Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.

Leveraging the local genetic structure for trans-ancestry association mapping.

Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.

Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics.

Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.

XPXP: improving polygenic prediction by cross-population and cross-phenotype analysis.

MOTIVATION: As increasing sample sizes from genome-wide association studies (GWASs), polygenic risk scores (PRSs) have shown great potential in personalized medicine with disease risk prediction, prevention and treatment. However, the PRS constructed using European samples becomes less accurate when it is applied to individuals from non-European populations. It is an urgent task to improve the accuracy of PRSs in under-represented populations, such as African populations and East Asian populations. RESULTS: In this article, we propose a cross-population and cross-phenotype (XPXP) method for construction of PRSs in under-represented populations. XPXP can construct accurate PRSs by leveraging biobank-scale datasets in European populations and multiple GWASs of genetically correlated phenotypes. XPXP also allows to incorporate population-specific and phenotype-specific effects, and thus further improves the accuracy of PRS. Through comprehensive simulation studies and real data analysis, we demonstrated that our XPXP outperformed existing PRS approaches. We showed that the height PRSs constructed by XPXP achieved 9% and 18% improvement over the runner-up method in terms of predicted R2 in East Asian and African populations, respectively. We also showed that XPXP substantially improved the stratification ability in identifying individuals at high genetic risk of type 2 diabetes. AVAILABILITY AND IMPLEMENTATION: The XPXP software and all analysis code are available at github.com/YangLabHKUST/XPXP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Witnessing localization of a quantum state via quantum speed limits in a driven avoided-level crossing system.

In this work, we investigate the witnessing of the localization of quantum states through quantum speed limits (QSLs) in a two-level driven avoided-level crossing system. As the characteristic natures of the localized quantum states, the QSL presents the periodic oscillations and coherence. The coherence partition of QSL is much bigger than the population partition of QSL. Our study gives us the possibilities to manipulate dynamics of quantum states locally by employing the coherent destruction of tunneling, which is significant in quantum information process. In addition, we analyze the effects of the rotating-wave approximation and the generalized Van Vleck approach on QSL and show that they wipe out the quantum coherence.

Efficient dewatering of waste gasification fine slag based on mechanical pressure-vacuum fields: Dewatering characteristics, energy optimization and potential environmental benefits.

Landfill is the major waste disposal method of high-moisture coal gasification fine slag (GFS) which causes the pollution of soil and water and brings the waste of resources. GFS efficient dewatering is an urgent problem to be solved, which is beneficial to realize its resource utilization. In this paper, mechanical pressure and vacuum coupling energy fields are applied to carry out the dewatering processes of GFS. The pressure field provides strong power for water migration, which makes water leave the particle system, while the vacuum field provides traction for water removal from system. The fine slag produced from Coal-to-methanol (named JC) with larger size particles tends to form "bridging" frameworks among particles, which provides water occurrence space and increases the moisture migration resistance. The mechanical dewatering process has an energy advantage interval, when the sample moisture is reduced to a certain degree, the mechanical force field is mainly used for particle friction and breakage but not for moisture migration. Through dewatering process energy optimization, high moisture gasification fine slag can be removed about 15% water within 30s and energy consumption of efficient dewatering is 2.63 kJ/g which is much lower than that of drying. Efficient dewatering is benefit to the GFS recycling which reduces hazardous materials release to environment. The potential effects of high efficiency dewatering process on GFS resource utilization and the possible eco-design framework for products recycled from the waste GFS were proposed. The research results will provide theoretical guidance for the gasification fine slag efficient dewatering and is benefit to the environment.

Bayesian weighted Mendelian randomization for causal inference based on summary statistics.

MOTIVATION: The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. RESULTS: We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. AVAILABILITY AND IMPLEMENTATION: The BWMR software is available at https://github.com/jiazhao97/BWMR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantum Speed Limit Quantified by the Changing Rate of Phase.

The quantum speed limit is important in determining the minimum evolution time of a quantum system, and thus is essential for quantum community. In this Letter, we derive a novel unified quantum speed limit bound for Hermitian and non-Hermitian quantum systems. The bound is quantified by the changing rate of phase of the quantum system, which represents the transmission mode of the quantum states over their evolution. The bound leads to further insights beyond the previous bounds on concrete evolution modes of the quantum system, such as horizontal or parallel transition or horizontal joining of the two quantum states in Hilbert space. The bound is linked to the feasibility of the evolutions of the state vectors, and provides a tighter upper bound. In addition, the generalized Margolus-Levitin bound is discussed.

Identifying the causal effects of driver distraction on hazardous actions at intersections based on propensity score weighting.

