Using genetic association data to guide drug discovery and development: Review of methods and applications

GWAS advancements to investigate disease associations and biological mechanisms

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Identification of lipid-modifying drug targets for autoimmune diseases: insights from drug target mendelian randomization

BACKGROUNDS

A growing body of evidence has highlighted the interactions of lipids metabolism and immune regulation. Nevertheless, there is still a lack of evidence regarding the causality between lipids and autoimmune diseases (ADs), as well as their possibility as drug targets for ADs.

OBJECTIVES

This study was conducted to comprehensively understand the casual associations between lipid traits and ADs, and evaluate the therapeutic possibility of lipid-lowering drug targets on ADs.

METHODS

Genetic variants for lipid traits and variants encoding targets of various lipid-lowering drugs were derived from Global Lipid Genetics Consortium (GLGC) and verified in Drug Bank. Summary data of ADs were obtained from MRC Integrative Epidemiology Unit (MER-IEU) database and FinnGen consortium, respectively. The causal inferences between lipid traits/genetic agents of lipid-lowering targets and ADs were evaluated by Mendelian randomization (MR), summary data-based MR (SMR), and multivariable MR (MVMR) analyses. Enrichment analysis and protein interaction network were employed to reveal the functional characteristics and biological relevance of potential therapeutic lipid-lowering targets.

RESULTS

There was no evidence of causal effects regarding 5 lipid traits and 9 lipid-lowering drug targets on ADs. Genetically proxied 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibition was associated with a reduced risk of rheumatoid arthritis (RA) in both discovery (OR [odds ratio] = 0.45, 95%CI: 0.32, 0.63, P = 6.79 × 10- 06) and replicate datasets (OR = 0.37, 95%CI: 0.23, 0.61, P = 7.81 × 10- 05). SMR analyses supported that genetically proxied HMGCR inhibition had causal effects on RA in whole blood (OR = 0.48, 95%CI: 0.29, 0.82, P = 6.86 × 10- 03) and skeletal muscle sites (OR = 0.75, 95%CI: 0.56, 0.99, P = 4.48 × 10- 02). After controlling for blood pressure, body mass index (BMI), smoking and drinking alchohol, HMGCR suppression showed a direct causal effect on a lower risk of RA (OR = 0.33, 95%CI: 0.40, 0.96, P = 0.042).

CONCLUSIONS

Our study reveals causal links of genetically proxied HMGCR inhibition (lipid-lowering drug targets) and HMGCR expression inhibition with a decreased risk of RA, suggesting that HMGCR may serve as candidate drug targets for the treatment and prevention of RA.

Unraveling the role of plasma proteins in dementia: insights from two cohort studies in the UK, with causal evidence from Mendelian randomization

Population-based proteomics offer a groundbreaking avenue to predict dementia onset. This study employed a proteome-wide, data-driven approach to investigate protein-dementia associations in 229 incident all-cause dementia (ACD) among 3,249 participants from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up, then validated in 1,506 incident ACD among 52,745 individuals from the UK Biobank (UKB) over median 13.7 years. NEFL and RPS6KB1 were robustly associated with incident ACD; MMP12 was associated with vascular dementia in ELSA. Additional markers EDA2R and KIM1 (HAVCR1) were identified from sensitivity analyses. Combining NEFL and RPS6KB1 with other factors yielded high predictive accuracy (area under the curve (AUC)=0.871) for incident ACD. Replication in the UKB confirmed associations between identified proteins with various dementia subtypes. Results from reverse Mendelian Randomization also supported the role of several proteins as early dementia biomarkers. These findings underscore proteomics' potential in identifying novel risk screening targets for dementia.

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region

Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.

Allergic Diseases and Chronic Adenotonsillar Diseases: A Mendelian Randomization Study

OBJECTIVE

The existing epidemiological evidence regarding the intricate relationship between allergic diseases and chronic adenotonsillar diseases (CATD) remains inconclusive. Herein, the objective of our study is to explore the causal association using Mendelian randomization (MR).

