BACKGROUND AND OBJECTIVE:Observational studies have found that depression, anxiety, and neuroticism are prospectively associated with lower urinary tract symptoms (LUTS) among women, but are limited by confounding and measurement errors. We examined whether genetic liability for these psychiatric traits is associated with LUTS among women. METHODS:Participants were from the mothers cohort of the Avon Longitudinal Study of Parents and Children (ALSPAC). Polygenic risk scores (PRSs) for depression, anxiety, and neuroticism were calculated using summary data from genome-wide association studies. LUTS were assessed using validated questionnaires at two time points: 2002-2004 (n = 4758; median age 40 yr, interquartile range [IQR] 37-43) and 2011-2012 (n = 3026; median age 50 yr, IQR 47-53). Separate logistic regression models, adjusted for age and population structure, were fitted to examine the association of each PRS with each LUTS. KEY FINDINGS AND LIMITATIONS:The neuroticism PRS was associated with nocturia at both time points [odds ratio and 95% confidence intervals=1.24 (1.10,1.40) and 1.21 (1.07,1.37)], and with any urinary incontinence (UI), any urgency, and mixed UI (2002-04) [1.14 (1.05,1.23), 1.12 (1.02,1.23), and 1.23 (1.07,1.43), respectively]. The depression PRS was associated with nocturia [OR 1.33, 95% CI 1.17-1.50) and 1.24 (1.09,1.40)] and any UI [1.09 (1.01,1.18); 1.11 (1.02,1.20)] at both time points, and with any urgency (2011-12) [1.13 (1.03,1.24)]. The anxiety PRS was associated with mixed UI (2002-04) [1.20 (1.03,1.39)]. Limitations include PRS not capturing all genetic liability, potential pleiotropic effects, and restriction of the cohort to parous women of predominantly European ancestry. CONCLUSIONS AND CLINICAL IMPLICATIONS:Genetic liability for depression, anxiety, and neuroticism is associated with higher risk of LUTS among women. This finding indicates a potential shared aetiology or a possible contribution of mental health problems in the development of LUTS.

BACKGROUND:There is evidence that adverse childhood experiences (ACEs) are associated with lower urinary tract symptoms (LUTS) in adulthood, but few studies have explored these associations in adolescence. Little is known about the biological mechanisms that could explain these associations. METHODS:We used data from the Avon Longitudinal Study of Parents and Children (n = 4745) on ACEs (from birth to age 8 years), LUTS at age 14 years [any urinary incontinence (UI), daytime and bedwetting, urgency, nocturia, frequent urination, voiding postponement, and low voiding volume], and inflammatory biomarkers interleukin-6 (IL-6) and C-reactive protein (CRP) measured at age 9 years. We examined associations between the summed ACE score and LUTS, and inflammation and LUTS. We then evaluated the mediating effects of IL-6 and CRP. RESULTS:Higher ACE scores were associated with increased odds of LUTS, e.g. a one-unit increase in the ACE score was associated with an increased odds of any UI [odds ratio (OR) 1.16, 95% confidence interval (CI) 1.03-1.30]. Higher levels of IL-6 were associated with increased odds of LUTS, e.g. any UI (OR 1.24, 95% CI 1.05-1.47). There was weak evidence that the associations between ACE score and LUTS were mediated by IL-6 (e.g. any UI ORnatural_indirect_effect 1.03, 95% CI 1.00-1.06). There was no evidence that CRP was associated with LUTS or mediated the association between ACE score and LUTS. CONCLUSION:This study reports novel findings that point to inflammation as being a possible mechanism on the causal pathway from ACEs to LUTS. Early intervention is needed in childhood to prevent LUTS persisting into adolescence.

Objective We aimed to examine the associations between family adversity in infancy and subsequent daytime soiling at primary school age. We also examined factors on the causal pathway between family adversity and soiling, specifically the child’s emotional/behaviour problems and constipation.Design This prospective cohort study included children from the Avon Longitudinal Study of Parents and Children (N=10 033) with data on the Family Adversity Index (FAI; birth to age 2), daytime soiling at age 7.5, emotional/behaviour problems at age 4, and constipation at age 6. We examined the relationships between the FAI, soiling, emotional/behaviour problems and constipation. We then evaluated the mediating effects of emotional/behaviour problems and constipation. All analyses were adjusted for child and family-related confounders.Results Daytime soiling occurred in 7% of children. A one-unit increase in infancy FAI was associated with increased odds of 12% for daytime soiling at age 7.5 (OR 1.12, 95% CI 1.07 to 1.17), emotional/behaviour problems score at age 4 (beta 0.52, 95% CI 0.47 to 0.58) and increased odds of constipation at age 6 (OR 1.07, 95% CI 1.03 to 1.11). The presence of constipation at age 6 was strongly associated with increased odds of soiling at age 7.5 (OR 3.34, 95% CI 2.68 to 4.16). There was weak evidence that the associations between FAI and daytime soiling were mediated by emotional/behaviour problems and constipation (ORnatural_indirect_effect 1.03, 95% CI 0.99 to 1.07).Conclusions Clinicians should be aware that exposure to family adversity in the early years places children at greater risk of subsequent constipation and soiling.

