Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

A novel constraint-based structure learning algorithm using marginal causal prior knowledge

Causal discovery with prior knowledge is important for improving performance. We consider the incorporation of marginal causal relations, which correspond to the presence or absence of directed paths in a causal model. We propose the Marginal Prior Causal Knowledge PC (MPPC) algorithm to incorporate marginal causal relations into a constraint-based structure learning algorithm. We provide the theorems of conditional independence properties by combining observational data and marginal causal relations. We compare the MPPC algorithm with other structure learning methods in both simulation studies and real-world networks. The results indicate that, compare with other constraint-based structure learning methods, MPPC algorithm can incorporate marginal causal relations and is more effective and more efficient.

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

MRSL: a causal network pruning algorithm based on GWAS summary data

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study

Rationale & objective

A causal relationship concerning diabetic retinopathy (DR) and diabetic nephropathy (DN) has been studied in many epidemiological observational studies. We conducted a two-sample mendelian randomization study from the perspective of genetics to assess these associations.

Methods

20 independent single nucleotide polymorphisms (SNPs) associated with diabetic retinopathy were selected from the FinnGen consortium. Summary-level data for diabetic nephropathy were obtained from the publicly available genome-wide association studies (GWAS) database, FinnGen and CKDGen consortium. Inverse variance weighted (IVW) was selected as the primary analysis. MR-Egger, weighted median (WM), simple mode and weighted mode were used as complementary methods to examine causality. Additionally, sensitivity analyses including Cochran's Q test, MR-Egger, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analyses were conducted to guarantee the accuracy and robustness of our MR analysis.

Results

Our current study demonstrated positive associations of genetically predicted diabetic retinopathy with diabetic nephropathy (OR=1.32; P=3.72E-11), type 1 diabetes with renal complications (OR=1.96; P= 7.11E-11), and type 2 diabetes with renal complications (OR=1.26, P=3.58E-04). Further subtype analysis and multivariate mendelian randomization (MVMR) also reached the same conclusion. A significant casualty with DN was demonstrated both in non-proliferative DR (OR=1.07, P=0.000396) and proliferative DR (OR=1.67, P=3.699068E-14). All the findings were robust across several sensitivity analyses.

Conclusion

Consistent with previous clinical studies, our findings revealed a positive correlation between DR and DN, providing genetic evidence for the non-invasive nature of DR in predicting DN.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

The genetic etiology of periodic limb movement in sleep

STUDY OBJECTIVES

Periodic limb movement in sleep is a common sleep phenotype characterized by repetitive leg movements that occur during or before sleep. We conducted a genome-wide association study (GWAS) of periodic limb movements in sleep (PLMS) using a joint analysis (i.e., discovery, replication, and joint meta-analysis) of four cohorts (MrOS, the Wisconsin Sleep Cohort Study, HypnoLaus, and MESA), comprised of 6843 total subjects.

METHODS

The MrOS study and Wisconsin Sleep Cohort Study (N = 1745 cases) were used for discovery. Replication in the HypnoLaus and MESA cohorts (1002 cases) preceded joint meta-analysis. We also performed LD score regression, estimated heritability, and computed genetic correlations between potentially associated traits such as restless leg syndrome (RLS) and insomnia. The causality and direction of the relationships between PLMS and RLS was evaluated using Mendelian randomization.

RESULTS

We found 2 independent loci were significantly associated with PLMS: rs113851554 (p = 3.51 × 10-12, β = 0.486), an SNP located in a putative regulatory element of intron eight of MEIS1 (2p14); and rs9369062 (p = 3.06 × 10-22, β = 0.2093), a SNP located in the intron region of BTBD9 (6p12); both of which were also lead signals in RLS GWAS. PLMS is genetically correlated with insomnia, risk of stroke, and RLS, but not with iron deficiency. Pleiotropy adjusted Mendelian randomization analysis identified a causal effect of RLS on PLMS.

CONCLUSIONS

Because PLMS is more common than RLS, PLMS may have multiple causes and additional studies are needed to further validate these findings.

Association of red cell distribution width with the risk of 3-month readmission in patients with heart failure: A retrospective cohort study

Background

In recent years, red cell distribution width (RDW) has been found to be associated with the prognosis of patients with heart failure (HF) in Western countries. However, evidence from Asia is limited. We aimed to investigate the relationship between RDW and the risk of 3-month readmission in hospitalized Chinese HF patients.

Methods

We retrospectively analyzed HF data from the Fourth Hospital of Zigong, Sichuan, China, involving 1,978 patients admitted for HF between December 2016 and June 2019. The independent variable in our study was RDW, and the endpoint was the risk of readmission within 3 months. This study mainly used a multivariable Cox proportional hazards regression analysis. Smoothed curve fitting was then used to assess the dose-response relationship between RDW and the risk of 3-month readmission.

Results

In the original cohort of 1,978 patients with HF (42% male and 73.1% aged ≥70 years), 495 patients (25.0%) were readmitted within 3 months after discharge. Smoothed curve fitting showed a linear correlation between RDW and the risk of readmission within 3 months. In the multivariable-adjusted model, every 1% increase in RDW was associated with a 9% increased risk of readmission within 3 months (hazard ratio = 1.09, 95% confidence interval: 1.00-1.15; P < 0.005).

Conclusions

A higher RDW value was significantly associated with a greater risk of 3-months readmission in hospitalized patients with HF.

Confirmation of the superior performance of the causal Graphical Analysis Using Genetics (cGAUGE) pipeline in comparison to various competing alternatives

Various methods exist that utilise information from genetic predictors to help identify potential causal relationships between measured biological or clinical traits. Here we conduct computer simulations to investigate the performance of a recently proposed causal Graphical Analysis Using Genetics (cGAUGE) pipeline, used as a precursor to Mendelian randomization analysis, in comparison to our previously proposed Bayesian Network approach for addressing this problem. We use the same simulation (and analysis) code as was used by the developers of cGAUGE, adding in a comparison with the Bayesian Network approach. Overall, we find the optimal method (in terms of giving high power and low false discovery rate) is the cGAUGE pipeline followed by subsequent analysis using the MR-PRESSO Mendelian randomization approach.

Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness.