Genome-wide interaction study of smoking behavior and non-small cell lung cancer risk in Caucasian population.

Non-small cell lung cancer is the most common type of lung cancer. Both environmental and genetic risk factors contribute to lung carcinogenesis. We conducted a genome-wide interaction analysis between single nucleotide polymorphisms (SNPs) and smoking status (never- versus ever-smokers) in a European-descent population. We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13336 non-small cell lung cancer cases. Candidate SNPs with P-value <0.001 were further analyzed using a standard case-control interaction analysis including 13970 controls. The significant SNPs with P-value <3.5 × 10-5 (correcting for multiple tests) from the case-control analysis in the discovery stage were further validated using an independent replication dataset comprising 5377 controls and 3054 non-small cell lung cancer cases. We further stratified the analysis by histological subtypes. Two novel SNPs, rs6441286 and rs17723637, were identified for overall lung cancer risk. The interaction odds ratio and meta-analysis P-value for these two SNPs were 1.24 with 6.96 × 10-7 and 1.37 with 3.49 × 10-7, respectively. In addition, interaction of smoking with rs4751674 was identified in squamous cell lung carcinoma with an odds ratio of 0.58 and P-value of 8.12 × 10-7. This study is by far the largest genome-wide SNP-smoking interaction analysis reported for lung cancer. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention. The results from our study reinforce that gene-smoking interactions play important roles in the etiology of lung cancer and account for part of the missing heritability of this disease.

Can polymorphisms in the fatty acid desaturase (FADS) gene cluster alter the effects of fish oil supplementation on plasma and erythrocyte fatty acid profiles? An exploratory study.

PURPOSE: The enzymes encoded by fatty acid desaturases (FADS) genes determine the desaturation of long-chain polyunsaturated fatty acids (LCPUFA). We investigated if haplotype and single nucleotide polymorphisms (SNPs) in FADS gene cluster can influence LCPUFA status in infants who received either fish oil or placebo supplementation. METHODS: Children enrolled in the Infant Fish Oil Supplementation Study (IFOS) were randomly allocated to receive either fish oil or placebo from birth to 6 months of age. Blood was collected at 6 months of age for the measurement of fatty acids and for DNA extraction. A total of 276 participant DNA samples underwent genotyping, and 126 erythrocyte and 133 plasma fatty acid measurements were available for analysis. Twenty-two FADS SNPs were selected on the basis of literature and linkage disequilibrium patterns identified from the HapMap data. Haplotype construction was completed using PHASE. RESULTS: For participants allocated to the fish oil group who had two copies of the FADS1 haplotype consisting of SNP minor alleles, DHA levels were significantly higher compared to other haplotypes. This finding was not observed for the placebo group. Furthermore, for members of the fish oil group only, the minor homozygous carriers of all the FADS1 SNPs investigated had significantly higher DHA than other genotypes (rs174545, rs174546, rs174548, rs174553, rs174556, rs174537, rs174448, and rs174455). CONCLUSIONS: Overall results of this preliminary study suggest that supplementation with fish oil may only significantly increase DHA in minor allele carriers of FADS1 SNPs. Further research is required to confirm this novel finding.

Genetic modifiers of radon-induced lung cancer risk: a genome-wide interaction study in former uranium miners.

