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PubMed2024

Multiple sclerosis and abnormal spermatozoa: A bidirectional two-sample mendelian randomization study.

OBJECTIVE: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, and previous observational epidemiological studies have suggested an association between MS and male infertility; male infertility due to sperm abnormalities may result from a number of aetiological factors, such as genetics, autoimmune factors, etc., and there are currently no studies to assess whether MS is associated with sperm abnormalities in men. Therefore, we performed a Mendelian randomization (MR) analysis to assess the causal relationship between MS and abnormal spermatozoa. METHODS: In this study, independent single nucleotide polymorphisms (SNPs) strongly associated with multiple sclerosis (MS) were identified by mining public genome-wide association study repositories and used as instrumental variables to explore causality. The causal effect of MS on sperm abnormalities was systematically assessed using two-sample Mendelian randomization (MR) techniques, and various analytical models such as inverse variance weighting (IVW), MR-Egger and MR-PRESSO were implemented to dissect the association. In addition, a wide range of sensitivity tests, including Cochran's Q test to detect heterogeneity, MR-Egger intercept analysis to assess bias, leave-one-out to test model robustness, and funnel plot analysis to detect potential publication bias, were implemented to ensure the robustness and reliability of the causal inference results. RESULTS: There was a significant causal relationship between MS and abnormal sperm (OR 1.090, 95% CI [1.017-1.168], p = 0.014); The accuracy and robustness of the results were confirmed by sensitivity analysis. CONCLUSION: Here we show that there appears to be a causal relationship between multiple sclerosis and abnormal spermatozoa. MS as a chronic disease has a higher risk of concomitant sperm abnormalities in its male patients, and reproductive and fertility issues in men with MS should receive special attention from clinicians.

Humans↗
PubMed2025

Genotype by Environment Interactions in Gene Regulation Underlie the Response to Soil Drying in the Model Grass Brachypodium distachyon.

Gene expression is a quantitative trait under the control of genetic and environmental factors and their interaction, so-called genotype and environment (G × E). Understanding the mechanisms driving G × E is fundamental for ensuring stable crop performance across environments and for predicting the response of natural populations to climate change. Gene expression is regulated through complex molecular networks, yet the interactions between genotype and environment in gene regulation are rarely considered, particularly at the genome scale. Current frameworks and experimental designs often lack power to explicitly test network rewiring or to systematically compare regulatory networks. Here, we leverage a highly replicated RNA-sequencing dataset to model genome-scale gene expression variation between two natural accessions of the model grass Brachypodium distachyon and their response to soil drying. We first identified genotypic, environmental, and G × E effects on physiological, metabolic, and gene expression traits. We identify patterns of conservation-or variation-in gene coexpression networks and link these coexpression features to physiological traits. We further develop predictions of gene-gene interactions using causal inference and screen for interactions specific to-or with higher affinity in-a single genotype, treatment, or their interaction, G × E. Our analyses identify variation in candidate gene regulatory networks that may shape the evolution of environmental response in B. distachyon. We highlight the environmentally dependent regulatory control of several metabolic traits shown previously to play a role in drought acclimation. The framework presented here provides a scalable approach for more complex comparisons, particularly with the growing availability of large datasets from technologies such as single-cell transcriptomics.

Brachypodium↗
PubMed1998

Causal modeling of epidemiological data on psychiatric disorders.

This paper reviews the logic of causal inference from epidemiological data. I maintain that the clearest causal statements can be made when the philosophical causal principles of association, direction and isolation are upheld in epidemiological research. After reviewing the argument by Holland that only experimental manipulation affords clear causal claims, I examine the utility of structural equation models and longitudinal methods for making causal claims from non-experimental data. This examination leads to the conclusion that mental health epidemiologists should begin to incorporate intervention trials into the last phases of their research programmes when they want to make strong causal claims.

Humans↗
PubMed2000

Social support, coping and subjective well-being in patients with rheumatic diseases.

