MR-PheWAS: exploring the causal effect of SUA level on multiple disease outcomes by using genetic instruments in UK Biobank

Abstract

ObjectivesWe aimed to investigate the role of serum uric acid (SUA) level in a broad spectrum of disease outcomes using data for 120 091 individuals from UK Biobank.MethodsWe performed a phenome-wide association study (PheWAS) to identify disease outcomes associated with SUA genetic risk loci. We then implemented conventional Mendelianrandomisation (MR) analysis to investigate the causal relevance between SUA level and disease outcomes identified from PheWAS. We next applied MR Egger analysis to detect and account for potential pleiotropy, which conventional MR analysis might mistake for causality, and used the HEIDI (heterogeneity in dependent instruments) test to remove cross-phenotype associations that were likely due to genetic linkage.ResultsOur PheWAS identified 25 disease groups/outcomes associated with SUA genetic risk loci after multiple testing correction (P<8.57e-05). Our conventional MR analysis implicated a causal role of SUA level in three disease groups: inflammatory polyarthropathies (OR=1.22, 95% CI 1.11 to 1.34), hypertensive disease (OR=1.08, 95% CI 1.03 to 1.14) and disorders of metabolism (OR=1.07, 95% CI 1.01 to 1.14); and four disease outcomes: gout (OR=4.88, 95% CI 3.91 to 6.09), essential hypertension (OR=1.08, 95% CI 1.03 to 1.14), myocardial infarction (OR=1.16, 95% CI 1.03 to 1.30) and coeliac disease (OR=1.41, 95% CI 1.05 to 1.89). After balancing pleiotropic effects in MR Egger analysis, only gout and its encompassing disease group of inflammatory polyarthropathies were considered to be causally associated with SUA level. Our analysis highlighted a locus (ATXN2/S2HB3) that may influence SUA level and multiple cardiovascular and autoimmune diseases via pleiotropy.ConclusionsElevated SUA level is convincing to cause gout and inflammatory polyarthropathies, and might act as a marker for the wider range of diseases with which it associates. Our findings support further investigation on the clinical relevance of SUA level with cardiovascular, metabolic, autoimmune and respiratory diseases.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Techniques in causal inference such as instrumental variable analysis can also be used to address issues of confounding in EHR . Recently, researchers have used particular genetic variants as instrumental variables in analyses relating variables such as hormone levels to phenotypes of interest . Mendelian randomization analysis is then used to explore potential causal relationships .…”

“…39,40 Recently, researchers have used particular genetic variants as instrumental variables in analyses relating variables such as hormone levels to phenotypes of interest. 41 Mendelian randomization analysis is then used to explore potential causal relationships. 42 Marginal structural models can be used to address confounding by time-dependent variables and has recently been applied to EHR in Sperrin et al 43,44 Techniques for reducing and eliminating confounding often assume that the potential confounders are measured.…”

“…A common goal of EHR-based analyses is to study the associations between specific phenotypes and variants at a particular gene region or across the genome, and this analysis is often performed using linear or logistic regression or using mixed linear model association (MLMA) analysis. 38,41,[121][122][123] Firth-corrected logistic regression may prove useful for modeling rare binary outcomes or settings in which there is strong covariate separation, and its application to PheWAS is demonstrated in Fritsche et al 124 Recently, Dey et al proposed a fast alternative to Firth-penalized regression to stabilize estimation for PheWAS studies using saddle-point approximation (SPA) that is useful for handling extremely unbalanced case-control data. 125 These methods can be applied in many other modeling settings as well.…”

Developing and Evaluating Mappings of ICD-10 and ICD-10-CM Codes to PheCodes

“…Subsequent studies have used PheCodes to replicate hundreds of known genotype-phenotype associations and discover dozens of new ones, some that have been validated in follow-up investigations. [5][6][7][8][9][10][11][12][13][14][15][16]…”

“…et al used the map to demonstrate a method that adjusts for case-control imbalances in a large genome-wide PheWAS [37]. Li et al used the same map to estimate the causal effects of elevated serum uric acid across the phenome [16]. …”