MRPC: An R Package for Inference of Causal Graphs

Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes

Current Mendelian randomization (MR) methods do not reflect complex relationships among multiple exposures and outcomes as is typical for real-life applications. We introduce MrDAG, a Bayesian causal graphical model for summary-level MR analysis to detect dependency relations within the exposures, the outcomes, and between them to improve causal effects estimation. MrDAG combines three causal inference strategies. It uses genetic variation as instrumental variables to account for unobserved confounders. It performs structure learning to detect and orientate the direction of the dependencies within the exposures and the outcomes. Finally, interventional calculus is employed to derive principled causal effect estimates. In MrDAG the directionality of the causal effects between the exposures and the outcomes is assumed known, i.e., the exposures can only be potential causes of the outcomes, and no reverse causation is allowed. In the simulation study, MrDAG outperforms recently proposed one-outcome-at-a-time and multi-response multi-variable Bayesian MR methods as well as causal graphical models under the constraint on edges' orientation from the exposures to the outcomes. MrDAG was motivated to unravel how lifestyle and behavioral exposures impact mental health. It highlights first, education and second, smoking as effective points of intervention given their important downstream effects on mental health. It also enables the identification of a novel path between smoking and the genetic liability to schizophrenia and cognition, demonstrating the complex pathways toward mental health. These insights would have been impossible to delineate without modeling the paths between multiple exposures and outcomes at once.

Clock Proteins Have the Potential to Improve Term Delivery Date Prediction: A Proof-of-Concept Study

Our ability to accurately predict the delivery date of term pregnancies is limited by shortcomings of modern-day clinical tools and due date estimation methods. The pregnancy clock is a series of coordinated and harmonized signals between mother, fetus, and placenta that regulate the length of gestation. Clock proteins are thought to be important mediators of these signals, yet few studies have investigated their potential utility as predictors of term delivery date. In this study, we performed a cross-sectional proteome analysis of 2648 serum samples collected between 18 and 28 weeks of gestation from mothers who delivered at term. The cohort included pregnancies both with and without complications. A total of 15 proteins of diverse functionalities were shown to have a direct association with time to birth (TTB), 11 of which have not been previously linked to gestational age. The protein A Distintegrin and Metalloproteinase 12 (ADA12) was one of the 15 proteins shown to have an association with TTB. Mothers who expressed the highest levels of ADA12 in the cohort (90th percentile) gave birth earlier than mothers who expressed the lowest levels of ADA12 (10th percentile) at a statistically significant rate (median gestational age at birth 390/7 weeks vs. 393/7 weeks, p < 0.001). Altogether, these findings suggest that ADA12, as well as potentially other clock proteins, have the potential to serve as clinical predictors of term delivery date in uncomplicated pregnancies and represent an important step towards characterizing the role(s) of clock proteins in mediating pregnancy length.

Symptom contributors to quality of life in schizophrenia: Exploratory factor and network analyses

Individuals with schizophrenia and other associated disorders experience significant disturbance to their quality of life (QoL) due to a multitude of co-occurring symptoms. Popular evidence-based practices (EBPs) devote significant effort to reduce positive symptomatology in order to prevent relapse, while emerging research posits that other symptoms (cognitive deficits, negative and affective symptoms) are more indicative of QoL disturbance. This study sought to examine the impact of symptom constructs on QoL and attempt to infer directionality of influence via network analysis. A total of 102 recovery phase adult outpatients with schizophrenia spectrum disorders were assessed on positive, negative, and affective symptomatology, in addition to QoL and cognitive abilities. Exploratory factor analysis and network analysis were performed to identify associations and infer directed influence between symptom constructs, and a directed acyclic graph was constructed to observe associations between symptom domains and QoL. Factor analysis results indicated that individual measures align with their respective symptom constructs. Strong factor correlations were found between QoL and the negative and affective symptom constructs, with weaker associations found between positive symptoms and cognition. Visualization of the network structure illustrated QoL as the central cluster of the network, and examination of the weighted edges found the strongest connectivity between QoL, negative symptomatology, and affective symptoms. More severe negative and affective symptoms were most directly linked with poorer QoL and may prove to be integral in attaining positive outcomes in schizophrenia treatment. Incorporation of psychosocial treatments in addition to pharmacotherapy may prove effective in targeting negative and affective symptoms.

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study

OBJECTIVES

To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort.

STUDY DESIGN

The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort.

RESULTS

The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001).

CONCLUSION

A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations.