Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework

Relationship between plasma brain-derived neurotrophic factor levels and neurological disorders: An investigation using Mendelian randomisation

Background

Altered brain-derived neurotrophic factor (BDNF) concentrations have been detected in the central nervous system tissues and peripheral blood. These alterations are associated with a series of neurological disorders.

Objective

To investigate the potential causal relationships between genetically determined plasma BDNF levels and various neurological diseases using a two-sample Mendelian randomisation study.

Methods

We selected single nucleotide polymorphisms strongly related to plasma BDNF levels as instrumental variables. Within the Mendelian randomisation framework, we used summary-level statistics for exposure (plasma BDNF levels) and outcomes (neurological disorders).

Results

We observed suggestive evidence of a relation between higher plasma BDNF levels and less risk of nontraumatic intracranial haemorrhage (nITH) (odds ratio [OR] = 0.861, 95 % confidence interval [CI]: 0.774-0.958, P = 0.006, PFDR = 0.078), epilepsy (OR = 0.927, 95 % CI: 0.880-0.976, P = 0.004, PFDR = 0.078), focal epilepsy (OR = 0.928, 95 % CI: 0.874-0.986, P = 0.016, PFDR = 0.139), and non-lesional focal epilepsy (OR = 0.981, 95 % CI: 0.964-0.999, P = 0.041, PFDR = 0.267). Combined with the UK Biobank dataset, the association of plasma BDNF levels with nITH remained significant (OR = 0.88, 95 % CI: 0.81-0.96, P < 0.01). The combined analysis of three consortium datasets demonstrated a considerable impact of plasma BDNF on epilepsy (OR = 0.94, 95 % CI: 0.90-0.98, P < 0.01) and a suggestive impact on focal epilepsy (OR = 0.94, 95 % CI: 0.89-0.99, P = 0.02). However, there was no apparent correlation between plasma BDNF levels and other neurological disorders or related subtypes.

Conclusions

Our study supports a possible causal relationship between elevated plasma BDNF levels and a reduced risk of nITH, epilepsy, and focal epilepsy.

Gut microbiota and cognitive performance: A bidirectional two-sample Mendelian randomization

PURPOSE

Previous studies have suggested a potential association between gut microbiota and neurological and psychiatric disorders. However, the causal relationship between gut microbiota and cognitive performance remains uncertain.

METHODS

A two-sample Mendelian randomization (MR) study used SNPs linked to gut microbiota (n = 18,340) and cognitive performance (n = 257,841) from recent GWAS data. Inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode were employed. Heterogeneity was assessed via Cochran's Q test for IVW. Results were shown with funnel plots. Outliers were detected through leave-one-out method. MR-PRESSO and MR-Egger intercept tests were conducted to address horizontal pleiotropy influence.

LIMITATIONS

Limited to European populations, generic level, and potential confounding factors.

RESULTS

IVW analysis revealed detrimental effects on cognitive perfmance associated with the presence of genus Blautia (P = 0.013, 0.966[0.940-0.993]), Catenibacterium (P = 0.035, 0.977[0.956-0.998]), Oxalobacter (P = 0.043, 0.979[0.960-0.999]). Roseburia (P < 0.001, 0.935[0.906-0.965]), in particular, remained strongly negatively associated with cognitive performance after Bonferroni correction. Conversely, families including Bacteroidaceae (P = 0.043, 1.040[1.001-1.081]), Rikenellaceae (P = 0.047, 1.026[1.000-1.053]), along with genera including Paraprevotella (P = 0.044, 1.020[1.001-1.039]), Ruminococcus torques group (P = 0.016, 1.062[1.011-1.115]), Bacteroides (P = 0.043, 1.040[1.001-1.081]), Dialister (P = 0.027, 1.039[1.004-1.074]), Paraprevotella (P = 0.044, 1.020[1.001-1.039]) and Ruminococcaceae UCG003 (P = 0.007, 1.040[1.011-1.070]) had a protective effect on cognitive performance.

CONCLUSIONS

Our results suggest that interventions targeting specific gut microbiota may offer a promising avenue for improving cognitive function in diseased populations. The practical application of these findings has the potential to enhance cognitive performance, thereby improving overall quality of life.

The associations between functional dyspepsia and potential risk factors: A comprehensive Mendelian randomization study

BACKGROUND

Previous cross-sectional studies have identified multiple potential risk factors for functional dyspepsia (FD). However, the causal associations between these factors and FD remain elusive. Here we aimed to fully examine the causal relationships between these factors and FD utilizing a two-sample MR framework.

METHODS

A total of 53 potential FD-related modifiable factors, including those associated with hormones, metabolism, disease, medication, sociology, psychology, lifestyle and others were obtained through a comprehensive literature review. Independent genetic variants closely linked to these factors were screened as instrumental variables from genome-wide association studies (GWASs). A total of 8875 FD cases and 320387 controls were available for the analysis. The inverse variance weighted (IVW) method was employed as the primary analytical approach to assess the relationship between genetic variants of risk factors and the FD risk. Sensitivity analyses were performed to evaluate the consistency of the findings using the weighted median model, MR-Egger and MR-PRESSO methods.

RESULTS

Genetically predicted depression (OR 1.515, 95% confidence interval (CI) 1.231 to 1.865, p = 0.000088), gastroesophageal reflux disease (OR 1.320, 95%CI 1.153 to 1.511, p = 0.000057) and years of education (OR 0.926, 95%CI 0.894 to 0.958, p = 0.00001) were associated with risk for FD in univariate MR analyses. Multiple medications, alcohol consumption, poultry intake, bipolar disorder, mood swings, type 1 diabetes, elevated systolic blood pressure and lower overall health rating showed to be suggestive risk factors for FD (all p<0.05 while ≥0.00167). The positive causal relationship between depression, years of education and FD was still significant in multivariate MR analyses.

CONCLUSIONS

Our comprehensive MR study demonstrated that depression and lower educational attainment were causal factors for FD at the genetic level.

Mendelian-randomization study revealed causal relationship between nonalcoholic fatty liver disease and osteoporosis/fractures

BACKGROUND

Patients with nonalcoholic fatty liver disease (NAFLD) are reported to have a higher risk of osteoporosis/fractures; however, the causal relationship remains unclear.

METHODS

Publicly available genome-wide association studies (GWASs) were used for Mendelian randomization (MR) analysis. GWASs of NAFLD and fractures were obtained from the FinnGen Consortium. GWASs of bone mineral density (BMD) were derived from a meta-analysis. GWASs of obesity, diabetes, liver function, and serum lipid-related metrics were used to clarify whether the accompanying NAFLD symptoms contributed to fractures. Moreover, two additional GWASs of NAFLD were applied.

RESULTS

A causal association was not observed between NAFLD and BMD using GWASs from the FinnGen Consortium. However, a causal relationship between NAFLD and femoral neck-BMD (FN-BMD), a suggestive relationship between fibrosis and FN-BMD, and between NAFLD and osteoporosis were identified in replication GWASs. Genetically proxied body mass index (BMI), high-density lipoprotein (HDL), and hip circumference increased the likelihood of lower limb fractures. The waist-to-hip ratio decreased, whereas glycated hemoglobin (HbA1C) and homeostasis model assessment of β-cell function (HOMA-B) increased the risk of forearm fractures. Low-density lipoprotein (LDL) reduced, whereas HbA1C increased the incidence of femoral fractures. Alkaline phosphatase (ALP) raised the risk of foot fractures. However, after a multivariate MR analysis (adjusted for BMI), all the relationships became insignificant.

