We conducted a genome-wide association analysis of 7 subfractions of
low density lipoproteins (LDLs) and 3 subfractions of
intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to
statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of
Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1,
APOE, LPA, and CETP) as containing variants that are very strongly associated with
lipoprotein subfractions (log(10)Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (
APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns--both in our original data and in a replication cohort--consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with
LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to
statins showed much weaker signals (none exceeding log(10)Bayes Factor of 6). However, two SNPs (in
APOE and LPA) previously-reported to be associated with
LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to
statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html.