Search

Perturbation of the insulin/insulin-like growth factor (IGF) signaling system is often cited as a mechanism driving breast cancer risk. A systematic review identified prospective cohort studies and Mendelian randomization studies that examined the effects of insulin/IGF signaling (IGF, their binding proteins (IGFBP), and markers of insulin resistance] on breast cancer risk. Meta-analyses generated effect estimates; risk of bias was assessed and the Grading of Recommendations Assessment, Development and Evaluation system applied to evaluate the overall quality of the evidence. Four Mendelian randomization and 19 prospective cohort studies met our inclusion criteria. Meta-analysis of cohort studies confirmed that higher IGF-1 increased risk of breast cancer; this finding was supported by the Mendelian randomization studies. IGFBP-3 did not affect breast cancer. Meta analyses for connecting-peptide and fasting insulin showed small risk increases, but confidence intervals were wide and crossed the null. The quality of evidence obtained ranged from ‘very low’ to ‘moderate’. There were insufficient studies to examine other markers of insulin/IGF signaling. These findings do not strongly support the biological plausibility of the second part of the physical activity—insulin/IGF signaling system—breast cancer pathway. Robust conclusions cannot be drawn due to the dearth of high quality studies.See related article by Swain et al., p. 2106

Supplementary Material and Methods includes search terms; excluded papers; study characteristics; risk of bias; forest plots; funnel plots. Supplementary Table 1: Search terms used for each component of the systematic review. Supplementary Table 2: Papers Excluded at Full Text Screen.* Supplementary Table 3A: Study characteristics of Mendelian randomisation studies. Supplementary Table 3B: Study characteristics of prospective cohort studies. Supplementary Table 4. Risk of Bias: Prospective Cohort Studies. Figure 1: Funnel plots. Figure 2: IGF-1 and breast cancer risk in models that adjust for IGFBP-3. Figure 3: IGF-1 and breast cancer risk by menopause status at blood draw. Figure 4: IGFBP-3 and breast cancer risk by menopause status at blood draw

Physical activity may reduce the risk of developing breast cancer via its effect on the insulin/insulin-like growth factor (IGF) signaling system. A systematic review searched for randomized controlled trials (RCT), Mendelian randomization and prospective cohort studies that examined the effects of physical activity on insulin/IGF signaling [IGFs, their binding proteins (IGFBP), and markers of insulin resistance] in adult women. Meta-analyses were performed to generate effect estimates. Risk of bias was assessed, and the Grading of Recommendations Assessment, Development, and Evaluation system used to determine the overall quality of the evidence. Fifty-eight RCTs met our inclusion criteria, no observational or Mendelian randomization studies met the criteria for inclusion. Meta-analyses indicated that physical activity interventions (vs. control) reduced fasting insulin, the Homeostatic Model Assessment for Insulin Resistance and fasting glucose. Physical activity increased IGF-1, but there was no clear effect on IGFBP-3 or the ratio of IGF-1:IGFBP-3. Strong evidence was only established for fasting insulin and insulin resistance. Further research is needed to examine the effect of physical activity on C-peptide and HBA1c in women. Reductions in fasting insulin and insulin resistance following exercise suggest some biological plausibility of the first part of the physical activity–insulin/IGF signaling–breast cancer pathway.See related article by Drummond et al., p. 2116

Supplementary Figure from Linking Physical Activity to Breast Cancer Risk via the Insulin/Insulin-like Growth Factor Signaling System, Part 2: The Effect of Insulin/Insulin-like Growth Factor Signaling on Breast Cancer Risk

Supplementary Figure from Linking Physical Activity to Breast Cancer Risk via Insulin/Insulin-Like Growth Factor Signaling System, Part 1: The Effect of Physical Activity on the Insulin/Insulin-Like Growth Factor Signaling System

Supplementary Figure 5B presents forest plots for adiponectin and breast cancer risk, excluding studies where exogenous hormone use status was unknown

GRADE appraisal for physical activity–inflammatory biomarkers pathways.