CONCLUSIONS

NAFLD caused reduced BMD, and genetically predicted HDL, LDL, HbA1C, HOMA-B, ALP, hip circumference, and waist-to-hip ratio causally increased the risk of fractures. BMI may mediate causal relationships. Larger GWASs are required to verify this finding.

SGLT2 inhibition, plasma proteins, and heart failure: a proteome-wide Mendelian Randomization and colocalization study

Objective

To investigate the causal contributions of Sodium-glucose cotransporter 2 (SGLT2) inhibition on Heart Failure (HF) and identify the circulating proteins that mediate SGLT2 inhibition's effects on HF.

Methods

Applying a two-sample, two-step Mendelian Randomization (MR) analysis, we aimed to estimate: (1) the causal impact of SGLT2 inhibition on HF; (2) the causal correlation of SGLT2 inhibition on 4,907 circulating proteins; (3) the causal association of SGLT2 inhibition-driven plasma proteins on HF. Genetic variants linked to SGLT2 inhibition derived from the previous studies. The 4,907 circulating proteins were derived from the deCODE study. Genetic links to HF were obtained through the Heart Failure Molecular Epidemiology for Therapeutic Targets (HERMES) consortium.

Results

SGLT2 inhibition demonstrated a lower risk of HF (odds ratio [OR] = 0.44, 95% CI [0.26, 0.76], P = 0.003). Among 4,907 circulating proteins, we identified leucine rich repeat transmembrane protein 2 (LRRTM2), which was related to both SGLT2 inhibition and HF. Mediation analysis revealed that the impact of SGLT2 inhibition on HF operates indirectly through LRRTM2 [β = -0.20, 95% CI (-0.39, -0.06), P = 0.02] with a mediation proportion of 24.6%. Colocalization analysis provided support for the connections between LRRTM2 and HF.

Conclusion

The study indicated a causative link between SGLT2 inhibition and HF, with plasma LRRTM2 potentially serving as a mediator.

Effects of genetically predicted posttraumatic stress disorder on autoimmune phenotypes

Observational studies suggest that posttraumatic stress disorder (PTSD) increases risk for various autoimmune diseases. Insights into shared biology and causal relationships between these diseases may inform intervention approaches to PTSD and co-morbid autoimmune conditions. We investigated the shared genetic contributions and causal relationships between PTSD, 18 autoimmune diseases, and 3 immune/inflammatory biomarkers. Univariate MiXeR was used to contrast the genetic architectures of phenotypes. Genetic correlations were estimated using linkage disequilibrium score regression. Bi-directional, two-sample Mendelian randomization (MR) was performed using independent, genome-wide significant single nucleotide polymorphisms; inverse variance weighted and weighted median MR estimates were evaluated. Sensitivity analyses for uncorrelated (MR PRESSO) and correlated horizontal pleiotropy (CAUSE) were also performed. PTSD was considerably more polygenic (10,863 influential variants) than autoimmune diseases (median 255 influential variants). However, PTSD evidenced significant genetic correlation with nine autoimmune diseases and three inflammatory biomarkers. PTSD had putative causal effects on autoimmune thyroid disease (p = 0.00009) and C-reactive protein (CRP) (p = 4.3 × 10-7). Inferences were not substantially altered by sensitivity analyses. Additionally, the PTSD-autoimmune thyroid disease association remained significant in multivariable MR analysis adjusted for genetically predicted inflammatory biomarkers as potential mechanistic pathway variables. No autoimmune disease had a significant causal effect on PTSD (all p values > 0.05). Although causal effect models were supported for associations of PTSD with CRP, shared pleiotropy was adequate to explain a putative causal effect of CRP on PTSD (p = 0.18). In summary, our results suggest a significant genetic overlap between PTSD, autoimmune diseases, and biomarkers of inflammation. PTSD has a putative causal effect on autoimmune thyroid disease, consistent with existing epidemiologic evidence. A previously reported causal effect of CRP on PTSD is potentially confounded by shared genetics. Together, results highlight the nuanced links between PTSD, autoimmune disorders, and associated inflammatory signatures, and suggest the importance of targeting related pathways to protect against disease and disability.

Association of glucose-lowering drug target and risk of gastrointestinal cancer: a mendelian randomization study

BACKGROUND & AIMS

Glucose-lowering drug is associated with various cancers, but the causality with gastrointestinal cancer risk is rarely reported. We aimed to explore the causality between them in this Mendelian randomization (MR) study.

METHODS

Two-sample MR, summary-data-based (SMR), mediation MR, and colocalization analyses was employed. Ten glucose-lowering drug targets (PPARG, DPP4, GLP1R, INSR, SLC5A2, ABCC8, KCNJ11, ETFDH, GPD2, PRKAB1) and seven types of gastrointestinal cancer (anal carcinoma, cardia cancer, gastric cancer, hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), pancreatic cancer, rectum cancer) were included. Patients with gastrointestinal cancers from six different large GWAS databases, including the UK Biobank and Finnish cohorts were incorporated, for discovery and external validation. Meta-analysis was employed to integrate the results from both discovery and validation cohorts, thereby ensuring the reliability of findings.

RESULTS

ABCC8/KCNJ11 were associated with pancreatic cancer risk in both two-sample MR (odds ratio (OR): 15.058, per standard deviation unit (SD) change of glucose-lowering durg target perturbation equivalent to 1 SD unit of HbA1c lowering; 95% confidence interval (95% CI): 3.824-59.295; P-value = 0.0001) and SMR (OR: 1.142; 95% CI: 1.013-1.287; P-value = 0.030) analyses. The mediation effect of body mass index (OR: 0.938; 95% CI: 0.884-0.995; proportion of mediation effect: 3.001%; P-value = 0.033) on ABCC8/KCNJ11 and pancreatic cancer was uncovered. Strong connections of DPP4 with anal carcinoma (OR: 0.123; 95% CI: 0.020-0.745; P-value = 0.023) and ICC (OR: 7.733; 95% CI: 1.743-34.310; P-value = 0.007) were detected. PPARG was associated with anal carcinoma (OR: 12.909; 95% CI: 3.217-51.795; P-value = 0.0003), HCC (OR: 36.507; 95% CI: 8.929-149.259; P-value < 0.0001), and pancreatic cancer (OR: 0.110; 95% CI: 0.071-0.172; P-value < 0.0001). SLC5A2 was connected with pancreatic cancer (OR: 8.096; 95% CI: 3.476-18.857; P-value < 0.0001). Weak evidence indicated the connections of GLP1R, GPD2, and PRKAB1 with anal carcinoma, cardia cancer, ICC, and rectum cancer. In addition, the corresponding results were consistently validated in both the validation cohorts and the integrated outcomes.

CONCLUSIONS

Some glucose-lowering drugs were associated with gastrointestinal cancer risk, which might provide new ideas for gastrointestinal cancer treatment.

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.

Obesity, Glycemic Traits, Lifestyle Factors, and Risk of Facial Aging: A Mendelian Randomization Study in 423,999 Participants

BACKGROUND

Several recent observational studies have associated obesity, lifestyle factors (smoking, sleep duration, and alcohol drinking), and glycemic traits with facial aging. However, whether this relationship is causal due to confounding and reverse causation is yet to be substantiated.