Road traffic noise exposure has been associated with multiple adverse outcomes in epidemiological studies. However, the underlying biological mechanisms remain unclear. The aim of this study was to investigate the association between road traffic noise exposure and cord blood and child blood DNA methylation (DNAm). Data from six European studies (BAMSE, Generation R, HELIX, INMA, LISA, PIAMA) were used to perform the discovery epigenome-wide meta-analysis. Prenatal, infancy, and recent road traffic noise exposure was assessed at the residential addresses. Blood DNAm was measured using the Illumina 450 K or EPIC arrays. To identify differentially methylated positions (DMPs), we fitted robust linear regression models for each cohort, and the results were subsequently meta-analyzed. Differentially methylated regions (DMRs) were identified using Comb-p and DMRcate. Findings were then looked-up in the independent ALSPAC cohort, in which noise was measured categorically. A total of 1477 newborns with DNAm data in cord blood, and 1129 and 2065 with DNAm in child blood (age 4-6 and age 8-10 years, respectively) were included in the discovery meta-analysis. We did not observe genome-wide significant (False Discovery Rate (FDR) < 0.05) DMPs associated with road traffic noise exposure. However, 46 DMPs reached suggestive significance (P < 1 × 10-5) across different time windows. One CpG site (cg09400092, annotated to SSTR1) associated with recent noise exposure at age 8-10 years was also significantly associated in the ALSPAC cohort (same direction of association with P = 0.00165). In addition, we identified a total of 93 FDR significant DMRs, of which 14 were nominally significant in the ALSPAC study. In conclusion, we observed suggestive evidence of an association between road traffic noise exposure and DNAm in child blood. This may indicate that differential DNAm plays a role in the biological mechanism underlying health effects of noise exposure.

Green space exposure has been associated with improved mental, physical and general health. However, the underlying biological mechanisms remain largely unknown. The aim of this study was to investigate the association between green space exposure and cord and child blood DNA methylation.Data from eight European birth cohorts with a total of 2,988 newborns and 1,849 children were used. Two indicators of residential green space exposure were assessed: (i) surrounding greenness (satellite-based Normalized Difference Vegetation Index (NDVI) in buffers of 100 m and 300 m) and (ii) proximity to green space (having a green space ≥ 5,000 m2 within a distance of 300 m). For these indicators we assessed two exposure windows: (i) pregnancy, and (ii) the period from pregnancy to child blood DNA methylation assessment, named as cumulative exposure. DNA methylation was measured with the Illumina 450K or EPIC arrays. To identify differentially methylated positions (DMPs) we fitted robust linear regression models between pregnancy green space exposure and cord blood DNA methylation and between cumulative green space exposure and child blood DNA methylation. Two sensitivity analyses were conducted: (i) without adjusting for cellular composition, and (ii) adjusting for air pollution. Cohort results were combined through fixed-effect inverse variance weighted meta-analyses. Differentially methylated regions (DMRs) were identified from meta-analysed results using the Enmix-combp and DMRcate methods.There was no statistical evidence of pregnancy or cumulative exposures associating with any DMP (False Discovery Rate, FDR, p-value < 0.05). However, surrounding greenness exposure was inversely associated with four DMRs (three in cord blood and one in child blood) annotated to ADAMTS2, KCNQ1DN, SLC6A12 and SDK1 genes. Results did not change substantially in the sensitivity analyses.Overall, we found little evidence of the association between green space exposure and blood DNA methylation. Although we identified associations between surrounding greenness exposure with four DMRs, these findings require replication.

Observational studies report prospective associations of neurodevelopmental and psychiatric traits with paediatric incontinence and constipation, but unmeasured and residual confounding may limit observational measures. Here, we use a prospective birth cohort study to investigate whether common variant genetic liability for a range of neurodevelopmental and psychiatric conditions are associated with paediatric incontinence and constipation. We used data from 7,857 participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) with data on genotype, incontinence, and constipation, and calculated Polygenic risk scores (PRS) for neurodevelopmental traits (ADHD, autism, intelligence) and psychiatric conditions (anxiety, depression, and OCD). Incontinence subtypes (daytime urinary incontinence [DUI], enuresis [any bedwetting and enuresis subtypes: monosymptomatic, non-monosymptomatic], faecal incontinence), and constipation, were assessed by parental reports at age 9 years and self-reports at age 14. PRS for ADHD (OR=1.14, 95% CI, 1.01 to 1.29, unadjusted p=0.040) and depression (OR=1.09, 95% CI, 1.00 to1.20, unadjusted p=0.063) were associated with DUI at age 9. PRS for autism (OR=1.19, 95% CI, 1.02 to 1.41, unadjusted p=0.032) and intelligence (OR=1.17, 95%, 0.99 to 1.38, unadjusted p=.06l) were associated with DUI at age 14. PRS for ADHD (OR=1.13, 95% CI, 1.03 to 1.24, unadjusted p=0.008) were associated with constipation at age 9. Within enuresis subtypes, PRS for autism were associated with MNE at age 9 (OR=1.15, 95% CI, 1.03 to 1.28, unadjusted p=0.012), but not NMNE (OR=0.93, 95% CI, 0.79 to 1.18, unadjusted p=0.335). No associations survived false discovery rate adjustment. The findings add to existing evidence that common variant genetic liability for neurodevelopmental traits and psychiatric conditions could be associated with paediatric incontinence and constipation. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was funded by the Medical Research Council (Integrative Epidemiology Unit), as part of Oliver Bastiani s PhD studentship, which also covered the cost for his access to ALSPAC. During this study, Jane Hvarregaard Christensen was supported by the Misses Anna and Dagny Hjerrild s Foundation. This work is supported by funding from the Medical Research Council (grant ref: MR/V033581/1: Mental Health and Incontinence). The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. Genomewide genotyping data was generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. This publication is the work of the authors, and they will serve as guarantors for the content of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). The funder had no role in the study design; collection, analysis and interpretation of data; writing of the report; and the decision to submit the article for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Avon Longitudinal Study of Parents and Children (ALSPAC) Ethics and Law Committee gave ethical approval for this study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Due to ALSPAC regulations, we cannot provide access to this data but if others wish to access this data they may contact the involved cohort study (ALSPAC) at https://www.bristol.ac.uk/alspac/researchers/access/, where they may apply to use this data also.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits. This article presents a framework to conduct GWAS of longitudinal data where the trait of interest follows a non-linear change over time. The framework is applied to childhood BMI, identifying 13 loci with age-varying genetic effects.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

Background Tumour-promoting inflammation is a "hallmark" of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. Methods We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 x 10(-8)) cis-acting SNPs (i.e., in or +/- 250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r(2) < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value ("q-value") <0.05 was used as a threshold to define "strong evidence" to support associations and 0.05 <= q-value < 0.20 to define "suggestive evidence". A colocalisation posterior probability (PPH4) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis. Findings We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10-1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20-1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53-0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88-0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48-4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value >= 0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. Interpretation Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated. Copyright (c) 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