Dose–response curves for effects of inflammatory biomarkers on breast cancer risk. Dose–response curves for (A) CRP, (B) leptin, and (C) adiponectin.

Forest plots for effects of physical activity compared with usual activity control in RCTs. A forest plot for (A) TNFα, (B) IL6, and (C) CRP.

Forest plots for effects of inflammatory biomarkers on breast cancer risk. Forest plots for (A) leptin and (B) adiponectin.

Supplementary Table 2C presents the study characteristics for non-randomised interventions

Supplementary Table 2A presents the study characteristics of the Mendelian randomization studies

Supplementary Figure 4 presents funnel plots for physical activity to inflammatory biomarkers meta-analyses

Forest plots for effects of inflammatory biomarkers on breast cancer risk. Forest plots for (A) CRP, (B) TNFα, and (C) IL6.

PRISMA flow diagram. This figure incorporates literature search, screening and study selection.

Forest plots for effects of physical activity interventions compared with usual activity in RCTs. A forest plot for (A) adiponection and (B) leptin.

PRISMA flow diagram. This figure incorporates literature search, screening, and study selection. Reasons for full-text inclusion included duplicate study, publication type (e.g., review), study design (e.g., cross-sectional design), population (e.g., male only), intervention/exposure (e.g., physical activity and diet), comparator (e.g., diet or medication), and outcomes (e.g., no inflammation measures).

GRADE appraisal for inflammatory biomarkers – breast cancer pathways.

BackgroundThe causal relevance of polyunsaturated fatty acids (PUFAs) for risk of site-specific cancers remains uncertain.MethodsUsing 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.FindingsGenetically elevated PUFA desaturase activity was associated (P < 0.0007) with higher risk (OR [95% confidence 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.InterpretationThe 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.FundingCancer Resesrch UK (C52724/A20138, C18281/A19169). UK Medical Research Council (MR/P014054/1). National Institute for Health Research (NIHR202411). UK Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4). National Cancer Institute (R00 CA215360). National Institutes of Health (U01 CA164973, R01 CA60987, R01 CA72520, U01 CA74806, R01 CA55874, U01 CA164973 and U01 CA164973).

The protective effect of physical activity on breast cancer incidence may partially be mediated by inflammation. Systematic searches of Medline, EMBASE, and SPORTDiscus were performed to identify intervention studies, Mendelian randomization studies, and prospective cohort studies that examined the effects of physical activity on circulating inflammatory biomarkers in adult women. Meta-analyses were performed to generate effect estimates. Risk of bias was assessed, and the Grading of Recommendations Assessment, Development, and Evaluation system was used to determine the overall quality of the evidence. Thirty-five intervention studies and one observational study met the criteria for inclusion. Meta-analyses of randomized controlled trials (RCT) indicated that, compared with control groups, exercise interventions reduced levels of C-reactive protein (CRP) [standardized mean difference (SMD) = −0.27, 95% confidence interval (CI) = −0.62 to 0.08), tumor necrosis factor alpha (TNFα, SMD = −0.63, 95% CI = −1.04 to −0.22), interleukin-6 (IL6, SMD = −0.55, 95% CI = −0.97 to −0.13) and leptin (SMD = −0.50, 95% CI = −1.10 to 0.09). Owing to heterogeneity in effect estimates and imprecision, evidence strength was graded as low (CRP, leptin) or moderate (TNFα and IL6). High-quality evidence indicated that exercise did not change adiponectin levels (SMD = 0.01, 95% CI = −0.14 to 0.17). These findings provide support for the biological plausibility of the first part of the physical activity—inflammation—breast cancer pathway.

