Biology subjects

Zheng, J.

Publications and source records attributed to Zheng, J..

At least 307 records · Page 17Linked to original sources

bioRxiv2020

CRISPR-based Live Imaging of Epigenetic Modification-Mediated Genome Reorganization

Epigenetic modifications play an essential role in chromatin architecture and dynamics. The role of epigenetic modification in chromatin organization has been studied by Hi-C from population cells, but imaging techniques to study their correlation and regulation in single living cells are lacking. Here we develop a CRISPR-based EpiGo (Epigenetic perturbation induced Genome organization) system to track epigenetic modification-mediated relocation, interaction or reorganization of genomic regions in living cells. EpiGo-KRAB is sufficient to induce the relocation of genomic loci to HP1 condensates and trigger genomic interactions. EpiGo-KRAB also triggers the induction of H3K9me3 at large genomic regions, which decorate on the surface of HP1 condensates possibly driven by phase separation.

molecular biology↗

bioRxiv2020

Genetic predictors of participation in optional components of UK Biobank

Large studies (e.g. UK Biobank) are increasingly used for GWAS and Mendelian randomization (MR) studies. Selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P<6x10-9), including loci with known links to intelligence and Alzheimers disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimers and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

genetics↗

bioRxiv2020

An unorthodox mechanism underlying voltage sensitivity of TRPV1 ion channel

While the capsaicin receptor TRPV1 channel is a polymodal nociceptor for heat, capsaicin, and proton, the channels responses to each of these stimuli are profoundly regulated by membrane potential, damping or even prohibiting its response at negative voltages and amplifying its response at positive voltages. Though voltage sensitivity plays an important role is shaping pain responses, how voltage regulates TRPV1 activation remains unknown. Here we showed that the voltage sensitivity of TRPV1 does not originate from the S4 segment like classic voltage-gated ion channels; instead, outer pore acidic residues directly partake in voltage-sensitive activation, with their negative charges collectively constituting the observed gating charges. Voltage-sensitive outer pore movement is titratable by extracellular pH and is allosterically coupled to channel activation, likely by influencing the upper gate in the ion selectivity filter. Elucidating this unorthodox voltage-gating process provides a mechanistic foundation for understanding polymodal gating and opens the door to novel approaches regulating channel activity for pain managements.

biophysics↗

bioRxiv2019

TRA2A-induced upregulation of LINC00662 regulates blood-brain barrier permeability by affecting ELK4 mRNA stability in Alzheimer's microenvironment

The blood-brain barrier (BBB) has an important significance in maintenance and regulation of the neural microenvironment. The occurrence of BBB disruption is the pathological change of early Alzheimers disease (AD). RNA-binding proteins and long non-coding RNAs are closely related to the regulation of BBB permeability. Our study was performed to demonstrate TRA2A/LINC00662/ELK4 axis that regulates BBB permeability in AD microenvironment. In A{beta}1-42-incubated microvascular endothelial cells (ECs) of BBB model in vitro, TRA2A and LINC00662 were enriched. TRA2A increased the stability of LINC00662 by binding with it. The knockdown of either TRA2A or LINC00662 decreased the BBB permeability via upregulating the levels of tight junction-related proteins. ELK4 was downregulated in BBB model in vitro in AD microenvironment. LINC00662 mediated the degradation of ELK4 mRNA by SMD pathway. The downregulated ELK4 increased the permeability of BTB by inducing the tight junction-related proteins. TRA2A/LINC00662/ELK4 axis is important in the regulation of BBB permeability in AD microenvironment, which would be a new molecular target for AD treatment.

molecular biology↗

bioRxiv2019

Using genetics to disentangle the complex relationship between food choices and health status

Despite food choices being one of the most important factors influencing health, efforts to identify individual food groups and dietary patterns that cause disease have been challenging, with traditional nutritional epidemiological approaches plagued by biases and confounding. After identifying 302 individual genetic determinants of dietary intake in 445,779 individuals in the UK Biobank study, we develop a statistical genetics framework that enables us, to directly assess the impact of food choices on health outcomes. We show that the biases which affect observational studies extend also to GWAS, genetic correlations and causal inference through genetics, which can be corrected by applying our methods. Finally, by applying Mendelian Randomization approaches to the corrected results we identify some of the first robust causal associations between eating patterns and cancer, heart disease, obesity, and several other health related risk factors, distinguishing between the effects of specific foods or dietary patterns.

