Strategies to investigate and mitigate collider bias in genetic and Mendelian randomisation studies of disease progression

Estimating Causal Effects on a Disease Progression Trait Using Bivariate Mendelian Randomisation

Genome-wide association studies (GWAS) have provided large numbers of genetic markers that can be used as instrumental variables in a Mendelian Randomisation (MR) analysis to assess the causal effect of a risk factor on an outcome. An extension of MR analysis, multivariable MR, has been proposed to handle multiple risk factors. However, adjusting or stratifying the outcome on a variable that is associated with it may induce collider bias. For an outcome that represents progression of a disease, conditioning by selecting only the cases may cause a biased MR estimation of the causal effect of the risk factor of interest on the progression outcome. Recently, we developed instrument effect regression and corrected weighted least squares (CWLS) to adjust for collider bias in observational associations. In this paper, we highlight the importance of adjusting for collider bias in MR with a risk factor of interest and disease progression as the outcome. A generalised version of the instrument effect regression and CWLS adjustment is proposed based on a multivariable MR model. We highlight the assumptions required for this approach and demonstrate its utility for bias reduction. We give an illustrative application to the effect of smoking initiation and smoking cessation on Crohn's disease prognosis, finding no evidence to support a causal effect.

Association Between Autoimmune Thyroid disease and Oral Lichen Planus: A Multi-Omic Genetic Analysis

OBJECTIVE

The oral mucosa mirrors a range of latent systemic disorders, with potential clinical associations noted between autoimmune thyroid disease (AITD) and oral lichen planus (OLP). This study aims to explore the genetic relationship and underlying mechanisms mediating these conditions.

METHODS

A 2-step Mendelian randomization (MR) analysis was conducted to elucidate the genetic relationship and mediating factors between AITD and OLP. Linkage disequilibrium score regression was employed to assess heritability and genetic correlations among phenotypes, followed by genetic colocalization analysis to discern shared genetic variants. A transcriptome-wide association study (TWAS) was also performed to pinpoint gene expression profiles.

RESULTS

Genetically predicted AITD is associated with an increased risk of OLP (OR[95% CI]: 1.44[1.19,1.74], P=1.61 × 10-4), potentially mediated by hypothyroidism. There is strong evidence of colocalization between AITD and OLP, with a shared PTPN22 gene variant driving this association. TWAS further identified RNASET2 and FGFR1OP within the HLA region as high-confidence shared genes for both conditions.

CONCLUSION

Our study provides novel evidence of a possible genetic association between AITD and OLP. These findings highlight the critical role of endocrine alterations in maintaining oral immune homeostasis, though further research is warranted to validate our findings.

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.

Impact of Remnant Cholesterol on Cardiovascular Risk in Diabetes

PURPOSE OF REVIEW

Individuals with diabetes face increased risk of atherosclerotic cardiovascular disease (ASCVD), in part due to hyperlipidemia. Even after LDL cholesterol-lowering, residual ASCVD risk persists, part of which may be attributed to elevated remnant cholesterol. We describe the impact of elevated remnant cholesterol on ASCVD risk in diabetes.

RECENT FINDINGS

Preclinical, observational, and Mendelian randomization studies robustly suggest that elevated remnant cholesterol causally increases risk of ASCVD, suggesting remnant cholesterol could be a treatment target. However, the results of recent clinical trials of omega-3 fatty acids and fibrates, which lower levels of remnant cholesterol in individuals with diabetes, are conflicting in terms of ASCVD prevention. This is likely partly due to neutral effects of these drugs on the total level of apolipoprotein B(apoB)-containing lipoproteins. Elevated remnant cholesterol remains a likely cause of ASCVD in diabetes. Remnant cholesterol-lowering therapies should also lower apoB levels to reduce risk of ASCVD.

Heterogeneity of animal experiments and how to deal with it

Heterogeneity of study samples is ubiquitous in animal experiments. Here, we discuss the different options of how to deal with heterogeneity in the statistical analysis of a single experiment. Specifically, data from different sub-groups (e.g. sex, strain, age cohorts) may be analysed separately, heterogenization factors may be ignored and data pooled for analysis, or heterogenization factors may be included as additional variables in the statistical model. The cost of ignoring a heterogenization factor is an inflated estimate of the variance and a consequent loss of statistical power. Therefore, it is usually preferable to include the heterogenization factor in the statistical model, especially if the heterogenization factor has been introduced intentionally (e.g. using both sexes). If heterogenization factors are included, they can be treated either as fixed factors in an analysis of variance design or sometimes as random effects in mixed effects regression models. Finally, for an appropriate sample size estimation, it is necessary to decide whether to treat heterogenization factors as nuisance variables, or whether the experiment should be powered to be able to detect not only the main effect of the treatment but also interactions between heterogenization factors and the treatment variable.