METHODS

Employing data from large genome-wide association studies, a comprehensive two-sample bidirectional MR study was conducted. The studied traits encompassed allergic rhinitis (cases n = 9707, controls n = 331173), allergic asthma (cases n = 8525, controls n = 193857), allergic conjunctivitis (cases n = 18321, controls n = 324178), atopic dermatitis (cases n = 11964, controls n = 306909), and CATD (cases n = 38983, controls n = 258553). All the patients were of European descent and participants in cohort studies. The primary analysis was executed using inverse-variance-weighted MR. Furthermore, six additional MR methods (MR-Egger, weighted median, simple mode, weighted mode, MR pleiotropy residual sum and outlier, MR robust adjusted profile score) were employed to ensure the reliability and detect potential horizontal pleiotropy within the results. The estimates obtained from the MR analysis were factored into the overall effect calculation.

RESULTS

Genetically anticipated outcomes demonstrated a significant association between CATD risk and allergic rhinitis (OR = 1.141, p = 6.30E-06), allergic asthma (OR = 1.115, p = 8.31E-05), allergic conjunctivitis (OR = 1.197, p = 8.69E-07), and a suggestive association with atopic dermatitis (OR = 1.053, p = 0.040). However, no substantial correlation was observed in the reverse direction.

CONCLUSIONS

Findings of our study provide evidence supporting a causal role of allergic diseases in the development of CATD, whereas the converse relationship does not appear to hold true.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:2653-2658, 2024.

Refining the impact of genetic evidence on clinical success

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Evaluation of Plasma Biomarkers for Causal Association With Peripheral Artery Disease

BACKGROUND

Hundreds of biomarkers for peripheral artery disease (PAD) have been reported in the literature; however, the observational nature of these studies limits causal inference due to the potential of reverse causality and residual confounding. We sought to evaluate the potential causal impact of putative PAD biomarkers identified in human observational studies through genetic causal inference methods.

METHODS

Putative circulating PAD biomarkers were identified from human observational studies through a comprehensive literature search based on terms related to PAD using PubMed, Cochrane, and Embase. Genetic instruments were generated from publicly available genome-wide association studies of circulating biomarkers. Two-sample Mendelian randomization was used to test the association of genetically determined biomarker levels with PAD using summary statistics from a genome-wide association study of 31 307 individuals with and 211 753 individuals without PAD in the Veterans Affairs Million Veteran Program and replicated in data from FinnGen comprised of 11 924 individuals with and 288 638 individuals without PAD.

RESULTS

We identified 204 unique circulating biomarkers for PAD from the observational literature, of which 173 were genetically instrumented using genome-wide association study results. After accounting for multiple testing (false discovery rate, <0.05), 10 of 173 (5.8%) biomarkers had significant associations with PAD. These 10 biomarkers represented categories including plasma lipoprotein regulation, lipid homeostasis, and protein-lipid complex remodeling. Observational literature highlighted different pathways including inflammatory response, negative regulation of multicellular organismal processes, and regulation of response to external stimuli.

CONCLUSIONS

Integrating human observational studies and genetic causal inference highlights several key pathways in PAD pathophysiology. This work demonstrates that a substantial portion of biomarkers identified in observational studies are not well supported by human genetic evidence and emphasizes the importance of triangulating evidence to understand PAD pathophysiology. Although the identified biomarkers offer insights into atherosclerotic development in the lower limb, their specificity to PAD compared with more widespread atherosclerosis requires further study.

Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer

BACKGROUND

This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation.

METHODS

Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.

RESULTS

Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication.

IMPLICATIONS

Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value.

HIGHLIGHTS

The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

Proteome-wide Mendelian randomization highlights AIF1 and HLA-DQA2 as targets for primary sclerosing cholangitis

BACKGROUND

Primary sclerosing cholangitis (PSC) is a kind of cholestatic liver disease without effective therapies and its pathogenesis is largely unknown.

METHODS

We performed the proteome-wide Mendelian randomization (MR) design to estimate the causal associations of protein levels with PSC risk. Therein, genetic associations with 4,907 plasma protein levels were extracted from a proteome-wide genome-wide association study (GWAS) with 35,559 individuals and those with PSC were obtained from the International PSC Study Group (2,871 cases and 12,019 controls) and the FinnGen study (1,491 cases and 301,383 controls). The colocalization analysis was performed to detect causal variants shared by proteins and PSC. The identified proteins were further enriched in pathways and diseases. A phenome-wide association screening was performed and potential drugs were assessed as well.