This review synthesized and appraised the evidence for an effect of inflammation on breast cancer risk. Systematic searches identified prospective cohort and Mendelian randomization studies relevant to this review. Meta-analysis of 13 biomarkers of inflammation were conducted to appraise the evidence for an effect breast cancer risk; we examined the dose–response of these associations. Risk of bias was evaluated using the ROBINS-E tool and the quality of evidence was appraised with Grading of Recommendations Assessment, Development, and Evaluation. Thirty-four observational studies and three Mendelian randomization studies were included. Meta-analysis suggested that women with the highest levels of C-reactive protein (CRP) had a higher risk of developing breast cancer [risk ratio (RR) = 1.13; 95% confidence interval (CI), 1.01–1.26] compared with women with the lowest levels. Women with highest levels of adipokines, particularly adiponectin (RR = 0.76; 95% CI, 0.61–0.91) had a reduced breast cancer risk, although this finding was not supported by Mendelian randomization analysis. There was little evidence of an effect of cytokines, including TNFα and IL6, on breast cancer risk. The quality of evidence for each biomarker ranged from very low to moderate. Beyond CRP, the published data do not clearly support the role of inflammation in the development of breast cancer.See related article by XXXX et al., p. 000

Background: Human, animal, and cell experimental studies; human biomarker studies; and genetic studies complement epidemiologic findings and can offer insights into biological plausibility and pathways between exposure and disease, but methods for synthesizing such studies are lacking. We, therefore, developed a methodology for identifying mechanisms and carrying out systematic reviews of mechanistic studies that underpin exposure–cancer associations.Methods: A multidisciplinary team with expertise in informatics, statistics, epidemiology, systematic reviews, cancer biology, and nutrition was assembled. Five 1-day workshops were held to brainstorm ideas; in the intervening periods we carried out searches and applied our methods to a case study to test our ideas.Results: We have developed a two-stage framework, the first stage of which is designed to identify mechanisms underpinning a specific exposure–disease relationship; the second stage is a targeted systematic review of studies on a specific mechanism. As part of the methodology, we also developed an online tool for text mining for mechanism prioritization (TeMMPo) and a new graph for displaying related but heterogeneous data from epidemiologic studies (the Albatross plot).Conclusions: We have developed novel tools for identifying mechanisms and carrying out systematic reviews of mechanistic studies of exposure–disease relationships. In doing so, we have outlined how we have overcome the challenges that we faced and provided researchers with practical guides for conducting mechanistic systematic reviews.Impact: The aforementioned methodology and tools will allow potential mechanisms to be identified and the strength of the evidence underlying a particular mechanism to be assessed. Cancer Epidemiol Biomarkers Prev; 26(11); 1667–75. ©2017 AACR.

A guide to using the methodology

Funding Information: We acknowledge all the participants and researchers of the 23 biobanks that have contributed to the GBMI. Biobank-specific acknowledgments are included in the Data S3 . We thank H. Huang, A.R. Martin, B.M. Neale, Y. Okada, K. Tsuo, J.C. Ulirsch, Y. Wang, and all the members of Finucane and Daly labs for their helpful feedback. M.K. was supported by a Nakajima Foundation Fellowship and the Masason Foundation . H.K.F. was funded by NIH grant DP5 OD024582 . Publisher Copyright: © 2022 The Author(s) Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10−4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.

Perturbation of the insulin/insulin-like growth factor (IGF) signaling system is often cited as a mechanism driving breast cancer risk. A systematic review identified prospective cohort studies and Mendelian randomization studies that examined the effects of insulin/IGF signaling (IGF, their binding proteins (IGFBP), and markers of insulin resistance] on breast cancer risk. Meta-analyses generated effect estimates; risk of bias was assessed and the Grading of Recommendations Assessment, Development and Evaluation system applied to evaluate the overall quality of the evidence. Four Mendelian randomization and 19 prospective cohort studies met our inclusion criteria. Meta-analysis of cohort studies confirmed that higher IGF-1 increased risk of breast cancer; this finding was supported by the Mendelian randomization studies. IGFBP-3 did not affect breast cancer. Meta analyses for connecting-peptide and fasting insulin showed small risk increases, but confidence intervals were wide and crossed the null. The quality of evidence obtained ranged from ‘very low’ to ‘moderate’. There were insufficient studies to examine other markers of insulin/IGF signaling. These findings do not strongly support the biological plausibility of the second part of the physical activity—insulin/IGF signaling system—breast cancer pathway. Robust conclusions cannot be drawn due to the dearth of high quality studies. See related article by Swain et al., p. 2106