There is evidence that children with autism/autistic traits have higher risks of incontinence and constipation, but no studies have examined this in a large community-based cohort. Aim/Research question: are autistic traits and diagnosed autism prospectively associated with increased odds of incontinence and constipation in children and adolescents? This was a population-based cohort study based on data from the Avon Longitudinal Study of Parents and Children (n = 4233–4490 at age 9 years; n = 3403–3697 at age 14). We used multivariable logistic regression to examine associations of parent-reported autistic traits (sociability, repetitive behaviours, social-communication, coherence) (at ages 3–9 years) and autism with incontinence (bedwetting, daytime-wetting, soiling) and constipation (parent-reported at age 9, self-reported at age 14). We adjusted for parity, maternal age at delivery, child’s sex and developmental level, maternal depression, and anxiety (antenatal and postnatal), and indicators of family socioeconomic status. Social-communication and speech coherence difficulties showed the strongest associations with incontinence, e.g., adjusted odds ratio (OR) and 95

Observational studies have suggested a protective role for eosinophils in colorectal cancer (CRC) development and implicated neutrophils, but the causal relationships remain unclear. Here, we aimed to estimate the causal effect of circulating white blood cell (WBC) counts (N = ~550 000) for basophils, eosinophils, monocytes, lymphocytes and neutrophils on CRC risk (N = 52 775 cases and 45 940 controls) using Mendelian randomisation (MR). For comparison, we also examined this relationship using individual-level data from UK Biobank (4043 incident CRC cases and 332 773 controls) in a longitudinal cohort analysis. The inverse-variance weighted (IVW) MR analysis suggested a protective effect of increased basophil count and eosinophil count on CRC risk [OR per 1-SD increase: 0.88, 95% CI: 0.78-0.99, P = .04; OR: 0.93, 95% CI: 0.88-0.98, P = .01]. The protective effect of eosinophils remained [OR per 1-SD increase: 0.88, 95% CI: 0.80-0.97, P = .01] following adjustments for all other WBC subtypes, to account for genetic correlation between the traits, using multivariable MR. A protective effect of increased lymphocyte count on CRC risk was also found [OR: 0.84, 95% CI: 0.76-0.93, P = 6.70e-4] following adjustment. Consistent with MR results, a protective effect for eosinophils in the cohort analysis in the fully adjusted model [RR per 1-SD increase: 0.96, 95% CI: 0.93-0.99, P = .02] and following adjustment for the other WBC subtypes [RR: 0.96, 95% CI: 0.93-0.99, P = .001] was observed. Our study implicates peripheral blood immune cells, in particular eosinophils and lymphocytes, in CRC development, highlighting a need for mechanistic studies to interrogate these relationships.

Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk. We developed genetic instruments for three glucose-lowering drug targets (peroxisome proliferator activated receptor γ [PPARG]; sulfonylurea receptor 1 [ATP binding cassette subfamily C member 8 (ABCC8)]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using cis-acting genome-wide significant (p<5×10−8) SNPs permitted to be in weak linkage disequilibrium (r2<0.20). Summary genetic association estimates for these SNPs were obtained from genome-wide association study (GWAS) consortia for the following cancers: breast (122,977 cases, 105,974 controls); colorectal (58,221 cases, 67,694 controls); prostate (79,148 cases, 61,106 controls); and overall (i.e. site-combined) cancer (27,483 cases, 372,016 controls). Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically proxied drug target perturbation and cancer risk. Co-localisation analysis was employed to examine robustness of findings to violations of Mendelian randomisation (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as ‘strong’ and ‘weak’ evidence. In MR analysis, genetically proxied PPARG perturbation was weakly associated with higher risk of prostate cancer (for PPARG perturbation equivalent to a 1 unit decrease in inverse rank normal transformed HbA1c: OR 1.75 [95 https://bcac.ccge.medschl.cam.ac.uk/bcacdata/ ); and overall prostate cancer ( http://practical.icr.ac.uk/blog/ ). Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL (practical at icr.ac.uk). Summary genetic association data on type 2 diabetes from Vujkovic et al (Nat Genet, 2020) can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).

Background: Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. Methods: To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y. Results: The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. Conclusions: These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development. Funding: This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/ .