AIMS

We aimed to assess these relationships using Mendelian randomization (MR).

METHODS

For the instrumental variables, this paper selected independent single nucleotide polymorphisms (SNPs) linked to the exposures at a genome-wide state (P < 5 × 10-8) in equivalent genome-wide association studies (GWAS). Using the UK Biobank, we obtained summary-level data for facial aging on 423,999 individuals. The primary assessments were performed through the combination of complementing techniques (simple method approaches, weighted model, MR-Egger, and weighted median) and the inverse-variance-weighted method. Along with that, we examined the heterogeneity and horizontal pleiotropy through different types of sensitivity analyses.

RESULTS

The correlations were (a) facial aging for body mass index (BMI, OR = 1.054, 95% CI 1.044-1.64), (b) waist/hip ratio (OR = 1.056, 95% CI 1.023-1.091), and (c) smoking (OR = 1.023, 95% CI 1.007-1.039). Equally important, the correlations for waist/hip ratio remained robust after adjusting for the genetically predicted BMI (OR = 1.028, 95% CI 1.003-1.054). However, no causal effects of alcoholic drinking, glycemic traits, and sleep duration on facial aging were observed.

CONCLUSIONS

The outcomes shed light on the potential correlation of obesity and cigarette smoking with facial aging while putting forward a more comprehensive and credible foundation for the optimization of facial aging strategies.

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Causal relationship between gut microbiota and risk of esophageal cancer: evidence from Mendelian randomization study

BACKGROUND

The causative implications remain ambiguous. Consequently, this study aims to evaluate the putative causal relationship between gut microbiota and Esophageal cancer (EC).

METHODS

The genome-wide association study (GWAS) pertaining to the microbiome, derived from the MiBioGen consortium-which consolidates 18,340 samples across 24 population-based cohorts-was utilized as the exposure dataset. Employing the GWAS summary statistics specific to EC patients sourced from the GWAS Catalog and leveraging the two-sample Mendelian randomization (MR) methodology, the principal analytical method applied was the inverse variance weighted (IVW) technique. Cochran's Q statistic was utilized to discern heterogeneity inherent in the data set. Subsequently, a reverse MR analysis was executed.

RESULTS

Findings derived from the IVW technique elucidated that the Family Porphyromonadaceae (P = 0.048) and Genus Candidatus Soleaferrea (P = 0.048) function as deterrents against EC development. In contrast, the Genus Catenibacterium (P = 0.044), Genus Eubacterium coprostanoligenes group (P = 0.038), Genus Marvinbryantia (P = 0.049), Genus Ruminococcaceae UCG010 (P = 0.034), Genus Ruminococcus1 (P = 0.047), and Genus Sutterella (P = 0.012) emerged as prospective risk contributors for EC. To assess reverse causal effect, we used EC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between EC and seven different types of gut microbiota. The robustness of the MR findings was substantiated through comprehensive heterogeneity and pleiotropy evaluations.

CONCLUSIONS

This research identified certain microbial taxa as either protective or detrimental elements for EC, potentially offering valuable biomarkers for asymptomatic diagnosis and prospective therapeutic interventions for EC.

Bias and mean squared error in Mendelian randomization with invalid instrumental variables

Mendelian randomization (MR) is a statistical method that utilizes genetic variants as instrumental variables (IVs) to investigate causal relationships between risk factors and outcomes. Although MR has gained popularity in recent years due to its ability to analyze summary statistics from genome-wide association studies (GWAS), it requires a substantial number of single nucleotide polymorphisms (SNPs) as IVs to ensure sufficient power for detecting causal effects. Unfortunately, the complex genetic heritability of many traits can lead to the use of invalid IVs that affect both the risk factor and the outcome directly or through an unobserved confounder. This can result in biased and imprecise estimates, as reflected by a larger mean squared error (MSE). In this study, we focus on the widely used two-stage least squares (2SLS) method and derive formulas for its bias and MSE when estimating causal effects using invalid IVs. Using those formulas, we identify conditions under which the 2SLS estimate is unbiased and reveal how the independent or correlated pleiotropic effects influence the accuracy and precision of the 2SLS estimate. We validate these formulas through extensive simulation studies and demonstrate the application of those formulas in an MR study to evaluate the causal effect of the waist-to-hip ratio on various sleeping patterns. Our results can aid in designing future MR studies and serve as benchmarks for assessing more sophisticated MR methods.

The causality between CD8+NKT cells and CD16-CD56 on NK cells with hepatocellular carcinoma: a Mendelian randomization study

BACKGROUND

Hepatocellular carcinoma (HCC), which is featured with high morbidity and mortality worldwide, is a primary malignant tumor of the liver. Recently, there is a wealth of supporting evidence revealing that NK cell-related immune traits are strongly associated with the development of HCC, but the causality between them has not been proven.

METHODS

Two-sample Mendelian randomization (MR) study was performed to probe the causal correlation between NK cell-related immune traits and HCC. Genetic variations in NK cell-related immune traits were extracted from recent genome-wide association studies (GWAS) of individuals with European blood lineage. HCC data were derived from the UK Biobank Consortium's GWAS summary count data, including a total of 372,184 female and male subjects, with 168 cases and 372,016 controls, all of whom are of European ancestry. Sensitivity analysis was mainly used for heterogeneity and pleiotropy testing.

RESULTS

Our research indicated the causality between NK cell-related immune traits and HCC. Importantly, CD8+NKT cells had protective causal effects on HCC (OR = 0.9996;95%CI,0.9993-0.9999; P = 0.0489). CD16-CD56 caused similar effects on NK cells (OR = 0.9997;95%CI,0.9996-0.9999; P = 0.0117) as CD8+NKT cells. Intercepts from Egger showed no pleiotropy and confounding factors. Furthermore, insufficient evidence was found to support the existence of heterogeneity by Cochran's Q test.

CONCLUSION

MR analysis suggested that low CD8+NKT cells and CD16-CD56 expression on NK cells were linked with a higher risk of HCC.

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.

Causal relationship between gut microbiota and glioblastoma: a two-sample Mendelian randomization study

Background: Observational research and medical trials have suggested a connection between gut microbiota and glioblastoma, but it remains unclear if the relationship is causal. Method: A two-sample Mendelian randomization (MR) study was conducted by employing data from the MiBioGen consortium's largest genome-wide association study (n=18340) and the FinnGen consortium R8 release information (162 cases and 256,583 controls). Inverse variance weighted (IVW), weighted median estimator (WME), weighted model, MR-Egger, simple mode, and MR-PRESSO were used to determine the causal relationship between gut microbiota and glioblastoma. Reverse MR analysis was also performed on bacteria identified as causally related to glioblastoma. Results: Seven causal relationships were identified between genetic liability in the gut microbiota and glioblastoma, involving various bacterial families and genera. No significant causal effect was found on gut microbiota from glioblastoma, and no significant heterogeneity of instrumental variables (IVs) or horizontal pleiotropy was observed. Conclusion: A two-sample MR analysis reveals a causal association between the gut microbiota and glioblastoma, highlighting the need for more investigation to comprehend the processes behind this association.

Association of modifiable risk factors with obstructive sleep apnea: a Mendelian randomization study

BACKGROUND

The risk factors involved in obstructive sleep apnea (OSA) have not been clearly identified yet. We attempted to systematically investigate genetically predicted modifiable risk factors and lifestyle behaviors associated with OSA.