genetics↗

bioRxiv2019

GmPTF1 Modifies Root Architecture Responses to Phosphate Starvation in Soybean

Though root architecture modifications may be critically important for improving phosphorus (P) efficiency in crops, the regulatory mechanisms triggering these changes remain unclear. In this study, we demonstrate that genotypic variation in GmEXPB2 expression is strongly correlated with root elongation and P acquisition efficiency, and enhancing its transcription significantly improves soybean yield in the field. Promoter deletion analysis was performed using six 5 truncation fragments (P1-P6) of GmEXPB2 fused with the GUS reporter gene in transgenic hairy roots, which revealed that the P1 segment containing 3 E-box elements significantly enhances induction of gene expression in response to phosphate (Pi) starvation. Further experimentation demonstrated that GmPTF1, a bHLH transcription factor, is the regulatory factor responsible for the induction of GmEXPB2 expression in response to Pi starvation. In short, Pi starvation induced expression of GmPTF1, with the GmPTF1 product not only directly binding the E-box motif in the P1 region of the GmEXPB2 promoter, but also activating GUS expression in a dosage dependent manner. Further work with soybean transgenic composite plants showed that, altering GmPTF1 expression significantly impacted GmEXPB2 transcription, and thereby affected root growth, biomass and P uptake. Taken together, this work identifies a novel regulatory factor, GmPTF1, involved in changing soybean root architecture through regulation the expression of GmEXPB2. These findings contribute to understanding the molecular basis of root architecture modifications in response to P deficiency, and, in the process, suggest candidate genes and a promoter region to target for improving soybean yield through molecular breeding of P efficiency. One Sentence SummaryThe bHLH transcription factor GmPTF1 regulates the expression of {beta}-expansin gene GmEXPB2 to modify root architecture, and thus promote phosphate acquisition, and biomass in soybean.

physiology↗

bioRxiv2019

Expression of heat shock protein 70 is insufficient to extend Drosophila melanogaster longevity

It has been known for over 20 years that Drosophila melanogaster flies with twelve additional copies of the hsp70 gene encoding the 70 kDa heat shock protein lives longer after a non-lethal heat treatment. Since the heat treatment also induces the expression of additional heat shock proteins, the biological effect can be due either to HSP70 acting alone or in combination. This study used the UAS/GAL4 system to determine whether hsp70 is sufficient to affect the longevity and the resistance to thermal, oxidative or desiccation stresses of the whole organism. We observed that HSP70 expression in the nervous system or muscles has no effect on longevity or stress resistance but ubiquitous expression reduces the life span of males. We also observed that the down-regulation of Hsp70 using RNAi did not affect longevity.

genetics↗

bioRxiv2019

Population genomics and pathotypic evaluation of the bacterial leaf blight pathogen of rice

Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial blight disease, which reduces crop yield by up to 50% in rice production. Despite its substantial threat on food production worldwide, knowledge about its population structure, virulence diversity and the relationship between them is limited. We used whole-genome sequencing to explore the diversity and evolution of Xoo during the past 30 years in the main rice-planting areas of China. Six separate lineages were revealed by phylogenomic analysis, with CX-5 and CX-6 predominating in the population for decades. The recent sporadic outbreaks were respectively caused by Xoo derived from these lineages especially the two major ones. The lineage and sub-lineage distribution of isolates strongly correlated to their geographical origin, which was found to be mainly determined by the planting of the two major rice subspecies, indica and japonica. Large-scale virulence testing was conducted to evaluate the diversity of pathogenicity for Xoo. We found rapid virulence dynamics against rice, and its determinant factors including genetic background of Xoo, rice resistance genes and the planting environment of rice. Genetic background was investigated deeply by comparative genomics, which indicates that transposition events contributing the most to evolution of the Xoo genome and the rapid diversification of virulence. This study provided a good model to understand the evolution and dynamics of plant pathogens in the context of interaction with their hosts which are influenced by both geographical conditions and farming practices.

genomics↗

bioRxiv2019

Disentangling causal relationships between inflammatory markers and depression: a bidirectional Mendelian randomization analysis