Background The causal relevance of polyunsaturated fatty acids (PUFAs) for risk of site-specific cancers remains uncertain.Methods Using a Mendelian randomization (MR) framework, we assessed the causal relevance of PUFAs for risk of cancer in European and East Asian ancestry individuals. We defined the primary exposure as PUFA desaturase activity, proxied by rs174546 at the FADS locus. Secondary exposures were defined as omega 3 and omega 6 PUFAs that could be proxied by genetic polymorphisms outside the FADS region. Our study used summary genetic data on 10 PUFAs and 67 cancers, corresponding to 562,871 cases and 1,619,465 controls, collected by the Fatty Acids in Cancer Mendelian Randomization Collaboration. We estimated odds ratios (ORs) for cancer per standard deviation increase in genetically proxied PUFA exposures.Findings Genetically elevated PUFA desaturase activity was associated (P < 0.0007) with higher risk (OR [95% con-fidence interval]) of colorectal cancer (1.09 [1.07-1.11]), esophageal squamous cell carcinoma (1.16 [1.06-1.26]), lung cancer (1.06 [1.03-1.08]) and basal cell carcinoma (1.05 [1.02-1.07]). There was little evidence for associations with reproductive cancers (OR = 1.00 [95% CI: 0.99-1.01]; Pheterogeneity = 0.25), urinary system cancers (1.03 [0.99-1.06], Pheterogeneity = 0.51), nervous system cancers (0.99 [0.95-1.03], Pheterogeneity = 0.92) or blood cancers (1.01 [0.98-1.04], Pheterogeneity = 0.09). Findings for colorectal cancer and esophageal squamous cell carcinoma remained compatible with causality in sensitivity analyses for violations of assumptions. Secondary MR analyses highlighted higher omega 6 PUFAs (arachidonic acid, gamma-linolenic acid and dihomo-gamma-linolenic acid) as potential mediators. PUFA biosynthesis is known to interact with aspirin, which increases risk of bleeding and inflammatory bowel disease. In a phenome-wide MR study of non-neoplastic diseases, we found that genetic lowering of PUFA desaturase activity, mimicking a hypothetical intervention to reduce cancer risk, was associated (P < 0.0006) with increased risk of inflammatory bowel disease but not bleeding.Interpretation The PUFA biosynthesis pathway may be an intervention target for prevention of colorectal cancer and esophageal squamous cell carcinoma but with potential for increased risk of inflammatory bowel disease.Copyright (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Evidence for a role for vitamin D in non-alcoholic fatty liver disease (NAFLD) pathogenesis is conflicting. As Mendelian randomisation (MR) avoids many limitations of conventional observational studies, this two-sample bidirectional MR analysis was conducted to determine the following: (i) whether genetically predicted 25-hydroxyvitamin D [25(OH)D] levels are a risk factor for NAFLD, and (ii) whether genetic risk for NAFLD influences 25(OH)D levels. Single-nucleotide polymorphisms (SNPs) associated with serum 25(OH)D levels were obtained from the European ancestry-derived SUNLIGHT consortium. SNPs associated with NAFLD or NASH (p-value < 1 × 10−5) were extracted from previous studies and supplemented by genome-wide association studies (GWASs) performed in the UK Biobank. These GWASs were done both without (primary analysis) and with (sensitivity analysis) the population-level exclusion of other liver diseases (e.g., alcoholic liver diseases, toxic liver diseases, viral hepatitis, etc.). Subsequently, MR analyses were performed to obtain effect estimates using inverse variance weighted (IVW) random effect models. Cochran’s Q statistic, MR-Egger regression intercept, MR pleiotropy residual sum and outlier (MR-PRESSO) analyses were used to assess pleiotropy. No causal association of genetically predicted serum 25(OH)D (per standard deviation increase) with risk of NAFLD was identified in either the primary analysis: n = 2757 cases, n = 460,161 controls, odds ratio (95% confidence interval): 0.95 (0.76, −1.18), p = 0.614; or the sensitivity analysis. Reciprocally, no causal association was identified between the genetic risk of NAFLD and serum 25(OH)D levels, OR = 1.00 (0.99, 1.02, p = 0.665). In conclusion, this MR analysis found no evidence of an association between serum 25(OH)D levels and NAFLD in a large European cohort.

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 1622374 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).