METHODS

The association between 34 risk factors and OSA was evaluated using the two-sample Mendelian randomization (MR). Genetic variants for risk factors were acquired from European-descent genome-wide studies. Data sources for OSA were extracted from FinnGen study with 16,761 cases and 201,194 controls. The primary analysis chosen was the inverse-variance weighted method.

RESULTS

MR analyses provide evidence of genetically predicted poor overall health rating (odds ratio (OR), 2.82; 95% confidence interval (CI), 1.95-4.08), nap during day (OR, 2.01; 95% CI, 1.37-2.93), high body mass index (BMI) (OR, 1.14; 95% CI, 1.09-1.19), increased body fat mass (OR, 1.83; 95% CI, 1.83-2.05), elevated body water mass (OR, 1.50; 95% CI, 1.31-1.70) and hypertension (OR, 1.81; 95% CI, 1.34-2.45) were associated with higher OSA risk, while high education level (OR, 0.55; 95% CI, 0.40-0.75) correlated with reduced OSA risk. Suggestive evidence was obtained for smoking and waist-to-hip ratio (WHR) with higher OSA odds, and vigorous physical activity, and HDL cholesterol with lower OSA odds. After adjusting for BMI using multivariable MR analysis, the effects of smoking, WHR, vigorous physical activity, and HDL-cholesterol were fully attenuated.

CONCLUSIONS

This MR study indicates that overall health rating, nap during day, BMI, body fat mass, body water mass, hypertension, and education are causally associated with the risk of OSA, which means that these modifiable risk factors are key targets for OSA prevention.

Causal associations between liver traits and Colorectal cancer: a Mendelian randomization study

OBJECTIVE

This study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management.

METHODS

Genetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias.

RESULTS

The MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44-0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias.

CONCLUSION

The study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.

Mendelian randomization reveals no correlations between herpesvirus infection and idiopathic pulmonary fibrosis

BACKGROUND

Previous studies have found that the persistence of herpesvirus significantly increases the risk of idiopathic pulmonary fibrosis (IPF), but it is unclear whether this effect is causal. We conducted a two-sample Mendelian randomization (MR) study to evaluate the causal relationship between three herpesvirus infections and IPF.

METHODS

We used genome-wide association studies (GWAS) data from three independent datasets, including FinnGen cohort, Milieu Intérieur cohort, and 23andMe cohort, to screen for instrumental variables (IVs) of herpesvirus infection or herpesvirus-related immunoglobulin G (IgG) levels. Outcome dataset came from the largest meta-analysis of IPF susceptibility currently available.

RESULTS

In the FinnGen cohort, genetically predicted Epstein-Barr virus (EBV) (OR = 1.105, 95%CI: 0.897-1.149, p = 0.815), cytomegalovirus (CMV) (OR = 1.073, 95%CI: 0.926-1.244, p = 0.302) and herpes simplex (HSV) infection (OR = 0.906, 95%CI: 0.753-1.097, p = 0.298) were not associated with the risk of IPF. In the Milieu Intérieur cohort, we found no correlations between herpesvirus-related IgG EBV nuclear antigen-1 (EBNA1) (OR = 0.968, 95%CI: 0.782-1.198, p = 0.764), EBV viral capsid antigen (VCA) (OR = 1.061, 95CI%: 0.811-1.387, p = 0.665), CMV (OR = 1.108, 95CI%: 0.944-1.314, p = 0.240), HSV-1 (OR = 1.154, 95%CI: 0.684-1.945, p = 0.592) and HSV-2 (OR = 0.915, 95%CI: 0.793-1.056, p = 0.225) and IPF risk. Moreover, in the 23andMe cohort, no evidence of associations between mononucleosis (OR = 1.042, 95%CI: 0.709-1.532, p = 0.832) and cold scores (OR = 0.906, 95%CI: 0.603-1.362, p = 0.635) and IPF were found. Sensitivity analysis confirmed the robustness of our results.

CONCLUSIONS

This study provides preliminary evidence that EBV, CMV, and HSV herpesviruses, and herpesviruses-related IgG levels, are not causally linked to IPF. Further MR analysis will be necessary when stronger instrument variables and GWAS with larger sample sizes become available.

Exploring the Role of Plasma Lipids and Statin Interventions on Multiple Sclerosis Risk and Severity: A Mendelian Randomization Study

BACKGROUND AND OBJECTIVES

There has been considerable interest in statins because of their pleiotropic effects beyond their lipid-lowering properties. Many of these pleiotropic effects are predominantly ascribed to Rho small guanosine triphosphatases (Rho GTPases) proteins. We aimed to genetically investigate the role of lipids and statin interventions on multiple sclerosis (MS) risk and severity.

METHOD

We used two-sample Mendelian randomization (MR) to investigate (1) the causal role of genetically mimic both cholesterol-dependent (through low-density lipoprotein cholesterol (LDL-C) and cholesterol biosynthesis pathway) and cholesterol-independent (through Rho GTPases) effects of statins on MS risk and MS severity, (2) the causal link between lipids (high-density lipoprotein cholesterol [HDL-C] and triglycerides [TG]) levels and MS risk and severity, and (3) the reverse causation between lipid fractions and MS risk. We used summary statistics from the Global Lipids Genetics Consortium (GLGC), eQTLGen Consortium, and the International MS Genetics Consortium (IMSGC) for lipids, expression quantitative trait loci, and MS, respectively (GLGC: n = 188,577; eQTLGen: n = 31,684; IMSGC (MS risk): n = 41,505; IMSGC (MS severity): n = 7,069).

RESULTS

The results of MR using the inverse-variance weighted method show that genetically predicted RAC2, a member of cholesterol-independent pathway (OR 0.86 [95% CI 0.78-0.95], p-value 3.80E-03), is implicated causally in reducing MS risk. We found no evidence for the causal role of LDL-C and the member of cholesterol biosynthesis pathway on MS risk. The MR results also show that lifelong higher HDL-C (OR 1.14 [95% CI 1.04-1.26], p-value 7.94E-03) increases MS risk but TG was not. Furthermore, we found no evidence for the causal role of lipids and genetically mimicked statins on MS severity. There is no evidence of reverse causation between MS risk and lipids.

DISCUSSION

Evidence from this study suggests that RAC2 is a genetic modifier of MS risk. Because RAC2 has been reported to mediate some of the pleiotropic effects of statins, we suggest that statins may reduce MS risk through a cholesterol-independent pathway (that is, RAC2-related mechanism(s)). MR analyses also support a causal effect of HDL-C on MS risk.

Uncovering the complex relationship between balding, testosterone and skin cancers in men

Male-pattern baldness (MPB) is related to dysregulation of androgens such as testosterone. A previously observed relationship between MPB and skin cancer may be due to greater exposure to ultraviolet radiation or indicate a role for androgenic pathways in the pathogenesis of skin cancers. We dissected this relationship via Mendelian randomization (MR) analyses, using genetic data from recent male-only meta-analyses of cutaneous melanoma (12,232 cases; 20,566 controls) and keratinocyte cancers (KCs) (up to 17,512 cases; >100,000 controls), followed by stratified MR analysis by body-sites. We found strong associations between MPB and the risk of KC, but not with androgens, and multivariable models revealed that this relationship was heavily confounded by MPB single nucleotide polymorphisms involved in pigmentation pathways. Site-stratified MR analyses revealed strong associations between MPB with head and neck squamous cell carcinoma and melanoma, suggesting that sun exposure on the scalp, rather than androgens, is the main driver. Men with less hair covering likely explains, at least in part, the higher incidence of melanoma in men residing in countries with high ambient UV.