RESULTS

The results indicated that genetically predicted plasma levels of 14 proteins were positively associated with an increased risk of PSC and 8 proteins were inversely associated with PSC risk in both PSC GWAS data sets, and they all survived in sensitivity analyses. The colocalization indicated that AIF1 (allograft inflammatory factor 1) and HLA-DQA2 (major histocompatibility complex, class II, DQ alpha 2) were shared proteins with PSC, and they should be direct targets for PSC. The phenome-wide screening suggested that variants located at AIF1 or HLA-DQA2 region were closely associated with several autoimmune diseases, such as rheumatoid arthritis, implicating the shared pathogenesis among them.

CONCLUSIONS

Our study highly pinpointed two candidate targets (AIF1 and HLA-DQA2) for PSC.

Causal role of immune cells in obstructive sleep apnea hypopnea syndrome: Mendelian randomization study

BACKGROUND

Despite being one of the most prevalent sleep disorders, obstructive sleep apnea hypoventilation syndrome (OSAHS) has limited information on its immunologic foundation. The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies (GWAS) and genotyping techniques using high-density genetic markers (e.g., SNP or CNVs). But this tactic hasn't yet been applied to OSAHS. Using a Mendelian randomization analysis, we analyzed the causal link between immune cells and the illness in order to comprehend the immunological bases of OSAHS.

AIM

To investigate the immune cells' association with OSAHS via genetic methods, guiding future clinical research.

METHODS

A comprehensive two-sample mendelian randomization study was conducted to investigate the causal relationship between immune cell characteristics and OSAHS. Summary statistics for each immune cell feature were obtained from the GWAS catalog. Information on 731 immune cell properties, such as morphologic parameters, median fluorescence intensity, absolute cellular, and relative cellular, was compiled using publicly available genetic databases. The results' robustness, heterogeneity, and horizontal pleiotropy were confirmed using extensive sensitivity examination.

RESULTS

Following false discovery rate (FDR) correction, no statistically significant effect of OSAHS on immunophenotypes was observed. However, two lymphocyte subsets were found to have a significant association with the risk of OSAHS: Basophil %CD33dim HLA DR- CD66b- (OR = 1.03, 95%CI = 1.01-1.03, P < 0.001); CD38 on IgD+ CD24- B cell (OR = 1.04, 95%CI = 1.02-1.04, P = 0.019).

CONCLUSION

This study shows a strong link between immune cells and OSAHS through a gene approach, thus offering direction for potential future medical research.

Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study

Background

Numerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables.

Methods

The genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation.

Results

The IVW method's findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results' reliability was confirmed by thorough assessments of heterogeneity and pleiotropy.

Conclusions

For the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD.

A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization

BACKGROUND

Mendelian randomization is a popular method for causal inference with observational data that uses genetic variants as instrumental variables. Similarly to a randomized trial, a standard Mendelian randomization analysis estimates the population-averaged effect of an exposure on an outcome. Dividing the population into subgroups can reveal effect heterogeneity to inform who would most benefit from intervention on the exposure. However, as covariates are measured post-"randomization", naive stratification typically induces collider bias in stratum-specific estimates.

METHOD

We extend a previously proposed stratification method (the "doubly-ranked method") to form strata based on a single covariate, and introduce a data-adaptive random forest method to calculate stratum-specific estimates that are robust to collider bias based on a high-dimensional covariate set. We also propose measures based on the Q statistic to assess heterogeneity between stratum-specific estimates (to understand whether estimates are more variable than expected due to chance alone) and variable importance (to identify the key drivers of effect heterogeneity).

RESULT

We show that the effect of body mass index (BMI) on lung function is heterogeneous, depending most strongly on hip circumference and weight. While for most individuals, the predicted effect of increasing BMI on lung function is negative, it is positive for some individuals and strongly negative for others.

CONCLUSION

Our data-adaptive approach allows for the exploration of effect heterogeneity in the relationship between an exposure and an outcome within a Mendelian randomization framework. This can yield valuable insights into disease aetiology and help identify specific groups of individuals who would derive the greatest benefit from targeted interventions on the exposure.