The purpose of this cross-sectional study is to examine the relationship between social support, coping, and subjective well-being by testing three hypotheses: (1) social support influences subjective well-being via coping; (2) coping influences subjective well-being via social support; (3) there is a reciprocal relationship between social support and coping, and both concepts influence subjective well-being. Data were analyzed from 628 patients with one or more chronic rheumatic disorder(s) affecting the joints, in some patients combined with another rheumatic disease (no fibromyalgia). Although causal inferences are not possible, the results present a plausible causal sequence in supporting the second hypothesis. This is only true, however, for coping by awaiting/avoidance: coping by awaiting/avoidance led to less social support and this decrease in social support influenced subjective well-being negatively.

Adaptation, Psychological↗
PubMed2006

When similarity and causality compete in category-based property generalization.

Five experiments were performed to investigate the category-based generalization of nonblank properties, properties that were novel but that were attributed to existing category features with causal explanations. Experiments 1-3 tested how such explanations interact with the well-known effects of similarity on such generalizations. The results showed that when the causal explanations were used, standard effects of typicality (Experiment 1), diversity (Experiment 2), or similarity itself (Experiment 3) were almost completely eliminated. Experiments 4 and 5 demonstrated that category-based generalizations exhibit some of the standard properties of causal reasoning; for example, an effect (i.e., a novel category property) is judged to be more prevalent when its cause (i.e., an existing category feature) is also prevalent. These findings suggest that category-based property generalization is often an instance of causal inference.

Animals↗
PubMed2006

An integrative approach for causal gene identification and gene regulatory pathway inference.

MOTIVATION: Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms. RESULTS: We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network. AVAILABILITY: Source code is available upon request.

Chromosome Mapping↗
PubMed2004

Graphical models for causation, and the identification problem.

This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.

Bayes Theorem↗
PubMed2026

Comparing genome-wide significant and chemosensory variants as instruments for dietary patterns in Mendelian randomization.

BACKGROUND: Diet is a modifiable risk factor for cardiometabolic disease, yet establishing causality remains challenging. Mendelian randomization (MR) leverages genetic variants as instrumental variables (IVs) to enable causal inference. METHOD: Using two-sample MR, we assessed the causal effects of four principal component-derived dietary patterns (DPs)-Unhealthy, Healthy, Meat-based, Pescatarian-on cardiometabolic outcomes including body mass index, coronary artery disease, blood lipids, blood pressures, type 2 diabetes, fasting glucose and insulin, and glycated haemoglobin. Two sets of IVs were employed: conventional genome-wide significant variants associated with each DP, filtered for pleiotropy and directionality; and biologically informed variants in chemosensory receptor genes, given the role of taste and smell perception in food choice. RESULTS: Using conventional IVs, the Pescatarian DP was associated with reduced fasting insulin (βIVW = -0.10 pmol/L per SD increase in the Pescatarian DP score, 95% confidence interval -0.15, -0.04; P = 1.19 × 10-3), surviving multiple sensitivity analyses. Associations between the Unhealthy DP and elevated blood pressure and glycated haemoglobin should be interpreted cautiously; one of the two filtered IVs was strongly associated with caffeine intake, limiting the attribution of these findings to the DP itself. Chemosensory Receptor IVs yielded null findings, reflecting insufficient power. CONCLUSION: Evidence for causal effects of DPs on cardiometabolic traits was limited, with the strongest support for a protective effect of the Pescatarian DP on fasting insulin. Chemosensory IVs demonstrated limited utility for DPs, likely reflecting the heterogeneous and complex sensory profiles of overall diets. Future efforts should consider guideline-based dietary indices to facilitate interpretability and translation.

Humans↗
PubMed1992

Statistical validation of intermediate markers of precancer for use as endpoints in chemoprevention trials.

Using an intermediate marker of precancer as an endpoint for evaluating agents that may prevent cancer involves a presumption that the modification of the marker will be accompanied by a modification of cancer incidence. This presumption can hold only if the marker is on or very closely linked to a causal pathway. Epidemiologists have discussed the nature of evidence required to infer causal relationships, and we briefly survey their work. Studies relating exposure (E) to marker (M) provide only indirect evidence for causality. Those relating marker (M) to disease (D) are more relevant. We propose a new validation criterion based on an analysis of the three-way relationship of exposure (E), marker (M) and disease (D). We discuss the level of evidence required for using intermediate markers as endpoints for Phase II and Phase III trials, and propose very stringent criteria for Phase III trials. For Phase II trials, we propose less stringent criteria, but still recommend that the marker (M) should have been shown to have a strong association with disease (D).