A Bidirectional Mendelian Randomization Study of Sarcopenia-Related Traits and Knee Osteoarthritis

Background

With the development of population aging worldwide, sarcopenia and knee osteoarthritis (KOA), two age-related diseases, will continue to impose increasing medical and economic burdens on the society. Previous studies have discovered an association between the two, but the causality remains controversial, and it is difficult to eliminate confounding factors. Therefore, a Mendelian randomization (MR) study was conducted to overcome these confounding factors and investigate the causal relationship between sarcopenia and KOA.

Objective

The present work focused on assessing the causality between KOA and sarcopenia, so as to provide new strategies to prevent and treat these two conditions in clinic.

Methods

We registered the title with PROSPERO (ID: CRD42023421096). The two-sample bidirectional MR analysis was conducted in two steps, with sarcopenia being the exposure whereas KOA being the outcome in the first step, and vice versa in the second step. Genome-wide association studies (GWAS) data on low hand-grip strength (n=256,523), walking pace (n=459,915), appendicular lean mass (ALM, n=450,243), and KOA (n=403,124) were obtained from the UK Biobank. Methods such as the inverse variance weighted (IVW) and weighted median were utilized for assessing the causality of KOA with sarcopenia, and sensitivity analyses were also conducted.

Results

In the main MR analysis using the IVW method, evidence suggested that low hand-grip strength, walking pace, and ALM had adverse effects on KOA (p-value 0.0001, odds ratio (OR) 1.4569, 95% confidence interval (CI) 1.2007-1.7677 for low hand-grip strength; p-value 0.0003, OR 1.1500, 95% CI 1.050-1.183 for ALM; p-value 5.29E-19, OR 0.0932, 95% CI 0.0553-0.1572 for walking pace). However, there was no causality of KOA with sarcopenia in the opposite direction.

Conclusion

Our study suggests an obvious unidirectional causality of KOA with sarcopenia, and supports the notion that patients with sarcopenia are more susceptible to the development of KOA.

Type 2 Diabetes and Atrial Fibrillation: Evaluating Causal and Pleiotropic Pathways Using Mendelian Randomization

Background Observational associations between type 2 diabetes (T2D) and atrial fibrillation (AF) have been established, but causality remains undetermined. We performed Mendelian randomization (MR) to study causal effects of genetically predicted T2D on AF risk, independent of cardiometabolic risk factors. Methods and Results Instrumental variables included 182 uncorrelated single nucleotide polymorphisms associated with T2D at genome-wide significance (P <5×10-8). Genetic association estimates for cardiometabolic exposures were obtained from genome-wide association studies including 188 577 individuals for low-density lipoprotein-C, 694 649 individuals for body mass index, and 757 601 for systolic blood pressure. Two-sample, inverse-variance weighted MR formed the primary analyses. The MR-TRYX approach was used to dissect potential pleiotropic pathways, with multivariable MR performed to investigate cardiometabolic mediation. Genetically predicted T2D associated with increased AF liability in univariable MR (odds ratio [OR], 1.08 [95% CI, 1.02-1.13], P=0.003). Sensitivity analyses indicated potential pleiotropy, with radial MR identifying 4 outlier single nucleotide polymorphisms that were likely contributors. Phenomic scanning on MR-base and subsequent least absolute shrinkage and selection operator regression allowed prioritization of 7 candidate traits. The outlier-adjusted effect estimate remained consistent with the original inverse-variance weighted estimate (OR, 1.07 [95% CI, 1.02-1.12], P=0.008). On multivariable MR, T2D remained associated with increased AF liability after adjustment for low-density lipoprotein-C and body mass index. Following adjustment for systolic blood pressure, the relationship between T2D and AF became nonsignificant (OR, 1.04 [95% CI, 0.95-1.13], P=0.40). Conclusions These data provide novel genetic evidence that while T2D likely causally associates with AF, mediation via systolic blood pressure exists. Endeavoring to lower systolic blood pressure alongside achieving normoglycemia may provide particular benefit on AF risk in patients with T2D.

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.

The associations between modifiable risk factors and nonalcoholic fatty liver disease: A comprehensive Mendelian randomization study

BACKGROUND AND AIMS

Early identification of modifiable risk factors is essential for the prevention of nonalcoholic fatty liver disease (NAFLD). We aimed to systematically explore the relationships between genetically predicted modifiable risk factors and NAFLD.

APPROACH AND RESULTS

We applied univariable and multivariable Mendelian randomization analyses to explore the relationships between 35 modifiable risk factors and NAFLD. We also evaluated the combined results in three independent large genome-wide association studies. Genetically predicted alcohol frequency, elevated serum levels of liver enzymes, triglycerides, C-reactive protein, and obesity traits, including body mass index, waist circumference, and body fat mass, were associated with increased risks of NAFLD (all with p  < 0.05). Poor physical condition had a suggestive increased risk for NAFLD (odds ratio [OR] = 2.63, p  = 0.042). Genetically instrumented type 2 diabetes (T2DM), hypothyroidism, and hypertension all increased the risk for NAFLD, and the ORs (95% confidence interval) were 1.508 (1.20-1.90), 13.08 (1.53-111.65), and 3.11 (1.33-7.31) for a 1-U increase in log-transformed odds, respectively. The positive associations of T2DM and hypertension with NAFLD remained significant in multivariable analyses. The combined results from the discovery and two replication datasets further confirmed that alcohol frequency, elevated serum liver enzymes, poor physical condition, obesity traits, T2DM, and hypertension significantly increase the risk of NAFLD, whereas higher education and high-density lipoprotein cholesterol (HDL-cholesterol) could lower NAFLD risk.

CONCLUSIONS

Genetically predicted alcohol frequency, elevated serum liver enzymes, poor physical condition, obesity traits, T2DM, and hypertension were associated with an increased risk of NAFLD, whereas higher education and HDL-cholesterol were associated with a decreased risk of NAFLD.

Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility

Identification of protein quantitative trait loci (pQTL) helps understand the underlying mechanisms of diseases and discover promising targets for pharmacological intervention. For most important class of drug targets, genetic evidence needs to be generalizable to diverse populations. Given that the majority of the previous studies were conducted in European ancestry populations, little is known about the protein-associated genetic variants in East Asians. Based on data-independent acquisition mass spectrometry technique, we conduct genome-wide association analyses for 304 unique proteins in 2,958 Han Chinese participants. We identify 195 genetic variant-protein associations. Colocalization and Mendelian randomization analyses highlight 60 gene-protein-phenotype associations, 45 of which (75%) have not been prioritized in Europeans previously. Further cross-ancestry analyses uncover key proteins that contributed to the differences in the obesity-induced diabetes and coronary artery disease susceptibility. These findings provide novel druggable proteins as well as a unique resource for the trans-ancestry evaluation of protein-targeted drug discovery.

Reassessing the causal role of obesity in breast cancer susceptibility: a comprehensive multivariable Mendelian randomization investigating the distribution and timing of exposure

BACKGROUND

Previous Mendelian randomization (MR) studies on obesity and risk of breast cancer adopted a small number of instrumental variables and focused mainly on the crude total effect. We aim to investigate the independent causal effect of obesity on breast cancer susceptibility, considering the distribution of fat, covering both early and late life.

