter, yet no large-scale study of inherited variation across cancer sites has been conducted.

METHODS

We conducted a cross-cancer genomic analysis for the inflammation pathway based on 48 genome-wide association studies within the National Cancer Institute GAME-ON Network across five common cancer sites, with a total of 64 591 cancer patients and 74 467 control patients. Subset-based meta-analysis was used to account for possible disease heterogeneity, and hierarchical modeling was employed to estimate the effect of the subcomponents within the inflammation pathway. The network was visualized by enrichment map. All statistical tests were two-sided.

RESULTS

We identified three pleiotropic loci within the inflammation pathway, including one novel locus in Ch12q24 encoding SH2B3 (rs3184504), which reached GWAS significance with a P value of 1.78 x 10(-8), and it showed an association with lung cancer (P = 2.01 x 10(-6)), colorectal cancer (GECCO P = 6.72x10(-6); CORECT P = 3.32x10(-5)), and breast cancer (P = .009). We also identified five key subpathway components with genetic variants that are relevant for the risk of these five cancer sites: inflammatory response for colorectal cancer (P = .006), inflammation related cell cycle gene for lung cancer (P = 1.35x10(-6)), and activation of immune response for ovarian cancer (P = .009). In addition, sequence variations in immune system development played a role in breast cancer etiology (P = .001) and innate immune response was involved in the risk of both colorectal (P = .022) and ovarian cancer (P = .003).

CONCLUSIONS

Genetic variations in inflammation and its related subpathway components are keys to the development of lung, colorectal, ovary, and breast cancer, including SH2B3, which is associated with lung, colorectal, and breast cancer.

META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies

INTRODUCTION

Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher's inverse χ2-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.

SIMULATION AND POWER

We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon's rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.

APPLICATION

We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 "transmembrane transporter activity" as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 "acetylcholine receptor activity" but only when not corrected for multiple testing (all GSA-methods applied; p ≈ 0.02).

Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence

We conducted a 1000 Genomes-imputed genome-wide association study (GWAS) meta-analysis for nicotine dependence, defined by the Fagerström Test for Nicotine Dependence in 17 074 ever smokers from five European-ancestry samples. We followed up novel variants in 7469 ever smokers from five independent European-ancestry samples. We identified genome-wide significant association in the alpha-4 nicotinic receptor subunit (CHRNA4) gene on chromosome 20q13: lowest P=8.0 × 10(-9) across all the samples for rs2273500-C (frequency=0.15; odds ratio=1.12 and 95% confidence interval=1.08-1.17 for severe vs mild dependence). rs2273500-C, a splice site acceptor variant resulting in an alternate CHRNA4 transcript predicted to be targeted for nonsense-mediated decay, was associated with decreased CHRNA4 expression in physiologically normal human brains (lowest P=7.3 × 10(-4)). Importantly, rs2273500-C was associated with increased lung cancer risk (N=28 998, odds ratio=1.06 and 95% confidence interval=1.00-1.12), likely through its effect on smoking, as rs2273500-C was no longer associated with lung cancer after adjustment for smoking. Using criteria for smoking behavior that encompass more than the single 'cigarettes per day' item, we identified a common CHRNA4 variant with important regulatory properties that contributes to nicotine dependence and smoking-related consequences.

Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10(-15)), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10(-6)). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.