Anticarcinogenic Agents↗
PubMed2026

Modelling time-varying genetic effects on binary disease risk via functional Mendelian randomization.

MOTIVATION: Genome-wide association studies have identified thousands of genetic variants associated with complex traits, establishing Mendelian randomization (MR) as a powerful framework for causal inference using variants as natural experiments. However, existing MR methods treat causal effects as static, relying on cross-sectional exposure measurements and ignoring how genetic predispositions to disease operate dynamically across the life course. Recovering age-specific causal effect functions from longitudinal data requires combining functional data representations of exposure trajectories with instrumental variable estimation strategies suitable for binary disease endpoints, a methodological gap that has remained unaddressed. RESULTS: We develop a functional MR framework for binary outcomes that integrates functional principal component analysis with two-stage residual inclusion (2SRI), ensuring consistent estimation under the nonlinear logistic link function that renders standard instrumental variable estimators inconsistent. Simulations across different causal effect trajectory shapes, varying measurement densities, and varying instrument strengths demonstrate accurate recovery of time-varying genetically predicted effects with minimal bias. Applied to UK Biobank data, the framework identifies an age-specific causal effect of genetically predicted body mass index on type 2 diabetes risk concentrated in early mid-adulthood and progressively attenuating thereafter. Concordance between the proposed 2SRI estimator applied to type 2 diabetes and the established continuous-outcome functional MR estimator applied to the paired glycated haemoglobin marker in the same cohort provides indirect empirical support for the validity of the proposed approach. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package mvfmr, with a full tutorial vignette.

Mendelian Randomization Analysis↗
PubMed2025

Dietary patterns and risk of ischemic stroke: A two-sample Mendelian randomization study.

Diet and nutrition critically influence the development and outcomes of ischemic stroke (IS). However, observational studies often yield inconsistent findings due to confounding and measurement error. Mendelian randomization (MR) provides an alternative approach to strengthen causal inference. We conducted a 2-sample MR analysis to evaluate the causal associations between 22 dietary factors and IS risk. Genetic instruments for dietary exposures were derived from the UK Biobank genome-wide association study, and outcome data were obtained from the MEGASTROKE consortium. Inverse-variance weighted analysis served as the primary method, complemented by sensitivity analyses. Consumption of oily fish (β = -0.402, P = .022), cheese (β = -0.364, P = .001), dried fruit (β = -0.710, P = .0002), weekly red wine (β = -0.507, P = .024), and calcium supplements (β = -3.994, P = .015) was associated with reduced risk of IS. Conversely, dietary patterns characterized by high-sugar or high-protein intake showed suggestive associations with increased IS risk, although these did not remain significant after multiple-comparison correction. This 2-sample MR study provides evidence that specific dietary factors, including oily fish, cheese, dried fruit, red wine, and calcium, may reduce IS risk, while high-sugar and high-protein diets may confer increased risk. These findings underscore the importance of dietary management in stroke prevention and highlight the need for further studies to validate the role of potentially harmful dietary patterns.

Humans↗
PubMed2006

Causal reasoning in rats.

Empirical research with nonhuman primates appears to support the view that causal reasoning is a key cognitive faculty that divides humans from animals. The claim is that animals approximate causal learning using associative processes. The present results cast doubt on that conclusion. Rats made causal inferences in a basic task that taps into core features of causal reasoning without requiring complex physical knowledge. They derived predictions of the outcomes of interventions after passive observational learning of different kinds of causal models. These competencies cannot be explained by current associative theories but are consistent with causal Bayes net theories.

Animals↗
PubMed2006

Estimating causal effects from epidemiological data.

In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods -- standardisation and inverse probability weighting -- to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.

Causality↗
PubMed1995

Helicobacter pylori, gastroduodenal disease, and recurrent abdominal pain in children.