Distracted driving is a threat to traffic safety that can result in more traffic crashes. Although previous studies have been conducted to explore the relationship between driver distraction and hazardous driving actions, few studies are available to identify the causation between them. Thus, the study intended to evaluate the causal effects of distraction on hazardous driving actions at intersections based on the crash data extracted from the Crash Report Sampling System (2021-2022). The multinomial logit model was employed to reveal the factors contributing to driver distraction. Then, propensity score weighting was adopted to balance the factor distributions between distraction and non-distraction cases to identify the causal effects on hazardous actions. Results indicated that 1) the propensity of distraction is relevant to factors such as the driver's age, gender, vehicle type, speed limit, area, weather, and light condition, 2) driver distraction can significantly increase the probability of risky actions including speeding, running red lights, failing to obey stop signs, failing to yield, following too closely, and 3) the causal effects show great diversity for different distraction types. The findings serve to understand the influence mechanism of distraction on specific crash risks and develop countermeasures to reduce distraction and hazardous driving actions.

Evaluating the traffic safety performance of left-turn waiting areas at signalized intersections.

Left-turn waiting area (LWA) is an innovative traffic design that is popularly applied to improve the traffic capacity of signalized intersections in China. The traffic safety impacts of the LWA, however, have not been fully discussed in previous studies. Thus, the study aims to evaluate the safety performance of the LWA by means of the traffic conflict technique. A field investigation was conducted to collect the post-encroachment time (PET) of conflicts and relevant variables at the signalized intersections in Jinhua, China. The Chi-square and two sample t-tests were adopted to examine the difference in conflict distribution between the intersections with and without LWA. The random parameter ordered logit model was employed to identify the factors contributing to the risks of vehicular collisions. Results indicate that (1) intersections with LWA are generally associated with more merging conflicts; (2) there are no significant discrepancies in the PET values between intersections with and without LWA; and (3) factors such as the number of left-turn lanes, number of receiving lanes, conflict type, vehicle type, driving direction, stopping outside LWA and overtaking behavior are identified to significantly impact the traffic conflicts. The findings serve to develop the countermeasures to ensure the safe operation of LWA.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means.

INTRODUCTION: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. METHOD: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. RESULTS: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. CONCLUSIONS: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. PRACTICAL APPLICATIONS: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Pickle water ameliorates castor oil-induced diarrhea in mice by regulating the homeostasis of the gut microbiota and intestinal mucosal barrier.

Introduction: Diarrhea is a common clinical condition that can potentially be fatal. Current treatment options often have side effects, such as constipation and vomiting, and there remains a need for more effective therapies. Pickled vegetables, a famous traditional food in China, have been suggested in clinical studies to alleviate diarrhea in children, particularly through the use of pickle water (PW). However, the pharmacological effects and mechanisms of PW on intestinal health remain unclear. This study aimed to explore the protective effects of PW on castor oil-induced diarrhea in ICR mice and to investigate its potential mechanisms. Methods: To evaluate the antidiarrheal effects of PW, we used a castor oil-induced diarrhea model in ICR mice. Various indices were measured to assess the severity of diarrhea. After euthanizing the mice, oxidative stress markers in the ileum were assessed using biochemical methods, and the expression of tight junction-related proteins in the ileum was analyzed using Western blot. Additionally, 16S rRNA high-throughput sequencing was used to evaluate the diversity and composition of the intestinal flora. Results: The results showed that PW supplementation reduced body weight without significantly affecting organ index and liver function in the castor oil-induced diarrhea mice. PW also effectively reduced the dilution rate, diarrhea index, average loose stool grade, propelling distance of carbon powder, and intestinal propulsive rate while improving the pathological abnormality in the ileum. Furthermore, PW enhanced the activities of total antioxidant capacity (T-AOC), glutathione peroxidase (GSH-PX), and catalase (CAT) while reducing malonaldehyde (MDA) levels. PW also increased the expression of tight junction proteins zonula occludens-1 (ZO-1) and occludin in the ileum. Additionally, the analysis of 16S rDNA revealed that PW increased both α and ß diversity, improved the composition of the intestinal flora, and restored it to a normal level. Discussion: Collectively, dietary PW administration ameliorates Castor oil-induced diarrhea by restoring tight junctions between intestinal mucosal cells, suppressing oxidative stress, and regulating the composition of intestinal flora. These findings suggest that PW may be a promising strategy for managing diarrhea.

Identifying the reciprocal causation between hit-and-run behavior and crash injury severity.

OBJECTIVE: Hit-and-run behavior is believed to exacerbate the injury severity of traffic crashes due to the delayed emergency response for the victims. However, several previous studies indicated the opposite finding that hit-and-run crashes were associated with less severe injuries. The relevant studies mainly identified the statistical associations between hit-and-run behavior and injury severity without revealing causation between them. To this end, the study aims to explore the reciprocal causation between the two variables. METHOD: The two-stage probit model with endogenous regressors is employed to identify the reciprocal causation between hit-and-run behavior and crash injury severity for single- and two-vehicle crashes, respectively, with the use of crash data extracted from the Crash Report Sampling System and Fatality Analysis Reporting System (2016-2019). RESULTS: The results indicate that 1) for both single- and two-vehicle crashes, the fleeing behavior can significantly increase the injury severity of the victims in the crashes while the severe injury of the victims has a negative impact on the propensity of such behavior, 2) the propensity of hit-and-run behavior is influenced by various instrumental variables such as driver age, gender, alcohol involvement, weekday, area type, and light condition, and 3) crash injury severity is significantly related to the victim age, gender, and vehicle damage. CONCLUSIONS: There is a reciprocal causation between hit-and-run behavior and injury severity in traffic crashes. The analytical results can provide a reasonable explanation for the counterintuitive finding on hit-an-run crashes and help mitigate the injury severity.

