Martin, Richard M

Abstract 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 Background Glycemic traits—such as hyperinsulinemia, hyperglycemia, and type 2 diabetes—have been associated with higher colorectal cancer risk in observational studies; however, causality of these associations is uncertain. We used Mendelian randomization (MR) to estimate the causal effects of fasting insulin, 2-hour glucose, fasting glucose, glycated hemoglobin (HbA1c), and type 2 diabetes with colorectal cancer. Methods Genome-wide association study summary data were used to identify genetic variants associated with circulating levels of fasting insulin (n = 34), 2-hour glucose (n = 13), fasting glucose (n = 70), HbA1c (n = 221), and type 2 diabetes (n = 268). Using 2-sample MR, we examined these variants in relation to colorectal cancer risk (48 214 case patient and 64 159 control patients). Results In inverse-variance models, higher fasting insulin levels increased colorectal cancer risk (odds ratio [OR] per 1-SD = 1.65, 95% confidence interval [CI] = 1.15 to 2.36). We found no evidence of any effect of 2-hour glucose (OR per 1-SD = 1.02, 95% CI = 0.86 to 1.21) or fasting glucose (OR per 1-SD = 1.04, 95% CI = 0.88 to 1.23) concentrations on colorectal cancer risk. Genetic liability to type 2 diabetes (OR per 1-unit increase in log odds = 1.04, 95% CI = 1.01 to 1.07) and higher HbA1c levels (OR per 1-SD = 1.09, 95% CI = 1.00 to 1.19) increased colorectal cancer risk, although these findings may have been biased by pleiotropy. Higher HbA1c concentrations increased rectal cancer risk in men (OR per 1-SD = 1.21, 95% CI = 1.05 to 1.40), but not in women. Conclusions Our results support a causal effect of higher fasting insulin, but not glucose traits or type 2 diabetes, on increased colorectal cancer risk. This suggests that pharmacological or lifestyle interventions that lower circulating insulin levels may be beneficial in preventing colorectal tumorigenesis.

Abstract Background: Observational evidence has shown that smoking is a risk factor for breast and colorectal cancer. We used Mendelian randomization (MR) to examine causal associations between smoking and risks of breast and colorectal cancer. Methods: Genome-Wide Association Study summary data were used to identify genetic variants associated with lifetime amount of smoking (n = 126 variants) and ever having smoked regularly (n = 112 variants). Using two-sample MR, we examined these variants in relation to incident breast (122,977 cases/105,974 controls) and colorectal cancer (52,775 cases/45,940 controls). Results: In inverse-variance weighted models, a genetic predisposition to higher lifetime amount of smoking was positively associated with breast cancer risk [OR per 1-SD increment: 1.13; 95% confidence interval (CI): 1.00–1.26; P = 0.04]; although heterogeneity was observed. Similar associations were found for estrogen receptor–positive and estrogen receptor–negative tumors. Higher lifetime amount of smoking was positively associated with colorectal cancer (OR per 1-SD increment, 1.21; 95% CI, 1.04–1.40; P = 0.01), colon cancer (OR, 1.31; 95% CI, 1.11–1.55; P < 0.01), and rectal cancer (OR, 1.36; 95% CI, 1.07–1.73; P = 0.01). Ever having smoked regularly was not associated with risks of breast (OR, 1.01; 95% CI, 0.90–1.14; P = 0.85) or colorectal cancer (OR, 0.97; 95% CI, 0.86–1.10; P = 0.68). Conclusions: These findings are consistent with prior observational evidence and support a causal role of higher lifetime smoking amount in the development of breast and colorectal cancer. Impact: The results from this comprehensive MR analysis indicate that lifetime smoking is a causal risk factor for these common malignancies.

Abstract Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3–T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

Abstract Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all‐cause and prostate cancer‐specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high‐grade compared to low‐grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all‐cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer‐specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression.