Despite early interest, the evidence linking fatty acids to cardiovascular diseases remains controversial. We used Mendelian randomization to explore the involvement of polyunsaturated (PUFA) and monounsaturated (MUFA) fatty acids biosynthesis in the aetiology of several cardiovascular disease endpoints in up to 1,153,768 European and 212,453 East Asian ancestry individuals. As instruments, we selected single nucleotide polymorphisms (SNP) mapping to genes with well-known roles in PUFA (i.e. FADS1/2 and ELOVL2) and MUFA (i.e. SCD) biosynthesis. Our findings suggest that higher PUFA biosynthesis rate (proxied by rs174576 near FADS1/2) is related to higher odds of multiple cardiovascular diseases, particularly ischemic stroke, peripheral artery disease and venous thromboembolism, whereas higher MUFA biosynthesis rate (proxied by rs603424 near SCD) is related to lower odds of coronary artery disease among Europeans. Results were unclear for East Asians as most effect estimates were imprecise. By triangulating multiple approaches (i.e. uni-/multi-variable Mendelian randomization, a phenome-wide scan, genetic colocalization and within-sibling analyses), our results are compatible with higher low- density lipoprotein (LDL)-cholesterol (and possibly glucose) being a downstream effect of higher PUFA biosynthesis rate. Our findings indicate that genetically-determined PUFA and MUFA biosynthesis are involved in the aetiology of cardiovascular diseases and suggest LDL-cholesterol as a potential mediating trait between PUFA biosynthesis and cardiovascular diseases risk.

BackgroundEstimation of the average causal effect using instrumental variable (IV) analyses requires homogeneity of instrument-exposure and/or exposure-outcome relationships. Previous research explored the validity of homogeneity assumptions by testing IV-exposure interaction effects using a set of effect modifiers. However, this approach requires that modifiers are known and measured but evidence for interaction may also be observed through IV association with exposure variance without knowledge of the modifier.MethodsWe explored the utility of testing for IV-exposure variance effects as evidence against homogeneity through simulation. We also evaluated the approach of removing IVs from Mendelian randomization (MR) analyses that show strong association with exposure variance (hence are likely to have heterogeneous effects). Our methodology was applied to evaluate homogeneity assumptions of LDL, urate and glucose on cardiovascular disease, gout, and type 2 diabetes, respectively.ResultsUnder simulation, interaction of IV-exposure and exposure-outcome effects by a single modifier led to bias of the estimated average causal effect (ACE) which could be partially assessed by testing for IV-exposure variance effects. Bias of the ACE attenuated after removing instruments with strong exposure variance effects. In applied analyses, we found no strong evidence of bias from the ACE.ConclusionsWe find no strong evidence against estimating the ACE for LDL, urate and glucose on cardiovascular disease, gout, and type 2 diabetes. These approaches could be used in future MR analyses to gain improved understanding of the causal estimand.Key messagesHomogeneity of the instrument-exposure and/or exposure-outcome effect is necessary to estimate the average causal effect which is important for developing health interventionsPartial evidence against the homogeneity assumption can be obtained from testing for the instrument-exposure variance effect which may suggest the presence of effect modificationThis evidence can be used in two ways: i) as a falsification approach to determine if the homogeneity assumption may be violated. ii) to remove genetic instruments from Mendelian randomization analyses providing an estimate that is closer to the average causal effectAfter removing instruments with exposure variance effects, the Mendelian randomization effect of LDL, urate and glucose on coronary heart disease, gout, and type 2 diabetes, respectively showed little difference suggesting no strong evidence against the average causal effect