METHODS

Using an enlarged set of female-specific genetic variants associated with adult general [body mass index (BMI)] and abdominal obesity [waist-to-hip ratio (WHR) with and without adjustment for BMI, WHR and WHRadjBMI] as well as using sex-combined genetic variants of childhood obesity (childhood BMI), we performed a two-sample univariable MR to re-evaluate the total effect of each obesity-related exposure on overall breast cancer (Ncase = 133 384, Ncontrol = 113 789). We further looked into its oestrogen receptor (ER)-defined subtypes (NER+ = 69 501, NER- = 21 468, Ncontrol = 105 974). Multivariable MR was applied to estimate the independent causal effect of each obesity-related exposure on breast cancer taking into account confounders as well as to investigate the independent effect of adult and childhood obesity considering their inter-correlation.

RESULTS

In univariable MR, the protective effects of both adult BMI [odds ratio (OR) = 0.89, 95% CI = 0.83-0.96, P = 2.06 × 10-3] and childhood BMI (OR = 0.78, 95% CI = 0.70-0.87, P = 4.58 × 10-6) were observed for breast cancer overall. Comparable effects were found in ER+ and ER- subtypes. Similarly, genetically predicted adult WHR was also associated with a decreased risk of breast cancer overall (OR = 0.87, 95% CI = 0.80-0.96, P = 3.77 × 10-3), restricting to ER+ subtype (OR = 0.88, 95% CI = 0.80-0.98, P = 1.84 × 10-2). Conditional on childhood BMI, the effect of adult general obesity on breast cancer overall attenuated to null (BMI: OR = 1.00, 95% CI = 0.90-1.10, P = 0.96), whereas the effect of adult abdominal obesity attenuated to some extent (WHR: OR = 0.90, 95% CI = 0.82-0.98, P = 1.49 × 10-2; WHRadjBMI: OR = 0.92, 95% CI = 0.86-0.99, P = 1.98 × 10-2). On the contrary, an independent protective effect of childhood BMI was observed in breast cancer overall, irrespective of adult measures (adjusted for adult BMI: OR = 0.84, 95% CI = 0.77-0.93, P = 3.93 × 10-4; adjusted for adult WHR: OR = 0.84, 95% CI = 0.76-0.91, P = 6.57 × 10-5; adjusted for adult WHRadjBMI: OR = 0.80, 95% CI = 0.74-0.87, P = 1.24 × 10-7).

CONCLUSION

Although successfully replicating the inverse causal relationship between adult obesity-related exposures and risk of breast cancer, our study demonstrated such effects to be largely (adult BMI) or partly (adult WHR or WHRadjBMI) attributed to childhood obesity. Our findings highlighted an independent role of childhood obesity in affecting the risk of breast cancer as well as the importance of taking into account the complex interplay underlying correlated exposures.

Association of gut microbiome and primary liver cancer: A two-sample Mendelian randomization and case-control study

BACKGROUND AND AIMS

Observational epidemiology studies suggested a relationship between the gut microbiome and primary liver cancer. However, the causal relationship remains unclear because of confounding factors and reverse causality. We aimed to explore the causal role of the gut microbiome in the development of primary liver cancer, including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).

METHODS

Mendelian randomization (MR) study was conducted using summary statistics from genome-wide association studies (GWAS) of the gut microbiome and liver cancer, and sequencing data from a case-control study validated the findings. A 5-cohort GWAS study in Germany (N = 8956) served as exposure, whilst the UK biobank GWAS study (N = 456 348) served as an outcome. The case-control study was conducted at the First Affiliated Hospital of Wenzhou Medical University from December 2018 to October 2020 and included 184 HCC patients, 63 ICC patients and 40 healthy controls.

RESULTS

A total of 57 features were available for MR analysis, and protective causal associations were identified for Family_Ruminococcaceae (OR = 0.46 [95% CI, 0.26-0.82]; p = .009) and Genus_Porphyromonadaceae (OR = 0.59 [95% CI, 0.42-0.83]; p = .003) with HCC, and for Family_Porphyromonadaceae (OR = 0.36 [95% CI, 0.14-0.94]; p = .036) and Genus_Bacteroidetes (OR = 0.55 [95% CI, 0.34-0.90]; p = .017) with ICC respectively. The case-control study results showed that the healthy controls had a higher relative abundance of Family_Ruminococcaceae (p = .00033), Family_Porphyromonadaceae (p = .0055) and Genus_Bacteroidetes (p = .021) than the liver cancer patients.

CONCLUSIONS

This study demonstrates that Ruminococcaceae, Porphyromonadaceae and Bacteroidetes are related to a reduced risk of liver cancer (HCC or ICC), suggesting potential significance for the prevention and control of liver cancer.

Chickenpox and multiple sclerosis: A Mendelian randomization study

Observational studies have suggested a suspected association between varicella-zoster virus (VZV) infection and multiple sclerosis (MS), but the connection has remained unclear. The aim of the present study is to evaluate the causal relationship between chickenpox which is caused by VZV infection and MS. We performed a two-sample Mendelian randomization analysis to investigate the association of chickenpox with MS using summary statistics from genome-wide association studies (GWAS). The GWAS summary statistics data for chickenpox was from the 23andMe cohort including 107 769 cases and 15 982 controls. A large summary of statistical data from the International Multiple Sclerosis Genetics Consortium (IMSGC) was used as the outcome GWAS data set, including 14 802 MS cases and 26 703 controls. We found evidence of a significant association between genetically predicted chickenpox and risk of MS (odds ratio [OR] = 35.27, 95% confidence interval [CI] = 22.97-54.17, p = 1.46E-59). Our findings provided evidence indicating a causal effect of chickenpox on MS. Further elucidations of this association and underlying mechanisms are needed for identifying feasible interventions to promote MS prevention.

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

Understanding the assumptions underlying Mendelian randomization

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.

The role of body fat in multiple sclerosis susceptibility and severity: A Mendelian randomisation study

OBJECTIVE

The objective of this study was to explore the potential causal associations of body mass index, height, weight, fat mass, fat percentage and non-fat mass in the whole body, arms, legs and trunk (henceforth, 'anthropometric measures') with multiple sclerosis (MS) risk and severity. We also investigated the potential for reverse causation between anthropometric measures and MS risk.

METHODS

We conducted a two-sample univariable, multivariable and bidirectional Mendelian randomisation (MR) analysis.

RESULTS

A range of features linked to obesity (body mass index, weight, fat mass and fat percentage) were risk factors for MS development and worsened the disease's severity in MS patients. Interestingly, we were able to demonstrate that height and non-fat mass have no association with MS risk or MS severity. We demonstrated that the association between anthropometric measures and MS is not subject to bias from reverse causation.

CONCLUSIONS

Our findings provide evidence from human genetics that a range of features linked to obesity is an important contributor to MS development and MS severity, but height and non-fat mass are not. Importantly, these findings also identify a potentially modifiable factor that may reduce the accumulation of further disability and ameliorate MS severity.

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.

