Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. Methods: We performed genome-wide association studies (GWAS) for subsequent major adverse cardiovascular events (MACE) (Ncases=51,929, Ncntrl=39,980) and subsequent arterial ischemic stroke (AIS) Ncases=45,120, Ncntrl=46,789) after first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (pQTLs) to determine the effect of 1,463 plasma protein abundances on subsequent MACE using Mendelian randomization (MR). Results: Two variants were significantly associated with subsequent cardiovascular events: rs76472767 (OR=0.75, 95% CI = 0.64-0.85, p= 3.69×10-08) with subsequent AIS and rs13294166 (OR=1.52, 95% CI = 1.37-1.67, p=3.77×10-08) with subsequent MACE. Using MR, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 (effect OR= 0.77, 95% CI = 0.66-0.88, adj. p=0.05), and TNFRSF14 (effect OR=1.42, 95% CI = 1.24-1.60, adj. p=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. Conclusions: We found evidence that two proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Age-specific effects of weight-based body size on fracture risk in later life: a lifecourse Mendelian randomisation study.

Musculoskeletal conditions, including fractures, can have severe and long-lasting consequences. Higher body mass index in adulthood is widely acknowledged to be protective for most fracture sites. However, sources of bias induced by confounding factors may have distorted previous findings. Employing a lifecourse Mendelian randomisation (MR) approach by using genetic instruments to separate effects at different life stages, this investigation aims to explore how prepubertal and adult body size independently influence fracture risk in later life.Using data from a large prospective cohort, univariable and multivariable MR were conducted to simultaneously estimate the effects of age-specific genetic proxies for body size (n = 453,169) on fracture risk (n = 416,795). A two-step MR framework was additionally applied to elucidate potential mediators. Univariable and multivariable MR indicated strong evidence that higher body size in childhood reduced fracture risk (OR, 95% CI: 0.89, 0.82 to 0.96, P = 0.005 and 0.76, 0.69 to 0.85, P = 1 × 10- 6, respectively). Conversely, higher body size in adulthood increased fracture risk (OR, 95% CI: 1.08, 1.01 to 1.16, P = 0.023 and 1.26, 1.14 to 1.38, P = 2 × 10- 6, respectively). Two-step MR analyses suggested that the effect of higher body size in childhood on reduced fracture risk was mediated by its influence on higher estimated bone mineral density (eBMD) in adulthood.This investigation provides novel evidence that higher body size in childhood reduces fracture risk in later life through its influence on increased eBMD. From a public health perspective, this relationship is complex since obesity in adulthood remains a major risk factor for co-morbidities. Results additionally indicate that higher body size in adulthood is a risk factor for fractures. Protective effect estimates previously observed are likely attributed to childhood effects.

Lowering of Circulating Sclerostin May Increase Risk of Atherosclerosis and Its Risk Factors: Evidence From a Genome-Wide Association Meta-Analysis Followed by Mendelian Randomization.

OBJECTIVE: In this study, we aimed to establish the causal effects of lowering sclerostin, target of the antiosteoporosis drug romosozumab, on atherosclerosis and its risk factors. METHODS: A genome-wide association study meta-analysis was performed of circulating sclerostin levels in 33,961 European individuals. Mendelian randomization (MR) was used to predict the causal effects of sclerostin lowering on 15 atherosclerosis-related diseases and risk factors. RESULTS: We found that 18 conditionally independent variants were associated with circulating sclerostin. Of these, 1 cis signal in SOST and 3 trans signals in B4GALNT3, RIN3, and SERPINA1 regions showed directionally opposite signals for sclerostin levels and estimated bone mineral density. Variants with these 4 regions were selected as genetic instruments. MR using 5 correlated cis-SNPs suggested that lower sclerostin increased the risk of type 2 diabetes mellitus (DM) (odds ratio [OR] 1.32 [95% confidence interval (95% CI) 1.03-1.69]) and myocardial infarction (MI) (OR 1.35 [95% CI 1.01-1.79]); sclerostin lowering was also suggested to increase the extent of coronary artery calcification (CAC) (ß = 0.24 [95% CI 0.02-0.45]). MR using both cis and trans instruments suggested that lower sclerostin increased hypertension risk (OR 1.09 [95% CI 1.04-1.15]), but otherwise had attenuated effects. CONCLUSION: This study provides genetic evidence to suggest that lower levels of sclerostin may increase the risk of hypertension, type 2 DM, MI, and the extent of CAC. Taken together, these findings underscore the requirement for strategies to mitigate potential adverse effects of romosozumab treatment on atherosclerosis and its related risk factors.