OBJECTIVE: To assess the evidence for a cause-and-effect relationship between Helicobacter pylori infection and antral gastritis, peptic ulcer disease, and recurrent abdominal pain in children. DATA SOURCES: A MEDLINE search from January 1983 through July 1994 was used to identify pertinent English-language publications. Current Contents and selected specialty journals were searched manually. Editorials, reviews, case reports, abstracts, and letters to the editor were excluded. STUDY SELECTION: All studies in children (ie, 0 to 18 years) were included. DATA EXTRACTION: Hill's criteria for causal inference were used to determine the strength of the evidence for a causal relationship. DATA SYNTHESIS: In total, 45 studies (case series, cross-sectional surveys, and treatment trials) were retrieved. The rate ratio of antral gastritis in children with H pylori infection (compared with uninfected children) ranged from 1.9 to 71.0 (median, 4.6). The prevalence of H pylori infection in children with duodenal ulcer was high (range, 33% to 100%; median, 92%) compared with children with gastric ulcer (range, 11% to 75%; median, 25%). Prevalence rates of infection in children with recurrent abdominal pain were inconsistent (range, 0% to 81%; median, 22%), with lower rates in children meeting Apley's criteria (range, 0% to 9%; median, 6%). CONCLUSIONS: There is strong evidence for an association between H pylori infection and antral gastritis and duodenal ulcer disease in children; however, data from randomized, double-blind, placebo-controlled treatment trials are required. There is weak evidence for an association with gastric ulcer and weak or no evidence for an association with recurrent abdominal pain.

Abdominal Pain↗
PubMed2005

A graphical approach for evaluating effective connectivity in neural systems.

The identification of effective connectivity from time-series data such as electroencephalogram (EEG) or time-resolved function magnetic resonance imaging (fMRI) recordings is an important problem in brain imaging. One commonly used approach to inference effective connectivity is based on vector autoregressive models and the concept of Granger causality. However, this probabilistic concept of causality can lead to spurious causalities in the presence of latent variables. Recently, graphical models have been used to discuss problems of causal inference for multivariate data. In this paper, we extend these concepts to the case of time-series and present a graphical approach for discussing Granger-causal relationships among multiple time-series. In particular, we propose a new graphical representation that allows the characterization of spurious causality and, thus, can be used to investigate spurious causality. The method is demonstrated with concurrent EEG and fMRI recordings which are used to investigate the interrelations between the alpha rhythm in the EEG and blood oxygenation level dependent (BOLD) responses in the fMRI. The results confirm previous findings on the location of the source of the EEG alpha rhythm.

Brain↗
PubMed2004

A definition of causal effect for epidemiological research.

Estimating the causal effect of some exposure on some outcome is the goal of many epidemiological studies. This article reviews a formal definition of causal effect for such studies. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a discussion of sampling variability and a generalisation of this causal theory. The difference between association and causation is described-the redundant expression "causal effect" is used throughout the article to avoid confusion with a common use of "effect" meaning simply statistical association-and shows why, in theory, randomisation allows the estimation of causal effects without further assumptions. The article concludes with a discussion on the limitations of randomised studies. These limitations are the reason why methods for causal inference from observational data are needed.

Causality↗
PubMed1996

Epidemiological appraisal of studies of residential exposure to power frequency magnetic fields and adult cancers.

OBJECTIVES: To appraise epidemiological evidence of the purported association between residential exposure to power frequency magnetic fields and adult cancers. METHODS: Literature review and epidemiological evaluation. RESULTS: Seven epidemiological studies have been conducted on the risk of cancer among adults in relation to residential exposure to power frequency magnetic fields. Leukaemia was positively associated with magnetic fields in three case-control studies. The other two case-control studies and two cohort studies did not show such a link. Brain tumours and breast cancer have rarely been examined by these studies. Based on the epidemiological results, the analysis of the role of chance and bias, and the criteria for causal inferences, it seems that the evidence is not strong enough to support the putative causal relation between residential exposure to magnetic fields and adult leukaemia, brain tumours, or breast cancer. Inadequate statistical power is far more a concern than selection bias, information bias, and confounding in interpreting the results from these studies, and in explaining inconsistencies between studies. CONCLUSIONS: Our reviews suggested that the only way to answer whether residential exposure to magnetic fields is capable of increasing the risks of adult cancers is to conduct more studies carefully avoiding methodological flaws, in particular small sample size. We also suggested that the risk of female breast cancer should be the object of additional investigations, and that future studies should attempt to include information on exposure to magnetic fields from workplaces as well as residential exposure to estimate the effects of overall exposure to magnetic fields.

Bias↗
PubMed2004

Advances to Bayesian network inference for generating causal networks from observational biological data.

MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/

Algorithms↗