Integrating genomic variants and developmental milestones to predict cognitive and adaptive outcomes in autistic children

Although the first signs of autism are often observed as early as 18-36 months of age, there is a broad uncertainty regarding future development, and clinicians lack predictive tools to identify those who will later be diagnosed with co-occurring intellectual disability (ID). Here, we developed predictive models of ID in autistic children (n=5,633 from three cohorts), integrating different classes of genetic variants alongside developmental milestones. The integrated model yielded an AUC ROC=0.65, with this predictive performance cross-validated and generalised across cohorts. Positive predictive values reached up to 55%, accurately identifying 10% of ID cases. The ability to stratify the probabilities of ID using genetic variants was up to twofold greater in individuals with delayed milestones compared to those with typical development. These findings underscore the potential of models in neurodevelopmental medicine that integrate genomics and clinical observations to predict outcomes and target interventions.

Recurrent stroke prediction by applying a stroke polygenic risk score in the Japanese population

Background

Recently, various polygenic risk score (PRS)-based methods were developed to improve stroke prediction. However, current PRSs (including cross-ancestry PRS) poorly predict recurrent stroke. Here, we aimed to determine whether the best PRS for Japanese individuals can also predict stroke recurrence in this population by extensively comparing the methods and maximizing the predictive performance for stroke onset.

Methods

We used data from the BioBank Japan (BBJ) 1st cohort (n=179,938) to derive and optimize the PRSs using a 10-fold cross-validation. We integrated the optimized PRSs for multiple traits, such as vascular risk factors and stroke subtypes to generate a single PRS using the meta-scoring approach (metaGRS). We used an independent BBJ 2nd cohort (n=41,929) as a test sample to evaluate the association of the metaGRS with stroke and recurrent stroke.

Results

We analyzed recurrent stroke cases (n=174) and non-recurrent stroke controls (n=1,153) among subjects within the BBJ 2nd cohort. After adjusting for known risk factors, metaGRS was associated with stroke recurrence (adjusted OR per SD 1.18 [95% CI: 1.00-1.39, p=0.044]), although no significant correlation was observed with the published PRSs. We administered three distinct tests to consider the potential index event bias; however, the outcomes derived from these examinations did not provide any significant indication of the influence of index event bias. The high metaGRS group without a history of hypertension had a higher risk of stroke recurrence than that of the low metaGRS group (adjusted OR 2.24 [95% CI: 1.07-4.66, p=0.032]). However, this association was weak in the hypertension group (adjusted OR 1.21 [95% CI: 0.69-2.13, p=0.50]).

Conclusions

The metaGRS developed in a Japanese cohort predicted stroke recurrence in an independent cohort of patients. In particular, it predicted an increased risk of recurrence among stroke patients without hypertension. These findings provide clues for additional genetic risk stratification and help in developing personalized strategies for stroke recurrence prevention.

Impact of insomnia on ovarian cancer risk and survival: a Mendelian randomization study

BACKGROUND

Insomnia is the most common sleep disorder in patients with epithelial ovarian cancer (EOC). We investigated the causal association between genetically predicted insomnia and EOC risk and survival through a two-sample Mendelian randomization (MR) study.

METHODS

Insomnia was proxied using genetic variants identified in a genome-wide association study (GWAS) meta-analysis of UK Biobank and 23andMe. Using genetic associations with EOC risk and overall survival from the Ovarian Cancer Association Consortium (OCAC) GWAS in 66,450 women (over 11,000 cases with clinical follow-up), we performed Iterative Mendelian Randomization and Pleiotropy (IMRP) analysis followed by a set of sensitivity analyses. Genetic associations with survival and response to treatment in ovarian cancer study of The Cancer Genome Atlas (TCGA) were estimated controlling for chemotherapy and clinical factors.