Full text Figures and data Side by side Abstract eLife assessment eLife digest Introduction Methods Results Discussion Appendix 1 Data availability References Peer review Author response Article and author information Metrics Abstract Background: Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. Methods: To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y. Results: The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. Conclusions: These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development. Funding: This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/. eLife assessment The by Bull et al. provides valuable information on the relationship between metabolic features, in particular different lipoproteins and fatty acids, and colorectal cancer. They use solid methods and combine different data sources to analyze forward and reverse Mendelian randomizations that support their claims. https://doi.org/10.7554/eLife.87894.3.sa0 About eLife assessments eLife digest Colorectal cancer, or bowel cancer, is the fourth most common cause of death from cancer worldwide. Understanding how the cancer develops and recognizing early signs is essential, as people who receive treatment early on have higher survival rates. One way to boost early detection and disease survival rates is through identifying early colorectal cancer biomarkers. For example, metabolites produced when cells process nutrients have been shown to play a role in the development of colon cancer. Certain metabolites could therefore serve as biomarkers, which can be detected in routine blood tests. But first, scientists need to identify the exact metabolic processes involved in cancer development. Bull, Hazelwood et al. show that fat metabolites during early adulthood may help predict colorectal cancer risk. In the experiments, the team assessed the link between an individual's genetic risk for developing colorectal cancer and metabolites in their blood. By looking at data from over 6,000 individuals living in the UK, followed from early life into adulthood, they found higher fatty acid and low-density lipoprotein levels in young adults at risk of colorectal cancer. However, the results could not be replicated in a separate cohort study of middle-aged adults. Bull, Hazelwood et al. noted that many individuals in this older age group use fat-targeting drugs called statins, which may have obscured this connection. The study of Bull, Hazelwood et al. shows that colorectal cancer risk indicators may be present from adolescence to around 40 years, before most individuals are diagnosed. The results suggest this may be a window for early detection and preventive interventions. It also highlights that differences in fat metabolism, possibly linked to genetic differences, may underlie colorectal cancer risk. More studies are needed to better understand how and whether interventions targeting fat levels may help prevent colorectal cancer development. Introduction Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide and the fourth most common cause of death from cancer (Ferlay et al., 2015; Clinton et al., 2018). There is a genetic component to risk of the disease, which is thought to explain up to 35% of variability in CRC risk (Huyghe et al., 2019; Czene et al., 2002; Lichtenstein et al., 2000). In addition, modifiable lifestyle factors, including obesity, consumption of processed meat, and alcohol, are thought to increase CRC risk (Clinton et al., 2018; Händel et al., 2020; McNabb et al., 2020; Lauby-Secretan et al., 2016; Gui et al., 2023). However, the underlying biological pathways remain unclear, which limits targeted prevention strategies. While CRC has higher mortality rates when diagnosed at later stages, early-stage CRC or precancerous lesions are largely treatable, meaning CRC screening programmes have the potential to be highly effective (Meester et al., 2020; Cardoso et al., 2021). Due to the lack of known predictive biomarkers for CRC, wide-scale screening (if implemented at all) is expensive and often targeted crudely by age range. Identifying biomarkers predictive of CRC, or with causal roles in disease development, is therefore vital. One potential source of biomarkers for CRC risk is the circulating metabolome, which offers a dynamic insight into cellular processes and disease states. It is increasingly clear from mechanistic studies that both systemic and intracellular tumour metabolism play an important role in CRC development and progression (Qiu et al., 2009; Ward and Thompson, 2012). Interestingly, several major risk factors for CRC are known to have profound effects on metabolism (Rattray et al., 2017). For instance, obesity has been shown via conventional observational and Mendelian randomization (MR) analyses to strongly alter circulating metabolite levels (Gui et al., 2023; Singla et al., 2010; Papandreou et al., 2021; Ahmad et al., 2022). This suggests that the circulating metabolome may play a mediating role in the relationship between at least some common risk factors, such as obesity, and CRC – or at least might be a useful biomarker for disease or intermediates thereof. In particular, previous work has highlighted polyunsaturated fatty acids (PUFAs) as potentially having a role in CRC development. The term PUFA includes omega-3 and -6 fatty acids. Recent MR work has highlighted a possible link between PUFAs, in particular omega-6 PUFAs, and CRC risk (Haycock et al., 2023). Further investigating the relationship between CRC and circulating metabolites may therefore provide powerful insights into the causal pathways underlying disease risk or alternatively may be valuable in prediction and early diagnosis. MR is a genetic epidemiological approach used to evaluate causal relationships between traits (Yarmolinsky et al., 2018; Smith and Ebrahim, 2003). This method uses genetic variation as a proxy measure for traits in an instrumental variable framework to assess the causal relevance of the traits in disease development. As germline genetic variants are theoretically randomized between generations and fixed at conception, this approach should be less prone to bias and confounding than conventional analyses undertaken in an observational context. Conventionally, MR is used to investigate the effect of an exposure on a disease outcome. In reverse MR, genetic instruments proxy the association between liability to a disease and other traits (Holmes and Davey Smith, 2019). This approach can identify the biomarkers which cause the disease, are predictive for the disease, or have diagnostic potential (Holmes and Davey Smith, 2019). Given the suspected importance of the circulating metabolome in CRC development, employing both reverse MR and conventional forward MR for metabolites in the same study may be an efficient approach for revealing causal and predictive biomarkers for CRC. Although previous observational studies have investigated associations between the circulating metabolome and CRC risk, these studies may have been influenced by confounding bias, which should be less relevant to MR analyses (Gao et al., 2023; Rattner et al., 2022; Leichtle et al., 2012; Ritchie et al., 2010; Nishiumi et al., 2012; Ma et al., 2012; Bertini et al., 2012; Zhang et al., 2014; Farshidfar et al., 2016; Farshidfar et al., 2012). Additionally, these studies focussed on adults, who commonly take medications which may confound metabolite associations, further complicating interpretations. Here, we applied a reverse MR framework to identify circulating metabolites which are associated with CRC liability across different stages of the early life course (spanning childhood to young adulthood, when use of medications and CRC are both rare) using data from a birth cohort study. We then attempted to replicate these results using reverse two-sample MR in an independent cohort of middle-aged adults (UK Biobank). We then performed conventional 'forward' MR of metabolites onto CRC risk using large-scale cancer consortia data to identify the metabolites which may have a causal role in CRC development. Methods Study populations This study uses data from two cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (generation 1) cohort (individual-level data) and the UK Biobank cohort (summary-level data); plus summary-level data from a genome-wide association study (GWAS) meta-analysis of CRC comprising the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), Colorectal Transdisciplinary Study (CORECT), and Colon Cancer Family Registry (CCFR). ALSPAC is a population-based birth cohort study in which 14,541 pregnant women with an expected delivery date between 1 April 1991 and 31 December 1992 were recruited from the former Avon County of southwest England (Boyd et al., 2013). Since then, 13,988 offspring alive at 1 y have been followed repeatedly with questionnaire- and clinic-based assessments (Fraser et al., 2013; Northstone et al., 2019). Sufficient information was available on 6221 of these individuals to be included in our analysis, as metabolomics was not performed for all individuals in the ALSPAC study. Study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at the University of Bristol (Harris et al., 2009) REDCap is a secure, web-based software platform designed to support data capture for research studies. Offspring genotype was assessed using the Illumina HumanHap550 quad chip platform. Quality control measures included exclusion of participants with sex mismatch, minimal or excessive heterozygosity, disproportionately missing data, insufficient sample replication, cryptic relatedness, and non-European ancestry. Imputation was performed using the Haplotype Reference Consortium (HRC) panel. Offspring were considered for the current analyses if they had no older siblings in ALSPAC (203 excluded) and were of white ethnicity (based on reports by parents, 604 excluded) to reduce the potential for confounding by genotype. The study website contains details of all available data through a fully searchable data dictionary and variable search tool (http://www.bristol.ac.uk/alspac/researchers/our-data/). UK Biobank is a population-based cohort study based in 22 centres across the UK (Sudlow et al., 2015). The cohort is made up of around 500,000 adults aged 40–80 years old, who were enrolled between 2006 and 2010. Genotyping data is available for 488,377 participants (Bycroft et al., 2018). Participants were genotyped using one of two arrays – either the Applied Biosystems UK BiLEVE Axiom Array by Affymetrix (now part of Thermo Fisher Scientific) or the closely related Applied Biosystems UK Biobank Axiom Array. Approaches based on principal component analysis (PCA) were used to account for population structure. Individuals were excluded if reported sex differed from inferred sex based on genotyping data; if they had sex chromosome karyotypes which were not XX or XY; if they were outliers in terms of heterozygosity and missing rates; or if they had high relatedness to another participant. Multiallelic SNPs or those with a minor allele frequency of below 1% were removed. Imputation was performed using the UK10K haplotype and HRC reference panels. The GWAS meta-analysis for CRC included up to 52,775 cases and 45,940 controls (Huyghe et al., 2019; Huyghe et al., 2021). This sample excluded cases and controls from UK Biobank to avoid potential bias due to sample overlap which may be problematic in MR analyses (Burgess et al., 2016). Cases were diagnosed by a physician and recorded overall and by site (colon, 28,736 cases; proximal colon, 14,416 cases; distal colon, 12,879 cases; and rectal, 14,150 cases). Colon cancer included proximal colon (any primary tumour arising in the caecum, ascending colon, hepatic flexure, or transverse colon), distal colon (any primary tumour arising in the pleenic flexure, descending colon, or sigmoid colon), and colon cases with unspecified site. Rectal cancer included any primary tumour arising in the rectum or rectosigmoid junction (Huyghe et al., 2019). Approximately 92% of participants in the overall CRC GWAS were white European (~8% were East Asian). All participants included in site-specific CRC analyses were of European ancestry. Imputation was performed using the Michigan imputation server and HRC r1.0 reference panel. Regression models were further adjusted for age, sex, genotyping platform, and genomic principal components as described previously (Huyghe et al., 2019). Assessment of CRC genetic liability Genetic liability to CRC was based on single-nucleotide polymorphisms (SNPs) associated with CRC case status at genome-wide significance (p<5 × 10−8). A total of 108 independent SNPs reported by two major GWAS meta-analyses were eligible for inclusion in a CRC genetic risk score (GRS) (Huyghe et al., 2019; Law et al., 2019). The set of SNPs was filtered, excluding 36 SNPs that were in linkage disequilibrium based on R2 > 0.001 using the TwoSampleMR package (SNPs with the lowest p-values were retained) (Hemani et al., 2018). This left 72 SNPs independently associated with CRC (Supplementary file 1a), 65 of which were available in imputed ALSPAC genotype data post quality control. As GWAS of site-specific CRC have identified marked heterogeneity (Huyghe et al., 2021), GRS describing site-specific CRCs were constructed for sensitivity analyses using the same process outlined above. The GRS for colon cancer, rectal cancer, proximal colon cancer, and distal colon cancer were comprised of 38, 25, 20, and 24 variants, respectively (Supplementary file 1a). For overall CRC and site-specific CRC analyses, sensitivity analyses excluding any SNPs in the FADS cluster (i.e. within the gene regions of