PURPOSE: Radon is a risk factor for lung cancer and uranium miners are more exposed than the general population. A genome-wide interaction analysis was carried out to identify genomic loci, genes or gene sets that modify the susceptibility to lung cancer given occupational exposure to the radioactive gas radon. METHODS: Samples from 28 studies provided by the International Lung Cancer Consortium were pooled with samples of former uranium miners collected by the German Federal Office of Radiation Protection. In total, 15,077 cases and 13,522 controls, all of European ancestries, comprising 463 uranium miners were compared. The DNA of all participants was genotyped with the OncoArray. We fitted single-marker and in multi-marker models and performed an exploratory gene-set analysis to detect cumulative enrichment of significance in sets of genes. RESULTS: We discovered a genome-wide significant interaction of the marker rs12440014 within the gene CHRNB4 (OR = 0.26, 95% CI 0.11-0.60, p = 0.0386 corrected for multiple testing). At least suggestive significant interaction of linkage disequilibrium blocks was observed at the chromosomal regions 18q21.23 (p = 1.2 × 10-6), 5q23.2 (p = 2.5 × 10-6), 1q21.3 (p = 3.2 × 10-6), 10p13 (p = 1.3 × 10-5) and 12p12.1 (p = 7.1 × 10-5). Genes belonging to the Gene Ontology term "DNA dealkylation involved in DNA repair" (GO:0006307; p = 0.0139) or the gene family HGNC:476 "microRNAs" (p = 0.0159) were enriched with LD-blockwise significance. CONCLUSION: The well-established association of the genomic region 15q25 to lung cancer might be influenced by exposure to radon among uranium miners. Furthermore, lung cancer susceptibility is related to the functional capability of DNA damage signaling via ubiquitination processes and repair of radiation-induced double-strand breaks by the single-strand annealing mechanism.

Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development.

The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10-11 in overall lung cancer and OR=0.41, p value=9.71x10-11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10-12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10-11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.

Validation of standard operating procedures in a multicenter retrospective study to identify -omics biomarkers for chronic low back pain.

Chronic low back pain (CLBP) is one of the most common medical conditions, ranking as the greatest contributor to global disability and accounting for huge societal costs based on the Global Burden of Disease 2010 study. Large genetic and -omics studies provide a promising avenue for the screening, development and validation of biomarkers useful for personalized diagnosis and treatment (precision medicine). Multicentre studies are needed for such an effort, and a standardized and homogeneous approach is vital for recruitment of large numbers of participants among different centres (clinical and laboratories) to obtain robust and reproducible results. To date, no validated standard operating procedures (SOPs) for genetic/-omics studies in chronic pain have been developed. In this study, we validated an SOP model that will be used in the multicentre (5 centres) retrospective "PainOmics" study, funded by the European Community in the 7th Framework Programme, which aims to develop new biomarkers for CLBP through three different -omics approaches: genomics, glycomics and activomics. The SOPs describe the specific procedures for (1) blood collection, (2) sample processing and storage, (3) shipping details and (4) cross-check testing and validation before assays that all the centres involved in the study have to follow. Multivariate analysis revealed the absolute specificity and homogeneity of the samples collected by the five centres for all genetics, glycomics and activomics analyses. The SOPs used in our multicenter study have been validated. Hence, they could represent an innovative tool for the correct management and collection of reliable samples in other large-omics-based multicenter studies.

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers.

BACKGROUND: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. METHODS: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. RESULTS: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. CONCLUSIONS: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. IMPACT: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.

Compare and Contrast Meta Analysis (CCMA): A Method for Identification of Pleiotropic Loci in Genome-Wide Association Studies.

In recent years, genome-wide association studies (GWAS) have identified many loci that are shared among common disorders and this has raised interest in pleiotropy. For performing appropriate analysis, several methods have been proposed, e.g. conducting a look-up in external sources or exploiting GWAS results by meta-analysis based methods. We recently proposed the Compare & Contrast Meta-Analysis (CCMA) approach where significance thresholds were obtained by simulation. Here we present analytical formulae for the density and cumulative distribution function of the CCMA test statistic under the null hypothesis of no pleiotropy and no association, which, conveniently for practical reasons, turns out to be exponentially distributed. This allows researchers to apply the CCMA method without having to rely on simulations. Finally, we show that CCMA demonstrates power to detect disease-specific, agonistic and antagonistic loci comparable to the frequently used Subset-Based Meta-Analysis approach, while better controlling the type I error rate.

Targeted Resequencing and Functional Testing Identifies Low-Frequency Missense Variants in the Gene Encoding GARP as Significant Contributors to Atopic Dermatitis Risk.