Causal role of immune cells in generalized anxiety disorder: Mendelian randomization study

Background

Generalized anxiety disorder (GAD) is a prevalent emotional disorder that has received relatively little attention regarding its immunological basis. Recent years have seen the widespread use of high-density genetic markers such as SNPs or CNVs for genotyping, as well as the advancement of genome-wide association studies (GWAS) technologies, which have facilitated the understanding of immunological mechanisms underlying several major psychiatric disorders. Despite these advancements, the immunological basis of GAD remains poorly understood. In light of this, we aimed to explore the causal relationship between immune cells and the disease through a Mendelian randomization study.

Methods

The summary information for GAD (Ncase=4,666, Ncontrol=337,577) was obtained from the FinnGen dataset. Summary statistics for the characterization of 731 immune cells, including morphological parameters (MP=32), median fluorescence intensity (MFI=389), absolute cells (AC=118), and relative cells (RC=192), were derived from the GWAS catalog. The study involved both forward MR analysis, with immune cell traits as the exposure and GAD as the outcome, and reverse MR analysis, with GAD as the exposure and immune cell traits as the outcome. We performed extensive sensitivity analyses to confirm the robustness, heterogeneity, and potential multi-biological effects of the study results. Also, to control for false positive results during multiple hypothesis testing, we adopted a false discovery rate (FDR) to control for statistical bias due to multiple comparisons.

Results

After FDR correction, GAD had no statistically significant effect on immunophenotypes. Several phenotypes with unadjusted low P-values are worth mentioning, including decreased PB/PC levels on B cells(β=-0.289, 95%CI=0.044~0.194, P=0.002), reduced PB/PC AC in GAD patients (β=-0.270, 95% CI=0.77~0.92, P=0.000), and diminished PB/PC on lymphocytes (β=-0.315, 95% CI=0.77~0.93, P=0.001). GAD also exerted a causal effect on CD27 on IgD-CD38br (β=-0.155,95%CI=0.78~0.94,P=0.002), CD20-%B cell (β= -0.105,95% CI=0.77~0.94, P=0.002), IgD-CD38br%lymphocyte(β=-0.305, 95%CI=0.79~0.95, P=0.002), FSC-A level on granulocytes (β=0.200, 95%CI=0.75~0.91, P=8.35×10-5), and CD4RA on TD CD4+(β=-0.150, 95% CI=0.82~1.02, P=0.099). Furthermore, Two lymphocyte subsets were identified to be significantly associated with GAD risk: CD24+ CD27+ B cell (OR=1.066,95%CI=1.04~1.10,P=1.237×10-5),CD28+CD4+T cell (OR=0.927, 95%CI=0.89~0.96, P=8.085×10-5).

Conclusion

The study has shown the close association between immune cells and GAD through genetic methods, thereby offering direction for future clinical research.

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.

Association of iron homeostasis biomarkers in type 2 diabetes and glycaemic traits: a bidirectional two-sample Mendelian randomization study

BACKGROUND

Mendelian randomization (MR) studies show iron positively associated with type 2 diabetes (T2D) but included potentially biasing hereditary haemochromatosis variants and did not assess reverse causality.

METHODS

We assessed the relation of iron homeostasis with T2D and glycaemic traits bidirectionally, using genome-wide association studies (GWAS) of iron homeostasis biomarkers [ferritin, serum iron, total iron-binding capacity (TIBC), transferrin saturation (TSAT) (n ≤ 246 139)], T2D (DIAMANTE n = 933 970 and FinnGen n = 300 483), and glycaemic traits [fasting glucose (FG), 2-h glucose, glycated haemoglobin (HbA1c) and fasting insulin (FI) (n ≤ 209 605)]. Inverse variance weighting (IVW) was the main analysis, supplemented with sensitivity analyses and assessment of mediation by hepcidin.