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of casual inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genetic colocalisation is an important tool to test for shared genetic aetiology and is commonly used to strengthen causal inference in genetic studies of molecular traits and drug targets. However, the single causal variant assumption of the original colocalization method is a considerable limitation in genomic regions with multiple causal effects.We integrated conditional analyses (GCTA-COJO) and colocalisation analyses (coloc), into a novel analysis tool called Pair-Wise Conditional Colocalization (PWCoCo). PWCoCo performs conditional analyses to identify independent signals for the two tested traits in a genomic region and then conducts colocalisation of each pair of conditionally independent signals for the two traits using summary-level data. This allows for the stringent single-variant assumption to hold for each pair of colocalisation analysis.We found that the computational efficiency of PWCoCo is on average better than colocalisation with Sum of Single Effects Regression using Summary Stats (SuSiE-RSS), with greater gains in efficiency for high-throughput analysis. In a case study using GWAS data for multiple sclerosis and brain cortex-derived eQTLs (MetaBrain), we recapitulated all previously identified genes, which showcased the robustness of the method. We further found colocalisation evidence for secondary signals in nine additional loci, which was not identifiable in conventional GWAS and/or colocalisation.PWCoCo offers key improvements over existing methods, including: (1) robust colocalisation when the single variant assumption is violated; (2) independent colocalisation of secondary signals, which enables identification of novel disease-causing variants; (3) an easy-to-use and computationally efficient tool to test for colocalisation of high-dimensional omics data.

Physical activity may reduce the risk of developing breast cancer via its effect on the insulin/insulin-like growth factor (IGF) signaling system. A systematic review searched for randomized controlled trials (RCT), Mendelian randomization and prospective cohort studies that examined the effects of physical activity on insulin/IGF signaling [IGFs, their binding proteins (IGFBP), and markers of insulin resistance] in adult women. Meta-analyses were performed to generate effect estimates. Risk of bias was assessed, and the Grading of Recommendations Assessment, Development, and Evaluation system used to determine the overall quality of the evidence. Fifty-eight RCTs met our inclusion criteria, no observational or Mendelian randomization studies met the criteria for inclusion. Meta-analyses indicated that physical activity interventions (vs. control) reduced fasting insulin, the Homeostatic Model Assessment for Insulin Resistance and fasting glucose. Physical activity increased IGF-1, but there was no clear effect on IGFBP-3 or the ratio of IGF-1:IGFBP-3. Strong evidence was only established for fasting insulin and insulin resistance. Further research is needed to examine the effect of physical activity on C-peptide and HBA1c in women. Reductions in fasting insulin and insulin resistance following exercise suggest some biological plausibility of the first part of the physical activity–insulin/IGF signaling–breast cancer pathway. See related article by Drummond et al., p. 2116

Amino acids are key to protein synthesis, energy metabolism, cell signaling and gene expression; however, the contribution of specific maternal amino acids to fetal growth is unclear. We explored the effect of maternal circulating amino acids on fetal growth, proxied by birthweight, using two-sample Mendelian randomization and summary data from a genome-wide association study (GWAS) of serum amino acids levels (sample 1, n = 86,507) and a maternal GWAS of offspring birthweight, adjusting for fetal genotype effects (sample 2, n = 406,063 with maternal and/or fetal genotype effect estimates). A total of 106 independent single nucleotide polymorphisms (SNPs) robustly associated with 19 amino acids (p < 4.9 × 10−10) were used as genetic instrumental variables. Our results provide evidence that maternal circulating glutamine (59 g offspring birthweight increase per SD increase in maternal amino acid level, 95% CI: 7, 110) and serine (27 g, 95% CI: 9, 46) raise, while leucine (−59 g, 95% CI: -106, -11) and phenylalanine (−25 g, 95% CI: -47, -4) lower offspring birthweight. Our findings strengthen evidence for key roles of maternal circulating amino acids in healthy fetal growth.

