Empirical comparisons of multiple Mendelian randomization approaches in the presence of assortative mating

The effect of depression status on osteoarthritis: A powerful two-step Mendelian randomization study

Osteoarthritis (OA) is a common degenerative disease that affects millions of individuals worldwide.

OBJECTIVE

There is no conclusive epidemiological evidence regarding the relationship between OA, depression, and whole-body fat mass. In this study, we conducted a two-step Mendelian randomization analysis to determine the causal relationships between them.

DESIGN

The published summary-level data are from genome-wide association studies (GWAS). Our study included 357,957 samples and 10,828,862 SNPs. Finally, the outcome GWAS data for OA came from a GWAS on the genetic architecture of OA using UK Biobank data. This study included 50,508 samples and 15,845,511 SNPs. We used five different modes of analysis, including inverse variance weighted meta-analysis (IVW), MR-Egger regression, weighted median, simple mode, and weighted mode, to explore causal relationships.

RESULTS

We found a positive correlation between depression and body fat mass, with depression leading to body fat mass an increase in (IVW result: p = 3.39E-07, OR (95 % CI) =2.16 (1.61, 2.90)). We also found a positive correlation between body fat mass and OA, with body fat mass increasing the risk of OA (IVW result: p = 1.65E-33, OR (95 % CI) = 1.98 (1.77, 2.21). Body fat mass played an important role as a mediator in the causal relationship between depression and OA, with approximately 14 % of the risk of OA caused by depression being mediated by body fat mass.

CONCLUSIONS

Our study offers reliable evidence that depression has a detrimental impact on the risk of OA. Future research can support these associations from improving depressed effect, including social, biological, and behavioral factors, to reduce the risk of chronic diseases such as osteoarthritis. And we identified high-risk variation of alleles which associated with OA and depression can be used to predict disease and provide a basis for clinical intervention and treatment of OA.

Twin-based Mendelian Randomization Analyses Highlight Smoking's Effects on Blood DNA Methylation, with Putative Reverse Causation

Epigenome-wide association studies (EWAS) aim to identify differentially methylated loci associated with complex traits and disorders. EWAS of cigarette smoking shows some of the most widespread DNA methylation (DNAm) associations in blood. However, traditional EWAS cannot differentiate between causation and confounding, leading to ambiguity in etiological interpretations. Here, we apply an integrated approach combining Mendelian Randomization and twin-based Direction-of-Causation analyses (MR-DoC) to examine causality underlying smoking-associated blood DNAm changes in the Netherlands Twin Register (N=2577). Evidence across models suggests that current smoking's causal effects on DNAm likely drive many of the previous EWAS findings, implicating functional pathways relevant to several adverse health outcomes of smoking, including hemopoiesis, cell- and neuro-development, and immune regulation. Additionally, we find evidence of potential reverse causal influences at some DNAm sites, with 17 of these sites enriched for gene regulatory functional elements in the brain. The top three sites with evidence of DNAm's effects on smoking annotate to genes involved in G protein-coupled receptor signaling (GNG7, RGS3) and innate immune response (SLC15A4), elucidating potential biological risk factors for smoking. This study highlights the utility of integrating genotypic and DNAm measures in twin cohorts to clarify the causal relationships between health behaviors and blood DNAm.

A General Approach to Adjusting Genetic Studies for Assortative Mating

The effects of assortative mating (AM) on estimates from genetic studies has been receiving increasing attention in recent years. We extend existing AM theory to more general models of sorting and conclude that correct theory-based AM adjustments require knowledge of complicated, unknown historical sorting patterns. We propose a simple, general-purpose approach using polygenic indexes (PGIs). Our approach can estimate the fraction of genetic variance and genetic correlation that is driven by AM. Our approach is less effective when applied to Mendelian randomization (MR) studies for two reasons: AM can induce a form of selection bias in MR studies that remains after our adjustment; and, in the MR context, the adjustment is particularly sensitive to PGI estimation error. Using data from the UK Biobank, we find that AM inflates genetic correlation estimates between health traits and education by 14% on average. Our results suggest caution in interpreting genetic correlations or MR estimates for traits subject to AM.