Enhancer promoter interactome and Mendelian randomization identify network of druggable vascular genes in coronary artery disease

Coronary artery disease (CAD) is a multifactorial disorder, which is partly heritable. Herein, we implemented a mapping of CAD-associated candidate genes by using genome-wide enhancer-promoter conformation (H3K27ac-HiChIP) and expression quantitative trait loci (eQTL). Enhancer-promoter anchor loops from human coronary artery smooth muscle cells (HCASMC) explained 22% of the heritability for CAD. 3D enhancer-promoter genome mapping of CAD-genes in HCASMC was enriched in vascular eQTL genes. By using colocalization and Mendelian randomization analyses, we identified 58 causal candidate vascular genes including some druggable targets (MAP3K11, CAMK1D, PDGFD, IPO9 and CETP). A network analysis of causal candidate genes was enriched in TGF beta and MAPK pathways. The pharmacologic inhibition of causal candidate gene MAP3K11 in vascular SMC reduced the expression of athero-relevant genes and lowered cell migration, a cardinal process in CAD. Genes connected to enhancers are enriched in vascular eQTL and druggable genes causally associated with CAD.

Shared Genetic Etiology between Cortical Brain Morphology and Tobacco, Alcohol, and Cannabis Use

Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression. We used genomic structural equation modeling to model a "common SUB genetic factor" and investigated its genetic overlap with brain structure. Furthermore, we estimated SUB polygenic risk scores (PRS) and examined whether they predicted brain imaging traits using the Adolescent Behavior and Cognitive Development (ABCD) study. We identified 8 significant negative genetic correlations, including between (1) alcoholic drinks per week and average cortical thickness, and (2) intracranial volume with age of smoking initiation. We observed 5 positive genetic correlations, including those between (1) insula surface area and lifetime cannabis use, and (2) the common SUB genetic factor and pericalcarine surface area. SUB PRS were associated with brain structure variation in ABCD. Our findings highlight a shared genetic etiology between cortical brain morphology and SUB and suggest that genetic variants associated with SUB may be causally related to brain structure differences.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

Two-Sample Multivariable Mendelian Randomization Analysis Using R

Mendelian randomization is a framework that uses measured variation in genes for assessing and estimating the causal effect of an exposure on an outcome. Multivariable Mendelian randomization is an extension that can assess the causal effect of multiple exposures on an outcome, and can be advantageous when considering a set (>1) of potentially correlated candidate risk factors in evaluating the causal effect of each on a health outcome, accounting for measured pleiotropy. This can be seen, for example, in determining the causal effects of lipids and cholesterol on type 2 diabetes risk, where the correlated risk factors share genetic predictors. Similar to univariate Mendelian randomization, multivariable Mendelian randomization can be conducted using two-sample summary-level data where the gene-exposure and gene-outcome associations are derived from separate samples from the same underlying population. Here, we present a protocol for conducting a two-sample multivariable Mendelian randomization study using the 'MVMR' package in R and summary-level genetic data. We also provide a protocol for searching and obtaining instruments using available data sources in the 'MRInstruments' R package. Finally, we provide general guidelines and discuss the utility of performing a multivariable Mendelian randomization analysis for simultaneously assessing causality of multiple exposures. © 2021 Wiley Periodicals LLC. Basic Protocol: Performing a two-sample multivariable Mendelian randomization analysis using the 'MVMR' package in R and summarized genetic data Support Protocol 1: Installing the 'MVMR' R package Support Protocol 2: Obtaining instruments from the 'MRInstruments' R package.

Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization

The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biomarkers, and identifying biomarkers that have a causal relationship with COVID-19 is imperative. To bridge these gaps, we aim to examine the causal effects of miRNAs on COVID-19 severity in this study using two-sample Mendelian randomization approaches. Multiple studies with available GWAS summary statistics data were retrieved. Using circulating miRNA expression data as exposure, and severe COVID-19 cases as outcomes, we identified ten unique miRNAs that showed causality across three phenotype groups of COVID-19. Using expression data from an independent study, we validated and identified two high-confidence miRNAs, namely, hsa-miR-30a-3p and hsa-miR-139-5p, which have putative causal effects on developing cases of severe COVID-19. Using existing literature and publicly available databases, the potential causative roles of these miRNAs were investigated. This study provides a novel way of utilizing miRNA eQTL data to help us identify potential miRNA biomarkers to make better and early diagnoses and risk assessments of severe COVID-19 cases.

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N= 297K). Cell type, disease progression, and cellular process programs captured distinct heritability signals even within the same cell type, as we show in multiple complex diseases that affect the brain (Alzheimer’s disease, multiple sclerosis), colon (ulcerative colitis) and lung (asthma, idiopathic pulmonary fibrosis, severe COVID-19). The inferred disease enrichments recapitulated known biology and highlighted novel cell-disease relationships, including GABAergic neurons in major depressive disorder (MDD), a disease progression M cell program in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease progression immune cell type programs were associated, whereas for epithelial cells, disease progression programs were most prominent, perhaps suggesting a role in disease progression over initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Machine learning applications for therapeutic tasks with genomics data

Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.

Genetic Contribution of Endometriosis to the Risk of Developing Hormone-Related Cancers

Endometriosis is a common gynecological disorder that has been associated with endometrial, breast and epithelial ovarian cancers in epidemiological studies. Since complex diseases are a result of multiple environmental and genetic factors, we hypothesized that the biological mechanism underlying their comorbidity might be explained, at least in part, by shared genetics. To assess their potential genetic relationship, we performed a two-sample mendelian randomization (2SMR) analysis on results from public genome-wide association studies (GWAS). This analysis confirmed previously reported genetic pleiotropy between endometriosis and endometrial cancer. We present robust evidence supporting a causal genetic association between endometriosis and ovarian cancer, particularly with the clear cell and endometrioid subtypes. Our study also identified genetic variants that could explain those associations, opening the door to further functional experiments. Overall, this work demonstrates the value of genomic analyses to support epidemiological data, and to identify targets of relevance in multiple disorders.

MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates

Motivation

Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for differential uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters.

Results

Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming methods that either do not account for uncertainty or do not include null and junk clusters. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A post hoc hypothesis-generating search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster.

Availability and implementation

MR-Clust can be downloaded from https://github.com/cnfoley/mrclust.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational Tools for Causal Inference in Genetics

The advent of large-scale, phenotypically rich, and readily accessible data provides an unprecedented opportunity for epidemiologists, statistical geneticists, bioinformaticians, and also behavioral and social scientists to investigate the causes and consequences of disease. Computational tools and resources are an integral component of such endeavors, which will become increasingly important as these data continue to grow exponentially. In this review, we have provided an overview of computational software and databases that have been developed to assist with analyses in causal inference. This includes online tools that can be used to help generate hypotheses, publicly accessible resources that store summary-level information for millions of genetic markers, and computational approaches that can be used to leverage this wealth of data to study causal relationships.

Caution against examining the role of reverse causality in Mendelian Randomization

Recently, Mendelian Randomization (MR) has gained in popularity as a concept to assess the causal relationship between phenotypes in genetic association studies. An extension of standard MR methodology, the MR Steiger approach, has recently been developed to infer the causal direction between two phenotypes in prospective studies. Through simulation studies, we examined and quantified the ability of the MR Steiger approach to determine the causal direction between two phenotypes (i.e., effect direction). Through simulation studies, our results show that the MR Steiger approach may fail to correctly identify the direction of causality. This is true, especially in the presence of pleiotropy. We also applied the MR Steiger method to the COPDGene study, a case-control study of chronic obstructive pulmonary disease (COPD) in current and former smokers, to examine the role of smoking on lung function. We have created an R package on Github called reverseDirection which runs simulations for user-specified scenarios to examine when the MR Steiger approach can correctly determine the causal direction between two phenotypes in any user specified scenario. In summary, our results emphasize the importance of caution when the MR Steiger approach is used in to infer the direction of causality.