Gene-mapping studies have consistently identified a susceptibility locus for atopic dermatitis and other inflammatory diseases on chromosome band 11q13.5, with the strongest association observed for a common variant located in an intergenic region between the two annotated genes C11orf30 and LRRC32. Using a targeted resequencing approach we identified low-frequency and rare missense mutations within the LRRC32 gene encoding the protein GARP, a receptor on activated regulatory T cells that binds latent transforming growth factor-ß. Subsequent association testing in more than 2,000 atopic dermatitis patients and 2,000 control subjects showed a significant excess of these LRRC32 variants in individuals with atopic dermatitis. Structural protein modeling and bioinformatic analysis predicted a disruption of protein transport upon these variants, and overexpression assays in CD4+CD25- T cells showed a significant reduction in surface expression of the mutated protein. Consistently, flow cytometric (FACS) analyses of different T-cell subtypes obtained from atopic dermatitis patients showed a significantly reduced surface expression of GARP and a reduced conversion of CD4+CD25- T cells into regulatory T cells, along with lower expression of latency-associated protein upon stimulation in carriers of the LRRC32 A407T variant. These results link inherited disturbances of transforming growth factor-ß signaling with atopic dermatitis risk.

'Omics' biomarkers associated with chronic low back pain: protocol of a retrospective longitudinal study.

INTRODUCTION: Chronic low back pain (CLBP) produces considerable direct costs as well as indirect burdens for society, industry and health systems. CLBP is characterised by heterogeneity, inclusion of several pain syndromes, different underlying molecular pathologies and interaction with psychosocial factors that leads to a range of clinical manifestations. There is still much to understand in the underlying pathological processes and the non-psychosocial factors which account for differences in outcomes. Biomarkers that may be objectively used for diagnosis and personalised, targeted and cost-effective treatment are still lacking. Therefore, any data that may be obtained at the '-omics' level (glycomics, Activomics and genome-wide association studies-GWAS) may be helpful to use as dynamic biomarkers for elucidating CLBP pathogenesis and may ultimately provide prognostic information too. By means of a retrospective, observational, case-cohort, multicentre study, we aim to investigate new promising biomarkers potentially able to solve some of the issues related to CLBP. METHODS AND ANALYSIS: The study follows a two-phase, 1:2 case-control model. A total of 12 000 individuals (4000 cases and 8000 controls) will be enrolled; clinical data will be registered, with particular attention to pain characteristics and outcomes of pain treatments. Blood samples will be collected to perform -omics studies. The primary objective is to recognise genetic variants associated with CLBP; secondary objectives are to study glycomics and Activomics profiles associated with CLBP. ETHICS AND DISSEMINATION: The study is part of the PainOMICS project funded by European Community in the Seventh Framework Programme. The study has been approved from competent ethical bodies and copies of approvals were provided to the European Commission before starting the study. Results of the study will be reviewed by the Scientific Board and Ethical Committee of the PainOMICS Consortium. The scientific results will be disseminated through peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT02037789; Pre-results.

Heritability and Genome-Wide Association Analyses of Sleep Duration in Children: The EAGLE Consortium.

STUDY OBJECTIVES: Low or excessive sleep duration has been associated with multiple outcomes, but the biology behind these associations remains elusive. Specifically, genetic studies in children are scarce. In this study, we aimed to: (1) estimate the proportion of genetic variance of sleep duration in children attributed to common single nucleotide polymorphisms (SNPs), (2) identify novel SNPs associated with sleep duration in children, and (3) investigate the genetic overlap of sleep duration in children and related metabolic and psychiatric traits. METHODS: We performed a population-based molecular genetic study, using data form the EArly Genetics and Life course Epidemiology (EAGLE) Consortium. 10,554 children of European ancestry were included in the discovery, and 1,250 children in the replication phase. RESULTS: We found evidence of significant but modest SNP heritability of sleep duration in children (SNP h2 0.14, 95% CI [0.05, 0.23]) using the LD score regression method. A novel region at chromosome 11q13.4 (top SNP: rs74506765, P = 2.27e-08) was associated with sleep duration in children, but this was not replicated in independent studies. Nominally significant genetic overlap was only found (rG = 0.23, P = 0.05) between sleep duration in children and type 2 diabetes in adults, supporting the hypothesis of a common pathogenic mechanism. CONCLUSIONS: The significant SNP heritability of sleep duration in children and the suggestive genetic overlap with type 2 diabetes support the search for genetic mechanisms linking sleep duration in children to multiple outcomes in health and disease.