RESULTS

Iron homeostasis biomarkers were largely unrelated to T2D, although serum iron was potentially associated with higher T2D [odds ratio: 1.07 per standard deviation; 95% confidence interval (CI): 0.99 to 1.16; P-value: 0.078) in DIAMANTE only. Higher ferritin, serum iron, TSAT and lower TIBC likely decreased HbA1c, but were not associated with other glycaemic traits. Liability to T2D likely increased TIBC (0.03 per log odds; 95% CI: 0.01 to 0.05; P-value: 0.005), FI likely increased ferritin (0.29 per log pmol/L; 95% CI: 0.12 to 0.47; P-value: 8.72 x 10-4). FG likely increased serum iron (0.06 per mmol/L; 95% CI: 0.001 to 0.12; P-value: 0.046). Hepcidin did not mediate these associations.

CONCLUSION

It is unlikely that ferritin, TSAT and TIBC cause T2D although an association for serum iron could not be excluded. Glycaemic traits and liability to T2D may affect iron homeostasis, but mediation by hepcidin is unlikely. Corresponding mechanistic studies are warranted.

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.

Mendelian randomization for cardiovascular diseases: principles and applications

Large-scale genome-wide association studies conducted over the last decade have uncovered numerous genetic variants associated with cardiometabolic traits and risk factors. These discoveries have enabled the Mendelian randomization (MR) design, which uses genetic variation as a natural experiment to improve causal inferences from observational data. By analogy with the random assignment of treatment in randomized controlled trials, the random segregation of genetic alleles when DNA is transmitted from parents to offspring at gamete formation is expected to reduce confounding in genetic associations. Mendelian randomization analyses make a set of assumptions that must hold for valid results. Provided that the assumptions are well justified for the genetic variants that are employed as instrumental variables, MR studies can inform on whether a putative risk factor likely has a causal effect on the disease or not. Mendelian randomization has been increasingly applied over recent years to predict the efficacy and safety of existing and novel drugs targeting cardiovascular risk factors and to explore the repurposing potential of available drugs. This review article describes the principles of the MR design and some applications in cardiovascular epidemiology.

Targeting Longevity Gene SLC13A5: A Novel Approach to Prevent Age-Related Bone Fragility and Osteoporosis

Reduced expression of the plasma membrane citrate transporter SLC13A5, also known as INDY, has been linked to increased longevity and mitigated age-related cardiovascular and metabolic diseases. Citrate, a vital component of the tricarboxylic acid cycle, constitutes 1-5% of bone weight, binding to mineral apatite surfaces. Our previous research highlighted osteoblasts' specialized metabolic pathway facilitated by SLC13A5 regulating citrate uptake, production, and deposition within bones. Disrupting this pathway impairs bone mineralization in young mice. New Mendelian randomization analysis using UK Biobank data indicated that SNPs linked to reduced SLC13A5 function lowered osteoporosis risk. Comparative studies of young (10 weeks) and middle-aged (52 weeks) osteocalcin-cre-driven osteoblast-specific Slc13a5 knockout mice (Slc13a5cKO) showed a sexual dimorphism: while middle-aged females exhibited improved elasticity, middle-aged males demonstrated enhanced bone strength due to reduced SLC13A5 function. These findings suggest reduced SLC13A5 function could attenuate age-related bone fragility, advocating for SLC13A5 inhibition as a potential osteoporosis treatment.

Conditional inference in cis-Mendelian randomization using weak genetic factors

Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis-MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference that use many correlated variants to make robust inferences even in situations, where those variants have only weak effects on the exposure. In particular, we exploit the highly structured nature of genetic correlations in single gene regions to reduce the dimension of genetic variants using factor analysis. These genetic factors are then used as instrumental variables to construct tests for the causal effect of interest. Since these factors may often be weakly associated with the exposure, size distortions of standard t-tests can be severe. Therefore, we consider two approaches based on conditional testing. First, we extend results of commonly-used identification-robust tests for the setting where estimated factors are used as instruments. Second, we propose a test which appropriately adjusts for first-stage screening of genetic factors based on their relevance. Our empirical results provide genetic evidence to validate cholesterol-lowering drug targets aimed at preventing coronary heart disease.

MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner's curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.

Mendelian Randomization Analysis of Genetic Proxies of Thiazide Diuretics and the Reduction of Kidney Stone Risk

Importance

Clinical trial data have called into question the efficacy of thiazide diuretics for the prevention of kidney stones.

Objective

To identify whether there is an association between genetic proxies of thiazide diuretics and the risk of kidney stones.