FINDINGS

Insomnia was associated with higher risk of endometrioid EOC (OR = 1.60, 95% CI 1.05-2.45) and lower risk of high-grade serous EOC (HGSOC) and clear cell EOC (OR = 0.79 and 0.48, 95% CI 0.63-1.00 and 0.27-0.86, respectively). In survival analysis, insomnia was associated with shorter survival of invasive EOC (OR = 1.45, 95% CI 1.13-1.87) and HGSOC (OR = 1.4, 95% CI 1.04-1.89), which was attenuated after adjustment for body mass index and reproductive age. Insomnia was associated with reduced survival in TCGA HGSOC cases who received standard chemotherapy (OR = 2.48, 95% CI 1.13-5.42), but was attenuated after adjustment for clinical factors.

INTERPRETATION

This study supports the impact of insomnia on EOC risk and survival, suggesting treatments targeting insomnia could be pivotal for prevention and improving patient survival.

FUNDING

National Institutes of Health, National Cancer Institute. Full funding details are provided in acknowledgments.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

The Effects of FABP4 on Cardiovascular Disease in the Aging Population

PURPOSE OF REVIEW

Fatty acid-binding protein 4 (FABP4) plays a role in lipid metabolism and cardiovascular health. In this paper, we cover FABP4 biology, its implications in atherosclerosis from observational studies, genetic factors affecting FABP4 serum levels, and ongoing drug development to target FABP4 and offer insights into future FABP4 research.

RECENT FINDINGS

FABP4 impacts cells through JAK2/STAT2 and c-kit pathways, increasing inflammatory and adhesion-related proteins. In addition, FABP4 induces angiogenesis and vascular smooth muscle cell proliferation and migration. FABP4 is established as a reliable predictive biomarker for cardiovascular disease in specific at-risk groups. Genetic studies robustly link PPARG and FABP4 variants to FABP4 serum levels. Considering the potential effects on atherosclerotic lesion development, drug discovery programs have been initiated in search for potent inhibitors of FABP4. Elevated FABP4 levels indicate an increased cardiovascular risk and is causally related to acceleration of atherosclerotic disease, However, clinical trials for FABP4 inhibition are lacking, possibly due to concerns about available compounds' side effects. Further research on FABP4 genetics and its putative causal role in cardiovascular disease is needed, particularly in aging subgroups.

Vitamin D and human health: evidence from Mendelian randomization studies

We summarized the current evidence on vitamin D and major health outcomes from Mendelian randomization (MR) studies. PubMed and Embase were searched for original MR studies on vitamin D in relation to any health outcome from inception to September 1, 2022. Nonlinear MR findings were excluded due to concerns about the validity of the statistical methods used. A meta-analysis was preformed to synthesize study-specific estimates after excluding overlapping samples, where applicable. The methodological quality of the included studies was evaluated according to the STROBE-MR checklist. A total of 133 MR publications were eligible for inclusion in the analyses. The causal association between vitamin D status and 275 individual outcomes was examined. Linear MR analyses showed genetically high 25-hydroxyvitamin D (25(OH)D) concentrations were associated with reduced risk of multiple sclerosis incidence and relapse, non-infectious uveitis and scleritis, psoriasis, femur fracture, leg fracture, amyotrophic lateral sclerosis, anorexia nervosa, delirium, heart failure, ovarian cancer, non-alcoholic fatty liver disease, dyslipidemia, and bacterial pneumonia, but increased risk of Behçet's disease, Graves' disease, kidney stone disease, fracture of radium/ulna, basal cell carcinoma, and overall cataracts. Stratified analyses showed that the inverse association between genetically predisposed 25(OH)D concentrations and multiple sclerosis risk was significant and consistent regardless of the genetic instruments GIs selected. However, the associations with most of the other outcomes were only pronounced when using genetic variants not limited to those in the vitamin D pathway as GIs. The methodological quality of the included MR studies was substantially heterogeneous. Current evidence from linear MR studies strongly supports a causal role of vitamin D in the development of multiple sclerosis. Suggestive support for a number of other health conditions could help prioritize conditions where vitamin D may be beneficial or harmful.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Mendelian Randomization Applied to Neurology: Promises and Challenges

The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Investigation of genetic determinants of cognitive change in later life

Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.