BackgroundThe inflammatory markers C-reactive protein (CRP), interleukin-1 receptor antagonist (IL1-Ra), and interleukin-6 (IL-6) have been associated with depression risk in observational studies. The causal nature of these associations is unclear as conventional observational designs are susceptible to reverse causation and residual confounding. Bidirectional Mendelian randomization (MR) analysis uses genetic variants to proxy for risk factors to help elucidate the presence, magnitude, and direction of causal relationships between traits. MethodsWe performed bidirectional two-sample MR to examine causal associations between circulating CRP, IL1-Ra, and IL-6 and major depressive disorder (MDD) in 135,458 cases and 344,901 controls in the Psychiatric Genetics Consortium. Genetic instruments to proxy inflammatory markers and liability to MDD were constructed by obtaining single-nucleotide polymorphisms (SNPs) associated with these phenotypes in genome-wide association study meta-analyses. Wald ratios and inverse-variance weighted random-effects models were employed to generate causal effect estimates and various sensitivity analyses were performed to examine violations of MR assumptions. ResultsThere was evidence supporting a causal effect of circulating IL-6 on risk of MDD (per natural-log increase: OR 0.85, 95% CI: 0.75-0.96, P=0.007). Higher circulating levels of IL-6 as influenced by variants in the IL6R gene region represent lower cellular binding of IL-6 to its receptor and therefore the present results suggest that IL-6 increases the risk of MDD. We found limited evidence supporting a causal effect of CRP (1.06, 95% CI 0.93-1.22; P=0.36) or IL1-Ra (OR 0.95, 95% CI: 0.87-1.03, P=0.20) on risk of MDD. Reverse direction MR analyses suggested limited evidence for a causal effect of genetic liability to MDD on any of the inflammatory markers examined. ConclusionsThese findings support a causal role of IL-6-related pathways in development of major depressive disorder and suggest the possible efficacy of interleukin-6 inhibition as a therapeutic target for depression.

genomics↗

bioRxiv2019

Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Various computational and statistical approaches have been proposed to uncover the mutational patterns of rapidly evolving influenza viral genes. Nonetheless, the approaches mainly rely on sequence alignments which could potentially lead to spurious mutations obtained by comparing sequences from different clades that coexist during particular periods of time. To address this issue, we propose a phylogenetic tree-based pipeline that takes into account the evolutionary structure in the sequence data. Assuming that the sequences evolve progressively under a strict molecular clock, considering a competitive model that is based on a certain Markov model, and using a resampling approach to obtain robust estimates, we could capture statistically significant single-mutations and co-mutations during the sequence evolution. Moreover, by considering the results obtained from analyses that consider all paths and the longest path in the resampled trees, we can categorize the mutational sites and suggest their relevance. Here we applied the pipeline to investigate the 50 years of evolution of the HA sequences of influenza A/H3N2 viruses. In addition to confirming previous knowledge on the A/H3N2 HA evolution, we also demonstrate the use of the pipeline to classify mutational sites according to whether they are able to enhance antigenic drift, compensate other mutations that enhance antigenic drift, or both.

evolutionary biology↗

bioRxiv2019

A new colorectal cancer risk prediction model incorporating family history, personal and environmental factors

PurposeReducing colorectal cancer (CRC) incidence and mortality through early detection would improve efficacy if targeted. A CRC risk-prediction model incorporating personal, family, genetic and environmental risk factors could enhance prediction.\n\nMethodsWe developed risk-prediction models using population-based CRC cases (N=4,445) and controls (N=3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). A familial risk profile (FRP) was calculated to summarize individuals risk based on their CRC family history, family structure, germline mutation probability in major susceptibility genes, and a polygenic component. Using logistic regression, we developed risk models including individuals FRP or a binary CRC family-history (FH), and risk factors collected at recruitment. Model validation used follow-up data for population-(N=12,052) and clinic-based (N=5,584) relatives with no cancer history at recruitment, assessing calibration (E/O) and discrimination (AUC).\n\nResultsThe E/O (95% confidence interval [CI]) for FRP models for population-based relatives were 1.04 (0.74-1.45) and 0.86 (0.64-1.20) for men and women, and for clinic-based relatives 1.15 (0.87-1.58) and 1.04 (0.76-1.45). The age-adjusted AUC (95% CI) for FRP models in population-based relatives were 0.69 (0.60-0.78) and 0.70 (0.62-0.77), and for clinic-based relatives 0.77 (0.69-0.84) and 0.68 (0.60-0.76). The incremental values of AUC (95% CI) for FRP over FH models for population-based relatives were 0.08 (0.01-0.15) and 0.10 (0.04-0.16), and for clinic-based relatives 0.11 (0.05-0.17) and 0.11 (0.06-0.17).\n\nConclusionThe FRP-based model and FH-based model calibrate well in both settings. The FRP-based model provided better risk-prediction and discrimination than the FH-based model. A detailed family history may be useful for targeted risk-based screening and clinical management.