Design, Setting, and Participants

This genetic association study undertook a mendelian randomization analysis of derived exposures and outcomes from genome-wide association study summary statistics. Genetic proxies of thiazide diuretics were derived from the International Consortium for Blood Pressure. Kidney stone cases and controls were derived from the Million Veteran Program, UK Biobank, and the FinnGen study. These cross-sectional designs do not report a duration of follow-up. Data analysis was performed in May 2023.

Exposure

Genetic proxies of thiazide diuretics were genetic variants in the thiazide-sensitive sodium chloride cotransporter gene associated with systolic blood pressure. Genetic proxies of β-blockers and systolic blood pressure served as negative controls.

Main Outcomes and Measures

The main outcome was the odds of kidney stones. The secondary outcomes were serum laboratory values relevant to the treatment of kidney stones.

Results

The main analysis included up to 1 079 657 individuals, including 50 832 kidney stone cases and 1 028 825 controls. In a meta-analysis of all cohorts, genetic proxies of thiazide diuretics were associated with a lower odds of kidney stones (OR, 0.85; 95% CI, 0.81-0.89; P < .001). Genetic proxies of β-blockers (OR, 1.02; 95% CI, 0.96-1.07; P = .52) and systolic blood pressure (OR, 1.00; 95% CI, 1.00-1.01; P = .49) were not associated with kidney stones. Genetic proxies of thiazide diuretics were associated with higher serum calcium (β [SE], 0.051 [0.0092]; P < .001) and total cholesterol (β [SE], 0.065 [0.015]; P < .001), but lower serum potassium (β [SE], -0.073 [0.022]; P < .001).

Conclusions and Relevance

In this genetic association study, genetic proxies of thiazide diuretics were associated with reduced kidney stone risk. This finding reflects a drug effect over the course of a lifetime, unconstrained by the limited follow-up period of clinical trials.

Mendelian randomization in hepatology: A review of principles, opportunities, and challenges

Mendelian randomization has become a popular tool to assess causal relationships using existing observational data. While randomized controlled trials are considered the gold standard for establishing causality between exposures and outcomes, it is not always feasible to conduct a trial. Mendelian randomization is a causal inference method that uses observational data to infer causal relationships by using genetic variation as a surrogate for the exposure of interest. Publications using the approach have increased dramatically in recent years, including in the field of hepatology. In this concise review, we describe the concepts, assumptions, and interpretation of Mendelian randomization as related to studies in hepatology. We focus on the strengths and weaknesses of the approach for a non-statistical audience, using an illustrative example to assess the causal relationship between body mass index and NAFLD.

The identification of distinct protective and susceptibility mechanisms for hip osteoarthritis: findings from a genome-wide association study meta-analysis of minimum joint space width and Mendelian randomisation cluster analyses

BACKGROUND

Hip minimum joint space width (mJSW) provides a proxy for cartilage thickness. This study aimed to conduct a genome-wide association study (GWAS) of mJSW to (i) identify new genetic determinants of mJSW and (ii) identify which mJSW loci convey hip osteoarthritis (HOA) risk and would therefore be of therapeutic interest.

METHODS

GWAS meta-analysis of hip mJSW derived from plain X-rays and DXA was performed, stratified by sex and adjusted for age and ancestry principal components. Mendelian randomisation (MR) and cluster analyses were used to examine causal effect of mJSW on HOA.

FINDINGS

50,745 individuals were included in the meta-analysis. 42 SNPs, which mapped to 39 loci, were identified. Mendelian randomisation (MR) revealed little evidence of a causal effect of mJSW on HOA (ORIVW 0.98 [95% CI 0.82-1.18]). However, MR-Clust analysis suggested the null MR estimates reflected the net effect of two distinct causal mechanisms cancelling each other out, one of which was protective, whereas the other increased HOA susceptibility. For the latter mechanism, all loci were positively associated with height, suggesting mechanisms leading to greater height and mJSW increase the risk of HOA in later life.

INTERPRETATIONS

One group of mJSW loci reduce HOA risk via increased mJSW, suggesting possible utility as targets for chondroprotective therapies. The second group of mJSW loci increased HOA risk, despite increasing mJSW, but were also positively related to height, suggesting they contribute to HOA risk via a growth-related mechanism.

