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Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

ImportanceThe causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation.ObjectiveTo conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases.Data sourcesGenomewide association studies (GWAS) published up to January 15, 2015.Study selectionGWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available.Data extraction and synthesisSummary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population.Main outcomes and measuresOdds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation.ResultsSummary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]).Conclusions and relevanceIt is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

ImportanceThe causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation.ObjectiveTo conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases.Data sourcesGenomewide association studies (GWAS) published up to January 15, 2015.Study selectionGWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available.Data extraction and synthesisSummary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population.Main outcomes and measuresOdds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation.ResultsSummary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]).Conclusions and relevanceIt is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

BackgroundAssessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer.Methods and findingsWe identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01-1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15-2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79-1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84-0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25-2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results.ConclusionsOur results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior.

Body mass index (BMI) is inversely associated with lung cancer risk in observational studies, even though it increases the risk of several other cancers, which could indicate confounding by tobacco smoking or reverse causality. We used the two-sample Mendelian randomization (MR) approach to circumvent these limitations of observational epidemiology by constructing a genetic instrument for BMI, based on results from the GIANT consortium, which was evaluated in relation to lung cancer risk using GWAS results on 16,572 lung cancer cases and 21,480 controls. Results were stratified by histological subtype, smoking status and sex. An increase of one standard deviation (SD) in BMI (4.65 Kg/m(2)) raised the risk for lung cancer overall (OR = 1.13; P = 0.10). This was driven by associations with squamous cell (SQ) carcinoma (OR = 1.45; P = 1.2 × 10(-3)) and small cell (SC) carcinoma (OR = 1.81; P = 0.01). An inverse trend was seen for adenocarcinoma (AD) (OR = 0.82; P = 0.06). In stratified analyses, a 1 SD increase in BMI was inversely associated with overall lung cancer in never smokers (OR = 0.50; P = 0.02). These results indicate that higher BMI may increase the risk of certain types of lung cancer, in particular SQ and SC carcinoma.

Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats). [ABSTRACT FROM AUTHOR]

Background Several obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation. Methods and findings Genetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (OR.sub.SD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (OR.sub.SD : 1.56, 95% confidence interval [CI] 1.44-1.70), with comparable results for waist-to-hip ratio (OR.sub.SD : 1.63, 95% CI 1.40-1.90) and body fat percentage (OR.sub.SD : 1.66, 95% CI 1.44-1.90). This analysis further indicated that higher fasting insulin (OR.sub.SD : 1.82, 95% CI 1.30-2.55) and diastolic blood pressure (DBP; OR.sub.SD : 1.28, 95% CI 1.11-1.47), but not systolic blood pressure (OR.sub.SD : 0.98, 95% CI 0.84-1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose. Conclusions This study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk., Author(s): Mattias Johansson 1,*, Robert Carreras-Torres 1, Ghislaine Scelo 1, Mark P. Purdue 2, Daniela Mariosa 1, David C. Muller 3, Nicolas J. Timpson 4, Philip C. Haycock 4, Kevin [...]

Background Both positive and negative associations between higher body mass index (BMI) and Parkinson disease (PD) have been reported in observational studies, but it has been difficult to establish causality because of the possibility of residual confounding or reverse causation. To our knowledge, Mendelian randomisation (MR)-the use of genetic instrumental variables (IVs) to explore causal effects-has not previously been used to test the effect of BMI on PD. Methods and findings Two-sample MR was undertaken using genome-wide association (GWA) study data. The associations between the genetic instruments and BMI were obtained from the GIANT consortium and consisted of the per-allele difference in mean BMI for 77 independent variants that reached genome-wide significance. The per-allele difference in log-odds of PD for each of these variants was estimated from a recent meta-analysis, which included 13,708 cases of PD and 95,282 controls. The inverse-variance weighted method was used to estimate a pooled odds ratio (OR) for the effect of a 5-kg/m.sup.2 higher BMI on PD. Evidence of directional pleiotropy averaged across all variants was sought using MR-Egger regression. Frailty simulations were used to assess whether causal associations were affected by mortality selection. A combined genetic IV expected to confer a lifetime exposure of 5-kg/m.sup.2 higher BMI was associated with a lower risk of PD (OR 0.82, 95% CI 0.69-0.98). MR-Egger regression gave similar results, suggesting that directional pleiotropy was unlikely to be biasing the result (intercept 0.002; p = 0.654). However, the apparent protective influence of higher BMI could be at least partially induced by survival bias in the PD GWA study, as demonstrated by frailty simulations. Other important limitations of this application of MR include the inability to analyse non-linear associations, to undertake subgroup analyses, and to gain mechanistic insights. Conclusions In this large study using two-sample MR, we found that variants known to influence BMI had effects on PD in a manner consistent with higher BMI leading to lower risk of PD. The mechanism underlying this apparent protective effect warrants further study., Author(s): Alastair J. Noyce 1,2, Demis A. Kia 1, Gibran Hemani 3,4, Aude Nicolas 5, T. Ryan Price 5, Eduardo De Pablo-Fernandez 1, Philip C. Haycock 3,4, Patrick A. Lewis [...]