epidemiology↗

bioRxiv2019

New mechanism-based inhibitors of aspartate transcarbamoylase for anticancer drug development

Aspartate transcarbamoylase (ATCase) is a key enzyme which regulates and catalyzes the second step of de novo pyrimidine synthesis in all organisms. E. coli ATCase is a prototypic enzyme regulated by both product feedback and substrate cooperativity, whereas human ATCase is a potential anticancer target. Through structural and biochemical analyses, we revealed that R167/130s loop region in ATCase serves as a gatekeeper for the active site, playing a new and unappreciated role in feedback regulation. Based on virtual compound screening simultaneously targeting the new regulatory region and active site of human ATCase, two compounds were identified to exhibit strong inhibition of ATCase activity, proliferation of multiple cancer cell lines, and growth of xenograft tumors. Our work has not only revealed a previously unknown regulatory region of ATCase that helps explain feedback regulation, but also successfully guided the identification of new ATCase inhibitors for anticancer drug development using a dual-targeting strategy.

cancer biology↗

bioRxiv2019

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here, we estimated the effects of 1002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naive phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes (www.epigraphdb.org/pqtl/). Evaluation of data from historic drug development programmes showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of our approach in identifying and prioritising potential therapeutic targets.

genetics↗

bioRxiv2019

An integrative analysis of GWAS and intermediate molecular trait data reveals common molecular mechanisms supporting genetic similarity between seemingly unrelated complex traits

The rapid accumulation of Genome Wide Association Studies (GWAS) and association studies of intermediate molecular traits provides new opportunities for comparative analysis of the genetic basis of complex human phenotypes. Using a newly developed statistical framework called Sherlock-II that integrates GWAS with eQTL (expression Quantitative Trait Loci) and metabolite-QTL data, we systematically analyzed 445 GWAS datasets, and identified 1371 significant gene-phenotype associations and 308 metabolites-phenotype associations (passing a Q value cutoff of 1/3). This integrative analysis allows us to translate SNP-phenotype associations into functionally informative gene-phenotype association profiles. Genetic similarity analyses based on these profiles clustered phenotypes into sub-trees that reveal both expected and unexpected relationships. We employed a statistical approach to delineate sets of functionally related genes that contribute to the similarity between their association profiles. This approach suggested common molecular mechanisms that connect the phenotypes in a subtree. For example, we found that fasting insulin, fasting glucose, breast cancer, prostate cancer, and lung cancer clustered into a subtree, and identified cyclic AMP/GMP signaling that connects breast cancer and insulin, NAPDH oxidase/ROS generation that connects the three cancers, and apoptosis that connects all five phenotypes. Our approach can be used to assess genetic similarity and suggest mechanistic connections between phenotypes. It has the potential to improve the diagnosis and treatment of a disease by mapping mechanistic insights from one phenotype onto others based on common molecular underpinnings.

genetics↗

bioRxiv2019

Computed Tomography Measures of Inter-site tumor Heterogeneity for Classifying Outcomes in High-Grade Serous Ovarian Carcinoma: a Retrospective Study