Causal Influences of Same-Sex Attraction on Psychological Distress and Risky Sexual Behaviors: Evidence for Bidirectional Effects

Although health disparities among same-sex attracted compared to heterosexual individuals are typically explained by minority stress, there is limited evidence for a causal effect. This study investigated whether same-sex attraction was causally associated with psychological distress and risky sexual behavior using sociosexual behavior as a proxy. The sample comprised monozygotic and dizygotic twins and their non-twin siblings (n = 2036, 3780 and 2356, respectively) genotyped and assessed for same-sex attraction, psychological distress (anxiety and depressive symptoms), and risky sexual behavior. Causal influences were investigated with same-sex attraction as the predictor and psychological distress and risky sexual behavior as the outcomes in two separate Mendelian Randomization-Direction of Causation (MRDoC) models using OpenMx in R. The MRDoC model improves on the Mendelian Randomization and Direction of Causation twin models by allowing analyses of variables with similar genetic architectures, incorporating polygenic scores as instrumental variables and specifying pleiotropy and residual covariance. There were significant causal influences flowing from same-sex attraction to psychological distress and risky sexual behavior (standardized coefficients = 0.13 and 0.16; 95% CIs 0.03-0.23 and 0.08-0.25, respectively). Further analyses also demonstrated causal influences flowing from psychological distress and risky sexual behavior toward same-sex attraction. Causal influences from same-sex attraction to psychological distress and risky sexual behavior may reflect minority stress, which reinforces ongoing measures to minimize social disparities. Causal influences flowing in the opposite direction may reflect rejection sensitivity, stigma-inducing outcomes of risky sexual behavior, and recall bias; however, further research is required to specifically investigate these processes.

Bidirectional Causal Associations Between Same-Sex Attraction and Psychological Distress: Testing Moderation and Mediation Effects

Only one study has examined bidirectional causality between sexual minority status (having same-sex attraction) and psychological distress. We combined twin and genomic data from 8700 to 9700 participants in the UK Twins Early Development Study cohort at ≈21 years to replicate and extend these bidirectional causal effects using separate unidirectional Mendelian Randomization-Direction of Causation models. We further modified these models to separately investigate sex differences, moderation by childhood factors (retrospectively-assessed early-life adversity and prospectively-assessed childhood gender nonconformity), and mediation by victimization. All analyses were carried out in OpenMx in R. Same-sex attraction causally influenced psychological distress with significant reverse causation (beta = 0.19 and 0.17; 95% CIs = 0.09, 0.29 and 0.08, 0.25 respectively) and no significant sex differences. The same-sex attraction → psychological distress causal path was partly mediated by victimization (12.5%) while the reverse causal path was attenuated by higher childhood gender nonconformity (moderation coefficient = -0.09, 95% CI: -0.13, -0.04).

Micronutrients and risks of three main urologic cancers: A mendelian randomization study

Background

The effect of micronutrients on urologic cancers has been explored in observational studies. We conducted the two-sample mendelian randomization (TSMR) study to investigate whether micronutrients could causally influence the risk of urologic cancers.

Methods

Summary statistics for four micronutrients and three main urologic cancers outcomes were obtained from genome-wide association studies (GWAS). MR analyses were applied to explore the potential causal association between them. Sensitivity analyses using multiple methods were also conducted.

Results

Genetically predicted one SD increase in serum copper and iron concentrations was causally associated with increased risks of renal cell carcinoma (RCC) (OR = 3.021, 95%CI = 2.204-4.687, P < 0.001, male; OR = 2.231, 95%CI = 1.524-3.953, P < 0.001, female; OR = 1.595, 95%CI = 1.310-1.758, P = 0.0238, male; OR = 1.484, 95%CI = 1.197-2.337, P = 0.0210, female, respectively) and per SD increase in serum zinc levels was related to decreased risks of RCC (OR = 0.131, 95%CI = 0.0159-0.208, P < 0.001, male; OR = 0.124, 95%CI = 0.0434-0.356, P < 0.001, female). No significant results were observed between micronutrients and the risk of bladder cancer after Bonferroni correction. Additionally, per SD increase in serum zinc level was associated with a 5.8% higher risk of prostate cancer (PCa) [OR = 1.058, 95%CI = 1.002-1.116, P = 0.0403, inverse-variance weight (IVW)].

Conclusions

Micronutrients play a vital role in the development of urological tumors. Future studies are required to replicate the findings, explore the underlying mechanisms, and examine the preventive or therapeutic role of micronutrients in clinical settings.

Mendelian randomization

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.

Causal Associations Between Educational Attainment and 14 Urological and Reproductive Health Outcomes: A Mendelian Randomization Study

Background: The impact of educational attainment (EA) on multiple urological and reproductive health outcomes has been explored in observational studies. Here we used Mendelian randomization (MR) to investigate whether EA has causal effects on 14 urological and reproductive health outcomes.

Methods: We obtained summary statistics for EA and 14 urological and reproductive health outcomes from genome-wide association studies (GWAS). MR analyses were applied to explore the potential causal association between EA and them. Inverse variance weighted was the primary analytical method.