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

Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.

A Genome-Wide Association Study Meta-Analysis of Alpha Angle Suggests Cam-Type Morphology May Be a Specific Feature of Hip Osteoarthritis in Older Adults.

OBJECTIVE: To examine the genetic architecture of cam morphology using alpha angle (AA) as a proxy measure and conduct an AA genome-wide association study (GWAS) followed by Mendelian randomization (MR) to evaluate its causal relationship with hip osteoarthritis (OA). METHODS: Observational analyses examined associations between AA measurements derived from hip dual x-ray absorptiometry (DXA) scans from the UK Biobank study and radiographic hip OA outcomes and subsequent total hip replacement. Following these analyses, an AA GWAS meta-analysis was performed (N = 44,214) using AA measurements previously derived in the Rotterdam Study. Linkage disequilibrium score regression assessed the genetic correlation between AA and hip OA. Genetic associations considered significant (P < 5 × 10-8 ) were used as AA genetic instrument for 2-sample MR analysis. RESULTS: DXA-derived AA showed expected associations between AA and radiographic hip OA (adjusted odds ratio [OR] 1.63 [95% confidence interval (95% CI) 1.58, 1.67]) and between AA and total hip replacement (adjusted hazard ratio 1.45 [95% CI 1.33, 1.59]) in the UK Biobank study cohort. The heritability of AA was 10%, and AA had a moderate genetic correlation with hip OA (rg  = 0.26 [95% CI 0.10, 0.43]). Eight independent genetic signals were associated with AA. Two-sample MR provided weak evidence of causal effects of AA on hip OA risk (inverse variance weighted OR 1.84 [95% CI 1.14, 2.96], P = 0.01). In contrast, genetic predisposition for hip OA had stronger evidence of a causal effect on increased AA (inverse variance weighted ß = 0.09 [95% CI 0.04, 0.13], P = 4.58 × 10-5 ). CONCLUSION: Expected observational associations between AA and related clinical outcomes provided face validity for the DXA-derived AA measurements. Evidence of bidirectional associations between AA and hip OA, particularly for risk of hip OA on AA, suggests that hip shape modeling secondary to a genetic predisposition to hip OA contributes to the well-established relationship between hip OA and cam morphology in older adults.

A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field.

Mendelian randomization (MR) is an increasingly popular component of an epidemiologist's toolkit, used to provide evidence of a causal effect of one trait (an exposure, eg, body mass index [BMI]) on an outcome trait or disease (eg, osteoarthritis). Identifying these effects is important for understanding disease etiology and potentially identifying targets for therapeutic intervention. MR uses genetic variants as instrumental variables for the exposure, which should not be influenced by the outcome or confounding variables, overcoming key limitations of traditional epidemiological analyses. For MR to generate a valid estimate of effect, key assumptions must be met. In recent years, there has been a rapid rise in MR methods that aim to test, or are robust to violations of, these assumptions. In this review, we provide an overview of MR for a non-expert audience, including an explanation of these key assumptions and how they are often tested, to aid a better reading and understanding of the MR literature. We highlight some of these new methods and how they can be useful for specific methodological challenges in the musculoskeletal field, including for conditions or traits that share underlying biological pathways, such as bone and joint disease. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.