Effect of High-Temperature Storage on Electrical Characteristics of Hydrogen-Treated AlGaN/GaN High-Electron-Mobility Transistors.

In this paper, high-temperature storage of hydrogen-treated AlGaN/GaN HEMTs is conducted for the first time to study the effect of high temperature on the electrical characteristics of the devices after hydrogen treatment, and it is found that high-temperature storage can effectively reduce the impact of hydrogen on the devices. After hydrogen treatment, the output current and the maximum transconductance of the device increase, and the threshold voltage drifts negatively. However, after high-temperature treatment at 200 °C for 24 h, the output current, threshold voltage, and the maximum transconductance of the device all approach their initial values before hydrogen treatment. By using low-frequency noise analysis technology, the trap density of the hydrogen-treated AlGaN/GaN HEMT is determined to be 8.9 × 1023 cm-3·eV-1, while it changes to 4.46 × 1022 cm-3·eV-1 after high-temperature storage. We believe that the change in the electrical characteristics of the device in hydrogen is due to the passivation of hydrogen on the inherent trap of the device, and the variation in the electrical properties of the device in the process of high-temperature storage involves the influence of two effects, namely the dehydrogenation effect and the improvement of the metal-semiconductor interface caused by high temperatures.

Gentiopicroside modulates glucose homeostasis in high-fat-diet and streptozotocin-induced type 2 diabetic mice.

Gluconeogenesis is closely related to the occurrence and development of type 2 diabetes mellitus (T2DM). Gentiopicroside (GPS) is the main active secoiridoid glycoside in Gentiana manshurica Kitagawa, which can improve chronic complications associated with diabetes and regulate glucose metabolism. However, the effects and potential mechanisms by which GPS affects T2DM understudied and poorly understood. In this study, we systematically explored the pharmacological effects of GPS on T2DM induced by a high-fat diet (HFD) and streptozotocin (STZ) as well as explored its related mechanisms. The results showed that GPS supplementation discernibly decreased blood glucose levels, food intake and water consumption, ameliorated glucose intolerance, abnormal pyruvate tolerance, insulin resistance and dyslipidemia. Furthermore, GPS discernibly ameliorated pathological morphological abnormalities of the liver and pancreas, reduced hepatic steatosis and maintain the balance between α-cells and ß-cells in pancreas. Moreover, GPS significantly inhibited gluconeogenesis, as evidenced by the suppressed protein expression of phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase) in the liver. Additionally, the results of Western blot analysis revealed that GPS increased p-PI3K, p-AKT, and p-FOXO1 expression levels, and decreased FOXO1 expression at protein level in the liver. Furthermore, the results of the immunostaining and Western blot analysis demonstrated that GPS supplementation increased the expression of zonula occludens-1 (ZO-1) and occludin in the ileum. Collectively, these results indicate that GPS may inhibit hepatic gluconeogenesis by regulating the PI3K/AKT/FOXO1 signaling pathway and maintain intestinal barrier integrity, and ultimately improve T2DM. Together, these findings indicate that GPS is a potential candidate drug for the prevention and treatment of T2DM, and the results of our study will provide experimental basis for further exploration of the possibility of GPS as a therapeutic agent for T2DM.

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.

[Treatment of displaced midshaft clavicle fracture with convolution method].

OBJECTIVE: To explore the clinical effects of convolution method in treating displaced midshaft clavicle fracture. METHODS: From January 2018 to July 2019, 32 patients (21 males and 11 females) with displaced midshaft clavicle fracture were treated, ranging in age from 18 to 65 years old, with a median age of 41.5 years old. Nineteen patients had fractures on the left side and 13 patients had fractures on the right side. The duration from injury to treatment ranged from 30 minutes to 5 days. The patients were treated with convolution method and repeated push and rotation of the affected upper limb, followed by external fixation of clavicle band. The reduction and healing of the fracture and the functional recovery of shoulder joint were observed. RESULTS: After operation, fracture reduction was evaluated by X ray film. Seven patients had an excellent result, 20 good and 5 fair. All the patients were followed up, and the duration ranged from 14 to 18 weeks. The mean Neer score was 89.75±6.88, which included pain score of 32.66±2.54, functional score of 26.44±2.91, range of motion score of 22.38±2.06, and anatomy score of 8.43±0.84;17 patients receive an excellent result, 14 good and 1 fair. CONCLUSION: The rotation method combined with the repeated pushing and rotating of the upper limb of the affected side can well reduce the displaced fracture of the middle clavicle. Bandage and clavicle band can be performed after reduction. Functional exercise runs through the whole course of treatment. Functional recovery of shoulder joint is good and satisfactory therapeutic effect is achieved.