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 (ORMVMR = 1.28, 95% CI = 1.06‐1.55, PMVMR =.011), and an inverse effect on lung adenocarcinoma (ORMVMR = 0.86, 95% CI = 0.77‐0.96, PMVMR =.008) were observed. A weak increased risk of lung squamous cell carcinoma was observed for higher BMI in univariable Mendelian randomization (UVMR) analysis (ORUVMR = 1.19, 95% CI = 1.01‐1.40, PUVMR =.036), but this effect disappeared after adjustment of smoking (ORMVMR = 1.02, 95% CI = 0.90‐1.16, PMVMR =.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. What's new? Lung cancer risk appears to be inversely correlated with BMI, which could be due to a variety of factors. Here, the authors used Mendelian randomization (MR) to look for a causal effect of BMI on lung cancer. MR uses genetic variants as instrumental variables, and avoids the effects of confounding factors. However, linkage disequilibrium with causal variants may interfere with the results. After adjustment for smoking, the authors found a direct causal effect of BMI on small cell lung cancer, and an inverse effect on lung adenocarcinoma. These results highlight that the effect of BMI varies significantly depending on histology. [ABSTRACT FROM AUTHOR]

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases. Author summary: Genetic association studies have been successful in identifying many genetic variants associated with disease risk, but it has been far more challenging to determine the genes through which these act. This is important, because such genes may encode effective drug targets for these diseases. We used Mendelian randomization (MR) and colocalization, two methods which in combination exploit these genetic variants to estimate the causal effects of individual genes. We applied this approach to 12 neurological and psychiatric diseases using data from the AMP-AD and CMC brain expression quantitative locus dataset, which is large enough to provide robust evidence for the relationship between genetic variants and gene expression. We found a causal relationship between the change in expression of 47 genes and increased disease risk across the 12 diseases we tested. As drug targets with human genetic evidence are far more likely to be approved in clinical trials, these findings provide a valuable list of potential therapeutic targets, including the ACE, GPNMB, KCNQ5, RERE and SUOX genes. [ABSTRACT FROM AUTHOR]

Background: Observational studies have suggested an association between circulating vitamin D concentrations [25(OH)D] and risk of breast and prostate cancer, which was not supported by a recent Mendelian randomization (MR) analysis comprising 15 748 breast and 22 898 prostate-cancer cases. Demonstrating causality has proven challenging and one common limitation of MR studies is insufficient power.Methods: We aimed to determine whether circulating concentrations of vitamin D are causally associated with the risk of breast and prostate cancer, by using summary-level data from the largest ever genome-wide association studies conducted on vitamin D (N = 73 699), breast cancer (Ncase = 122 977) and prostate cancer (Ncase = 79 148). We constructed a stronger instrument using six common genetic variants (compared with the previous four variants) and applied several two-sample MR methods.Results: We found no evidence to support a causal association between 25(OH)D and risk of breast cancer [OR per 25 nmol/L increase, 1.02 (95% confidence interval: 0.97-1.08), P = 0.47], oestrogen receptor (ER)+ [1.00 (0.94-1.07), P = 0.99] or ER- [1.02 (0.90-1.16), P = 0.75] subsets, prostate cancer [1.00 (0.93-1.07), P = 0.99] or the advanced subtype [1.02 (0.90-1.16), P = 0.72] using the inverse-variance-weighted method. Sensitivity analyses did not reveal any sign of directional pleiotropy.Conclusions: Despite its almost five-fold augmented sample size and substantially improved statistical power, our MR analysis does not support a causal effect of circulating 25(OH)D concentrations on breast- or prostate-cancer risk. However, we can still not exclude a modest or non-linear effect of vitamin D. Future studies may be designed to understand the effect of vitamin D in subpopulations with a profound deficiency. [ABSTRACT FROM AUTHOR]