Mendelian Randomization as a Tool for Cardiovascular Research: A Review

Importance

Mendelian randomization (MR) is a statistical approach that has become increasingly popular in the field of cardiovascular disease research. It offers a way to infer potentially causal relationships between risk factors and outcomes using observational data, which is particularly important in cases where randomized clinical trials are not feasible or ethical. With the growing availability of large genetic data sets, MR has become a powerful and accessible tool for studying the risk factors for cardiovascular disease.

Observations

MR uses genetic variation associated with modifiable exposures or risk factors to mitigate biases that affect traditional observational study designs. The approach uses genetic variants that are randomly assigned at conception as proxies for exposure to a risk factor, mimicking a randomized clinical trial. By comparing the outcomes of individuals with different genetic variants, researchers may draw causal inferences about the effects of specific risk factors on cardiovascular disease, provided assumptions are met that address (1) the association between each genetic variant and risk factor and (2) the association of the genetic variants with confounders and (3) that the association between each genetic variant and the outcome only occurs through the risk factor. Like other observational designs, MR has limitations, which include weak instruments that are not strongly associated with the exposure of interest, linkage disequilibrium where genetic instruments influence the outcome via correlated rather than direct effects, overestimated genetic associations, and selection and survival biases. In addition, many genetic databases and MR studies primarily include populations genetically similar to European reference populations; improved diversity of participants in these databases and studies is critically needed.

Conclusions and Relevance

This review provides an overview of MR methodology, including assumptions, strengths, and limitations. Several important applications of MR in cardiovascular disease research are highlighted, including the identification of drug targets, evaluation of potential cardiovascular risk factors, as well as emerging methodology. Overall, while MR alone can never prove a causal relationship beyond reasonable doubt, MR offers a rigorous approach for investigating possible causal relationships in observational data and has the potential to transform our understanding of the etiology and treatment of cardiovascular disease.

The citrate transporter SLC13A5 as a therapeutic target for kidney disease: evidence from Mendelian randomization to inform drug development

BACKGROUND

Solute carrier family 13 member 5 (SLC13A5) is a Na+-coupled citrate co-transporter that mediates entry of extracellular citrate into the cytosol. SLC13A5 inhibition has been proposed as a target for reducing progression of kidney disease. The aim of this study was to leverage the Mendelian randomization paradigm to gain insight into the effects of SLC13A5 inhibition in humans, towards prioritizing and informing clinical development efforts.

METHODS

The primary Mendelian randomization analyses investigated the effect of SLC13A5 inhibition on measures of kidney function, including creatinine and cystatin C-based measures of estimated glomerular filtration rate (creatinine-eGFR and cystatin C-eGFR), blood urea nitrogen (BUN), urine albumin-creatinine ratio (uACR), and risk of chronic kidney disease and microalbuminuria. Secondary analyses included a paired plasma and urine metabolome-wide association study, investigation of secondary traits related to SLC13A5 biology, a phenome-wide association study (PheWAS), and a proteome-wide association study. All analyses were compared to the effect of genetically predicted plasma citrate levels using variants selected from across the genome, and statistical sensitivity analyses robust to the inclusion of pleiotropic variants were also performed. Data were obtained from large-scale genetic consortia and biobanks, with sample sizes ranging from 5023 to 1,320,016 individuals.

RESULTS

We found evidence of associations between genetically proxied SLC13A5 inhibition and higher creatinine-eGFR (p = 0.002), cystatin C-eGFR (p = 0.005), and lower BUN (p = 3 × 10-4). Statistical sensitivity analyses robust to the inclusion of pleiotropic variants suggested that these effects may be a consequence of higher plasma citrate levels. There was no strong evidence of associations of genetically proxied SLC13A5 inhibition with uACR or risk of CKD or microalbuminuria. Secondary analyses identified evidence of associations with higher plasma calcium levels (p = 6 × 10-13) and lower fasting glucose (p = 0.02). PheWAS did not identify any safety concerns.

CONCLUSIONS

This Mendelian randomization analysis provides human-centric insight to guide clinical development of an SLC13A5 inhibitor. We identify plasma calcium and citrate as biologically plausible biomarkers of target engagement, and plasma citrate as a potential biomarker of mechanism of action. Our human genetic evidence corroborates evidence from various animal models to support effects of SLC13A5 inhibition on improving kidney function.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.