Mendelian Randomization (MR) uses genetic instrumental variables to make causal inferences. Whilst sometimes referred to as “nature’s randomized trial”, it has distinct assumptions that make comparisons between the results of MR studies with those of actual randomized controlled trials (RCTs) invaluable. To scope the potential for (semi-)-automated triangulation of MR and RCT evidence, we minedClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications. We found that only 11% of completed RCTs identified inClinicalTrials.Govsubmitted their results to the database. Similarly low coverage was revealed for Semantic Medline (SemMedDB) semantic triples derived from MR and RCT publications –25% and 12%, respectively. Among intervention types that can be mimicked by MR, only trials of pharmaceutical interventions could be automatically matched to MR results due to insufficient annotation with MeSH ontology. A manual survey of the literature highlighted the potential for triangulation across a number of exposure/outcome pairs if these challenges can be addressed. We conclude that careful triangulation of MR with RCT evidence should involve consideration of similarity of phenotypes across study designs, intervention intensity and duration, study population demography and health status, comparator group, intervention goal and quality of evidence.

Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence “triangulation” presents a number of challenges for systematically identifying and integrating relevant information. We present ASQ (Annotated Semantic Queries), a natural language query interface to the integrated biomedical entities and epidemiological evidence in EpiGraphDB, which enables users to extract “claims” from a piece of unstructured text, and then investigate the evidence that could either support, contradict the claims, or offer additional information to the query. This approach has the potential to support the rapid review of pre-prints, grant applications, conference abstracts and articles submitted for peer review. ASQ implements strategies to harmonize biomedical entities in different taxonomies and evidence from different sources, to facilitate evidence triangulation and interpretation. ASQ is openly available athttps://asq.epigraphdb.org.

Background: The Research Excellence Framework (REF) in the United Kingdom is a system to assess the quality of research, requiring higher education institutions to select a sample of research outputs to submit for assessment. We tested whether prediction markets, in a novel application, could help institutions decide which research outputs to submit to the REF. Methods: Prediction markets are tools used to aggregate knowledge from a group who trade on the outcomes of future events. We ran six different prediction markets across three institutions in four different academic fields, asking participants to predict which papers in their department would score above a certain threshold in the REF.Results: Despite low participation in some markets, overall the prediction markets predicted mock REF outcomes with greater than 70% accuracy. We also compared the results of one market to random forests and logistic regression models trained to predict REF scores using bibliometrics, and found they had similar accuracy with prediction markets and high correlation with final market price, suggesting trades were partly informed by publication metrics. Conclusions: Our study provides a proof of concept that prediction markets can be used to forecast REF ratings, and that they capture similar information to other methods. Due to their pragmatic limitations, prediction markets are unlikely to entirely replace other methods of REF selection or research assessment, but they may offer a useful addition to REF selection and may be valuable in training contexts.

