The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma ...protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the ...need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (<ext-link ext-link-type="uri" xlink:href="http://www.mrbase.org">http://www.mrbase.org</ext-link>): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
Context: Tea has been proposed as an antihypertensive agent for individuals with elevated blood pressure, yet the evidence for this has not been systematically reviewed to date.
Objective: The aim of ...this review was to evaluate the effects of tea on blood pressure in individuals within the prehypertensive and hypertensive blood pressure ranges.
Data Sources: The CENTRAL, PubMed, Embase, and Web of Science databases were searched for all relevant studies published from 1946 to September 27, 2013.
Study Selection: The selection criteria included randomized controlled trials of adults whose blood pressure was within hypertensive or prehypertensive ranges and in which the applied intervention was green or black tea; controls consisting of placebo, minimal tea intervention, or no intervention; and a follow-up period of at least 2 months.
Data Extraction: Two reviewers independently extracted data on participants, interventions, comparators, outcomes, and study design. Mean differences (MDs) and 95% confidence intervals (95%CIs) were pooled to generate summary effect estimates.
Results: Meta-analyses of 10 trials (834 participants) showed statistically significant reductions in systolic blood pressure (MD −2.36 mmHg, 95%CI −4.20 to −0.52) and diastolic blood pressure (MD −1.77 mmHg, 95%CI −3.03 to −0.52) with tea consumption.
Conclusions: Consumption of green or black tea can reduce blood pressure in individuals within prehypertensive and hypertensive ranges, although further investigation with studies of longer duration and stronger methodological quality is warranted to confirm these findings.
Preclinical and epidemiological studies indicate a potential chemopreventive role of statins in epithelial ovarian cancer risk.
To evaluate the association of genetically proxied inhibition of ...3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase (ie, genetic variants related to lower function of HMG-CoA reductase, target of statins) with epithelial ovarian cancer among the general population and in BRCA1/2 mutation carriers.
Single-nucleotide polymorphisms (SNPs) in HMGCR, NPC1L1, and PCSK9 associated with low-density lipoprotein (LDL) cholesterol in a genome-wide association study (GWAS) meta-analysis (N ≤196 475) were used to proxy therapeutic inhibition of HMG-CoA reductase, Niemann-Pick C1-Like 1 (NPC1L1) and proprotein convertase subtilisin/kexin type 9 (PCSK9), respectively. Summary statistics were obtained for these SNPs from a GWAS meta-analysis of case-control analyses of invasive epithelial ovarian cancer in the Ovarian Cancer Association Consortium (OCAC; N = 63 347) and from a GWAS meta-analysis of retrospective cohort analyses of epithelial ovarian cancer among BRCA1/2 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA; N = 31 448). Across the 2 consortia, participants were enrolled between 1973 and 2014 and followed up through 2015. OCAC participants came from 14 countries and CIMBA participants came from 25 countries. SNPs were combined into multi-allelic models and mendelian randomization estimates representing lifelong inhibition of targets were generated using inverse-variance weighted random-effects models.
Primary exposure was genetically proxied inhibition of HMG-CoA reductase and secondary exposures were genetically proxied inhibition of NPC1L1 and PCSK9 and genetically proxied circulating LDL cholesterol levels.
Overall and histotype-specific invasive epithelial ovarian cancer (general population) and epithelial ovarian cancer (BRCA1/2 mutation carriers), measured as ovarian cancer odds (general population) and hazard ratio (BRCA1/2 mutation carriers).
The OCAC sample included 22 406 women with invasive epithelial ovarian cancer and 40 941 control individuals and the CIMBA sample included 3887 women with epithelial ovarian cancer and 27 561 control individuals. Median ages for the cohorts ranged from 41.5 to 59.0 years and all participants were of European ancestry. In the primary analysis, genetically proxied HMG-CoA reductase inhibition equivalent to a 1-mmol/L (38.7-mg/dL) reduction in LDL cholesterol was associated with lower odds of epithelial ovarian cancer (odds ratio OR, 0.60 95% CI, 0.43-0.83; P = .002). In BRCA1/2 mutation carriers, genetically proxied HMG-CoA reductase inhibition was associated with lower ovarian cancer risk (hazard ratio, 0.69 95% CI, 0.51-0.93; P = .01). In secondary analyses, there were no significant associations of genetically proxied inhibition of NPC1L1 (OR, 0.97 95% CI, 0.53-1.75; P = .91), PCSK9 (OR, 0.98 95% CI, 0.85-1.13; P = .80), or circulating LDL cholesterol (OR, 0.98 95% CI, 0.91-1.05; P = .55) with epithelial ovarian cancer.
