Data populations

Lipid GWAS bottom line statistics was indeed extracted from the brand new Mil Veteran Program (MVP) (doing 215,551 European some body) and Internationally Lipids Genetics Consortium (GLGC) (doing 188,577 genotyped people) . As the additional exposures for the multivariable MR analyses, i used Body mass index realization analytics out-of a beneficial meta-investigation of GWASs inside the as much as 795,640 people and many years at menarche conclusion statistics of an excellent meta-research out-of GWASs during the around 329,345 girls out-of Eu origins [17,23]. The new MVP acquired moral and study method acceptance in the Experienced Fling Main Institutional Opinion Panel in accordance with the prices intricate from the Report from Helsinki, and you can written agree try taken from every members. For the Willer and you may acquaintances and you may BCAC investigation set, i recommend an individual into number one GWAS manuscripts as well as their supplementary topic having information about consent protocols for every single of the respective cohorts. Info within these cohorts are located in the brand new S1 Text.

Lipid meta-studies

We performed a fixed-consequences meta-investigation between for every lipid attribute (Overall cholesterol levels [TC], LDL, HDL, and you will triglycerides [TGs]) during the GLGC plus the associated lipid attribute from the MVP cohort [several,22] utilizing the standard options in the PLINK . You will find specific genomic rising prices in these meta-study association analytics, however, linkage disequilibrium (LD)-score regression intercepts demonstrate that so it rising prices is in large region because of polygenicity and never society 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.