FADS1, FADS2, or FADS3) (Supplementary file 1a) were performed given a likely role for these SNPs in influencing circulating metabolite levels directly, in particular via lipid metabolism (i.e. not primarily due to CRC) (Lu et al., 2019; Zaytseva et al., 2018; ClinicalTrials.gov, 2022; Fhu and Ali, 2020; Chen et al., 2020; Kathiresan et al., 2009; Tanaka et al., 2009). Assessment of circulating metabolites Circulating metabolite measures were drawn from ALSPAC and UK Biobank using the same targeted metabolomics platform. In ALSPAC, participants provided non-fasting blood samples during a clinic visit while aged approximately 8 y, and fasting blood samples from clinic visits while aged approximately 16 y, 18 y, and 25 y. Proton nuclear magnetic resonance (1H-NMR) spectroscopy was performed on ethylenediaminetetraacetic acid (EDTA) plasma (stored at or below –70°C pre-processing) to quantify a maximum of 231 metabolites (Würtz et al., 2017). Quantified metabolites included the cholesterol and triglyceride content of lipoprotein particles; the concentrations and diameter/size of these particles; apolipoprotein B and apolipoprotein A-1 concentrations; as well as fatty acids and their ratios to total fatty acid concentration, branched chain and aromatic amino acids, glucose and pre-glycaemic factors including lactate and citrate, fluid balance factors including albumin and creatinine, and the inflammatory marker glycoprotein acetyls (GlycA). This metabolomics platform has limited coverage of fatty acids. In UK Biobank, EDTA plasma samples from 117,121 participants, a random subset of the original ∼500,000 who provided samples at assessment centres between 2006 and 2013, were analysed between 2019 and 2020 for levels of 249 metabolic traits (168 concentrations plus 81 ratios) using the same high-throughput 1H-NMR platform. Data pre-processing and QC steps are described previously (Würtz et al., 2017; Julkunen et al., 2021; Bycroft et al., 2018). To allow comparability between MR and GRS estimates, all metabolite measures were standardized and normalized using rank-based inverse normal transformation. For descriptive purposes in ALSPAC, body mass index (BMI) was calculated at each time point as weight (kg) divided by squared height (m2) based on clinic measures of weight to the nearest 0.1 kg using a Tanita scale and height measured in light clothing without shoes to the nearest 0.1 cm using a Harpenden stadiometer. CRC liability variants were combined into a GRS using PLINK 1.9, specifying the effect (risk raising) allele and coefficient (logOR) with estimates from the CRC GWAS used as external weights (Huyghe et al., 2019; Law et al., 2019). GRSs were calculated as the number of effect alleles (or dosages if imputed) at each SNP (0, 1, or 2) multiplied by its weighting, summing these, and dividing by the total number of SNPs used. Z-scores of GRS variables were calculated to standardize scoring. Statistical approach An overview of the study design is presented in Figure 1. To estimate the effect of increased genetic liability to CRC on circulating metabolites, we conducted a GRS analysis in ALSPAC and reverse two-sample MR analyses in UK Biobank. Estimates were interpreted within a 'reverse MR' framework (Holmes and Davey Smith, 2019), wherein results are taken to reflect 'metabolic features' of CRC liability which could capture causal or predictive metabolite–disease associations. To clarify the direction of metabolite–CRC associations, we additionally performed conventional 'forward' two-sample MR analyses to estimate the effect of circulating metabolites on CRC risk using large-scale GWAS data on metabolites and CRC. Figure 1 Download asset Open asset Study design. First, linear regression models were used to examine the relationship between genetic susceptibility to adult colorectal cancer (CRC) and circulating metabolites measured in the Avon Longitudinal Study of Parents and Children (ALSPAC) participants at age 8 y, 16 y, 18 y, and 25 y. Next, we performed a reverse Mendelian randomization analysis to identify metabolites influenced by CRC susceptibility in an independent population of adults. Finally, we performed a conventional (forward) Mendelian randomization analysis of circulating metabolites on CRC to identify metabolites causally associated with CRC risk. Consistent evidence across all three methodological approaches was interpreted to indicate a causal role for a given metabolite in CRC aetiology. Associations of CRC liability with circulating metabolites in early life Separate linear regression models with robust standard errors were used to estimate coefficients and 95% confidence intervals (95% CIs) for associations of GRSs with each metabolite as a dependent variable measured on the same individuals at age 8 y, 16 y, 18 y, and 25 y, adjusted for sex and age at the time of metabolite assessment. To aid interpretations, estimates were multiplied by 0.693 (loge2) to reflect SD-unit differences in metabolites per doubling of genetic liability to CRC (Burgess and Labrecque, 2018). The Benjamini–Hochberg method was used to adjust p-values for multiple testing and an adjusted p-value of <0.05 was used as a heuristic for evidence for association given current sample sizes (Benjamini and Hochberg, 1995). Reverse MR of the effects of CRC liability on circulating metabolites in middle adulthood 'Reverse' MR analyses (Holmes and Davey Smith, 2019) were conducted using UK Biobank for outcome datasets in two-sample MR to examine the effect of CRC liability on circulating metabolites. SNP-outcome (metabolite) estimates were obtained from a GWAS of metabolites in UK Biobank (Clayton et al., 2022; Borges et al., 2022a). Prior to GWAS, all metabolite measures were standardized and normalized using rank-based inverse normal transformation. Genetic association data for metabolites were retrieved using the MRC IEU UK Biobank GWAS pipeline (Data.bris, 2022). Full summary statistics are available via the IEU Open GWAS project (Holmes and Davey Smith, 2019; Elsworth et al., 2020). Up to three statistical methods were used to generate reverse MR estimates of the effect of CRC liability on circulating metabolites using the TwoSampleMR package (Hemani et al., 2016): random-effects inverse variance weighted (IVW), weighted-median, and weighted-mode, which each make differing assumptions about directional pleiotropy and SNP heterogeneity (Bowden et al., 2016; Hartwig et al., 2017). The IVW MR model will produce biased effect estimates in the presence of horizontal pleiotropy, that is, where one or more genetic variant(s) included in the instrument affect the outcome by a pathway other than through the exposure. In the weighted median model, each genetic variant is weighted according to its distance from the median effect of all genetic variants. Thus, the weighted median model will provide an unbiased estimate when at least 50% of the information in an instrument comes from genetic variants that are not horizontally pleiotropic. The weighted mode model uses a similar approach but weights genetic instruments according to the mean effect. In this model, over 50% of the weight of the genetic instrument can be contributed to by genetic variants which are horizontally pleiotropic, but the most common amount of pleiotropy must be zero (known as the Zero Modal Pleiotropy Assumption [ZEMPA]) (Hartwig et al., 2017). As above, estimates were multiplied by 0.693 (loge2) to reflect SD-unit differences in metabolites per doubling of genetic liability to CRC (Burgess and Labrecque, 2018). Forward MR of the effects of metabolites on CRC Forward MR analyses were conducted using summary statistics from UK Biobank for the same NMR-measured metabolites (SNP-exposure) and from GECCO/CORECT/CCFR as outlined above (SNP-outcome). We identified SNPs that were independently associated (R2 < 0.001 and p<5 × 10–8) with metabolites from a GWAS of 249 metabolites in UK Biobank described above. As before, we used up to three statistical methods to generate MR estimates of the effect of circulating metabolites on CRC risk (overall and site-specific): random-effects IVW, weighted median, and weighted mode. The Benjamini–Hochberg method was used to adjust p-values for multiple testing and an adjusted p-value of <0.05 was used as a heuristic for nominal evidence for a causal effect (Benjamini and Hochberg, 1995). MR outputs are beta coefficients representing the logOR for CRC per SD higher metabolite, exponentiated to reflect the OR for CRC per SD metabolite. MR analyses were performed in R version 4.0.3 (R Development Core Team, 2021) and GRS analyses in Stata 16.1 (StataCorp, College Station, TX). The ggforestplot R package was used to generate results visualizations (Scheinin et al., 2022). Results Associations of CRC liability with circulating metabolites in early life At the time the ALSPAC blood samples were taken, the mean age of participants was 7.5 y (N = 4767), 15.5 y (N = 2930), 17.8 y (N = 2613), and 24.5 y (N = 2559) for the childhood, early adolescence, late adolescence, and young adulthood time points, respectively. The proportion of participants which were male were 50.5, 47.4, 44.5, and 39.1% and mean BMI was 16.2, 21.4, 22.7, and 24.8 kg/m2 for each time point, respectively. The socio-demographic profile of ALSPAC offspring participants has been reported previously (Boyd et al., 2013). The mean and standard deviation (SD) values for metabolites on each measurement occasion in ALSPAC are shown in Supplementary file 1b. In the GRS analysis, there was no strong evidence of association of CRC liability with metabolites at age 8 y (Supplementary file 1c). At age 16 y, there was evidence for association with several lipid traits including higher cholesteryl esters to total lipids ratio in large low-density lipoprotein (LDL) (SD change per doubling CRC liability = 0.06, 95% CI = 0.02–0.10) and higher cholesterol in very small very low-density lipoprotein (VLDL) (SD change per doubling CRC liability = 0.06, 95% CI = 0.03–0.10). There was strong evidence for association with several traits at age 18 y including higher non-high-density lipoprotein (non-HDL) lipids, for example, a 1 doubling CRC liability was associated with higher levels of total cholesterol (SD change = 0.05 95% CI = 0.01–0.09), VLDL-cholesterol (SD change = 0.05, 95% CI = 0.01–0.09), LDL-cholesterol (SD change = 0.06, 95% CI = 0.02–0.09), apolipoproteins (apolipoprotein B [SD change = 0.06, 95% CI = 0.02–0.09]), and fatty acids (omega-3 [SD change = 0.08, 95% CI = 0.04–0.11], docosahexaenoic acid [DHA] [SD change = 0.05, 95% CI = 0.02–0.09]) (Supplementary file 1c). Figure 2 (Figure 2—figure supplements 1–6) shows results for all clinically validated metabolites. At age 25 y, there was no strong evidence of association of CRC liability with metabolites. In anatomical site-specific analyses, there was strong evidence for association of liability to colon cancer with omega-3 (SD change = 0.07, 95% CI = 0.03–0.11) and DHA (SD change = 0.07, 95% CI = 0.03–0.10) at age 18 y. There was little evidence for any associations at any other CRC site or age (Supplementary file 1c). When SNPs in the FADS cluster gene regions were excluded due to possible horizontal pleiotropy given the role of FADS in lipid metabolism, there was a reduction in strength of evidence for an association of liability to CRC with any metabolite measured, although estimates were in a largely consistent direction with the prior analysis (Supplementary file 1d). Figure 2 with 6 supplements see all Download asset Open asset Associations of genetic liability to adult colorectal cancer (based on a 72 single-nucleotide polymorphism [SNP] genetic risk score) with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y [N = 4767], 16 y [N = 2930], 18 y [N = 2613], and 25 y [N = 2559]). Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to colorectal cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05). Reverse MR of the effects of CRC liability on circulating metabolites in middle adulthood All instrument sets from the reverse MR analysis had an F-statistic greater than 10 (minimum F-statistic = 36, median = 40), suggesting that our analyses did not suffer from weak instrument bias (Supplementary file 1e). There was little evidence of an association of CRC liability (overall or by anatomical site) on any of the circulating metabolites investigated, including when the SNP in the FADS gene region was excluded, based on our pre-determined cut-off of FDR-P < 0.05; however, the direction of effect estimates was largely consistent with those seen in ALSPAC GRS analyses, with higher CRC liability weakly associated with higher non-HDLs, lipoproteins, and fatty acid levels (Supplementary file 1f and g). Figure 3 (Figure 3—figure supplements 1–3) shows the results for clinically validated metabolites. In subsite stratified analyses, there was strong evidence for a causal effect of genetic liability to proximal colon cancer on several traits, including total fatty acids (SD change per doubling of liability = 0.02, 95% CI = 0.01–0.04) and omega-6 fatty acids (SD change per doubling of liability = 0.03, 95% CI = 0.01–0.05). Figure 3 with 3 supplements see all Download asset Open asset Associations of genetic liability to colorectal cancer with clinically validated metabolic traits in an independent sample of adults (UK Biobank, N = 118,466, median age 58 y) based on reverse two-sample Mendelian randomization analyses. Estimates shown are beta coefficients representing the SD-unit difference in metabolic trait per doubling of liability to colorectal cancer. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05). Forwa

