GWAS <a href=""></a> bottom line statistics of 122,977 BC times and you can 105,974 control was extracted from the brand new Breast cancer Organization Consortium (BCAC)

Data communities

Lipid GWAS summary statistics was basically obtained from the brand new Million Veteran Program (MVP) (up to 215,551 European some one) in addition to Internationally Lipids Family genes Consortium (GLGC) (to 188,577 genotyped some one) . Once the even more exposures in multivariable MR analyses, i made use of Bmi summation analytics regarding good meta-research from GWASs from inside the as much as 795,640 people and age at menarche conclusion statistics out-of an excellent meta-research from GWASs inside doing 329,345 girls of Eu ancestry [17,23]. The fresh new MVP acquired ethical and study protocol approval throughout the Experienced Fling Main Organization Opinion Panel according to the standards detailed on the Report out of Helsinki, and you can authored agree are extracted from every participants. Into Willer and you will acquaintances and you may BCAC data kits, i send the reader with the number 1 GWAS manuscripts as well as their secondary point getting information on consent protocols each of their particular cohorts. Details during these cohorts have been in the S1 Text.

Lipid meta-study

We did a predetermined-outcomes meta-data ranging from per lipid characteristic (Overall cholesterol [TC], LDL, HDL, and you will triglycerides [TGs]) when you look at the GLGC and associated lipid feature from the MVP cohort [12,22] utilizing the default setup within the PLINK . There is some genomic rising prices throughout these meta-data organization statistics, however, linkage disequilibrium (LD)-score regression intercepts reveal that which inflation is in large area because of polygenicity rather than populace 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.

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