Data communities
Lipid GWAS realization statistics was extracted from the newest Billion Experienced System (MVP) (to 215,551 Western european some body) and Globally Lipids Genes Consortium (GLGC) (doing 188,577 genotyped somebody) . While the a lot more exposures when you look at the multivariable MR analyses, we used Body mass index summary statistics away from a great meta-data out of GWASs into the as much as 795,640 individuals and you can years on menarche summation statistics off an excellent meta-analysis off GWASs in the around 329,345 people away from Eu origins [17,23]. This new MVP obtained moral and read protocol approval about Veteran Affair Main Institutional Review Panel in accordance with the principles intricate from the Statement of Helsinki, and you may created consent is actually taken from all users. Toward Willer and you will acquaintances and you may BCAC data set, we recommend your reader on top GWAS manuscripts in addition to their secondary matter to possess informative data on agree protocols for every of the particular cohorts. Details on these cohorts come in the new S1 Text.
Lipid meta-data
I did a predetermined-effects meta-research between for each and every lipid characteristic (Full cholesterol [TC], LDL, HDL, and you can triglycerides [TGs]) into the GLGC and associated lipid characteristic on MVP cohort [several,22] using the standard settings inside PLINK . There is certainly specific genomic rising prices in these meta-research connection analytics, however, linkage disequilibrium (LD)-score regression intercepts reveal that so it inflation is in higher part due to 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.
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