Causal associations between risk factors and common diseases inferred from GWAS summary data

Abstract

Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called GSMR) that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases (sample sizes of up to 405,072). We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer’s disease, and bidirectional associations with opposite effects (e.g., higher BMI increases the risk of T2D but the effect of T2D on BMI is negative).

The genetics of the mood disorder spectrum: genome-wide association analyses of over 185,000 cases and 439,000 controls

“…19 We explored potential causal relationships between the mood disorders and 20 other traits using Mendelian randomisation. The interpretation of these analyses is 21 challenging, especially for complex traits, when the ascertainment of cases varies, 22 and when there are relatively few (< 20) variants used as instruments (for example, 23 in the PGC BD and down-sampled analyses presented) (41,67,68). Major 24 depressive disorder and bipolar disorder demonstrate considerable heterogeneity (as our subtype analyses show for bipolar disorder types 1 and 2), potentially 1 confounding the results of Mendelian randomisation.…”

“…Results 18 using LDSC are included in the Supplementary Tables. 19 Mendelian randomisation (GSMR) 20 Bidirectional Mendelian randomisation analyses were performed using the 21 GSMR option in GCTA to allow exploratory inference of the causal direction of 22 known relationships between mood disorder traits and other traits (41,42). 23 Specifically, the relationship between the mood disorder analyses (MOOD, combined 1 MDD, PGC BD) and schizophrenia, intelligence, educational attainment, body mass 2 index, and coronary artery disease were explored (Supplementary Methods) (32, 3 43-46).…”

Genetic drug target validation using Mendelian randomization

“…This GLS method is by no means the only relevant estimator function and one can "repurpose" many general MR methods for use in drug target MR. For example, the MR-base platform clumps variants to such a low level (e.g., R-squared of 0.001) that one can apply weighted regression solutions (e.g., IVW), foregoing LD correction 13 . Generalised Summarydata-based Mendelian Randomisation (GSMR) 52 Throughout this manuscript we used a type I of error rate of 0.05 (or 95% confidence interval) and did not correct for multiple testing. While appropriate multiplicity protection is important, by focussing on four thoroughly studied drug targets (NPC1L1, HMGCR, PCSK9, and CETP) there is an abundance of prior evidence on the expected CHD effect, making analytical control of the false positive rate less relevant.…”

MR-TRYX: A Mendelian randomization framework that exploits horizontal pleiotropy to infer novel causal pathways

“…The problem of instrumental variables being invalid due to horizontal pleiotropy has received much attention in MR analysis. Detecting and excluding such invalid instruments, based on whether they appear to be outliers in the analysis, is now a common strategy that exists in various forms 7,8,14,15,35 . We have shown here that outlier removal could, in some circumstances, compound rather than reduce bias, and misses an opportunity to better understand the traits under study.…”