Genetically proxied inhibition of HMG-CoA reductase was significantly associated with lower odds of epithelial ovarian cancer. However, these findings do not indicate risk reduction from medications that inhibit HMG-CoA reductase; further research is needed to understand whether there is a similar association with such medications.
Prostate cancer (PrCa) is the second most prevalent malignancy in men worldwide. Observational studies have linked the use of low-density lipoprotein cholesterol (LDL-c) lowering therapies with ...reduced risk of PrCa, which may potentially be attributable to confounding factors. In this study, we performed a drug target Mendelian randomisation (MR) analysis to evaluate the association of genetically proxied inhibition of LDL-c-lowering drug targets on risk of PrCa.
Single-nucleotide polymorphisms (SNPs) associated with LDL-c (P < 5 × 10-8) from the Global Lipids Genetics Consortium genome-wide association study (GWAS) (N = 1,320,016) and located in and around the HMGCR, NPC1L1, and PCSK9 genes were used to proxy the therapeutic inhibition of these targets. Summary-level data regarding the risk of total, advanced, and early-onset PrCa were obtained from the PRACTICAL consortium. Validation analyses were performed using genetic instruments from an LDL-c GWAS conducted on male UK Biobank participants of European ancestry (N = 201,678), as well as instruments selected based on liver-derived gene expression and circulation plasma levels of targets. We also investigated whether putative mediators may play a role in findings for traits previously implicated in PrCa risk (i.e., lipoprotein a (Lp(a)), body mass index (BMI), and testosterone). Applying two-sample MR using the inverse-variance weighted approach provided strong evidence supporting an effect of genetically proxied inhibition of PCSK9 (equivalent to a standard deviation (SD) reduction in LDL-c) on lower risk of total PrCa (odds ratio (OR) = 0.85, 95% confidence interval (CI) = 0.76 to 0.96, P = 9.15 × 10-3) and early-onset PrCa (OR = 0.70, 95% CI = 0.52 to 0.95, P = 0.023). Genetically proxied HMGCR inhibition provided a similar central effect estimate on PrCa risk, although with a wider 95% CI (OR = 0.83, 95% CI = 0.62 to 1.13, P = 0.244), whereas genetically proxied NPC1L1 inhibition had an effect on higher PrCa risk with a 95% CI that likewise included the null (OR = 1.34, 95% CI = 0.87 to 2.04, P = 0.180). Analyses using male-stratified instruments provided consistent results. Secondary MR analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR = 0.90 per SD reduction in PCSK9 expression, 95% CI = 0.86 to 0.95, P = 5.50 × 10-5) and circulating plasma levels of PCSK9 (OR = 0.93 per SD reduction in PCSK9 protein levels, 95% CI = 0.87 to 0.997, P = 0.04) on PrCa risk. Colocalization analyses identified strong evidence (posterior probability (PPA) = 81.3%) of a shared genetic variant (rs553741) between liver-derived PCSK9 expression and PrCa risk, whereas weak evidence was found for HMGCR (PPA = 0.33%) and NPC1L1 expression (PPA = 0.38%). Moreover, genetically proxied PCSK9 inhibition was strongly associated with Lp(a) levels (Beta = -0.08, 95% CI = -0.12 to -0.05, P = 1.00 × 10-5), but not BMI or testosterone, indicating a possible role for Lp(a) in the biological mechanism underlying the association between PCSK9 and PrCa. Notably, we emphasise that our estimates are based on a lifelong exposure that makes direct comparisons with trial results challenging.
Our study supports a strong association between genetically proxied inhibition of PCSK9 and a lower risk of total and early-onset PrCa, potentially through an alternative mechanism other than the on-target effect on LDL-c. Further evidence from clinical studies is needed to confirm this finding as well as the putative mediatory role of Lp(a).