Predicting survival using clinical risk scores and non-HLA immunogenetics

Previous studies of non-histocompatibility leukocyte antigen (HLA) gene single-nucleotide polymorphisms (SNPs) on subgroups of patients undergoing allogeneic haematopoietic stem cell transplantation (HSCT) revealed an association with transplant outcome. This study further evaluated the association of non-HLA polymorphisms with overall survival in a cohort of 762 HSCT patients using data on 26 polymorphisms in 16 non-HLA genes. When viewed in addition to an already established clinical risk score (EBMT-score), three polymorphisms: rs8177374 in the gene for MyD88-adapter-like (MAL; P=0.026), rs9340799 in the oestrogen receptor gene (ESR; P=0.003) and rs1800795 in interleukin-6 (IL-6; P=0.007) were found to be associated with reduced overall survival, whereas the haplo-genotype (ACC/ACC) in IL-10 was protective (P=0.02). The addition of these non-HLA polymorphisms in a Cox regression model alongside the EBMT-score improved discrimination between risk groups and increased the level of prediction compared with the EBMT-score alone (gain in prediction capability for EBMT-genetic-score 10.8%). Results also demonstrated how changes in clinical practice through time have altered the effects of non-HLA analysis. The study illustrates the significance of non-HLA genotyping prior to HSCT and the importance of further investigation into non-HLA gene polymorphisms in risk prediction.

CHRNA5 risk variant predicts delayed smoking cessation and earlier lung cancer diagnosis--a meta-analysis

BACKGROUND

Recent meta-analyses show strong evidence of associations among genetic variants in CHRNA5 on chromosome 15q25, smoking quantity, and lung cancer. This meta-analysis tests whether the CHRNA5 variant rs16969968 predicts age of smoking cessation and age of lung cancer diagnosis.

METHODS

Meta-analyses examined associations between rs16969968, age of quitting smoking, and age of lung cancer diagnosis in 24 studies of European ancestry (n = 29 072). In each dataset, we used Cox regression models to evaluate the association between rs16969968 and the two primary phenotypes (age of smoking cessation among ever smokers and age of lung cancer diagnosis among lung cancer case patients) and the secondary phenotype of smoking duration. Heterogeneity across studies was assessed with the Cochran Q test. All statistical tests were two-sided.

RESULTS

The rs16969968 allele (A) was associated with a lower likelihood of smoking cessation (hazard ratio [HR] = 0.95, 95% confidence interval [CI] = 0.91 to 0.98, P = .0042), and the AA genotype was associated with a four-year delay in median age of quitting compared with the GG genotype. Among smokers with lung cancer diagnoses, the rs16969968 genotype (AA) was associated with a four-year earlier median age of diagnosis compared with the low-risk genotype (GG) (HR = 1.08, 95% CI = 1.04 to 1.12, P = 1.1*10(-5)).

CONCLUSION

These data support the clinical significance of the CHRNA5 variant rs16969968. It predicts delayed smoking cessation and an earlier age of lung cancer diagnosis in this meta-analysis. Given the existing evidence that this CHRNA5 variant predicts favorable response to cessation pharmacotherapy, these findings underscore the potential clinical and public health importance of rs16969968 in CHRNA5 in relation to smoking cessation success and lung cancer risk.

Informed genome-wide association analysis with family history as a secondary phenotype identifies novel loci of lung cancer

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.

Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees

Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.

Kernel score statistic for dependent data

The kernel score statistic is a global covariance component test over a set of genetic markers. It provides a flexible modeling framework and does not collapse marker information. We generalize the kernel score statistic to allow for familial dependencies and to adjust for random confounder effects. With this extension, we adjust our analysis of real and simulated baseline systolic blood pressure for polygenic familial background. We find that the kernel score test gains appreciably in power through the use of sequencing compared to tag-single-nucleotide polymorphisms for very rare single nucleotide polymorphisms with <1% minor allele frequency.

