Analysis populations

Lipid GWAS conclusion statistics was basically taken from the brand new Mil Veteran System (MVP) (around 215,551 Eu individuals) and Worldwide Lipids Genes Consortium (GLGC) (doing 188,577 genotyped anybody) . Since the most exposures inside multivariable MR analyses, we utilized Body mass index realization analytics regarding a beneficial meta-investigation out of GWASs for the up to 795,640 some body and ages on menarche summation analytics of a great meta-data off GWASs from inside the up to 329,345 people off European origins [17,23]. The brand new MVP acquired ethical and study protocol recognition throughout the Experienced Affair Central Institutional Review Board in accordance with the principles detailed on Declaration regarding Helsinki, and you may authored agree is actually extracted from all the professionals https://datingranking.net/de/bdsm-sites-de/. To the Willer and acquaintances and you will BCAC analysis sets, we refer an individual with the primary GWAS manuscripts and their additional procedure to have all about agree protocols for each of the particular cohorts. Additional info on these cohorts can be found in the fresh new S1 Text.

Lipid meta-data

We performed a fixed-effects meta-data between each lipid characteristic (Total cholesterol levels [TC], LDL, HDL, and you will triglycerides [TGs]) from inside the GLGC together with corresponding lipid attribute regarding the MVP cohort [a dozen,22] utilizing the standard options from inside the PLINK . There can be some genomic inflation on these meta-analysis association analytics, however, linkage disequilibrium (LD)-get regression intercepts show that this inflation is within highest part because of polygenicity and not inhabitants stratification (S1 Fig).

MR analyses

MR analyses were performed using the TwoSampleMR R package version 0.4.13 ( . For all analyses, we used a 2-sample MR framework, with exposure(s) (lipids, BMI, age at menarche) and outcome (BC) genetic associations from separate cohorts. Unless otherwise noted, MR results reported in this manuscript used inverse-variance weighting assuming a multiplicative random effects model. For single-trait MR analyses, we additionally employed Egger regression , weighted median , and mode-based estimates. SNPs associated with each lipid trait were filtered for genome-wide significance (P < 5 ? 10 ?8 ) from the MVP lipid study , and then we removed SNPs in LD (r 2 < 0.001 in UK10K consortium) in order to obtain independent variants. All genetic variants were harmonized using the TwoSampleMR harmonization function with default parameters. Each of these independent, genome-wide significant SNPs was termed a genetic instrument. We estimated that these single-trait MR genetic instruments had 80% power to reject the null hypothesis, with a 1% error rate, for the following odds ratio (OR) increases in BC risk due to a standard deviation increase in lipid levels: HDL, 1.057; LDL, 1.058; TGs, 1.055; TC, 1.060 [30,31]. We tested for directional pleiotropy using the MR-Egger regression test . To reduce heterogeneity in our genetic instruments for single-trait MR, we employed a pruning procedure (S1 Text). Genetic instruments used in single-trait MR are listed in S1 Table. For multivariable MR experiments [32,33], we generated genetic instruments by first filtering the genotyped variants for those present across all data sets. For each trait and data set combination (Yengo and colleagues for BMI; Day and colleagues for age at menarche ; MVP and GLGC for HDL, LDL, and TGs), we then filtered for genome-wide significance (P < 5 ? 10 ?8 ) and for linkage disequilibrium (r 2 < 0.001 in UK10K consortium) . We performed tests for instrument strength and validity , and each multivariable MR experiment had sufficient instrument strength. We removed variants driving heterogeneity in the ratio of outcome/exposure effects causing instrument invalidity (S1 Text). Genetic instruments used in multivariable MR are listed in S2 Table. Because the MR methods and tests we employed are highly correlated, we did not apply a multiple testing correction to the reported P-values.