Osteoarthritis: Insights Offered by the Study of Bone Mass Genetics

PURPOSE OF REVIEW

This paper reviews how bone genetics has contributed to our understanding of the pathogenesis of osteoarthritis. As well as identifying specific genetic mechanisms involved in osteoporosis which also contribute to osteoarthritis, we review whether bone mineral density (BMD) plays a causal role in OA development.

RECENT FINDINGS

We examined whether those genetically predisposed to elevated BMD are at increased risk of developing OA, using our high bone mass (HBM) cohort. HBM individuals were found to have a greater prevalence of OA compared with family controls and greater development of radiographic features of OA over 8 years, with predominantly osteophytic OA. Initial Mendelian randomisation analysis provided additional support for a causal effect of increased BMD on increased OA risk. In contrast, more recent investigation estimates this relationship to be bi-directional. However, both these findings could be explained instead by shared biological pathways. Pathways which contribute to BMD appear to play an important role in OA development, likely reflecting shared common mechanisms as opposed to a causal effect of raised BMD on OA. Studies in HBM individuals suggest this reflects an important role of mechanisms involved in bone formation in OA development; however further work is required to establish whether the same applies to more common forms of OA within the general population.

Impact of serum calcium levels on total body bone mineral density: A mendelian randomization study in five age strata

BACKGROUND & AIMS

Mendelian randomization (MR) studies have reported the causal association between serum calcium levels and bone mineral density (BMD). The results showed that genetically increased serum calcium levels in individuals with normal calcium levels did not increase BMD and could even reduce BMD. However, whether there are differences in the association between serum calcium and BMD in different age strata remains unclear.

METHODS

We selected eight serum calcium genetic variants with genome-wide significance (P < 5.00E-08) as the potential instrumental variables. We conducted an MR analysis to evaluate the impact of serum calcium levels on total body BMD in five age strata, 0-15, 15-30, 30-45, 45-60, and ≥60 years, using large-scale serum calcium (61,079 individuals) and total body BMD genome-wide association study (66,628 individuals) datasets. For pleiotropy analysis, we used a manual method and four common statistical methods, namely the MR-Egger intercept, MR-PRESSO, heterogeneity, and Steiger filtering tests. For MR analysis, we selected four MR methods, namely inverse-variance weighted, weighted median, MR-Egger, and MR-PRESSO. In addition to the univariable MR analysis, we conducted a multivariate MR analysis taking into account the effect of serum parathyroid hormone levels.

RESULTS

Univariable MR analysis using the inverse-variance weighted method indicated that per 0.5-mg/dL increase (about 1 standard deviation) in serum calcium levels was statistically significantly associated with reduced total body BMD only in the ≥60 years stratum (effect estimate (beta) = -0.545, 95% confidence interval (CI): -0.892 to -0.198, P = 0.002). The weighted median regression (beta = -0.446, 95% CI: -0.821 to -0.094, P = 1.40E-02) and MR-PRESSO (beta = -0.545, 95% CI: -0.892 to -0.198, P = 0.022) MR methods further supported this suggestive association. The multivariable MR analysis also found a significant association between increased serum calcium levels and reduced total body BMD in the ≥60 years stratum (beta = -0.547, 95% CI: -0.934 to -0.16, P = 0.006).

CONCLUSIONS

Our results provide genetic evidence that increased serum calcium levels did not improve BMD in the general population and that the elevated serum calcium levels in generally healthy populations, especially in adults older than 60 years, may even reduce the BMD. Our results are comparable with those of recent MR findings.

Exploring the causal roles of circulating remnant lipid profile on cardiovascular and cerebrovascular diseases: Mendelian randomization study

Background

Causal evidence of circulating lipids especially the remnant cholesterol with cardiovascular and cerebrovascular disease (CVD) is lacking. This research aimed to explore the causal roles of extensive lipid traits especially the remnant lipids in CVD.

Methods

Two-sample Mendelian randomization (TSMR) analysis was performed based on large-scale meta-analysis datasets in European ancestry. The causal effect of 15 circulating lipid profiles including 6 conventional lipids and 9 remnant lipids on coronary heart disease (CHD) and ischemic stroke (IS), as well as the subtypes, was assessed.

Results

Apolipoprotein B (Apo B), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) were still important risk factors for CHD and myocardial infarction (MI) but not for IS. Apo B is the strongest which increased the CHD and MI risk by 44% and 41%, respectively. The odds ratios (ORs) of total TG on CHD and MI were 1.25 (95% confidence interval [CI], 1.13–1.38) and 1.24 (95% CI, 1.11–1.38), respectively. A one standard deviation difference increased TG in medium very-low-density lipoproteins (M.VLDL.TG), TG in small VLDL (S.VLDL.TG), TG in very small VLDL (XS.VLDL.TG), TG in intermediate-density lipoproteins (IDL.TG), TG in very large HDL (XL.HDL.TG), and TG in small HDL (S.HDL.TG) particles also robustly increased the risk of CHD and MI by 9–28% and 9–27%, respectively. TG in very/extremely large VLDL (XXL.VLDL.TG and XL.VLDL.TG) were insignificant or even negatively associated with CHD (in multivariable TSMR), and negatively associated with IS as well.

Conclusion

The remnant lipids presented heterogeneity and two-sided effects for the risk of CHD and IS that may partially rely on the particle size. The findings suggested that the remnant lipids were required to be intervened according to specific components. This research confirms the importance of remnant lipids and provides causal evidence for potential targets for intervention.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Exploring the causal pathway from bilirubin to CVD and diabetes in the UK biobank cohort study: Observational findings and Mendelian randomization studies

BACKGROUND AND AIMS

Some studies reported that mildly elevated serum bilirubin levels were associated with decreased risk of cardiovascular disease (CVD) and diabetes. Whether these are causal relationships remains unclear. This study aims to examine the causal effects of bilirubin on CVD, diabetes and their subtypes.

METHODS

The data we used in this study includes individual data from the UK Biobank cohort with 331,002 white British participants, and summary data from published genome wide associations studies (GWAS) findings. We used individual data to perform logistic regression for the observational study and two-stage least squares method for the Mendelian randomization (MR) study. We also performed several traditional MR methods and MR-TRYX by summary data.

RESULTS

The observational study supported the association relationships between bilirubin and CVD and diabetes and their subtypes. Results of MR showed strong evidence for negative causal associations of log_e total bilirubin with CVD [OR 0.92, 95%CI 0.88-0.95, p-value 2.15 × 10^-6], coronary heart disease [OR 0.90, 95%CI 0.85-0.96, p-value 1.54 × 10^-3] and hypertensive diseases [OR 0.91, 95%CI 0.88-0.95, p-value 5.89 × 10^-6], but no evidence for diabetes [OR 0.94, 95%CI 0.86-1.02, p-value 0.14] and its subtypes. We also obtained similar results for direct bilirubin. We found that blood pressure, cholesterol, C-reactive protein, alcohol and white blood cell count played important roles in the causal pathway from bilirubin to CVD. Two sample MR and sensitivity analyses showed consistent results with one sample MR.

CONCLUSIONS

Genetically determined bilirubin was negatively associated with the risk of CVD but had no evident causal association with diabetes in the UK Biobank cohort of white British.