AbstractO_ST_ABSBackgroundC_ST_ABSHigh grade serous ovarian carcinoma shows marked intra-tumoral heterogeneity which is associated with decreased survival and resistance to platinum-based chemotherapy. Pre-treatment quantification of spatial tumor heterogeneity by multiple tissue sampling is not clinically feasible. Using standard-of-care CT imaging to non-invasively quantify heterogeneity could have high clinical utility and would be highly cost-effective. Texture analysis measures local variations in computed tomography (CT) image intensity. Haralick texture methods are typically used to capture the heterogeneity of entire lesions; however, this neglects the possible presence of texture habitats within the lesion, and the differences between metastatic sites. The primary aim of this study was to develop texture analysis of intra-site and inter-site spatial heterogeneity from standard-of-care CT images and to correlate these measures with clinical and genomic features in patients with HGSOC. Methods and findingsWe analyzed the data from a retrospective cohort of 84 patients with HGSOC consisting of 46 patients from Memorial Sloan Kettering Cancer Center (MSKCC) and 38 non-MSKCC cases selected from The Cancer Imaging Archive (TCIA). Inclusion criteria consisted of FIGO stage II-IV HGSOC, attempted primary cytoreductive surgery, intravenous contrast-enhanced CT of abdomen and pelvis performed prior to surgery and availability of molecular tumor data analysed as per the Cancer Genome Atlas (TCGA) Research Network ovarian cancer project. Manual segmentation and image analysis was performed on 463 metastatic tumor sites from 84 patients. In the MSKCC cohort the median number of tumor sites was 7 (interquartile range 5-9) and 4 (interquartile range 3-4) in the TCIA patients. Sub-regions were produced within each tumor site by grouping voxels with similar Haralick texture using the Kernel K-means method. We derived statistical measures of intra- and inter-site tumor heterogeneity (IISTH) including cluster sites entropy (cSE), cluster sites standard deviation (cluDev) and cluster sites dissimilarity (cluDiss) from sub-regions identified within and between individual tumor sites. Unsupervised clustering was used to group patient IISTH measures into low, medium, high, and ultra-high heterogeneity clusters from each cohort. The IISTH measure cluDiss was an independent predictor of progression-free survival (PFS) in multivariable analysis in both datasets (MSKCC hazard ratio [HR] 1.04, 95% CI 1.01-1.06, P = 0.002; TCIA HR 1.05, 95% CI 1.00-1.10, P = 0.049). Low and medium IISTH clusters were associated with longer PFS in multivariable analysis (MSKCC HR 2.94, 90% CI 1.29-6.70, P = 0.009, TCIA HR 5.94, 95% CI 1.05-33.6, P = 0.044). IISTH measures were robust to differences in the CT imaging systems. Average Haralick textures contrast (TCIA HR 1.08, 95% CI 1.01-1.10, P = 0.019) and homogeneity (TCIA HR 1.09, 95% CI 1.02-1.16, P = 0.008) were associated with PFS in mutivariate analysis only in the TCIA dataset. All other average Haralick textures and total tumor volume were not associated with PFS in either dataset. ConclusionsTexture measures of intra- and inter-site tumor heterogeneity from standard of care CT images are correlated with shorter PFS in HGSOC patients. These quantitative methods are independent of the CT imaging system and can thus be applied in clinical practice. The methodology proposed here enables the non-invasive quantification of intra-tumoral heterogeneity and disease stratification for future experimental medicine studies and clinical trials, particularly in cases where total tumour volume and averaged textures have low predictive power. Author summaryO_ST_ABSWhy was this study done?C_ST_ABSO_LITumor heterogeneity is a feature of many solid malignancies including ovarian cancer. C_LIO_LIRecent genomic research suggests that intra-site tumor heterogeneity (heterogeneity within a single tumor site) and inter-site tumor heterogeneity (heterogeneity between different metastatic sites in the same patient) correlate with clinical outcome in HGSOC. C_LI What did the researchers do and find?O_LIWe developed quantitative and non-invasive image-analysis based measures for predicting outcome in HGSOC patients by combining image-based information from within and between multiple tumor sites. C_LIO_LIUsing datasets from two sources, we demonstrate that these image-based tumor heterogeneity measures predict progression free survival in patients with HGSOC. C_LI What do these findings mean?O_LINon-invasive measures of CT image heterogeneity may predict outcomes in HGSOC patients. C_LIO_LIWider application of these CT image heterogeneity measures could prove useful for stratifying patients to different therapies given that total tumour volume and averaged textures have low predictive power. C_LI

bioinformatics↗

bioRxiv2018

MR-TRYX: Exploiting horizontal pleiotropy to infer novel causal pathways

In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying novel pathways to the traits under investigation. Here, we developed the MR-TRYX framework, following the advice of William Bateson to \"TReasure Your eXceptions\". We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across thousands of complete GWAS summary datasets in the MR-Base database to systematically identify other (\"candidate\") traits that associate with the outliers. We developed a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways were uncovered with already established causal effects, validating the approach, but also novel putative causal pathways. Adjustment for pleiotropic pathways reduced the heterogeneity across the analyses.

