Baseline characteristics of the 366 controls in the CARE populations by quartiles of PTX3.

<p>IQR  =  interquartile range. MDRD-GFR  =  Modification of Diet in Renal Disease-Glomerular Filtration Rate.</p

Lack of association between PTX3 and short pentraxin.

<p>Spearman correlations revealed no association between plasma levels of the long pentraxin PTX3 and the short pentraxin CRP (A). Plasma PTX3 levels positively correlated with plasma SAA levels (B), and plasma CRP and SAA levels correlated strongly with each other (C). After exclusion of participants with high CRP levels (>10 mg/L), PTX3 showed no significant correlations with CRP and SAA (D and E), whereas CRP and SAA correlated positively with each other (F).</p

PTX3 associates negatively with obesity and metabolic syndrome in non-diabetic subject.

<p>PTX3 associates negatively with obesity but positively with diabetes mellitus (A). Mean plasma PTX3 levels among 779 individuals, comparing non-obese (BMI <30) and obese individuals (BMI ≥30) (left), or non-diabetic and diabetic control individuals (right). Mean values were estimated from linear regression models, adjusted for age, sex, smoking status, triglycerides, HDL cholesterol, and hypertension. Models for diabetes also adjusted for BMI. PTX3 negatively associates with obesity among non-diabetic individuals (B). Mean values of plasma PTX3 levels by BMI category in non-diabetic (left) and diabetic individuals (right) estimated from linear regression models. Mean values adjusted for age, sex, smoking status, triglycerides, HDL cholesterol, and hypertension. PTX3 associates negatively with the number of metabolic syndrome components (C). Mean values of plasma PTX3 levels according to presence of 0, 1, 2, 3, and 4 or more components of metabolic syndrome in all individuals (left) and non-diabetic individuals (right). Mean values estimated from linear regression models, adjusted for age, sex, and smoking status.</p

PTX3 levels associate adversely with metabolic syndrome components.

<p>Mean values of selected characteristics (BMI, waist circumference, triglyceride, and HDL cholesterol) by quartile of PTX3. Mean values were estimated from linear regression models, adjusted for age.</p

Inheritance model^# selected with the AIC.

#<p>Models (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>): “-” = null, basic, “sub” = subset, “inv. sub” = inverse subset, general.</p>∧<p>Migraine characteristics: aura, pulsation, unipain ( = unilateral pain), sound ( = phonophobia), light ( = photophobia), duration of 4–72 hours ( = longdur), nausea, aggravation by physical activity ( = aggrphys), severity inhibits daily activities ( = inhibit), ≥6 attacks/year ( = freq).</p><p>“*” indicates models with significant LLR test p-values (<0.05) after adjustment for multiple hypothesis testing (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>).</p

WGHS active migraineurs (N = 3,003^*) with aura or migraine characteristic.

<p>*An additional 2,119 WGHS participants reported prior but not active migraine compared with 18,172 WGHS participants reported never experiencing migraine.</p

Inheritance model^# selected with the BIC.

#<p>Models (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>): “-” = null, basic, “sub” = subset, “inv. sub” = inverse subset.</p>∧<p>Migraine characteristics: aura, pulsation, unipain ( = unilateral pain), sound ( = phonophobia), light ( = photophobia), duration of 4–72 hours ( = longdur), nausea, aggravation by physical activity ( = aggrphys), severity inhibits daily activities ( = inhibit), ≥6 attacks/year ( = freq).</p><p>“*” indicates models with significant LLR test p-values (<0.05) after adjustment for multiple hypothesis testing (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>).</p

Estimates (beta coefficients) for association in the WGHS from logistic models for each of the 12 candidate SNPs as predictors of migraine accompanied by aura or other characteristics (black bars), or not (gray bars).

<p>Significant associations are indicated with “*” (see also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#pgen.1004366.s005" target="_blank">Table S4</a>). Model selection results (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#pgen-1004366-t003" target="_blank">Tables 3</a> & <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#pgen-1004366-t004" target="_blank">4</a>) are indicated with outlines around each plot as follows: Non-null models selected using the BIC are indicated with a heavy red outline, while non-null models selected using the AIC are indicated with a thin black outline. “Subset” or “inverse subset” models (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>) are indicated with a solid outline, “basic” models are indicated with a dotted outline, and “general” models are indicated with a dashed outline. Migraine characteristics considered were aura, pulsating pain ( = pulsate), unilateral pain ( = unipain), phonophobia ( = sound), photophobia ( = light), duration of 4–72 hours ( = longdur), nausea, aggravation by physical activity ( = aggrphys), inhibition of daily activities ( = inhibit), ≥6 attacks/year ( = freq). The rightmost column (actmig) indicates association estimates for active migraineurs, irrespective of status for the characteristics (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>). The order of SNPs is derived from clustering as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#pgen.1004366.s001" target="_blank">Figure S1</a>. See also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004366#s4" target="_blank">Methods</a>.</p

A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians

<div><p>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 (log₁₀Bayes 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 log₁₀Bayes 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 <a href="http://stephenslab.uchicago.edu/software.html" target="_blank">http://stephenslab.uchicago.edu/software.html</a>.</p></div

Decomposition of the gain (or loss) from a multivariate analysis of 12 phenotypes vs a univariate analysis of LDL-C into two components: one from using more detailed measurements, and one from using a multivariate analysis.

<p>Plotted are (a) log₁₀ BF_av (the BF based on multivariate analysis of all 12 phenotypes) vs log₁₀ BF_ldl (the BF based on univariate analysis of LDL-C), (b) log₁₀ BF_uni (the BF based on univariate analysis of all 12 phenotypes) vs log₁₀ BF_ldl, and (c) log₁₀ BF_av vs log₁₀ BF_uni. SNPs are colored according to the nearest gene.</p