Background Human papilloma virus infection is known to influence oropharyngeal cancer (OPC) risk, likely via sexual transmission. However, sexual behaviour has been correlated with other risk factors including smoking and alcohol, meaning independent effects are difficult to establish. We aimed to evaluate the causal effect of sexual behaviour on the risk of OPC using Mendelian randomization (MR). Methods Genetic variants robustly associated with age at first sex (AFS) and the number of sexual partners (NSP) were used to perform both univariable and multivariable MR analyses with summary data on 2641 OPC cases and 6585 controls, obtained from the largest available genome-wide association studies (GWAS). Given the potential for genetic pleiotropy, we performed a number of sensitivity analyses: (i) MR methods to account for horizontal pleiotropy, (ii) MR of sexual behaviours on positive (cervical cancer and seropositivity for Chlamydia trachomatis ) and negative control outcomes (lung and oral cancer), (iii) Causal Analysis Using Summary Effect estimates (CAUSE), to account for correlated and uncorrelated horizontal pleiotropic effects, (iv) multivariable MR analysis to account for the effects of smoking, alcohol, risk tolerance and educational attainment. Results In univariable MR, we found evidence supportive of an effect of both later AFS (IVW OR = 0.4, 95%CI (0.3, 0.7), per standard deviation (SD), p = < 0.001) and increasing NSP (IVW OR = 2.2, 95%CI (1.3, 3.8) per SD, p = < 0.001) on OPC risk. These effects were largely robust to sensitivity analyses accounting for horizontal pleiotropy. However, negative control analysis suggested potential violation of the core MR assumptions and subsequent CAUSE analysis implicated pleiotropy of the genetic instruments used to proxy sexual behaviours. Finally, there was some attenuation of the univariable MR results in the multivariable models (AFS IVW OR = 0.7, 95%CI (0.4, 1.2), p = 0.21; NSP IVW OR = 0.9, 95%CI (0.5 1.7), p = 0.76). Conclusions Despite using genetic variants strongly related sexual behaviour traits in large-scale GWAS, we found evidence for correlated pleiotropy. This emphasizes a need for multivariable approaches and the triangulation of evidence when performing MR of complex behavioural traits.