BackgroundRelatively little is known about the long-term impacts of SARS-CoV-2 biology, including whether it increases the risk of cancer. This study aims to identify the molecular interactions between COVID-19 infections and cancer processes.Materials and MethodsWe integrated recent data on SARS-CoV-2 – host protein interactions, risk factors for critical illness, known oncogenes, tumor suppressor genes and cancer drivers in EpiGraphDB, a database of disease biology and epidemiology. We used these data to reconstruct the network of molecular links between SARS-CoV-2 infections and cancer processes in various tissues expressing the angiotensin-converting enzyme 2 (ACE2) receptor. We applied community detection algorithms and Gene Set Enrichment Analysis (GSEA) to identify cancer-relevant pathways that may be perturbed by SARS-CoV-2 infection.ResultsIn lung tissue, the results showed that 4 oncogenes are potentially targeted by SARS-CoV-2, and 92 oncogenes interact with other human genes targeted by SARS-CoV-2. We found evidence of potential SARS-CoV-2 interactions with Wnt and hippo signaling pathways, telomere maintenance, DNA replication, protein ubiquitination and mRNA splicing. Some of these pathways were potentially affected in multiple tissues.ConclusionsThe long-term implications of SARS-CoV-2 infection are still unknown, but our results point to the potential impact of infection on pathways relevant to cancer affecting cell proliferation, development and survival, favoring DNA degradation, preventing the repair of damaging events and impeding the translation of RNA into working proteins. This highlights the need for further research to investigate whether such effects are transient or longer lasting. Our results are openly available in the EpiGraphDB platform at https://epigraphdb.org/covid-cancer and the repository https://github.com/MRCIEU/covid-cancer (https://doi.org/10.5281/zenodo.6391588).

Real-time updated risk prediction of disease outcomes could lead to improvements in patient care and better resource management. Established monitoring during pregnancy at antenatal and intrapartum periods could be particularly amenable to benefits of this approach. This proof-of-concept study compared automated and manual prediction modelling approaches using data from the Collaborative Perinatal Project with exemplar application to hypoxic-ischaemic encephalopathy (HIE). Using manually selected predictors identified from previously published studies we obtained high HIE discrimination with logistic regression applied to antenatal only (0.71 AUC [95% CI 0.64-0.77]), antenatal and intrapartum (0.70 AUC [95% CI 0.64-0.77]), and antenatal, intrapartum and birthweight (0.73 AUC [95% CI 0.67-0.79]) data. In parallel, we applied a range of automated modelling methods and found penalised logistic regression had best discrimination and was equivalent to the manual approach but required little human input giving 0.75 AUC for antenatal only (95% CI 0.69, 0.81), 0.70 AUC for antenatal and intrapartum (95% CI 0.63, 0.78), and 0.74 AUC using antenatal, intrapartum, and infant birthweight (95% CI 0.65, 0.81). These results demonstrate the feasibility of developing automated prediction models which could be applied to produce disease risk estimates in real-time. This approach may be especially useful in pregnancy care but could be applied to any disease.

Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (Pcombined = 1.8 × 10−11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (Pcombined = 1.5 × 10−10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (Pcombined = 1.2 × 10−122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.

Gene prioritisation at GWAS loci necessities careful assembly and examination of different types of molecular evidence to arrive at a set of plausible candidates. In many human traits, common small-effect mutations may subtly dysregulate the function of the very same genes which are impacted by rare, large-effect mutations causing Mendelian disease of similar phenotype. However, information on gene-Mendelian disease associations, rare pathogenic mutations driving the disease, and the disease phenotype ontology is dispersed across many data sources and does not integrate easily with enrichment analysis.MendelVar is a new webserver facilitating transfer of knowledge from Mendelian disease research into interpretation of genetic associations from GWAS of complex traits. MendelVar allows querying of pre-defined or LD-determined genomic intervals against a comprehensive integrated database to find overlap with genes linked to Mendelian disease. Next, MendelVar looks for enrichment of any Human Phenotype Ontology, Disease Ontology and other ontology/pathway terms associated with identified Mendelian genes. In addition, MendelVar provides a list of all overlapping pathogenic and likely pathogenic variants for Mendelian disease sourced from ClinVar.Inclusion of information obtained from MendelVar in post-GWAS gene annotation pipelines can strengthen the case for causal importance of some genes. Moreover, as genes with Mendelian disease evidence may make for more successful drug targets, this may be particularly useful in drug discovery pipelines. Taking GWAS summary statistics for male-pattern baldness, intelligence and atopic dermatitis, we demonstrate the use of MendelVar in prioritizing candidate genes at these loci which are linked to relevant enriched ontology terms. MendelVar is freely available athttps://mendelvar.mrcieu.ac.uk/