FUNDING

Primarily funded by the Medical Research Council and Wellcome Trust.

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.

Deciphering the genetic architecture of atrial fibrillation offers insights into disease prediction, pathophysiology and downstream sequelae

Aims

The study aimed to discover novel genetic loci for atrial fibrillation (AF), explore the shared genetic etiologies between AF and other cardiovascular and cardiometabolic traits, and uncover AF pathogenesis using Mendelian randomization analysis.

Methods and results

We conducted a genome-wide association study meta-analysis including 109,787 AF cases and 1,165,920 controls of European ancestry and identified 215 loci, among which 91 were novel. We performed Genomic Structural Equation Modeling analysis between AF and four cardiovascular comorbidities (coronary artery disease, ischemic stroke, heart failure, and vneous thromboembolism) and found 189 loci shared across these diseases as well as a universal genetic locus shared by atherosclerotic outcomes (i.e., rs1537373 near CDKN2B). Three genetic loci (rs10740129 near JMJD1C, rs2370982 near NRXN3, and rs9931494 near FTO) were associated with AF and cardiometabolic traits. A polygenic risk score derived from this genome-wide meta-analysis was associated with AF risk (odds ratio 2.36, 95% confidence interval 2.31-2.41 per standard deviation increase) in the UK biobank. This score, combined with age, sex, and basic clinical features, predicted AF risk (AUC 0.784, 95% CI 0.781-0.787) in Europeans. Phenome-wide association analysis of the polygenic risk score identified many AF-related comorbidities of the circulatory, endocrine, and respiratory systems. Phenome-wide and multi-omic Mendelian randomization analyses identified associations of blood lipids and pressure, diabetes, insomnia, obesity, short sleep, and smoking, 27 blood proteins, one gut microbe (genus.Catenibacterium), and 11 blood metabolites with risk to AF.

Conclusions

This genome-wide association study and trans-omic Mendelian randomization analysis provides insights into disease risk prediction, pathophysiology and downstream sequelae.

Muscle Traits, Sarcopenia, and Sarcopenic Obesity: A Vitamin D Mendelian Randomization Study

(1) Background: Observational studies associate vitamin D deficiency with muscle disorders, while some clinical trial data support a minor association between the vitamin and skeletal muscle performance in healthy subjects. Vitamin D receptor knockout mice studies confirm the relationship between vitamin D and skeletal muscle; however, causal inference in humans is challenging due to the ethical implications of including vitamin D-deficient participants in randomized trials. This study uses genetic methods to safely explore causal underpinnings for the relationship between 25(OH)D concentrations and skeletal muscle-related traits, including grip strength and combined arm skeletal muscle mass, and extends this analysis to suspected pathophysiology in the form of probable sarcopenia and sarcopenic obesity. (2) Methods: We conducted Mendelian randomization (MR) analyses in up to 307,281 participants from the UK Biobank of whom 25,414 had probable sarcopenia and 16,520 had sarcopenic obesity. In total, 35 variants were used to instrument 25(OH)D and MR analyses conducted using multiple approaches. (3) Results: Genetic analyses provided support for a relationship between genetically predicted higher 25(OH)D and skeletal muscle traits, with linear MR analyses for grip strength showing 0.11 kg (95% CI 0.04, 0.19) greater contractile force per 10 unit higher 25(OH)D, while there was a modest association with skeletal muscle mass (0.01 kg (95% CI 0.003, 0.02) greater muscle mass). For probable sarcopenia risk, there was suggestive evidence for lower odds by higher 25(OH)D (OR 0.96 (95% CI 0.92, 1.00)); however, this did not reflect an association with sarcopenic obesity (OR 0.97 (95% CI 0.93, 1.02)), but was seen in probable sarcopenia cases who were not obese (OR 0.92 (95% CI 0.86, 0.98)). Results were similar across multiple MR approaches. (4) Conclusions: Our study supports a causal relationship between 25(OH)D and skeletal muscle health. While evidence for benefit did not extend to lower risk of sarcopenic obesity, effective vitamin D-deficiency prevention strategies may help reduce age-related muscle weakness.