Prostate cancer is the most common cancer in men in developed countries, and is a target for risk reduction strategies. The effects of alcohol consumption on prostate cancer incidence and survival remain unclear, potentially due to methodological limitations of observational studies. In this study, we investigated the associations of genetic variants in alcohol‐metabolising genes with prostate cancer incidence and survival. We analysed data from 23,868 men with prostate cancer and 23,051 controls from 25 studies within the international PRACTICAL Consortium. Study‐specific associations of 68 single nucleotide polymorphisms (SNPs) in 8 alcohol‐metabolising genes (Alcohol Dehydrogenases (ADHs) and Aldehyde Dehydrogenases (ALDHs)) with prostate cancer diagnosis and prostate cancer‐specific mortality, by grade, were assessed using logistic and Cox regression models, respectively. The data across the 25 studies were meta‐analysed using fixed‐effect and random‐effects models. We found little evidence that variants in alcohol metabolising genes were associated with prostate cancer diagnosis. Four variants in two genes exceeded the multiple testing threshold for associations with prostate cancer mortality in fixed‐effect meta‐analyses. SNPs within ALDH1A2 associated with prostate cancer mortality were rs1441817 (fixed effects hazard ratio, HRfixed = 0.78; 95% confidence interval (95%CI):0.66,0.91; p values = 0.002); rs12910509, HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.003); and rs8041922 (HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.002). These SNPs were in linkage disequilibrium with each other. In ALDH1B1, rs10973794 (HRfixed = 1.43; 95%CI:1.14,1.79; p values = 0.002) was associated with prostate cancer mortality in men with low‐grade prostate cancer. These results suggest that alcohol consumption is unlikely to affect prostate cancer incidence, but it may influence disease progression.

Circulating insulin‐like growth factors (IGFs) and their binding proteins (IGFBPs) are associated with prostate cancer. Using genetic variants as instruments for IGF peptides, we investigated whether these associations are likely to be causal. We identified from the literature 56 single nucleotide polymorphisms (SNPs) in the IGF axis previously associated with biomarker levels (8 from a genome‐wide association study [GWAS] and 48 in reported candidate genes). In ∼700 men without prostate cancer and two replication cohorts (N ∼ 900 and ∼9,000), we examined the properties of these SNPS as instrumental variables (IVs) for IGF‐I, IGF‐II, IGFBP‐2 and IGFBP‐3. Those confirmed as strong IVs were tested for association with prostate cancer risk, low (< 7) vs. high (≥ 7) Gleason grade, localised vs. advanced stage, and mortality, in 22,936 controls and 22,992 cases. IV analysis was used in an attempt to estimate the causal effect of circulating IGF peptides on prostate cancer. Published SNPs in the IGFBP1/IGFBP3 gene region, particularly rs11977526, were strong instruments for IGF‐II and IGFBP‐3, less so for IGF‐I. Rs11977526 was associated with high (vs. low) Gleason grade (OR per IGF‐II/IGFBP‐3 level‐raising allele 1.05; 95% CI: 1.00, 1.10). Using rs11977526 as an IV we estimated the causal effect of a one SD increase in IGF‐II (∼265 ng/mL) on risk of high vs. low grade disease as 1.14 (95% CI: 1.00, 1.31). Because of the potential for pleiotropy of the genetic instruments, these findings can only causally implicate the IGF pathway in general, not any one specific biomarker.

AbstractGenetic risk scores were used as unconfounded instruments for specific lipid traits (Mendelian randomization) to assess whether circulating lipids causally influence prostate cancer risk. Data from 22,249 prostate cancer cases and 22,133 controls from 22 studies within the international PRACTICAL consortium were analyzed. Allele scores based on single nucleotide polymorphisms (SNPs) previously reported to be uniquely associated with each of low‐density lipoprotein (LDL), high‐density lipoprotein (HDL), and triglyceride (TG) levels, were first validated in an independent dataset, and then entered into logistic regression models to estimate the presence (and direction) of any causal effect of each lipid trait on prostate cancer risk. There was weak evidence for an association between the LDL genetic score and cancer grade: the odds ratio (OR) per genetically instrumented standard deviation (SD) in LDL, comparing high‐ (≥7 Gleason score) versus low‐grade (<7 Gleason score) cancers was 1.50 (95% CI: 0.92, 2.46; P = 0.11). A genetically instrumented SD increase in TGs was weakly associated with stage: the OR for advanced versus localized cancer per unit increase in genetic risk score was 1.68 (95% CI: 0.95, 3.00; P = 0.08). The rs12916‐T variant in 3‐hydroxy‐3‐methylglutaryl‐CoA reductase (HMGCR) was inversely associated with prostate cancer (OR: 0.97; 95% CI: 0.94, 1.00; P = 0.03). In conclusion, circulating lipids, instrumented by our genetic risk scores, did not appear to alter prostate cancer risk. We found weak evidence that higher LDL and TG levels increase aggressive prostate cancer risk, and that a variant in HMGCR (that mimics the LDL lowering effect of statin drugs) reduces risk. However, inferences are limited by sample size and evidence of pleiotropy.