bioinformatics↗

bioRxiv2018

Hypothesis-free analysis of deep vein thrombosis aetiology: a Mendelian randomization study

BackgroundDeep vein thrombosis (DVT) is the formation of a thrombus/clot in the deep veins, when part of this clot breaks off it can travel to the lungs, resulting in pulmonary embolism. These two conditions together are known as venous thromboembolism (VTE), a leading cause of death and disability worldwide. Despite the prevalence of VTE, we do not fully understand what causes it and it is often overlooked as a major public health problem. Confirming and identifying risk factors associated with DVT is likely to lead to a reduction in the incidence, morbidity and mortality of VTE especially where these risk factors are modifiable. We can do this, by exploiting the availability of summary genetic data from genome-wide association studies (GWAS) of numerous phenotypes, including DVT, which permits hypothesis-free causal inference.\n\nObjectivesTo identify novel risk factors for DVT and to assess the causality of factors previously shown to be associated with DVT.\n\nMethodsTwo-sample Mendelian randomization (MR) was performed using summarised genetic data. Inverse variance weighted (IVW) estimates were calculated and validated by additional methods more robust to horizontal pleiotropy (MR Egger, simple mode, weighted mode, and weighted median). Bidirectional and heterogeneity sensitivity analyses were performed to further evaluate our findings.\n\nResultsForty-seven exposures passed an exposure-exposure correlation-adjusted Bonferroni P-value threshold (5.43E-05). These included previously hypothesised risk factors for DVT (e.g. body mass index, varicose veins, height, hyperthyroidism) and novel associations (e.g. prospective memory, basal metabolic rate).\n\nConclusionOur analyses confirmed causal associations of risk factors previously associated with DVT and highlighted several novel risk factors for the disease. Our study demonstrates the utility of using a hypothesis free Mendelian randomization approach for the identification of novel disease risk factors.

epidemiology↗

bioRxiv2018

Evaluating causal associations between previously reported risk factors and epithelial ovarian cancer: a Mendelian randomization analysis

BackgroundPreviously reported observational associations between risk factors and epithelial ovarian cancer (EOC) could reflect residual confounding, reverse causation, or measurement error. Mendelian randomization (MR) uses genetic variants as proxies for modifiable risk factors to strengthen causal inference in observational studies.\n\nMethodsWe used MR to evaluate the causal role of 13 previously reported risk factors in overall and histotype-specific EOC in up to 25,509 case subjects and 40,941 controls in the Ovarian Cancer Association Consortium. Inverse-variance weighted models were employed to generate effect estimates and MR-Egger, weighted median, and weighted mode were performed to examine evidence of horizontal pleiotropy. A Bonferroni-corrected P-value threshold was used to establish \"strong evidence\" (P<0.0038) and \"suggestive evidence\" (0.0038<P<0.05) for associations.\n\nResultsThere was strong or suggestive evidence that 9 of 13 risk factors were causally associated with overall or histotype-specific EOC. Genetic liability to endometriosis was strongly associated with EOC (OR per log odds higher liability: 1.27,95%CI:1.16-1.40;P=6.94x10-7) and lifetime smoking exposure was suggestively associated with EOC (OR per unit increase in smoking score:1.36,95%CI:1.04-1.78;P=0.02). In histotype-stratified analyses, the strongest associations found were between: height and clear cell carcinoma (OR per SD increase:1.36,95%CI:1.15-1.61;P=0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset:1.09,95% CI:1.02-1.16;P=0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per log odds higher liability:0.74,95% CI:0.62-0.90;P=0.002). There was little evidence that genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone-binding globulin were associated with ovarian cancer or its subtypes.\n\nConclusionsOur comprehensive examination of possible etiological drivers of ovarian carcinogenesis supports a causal role for few of these factors in epithelial ovarian cancer and suggests distinct etiologies across histotypes.

epidemiology↗