Bidirectional Mendelian Randomization for Metabolic Traits

To test for causal inferences between UL and the above-described 20 metabolic and anthropometric traits, we performed bi-directional two-sample Mendelian randomisation. These analyses were completed using ‘TwoSampleMR’ R library (0.5.6)⁶⁰ (https://mrcieu.github.io/TwoSampleMR/). To avoid possible bias from overlapping samples, we extracted genetic instruments for UL from the GWAS results obtained in FinnGen, and for other, mostly UKBB-based traits from the GWAS database provided by the MRC IEU and integrated them into TwoSampleMR. LD pruning was completed using European population reference, a threshold of r² = 0.001, and a clumping window of 10 kb, as set as default in ‘clump_data’ function; the numbers of SNPs available for the analyses are listed in Table S12. The inverse variance-weighted (IVW) method was considered the primary analysis. In sensitivity analyses, we derived causal estimates using MR Egger (implemented in TwoSampleMR), MR-PRESSO (1.0)^{43 (link)}, and MRMix (0.1.0)^{44 (link)} methods for the traits showing FDR-significant causal effects on UL in the primary analysis. The sensitivity analyses were conducted using the same sets of instruments that were used in the primary IVW analysis using an identical LD pruning approach. The estimates obtained in the sensitivity analyses were required to be in a matching direction with the IVW estimates to conclude a reliable causal effect. Egger intercepts were evaluated to assess horizontal pleiotropy. Cochran’s Q statistics were derived using ‘mr_heterogeneity’ function to test for heterogeneity. To screen for highly influential variants that could drive the association, for example, due to horizontal pleiotropy, we performed leave-one-out analyses using ‘mr_leaveoneout’ function. We also estimated the multivariable effects of fat-free mass, fat mass, BMI, and estradiol level on UL risk using TwoSampleMR. LD pruning was conducted with the same settings as described above. We used data from FinnGen to extract variant associations with UL, from the MRC IEU GWAS database to extract variant associations with fat-free mass, fat mass, and BMI, and from a Study by ref. ^{61 (link)}. to extract variant associations with estradiol.