In the Selenium and Vitamin E Cancer Prevention Trial (SELECT), selenium supplementation (causing a median 114 μg/L increase in circulating selenium) did not lower overall prostate cancer risk, but ...increased risk of high-grade prostate cancer and type 2 diabetes. Mendelian randomization analysis uses genetic variants to proxy modifiable risk factors and can strengthen causal inference in observational studies. We constructed a genetic instrument comprising 11 single nucleotide polymorphisms robustly (P < 5 × 10-8) associated with circulating selenium in genome-wide association studies. In a Mendelian randomization analysis of 72 729 men in the PRACTICAL Consortium (44 825 case subjects, 27 904 control subjects), 114 μg/L higher genetically elevated circulating selenium was not associated with prostate cancer (odds ratio OR = 1.01, 95% confidence interval CI = 0.89 to 1.13). In concordance with findings from SELECT, selenium was weakly associated with advanced (including high-grade) prostate cancer (OR = 1.21, 95% CI = 0.98 to 1.49) and type 2 diabetes (OR = 1.18, 95% CI = 0.97 to 1.43; in a type 2 diabetes genome-wide association study meta-analysis with up to 49 266 case subjects and 249 906 control subjects). Our Mendelian randomization analyses do not support a role for selenium supplementation in prostate cancer prevention and suggest that supplementation could have adverse effects on risks of advanced prostate cancer and type 2 diabetes.
Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In ...order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis.
Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer).
There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses.
Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiology. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for cancer prevention.
IntroductionCompared with the traditional drug development pathway, investigating alternative uses for existing drugs (ie, drug repurposing) requires substantially less time, cost and resources. ...Immune checkpoint inhibitors are licensed for the treatment of certain breast, colorectal, head and neck, lung and melanoma cancers. These drugs target immune checkpoint proteins to reduce the suppression of T cell activation by cancer cells. As T cell suppression is a hallmark of cancer common across anatomical sites, we hypothesise that immune checkpoint inhibitors could be repurposed for the treatment of additional cancers beyond the ones already indicated.Methods and analysisWe will use two-sample Mendelian randomisation to investigate the effect of genetically proxied levels of protein targets of two immune checkpoint inhibitors—programmed cell death protein 1 and programmed death ligand 1—on survival of seven cancer types (breast, colorectal, head and neck, lung, melanoma, ovarian and prostate). Summary genetic association data will be obtained from prior genome-wide association studies of circulating protein levels and cancer survival in populations of European ancestry. Various sensitivity analyses will be performed to examine the robustness of findings to potential violations of Mendelian randomisation assumptions, collider bias and the impact of alternative genetic instrument construction strategies. The impact of treatment history and tumour stage on the findings will also be investigated using summary-level and individual-level genetic data where available.Ethics and disseminationNo separate ethics approval will be required for these analyses as we will be using data from previously published genome-wide association studies which individually gained ethical approval and participant consent. Results from analyses will be submitted as an open-access peer-reviewed publication and statistical code will be made freely available on the completion of the analysis.
Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of ...disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.
Various risk factors have been associated with epithelial ovarian cancer risk in observational epidemiological studies. However, the causal nature of the risk factors reported, and thus their ...suitability as effective intervention targets, is unclear given the susceptibility of conventional observational designs to residual confounding and reverse causation. Mendelian randomization (MR) uses genetic variants as proxies for risk factors to strengthen causal inference in observational studies. We used MR to evaluate the association of 12 previously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular factors) with risk of invasive epithelial ovarian cancer, invasive epithelial ovarian cancer histotypes, and low malignant potential tumours.
Genetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly (P < 5 × 10-8) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case-control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR-Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence (P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio OR per 50% higher odds liability: 1.10, 95% CI 1.06-1.15; P = 6.94 × 10-7) and suggestive evidence that lifetime smoking exposure was associated with an increased risk of invasive epithelial ovarian cancer (OR per unit increase in smoking score: 1.36, 95% CI 1.04-1.78; P = 0.02). In analyses examining histotypes and low malignant potential tumours, the strongest associations found were between height and clear cell carcinoma (OR per SD increase: 1.36, 95% CI 1.15-1.61; P = 0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset: 1.09, 95% CI 1.02-1.16; P = 0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per 50% higher odds liability: 0.89, 95% CI 0.82-0.96; P = 0.002). There was little evidence for an association of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone binding globulin with ovarian cancer or its subtypes. The primary limitations of this analysis include the modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the association of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptive use, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk.
Our comprehensive examination of possible aetiological drivers of ovarian carcinogenesis using germline genetic variants to proxy risk factors supports a role for few of these factors in invasive epithelial ovarian cancer overall and suggests distinct aetiologies across histotypes. The identification of novel risk factors remains an important priority for the prevention of epithelial ovarian cancer.
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