Results: Genetically predicted one standard deviation (SD) increase in EA was causally associated with a higher risk of prostate cancer [odds ratio (OR) 1.14, 95% confidence interval (CI) 1.05–1.25, P = 0.003] and a reduced risk of kidney stone (OR 0.73, 95% CI 0.62–0.87, P < 0.001) and cystitis (OR 0.76, 95% CI 0.67–0.86, P < 0.001) after Bonferroni correction. EA was also suggestively correlated with a lower risk of prostatitis (OR 0.76, 95% CI 0.59–0.98, P = 0.037) and incontinence (OR 0.64, 95% CI 0.47–0.87, P = 0.004). For the bioavailable testosterone levels and infertility, sex-specific associations were observed, with genetically determined increased EA being related to higher levels of testosterone in men (β 0.07, 95% CI 0.04–0.10, P < 0.001), lower levels of testosterone in women (β −0.13, 95% CI−0.16 to−0.11, P < 0.001), and a lower risk of infertility in women (OR 0.74, 95% CI 0.64–0.86, P < 0.001) but was not related to male infertility (OR 0.79, 95% CI 0.52–1.20, P = 0.269) after Bonferroni correction. For bladder cancer, kidney cancer, testicular cancer, benign prostatic hyperplasia, and erectile dysfunction, no causal effects were observed.

Conclusions: EA plays a vital role in urological diseases, especially in non-oncological outcomes and reproductive health. These findings should be verified in further studies when GWAS data are sufficient.

Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain

Osteoarthritis (OA) and major depression (MD) are two debilitating disorders that frequently co-occur and affect millions of the elderly each year. Despite the greater symptom severity, poorer clinical outcomes, and increased mortality of the comorbid conditions, we have a limited understanding of their etiologic relationships. In this study, we conducted the first cross-disorder investigations of OA and MD, using genome-wide association data representing over 247K cases and 475K controls. Along with significant positive genome-wide genetic correlations (r g = 0.299 ± 0.026, p = 9.10 × 10-31), Mendelian randomization (MR) analysis identified a bidirectional causal effect between OA and MD (βOA → MD = 0.09, SE = 0.02, z-score p-value < 1.02 × 10-5; βMD → OA = 0.19, SE = 0.026, p < 2.67 × 10-13), indicating genetic variants affecting OA risk are, in part, shared with those influencing MD risk. Cross-disorder meta-analysis of OA and MD identified 56 genomic risk loci (P meta ≤ 5 × 10-8), which show heightened expression of the associated genes in the brain and pituitary. Gene-set enrichment analysis highlighted "mechanosensory behavior" genes (GO:0007638; P gene_set = 2.45 × 10-8) as potential biological mechanisms that simultaneously increase susceptibility to these mental and physical health conditions. Taken together, these findings show that OA and MD share common genetic risk mechanisms, one of which centers on the neural response to the sensation of mechanical stimulus. Further investigation is warranted to elaborate the etiologic mechanisms of the pleiotropic risk genes, as well as to develop early intervention and integrative clinical care of these serious conditions that disproportionally affect the aging population.

Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being

Resilience and well-being are strongly related. People with higher levels of well-being are more resilient after stressful life events or trauma and vice versa. Less is known about the underlying sources of overlap and causality between the constructs. In a sample of 11.304 twins and 2.572 siblings from the Netherlands Twin Register, we investigated the overlap and possible direction of causation between resilience (i.e. the absence of psychiatric symptoms despite negative life events) and well-being (i.e. satisfaction with life) using polygenic score (PGS) prediction, twin-sibling modelling, and the Mendelian Randomization Direction of Causality (MR-DoC) model. Longitudinal twin-sibling models showed significant phenotypic correlations between resilience and well-being (.41/.51 at time 1 and 2). Well-being PGS were predictive for both well-being and resilience, indicating that genetic factors influencing well-being also predict resilience. Twin-sibling modeling confirmed this genetic correlation (0.71) and showed a strong environmental correlation (0.93). In line with causality, both genetic (51%) and environmental (49%) factors contributed significantly to the covariance between resilience and well-being. Furthermore, the results of within-subject and MZ twin differences analyses were in line with bidirectional causality. Additionally, we used the MR-DoC model combining both molecular and twin data to test causality, while correcting for pleiotropy. We confirmed the causal effect from well-being to resilience, with the direct effect of well-being explaining 11% (T1) and 20% (T2) of the variance in resilience. Data limitations prevented us to test the directional effect from resilience to well-being with the MR-DoC model. To conclude, we showed a strong relation between well-being and resilience. A first attempt to quantify the direction of this relationship points towards a bidirectional causal effect. If replicated, the potential mutual effects can have implications for interventions to lower psychopathology vulnerability, as resilience and well-being are both negatively related to psychopathology.