Genetic factors in individual radiation sensitivity

Cancer risk and radiation sensitivity are often associated with alterations in DNA repair, cell cycle, or apoptotic pathways. Interindividual variability in mutagen or radiation sensitivity and in cancer susceptibility may also be traced back to polymorphisms of genes affecting e.g. DNA repair capacity. We studied possible associations between 70 polymorphisms of 12 DNA repair genes with basal and initial DNA damage and with repair thereof. We investigated DNA damage induced by ionizing radiation in lymphocytes isolated from 177 young lung cancer patients and 169 cancer-free controls. We also sought replication of our findings in an independent sample of 175 families (in total 798 individuals). DNA damage was assessed by the Olive tail moment (OTM) of the comet assay. DNA repair capacity (DRC) was determined for 10, 30 and, 60min of repair. Genes involved in the single-strand-repair pathway (SSR; like XRCC1 and MSH2) as well as genes involved in the double-strand-repair pathway (DSR; like RAD50, XRCC4, MRE11 and ATM) were found to be associated with DNA damage. The most significant association was observed for marker rs3213334 (p=0.005) of XRCC1 with basal DNA damage (B), in both cases and controls. A clear additive effect on the logarithm of OTM was identified for the marker rs1001581 of the same LD-block (p=0.039): BCC=-1.06 (95%-CI: -1.16 to -0.96), BCT=-1.02 (95%-CI: -1.11 to -0.93) and BTT=-0.85 (95%-CI: -1.01 to -0.68). In both cases and controls, we observed significantly higher DNA basal damage (p=0.007) for carriers of the genotype AA of marker rs2237060 of RAD50 (involved in DSR). However, this could not be replicated in the sample of families (p=0.781). An alteration to DRC after 30min of repair with respect to cases was observed as borderline significant for marker rs611646 of ATM (involved in DSR; p=0.055), but was the most significant finding in the sample of families (p=0.009). Our data indicate that gene variation impacts measurably on DNA damage and repair, suggesting at least a partial contribution to radiation sensitivity and lung cancer susceptibility.

Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R

Among the single-nucleotide polymorphisms (SNPs) previously reported to be associated with body mass index (BMI) and obesity, we focus on a common risk variant rs7566605 upstream of the insulin-induced gene 2 (INSIG2) gene and a rare protective variant rs2229616 on the melanocortin-4 receptor (MC4R) gene. INSIG2 is involved in adipogenesis and MC4R effects hormonal appetite control in response to the amount of adipose tissue. The influence of rs2229616 (MC4R) on BMI and obesity has been confirmed repeatedly and insight into the underlying mechanism provided. However, a main effect of rs7566605 (INSIG2) is under debate because of inconsistent replications of association. Interaction of rs7566605 with age may offer an explanation. SNP-age and SNP-SNP interaction models were tested on independent individuals from three population-based longitudinal cohorts, restricting the analysis to an observed age of 25-74 years. KORA S3/F3, KORA S4/F4 (Augsburg, Germany, 1994-2005, 1999-2008), and Framingham-Offspring data (Framingham, USA, 1971-2001) were analysed, with a total sample size of N=6926 in the joint analysis. The effect of interaction between rs7566605 and age on BMI and obesity status is significant and consistent across studies. This new evidence for rs7566605 (INSIG2) complements previous research. In addition, the interaction effect of rs7566605 with the MC4R variant rs2229616 on BMI was observed. This effect size was three times larger than that in a previously reported single-locus main effect of rs2229616. This leads to the conclusion that SNP-age or SNP-SNP interactions can mask genetic effects for complex diseases if left unaccounted for.

A network-based kernel machine test for the identification of risk pathways in genome-wide association studies

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

Comparison of three summary statistics for ranking genes in genome-wide association studies

Problems associated with insufficient power have haunted the analysis of genome-wide association studies and are likely to be the main challenge for the analysis of next-generation sequencing data. Ranking genes according to their strength of association with the investigated phenotype is one solution. To obtain rankings for genes, researchers can draw from a wide range of statistics summarizing the relationships between variants mapped to a gene and the phenotype. Hence, it is of interest to explore the performance of these statistics in the context of rankings. To this end, we conducted a simulation study (limited to genes of equal sizes) of three different summary statistics examining the ability to rank genes in a meaningful order. The weighted sum of squared marginal score test (Pan, 2009), RareCover algorithm (Bahtia et al., 2010) and the elastic net regularization (Zou and Hastie, 2005) were chosen, because they can handle common as well as rare variants. The test based on the score statistic outperformed both other methods in almost all investigated scenarios. It was the only measure to consistently detect genes with interacting causal variants. However, the RareCover algorithm proved better at identifying genes including causal variants with small effect sizes and low minor allele frequency than the weighted sum of squared marginal score test. The performance of the elastic net regularization was unimpressive for all but the simplest scenarios.