DNA methylation (DNAm) is influenced by genetic and environmental factors, and can be used to understand interindividual variability in genomic regulation. Co-methylation between DNAm sites is a known phenomenon, but the architecture of relationships between the approximately 450,000 (450k) sites commonly measured in epidemiological studies has not been described. We investigate whether interindividual co-methylation structure amongst the 450k sites changes with age, whether it differs between UK-born White (n=849, 910, 921 and 424) and Pakistani ancestry (n=439) individuals, and how it relates to genome regulation. We find stability between birth and adolescence, across cohorts, and between two ethnic groups. Highly correlated DNAm sites in close proximity are heritable, but these relationships are weakly influenced by nearby genetic variants, and are enriched for transcription factor (TF) binding sites related to regulation of short RNAs transcribed by RNA polymerase III. Highly correlated sites that are distant, or on different chromosomes (in trans), are driven by common and unique environmental factors, with methylation at these sites less likely to be driven by genotype. Trans co-methylated DNAm sites are enriched for multiple TF binding sites and for inter-chromosomal chromatin contact sites, suggesting DNA co-methylation of distant sites may relate to long-range cooperative TF interactions. We conclude that DNA co-methylation has a stable structure from birth to adolescence, and between UK-born White and Pakistani individuals. This stable structure might have implications for future design and interpretation of epigenetic studies. We hypothesise that co-methylation may have roles in genome regulation in humans, including 3D chromatin architecture.