Estimated correlation coefficients, obtained using the general linear model with different variance-covariance structures, for the five metabolic subsets defined for apolipoprotein B.

<p>A. GLM with metabolic-subset specific correlation coefficients defined by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e003" target="_blank">2</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e009" target="_blank">3</a>); B. GLM with common correlation coefficients across the first three metabolic-subsets; C. GLM with no metabolic-subset dependent correlation coefficients, i.e., the null model defined by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e003" target="_blank">2</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e011" target="_blank">4</a>).</p

Box-plots of the core LL module expression.

<p>Heterogeneous mean expression values and variances are observed.</p

Power of the linear regression and GLM-based test statistics for different co-expression dynamics and sample sizes.

<p>Power of the linear regression and GLM-based test statistics for different co-expression dynamics and sample sizes.</p

Histograms of the observed values for 3-hydroxybutyrate, linoleic acid, large HDL particles, small HDL particles, small LDL particles, and total cholesterol in large HDL.

<p>Histograms of the observed values for 3-hydroxybutyrate, linoleic acid, large HDL particles, small HDL particles, small LDL particles, and total cholesterol in large HDL.</p

Type I error probabilities for the linear regression and the GLM-based test statistics by module size and sample size.

<p>Type I error probabilities for the linear regression and the GLM-based test statistics by module size and sample size.</p

2q33.1 and 5q23.2 loci cohort-wise ADVANCED model effect estimates and meta-analysis results with systolic blood pressure (SBP).^$

<p>Genomic positions are based on the human genome build 36. Alleles are reported on the forward strand of the reference genome. The effects are reported for the alleles increasing risk for IA in the Yasuno et al. studies <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002563#pgen.1002563-Yasuno1" target="_blank">[12]</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002563#pgen.1002563-Yasuno2" target="_blank">[13]</a>. Risk alleles are aligned according to the forward strand of the reference genome. Minor allele frequencies (MAF) are based on from the HapMap Phase II CEU population data.</p>$<p>Diastolic blood pressure (DBP) and mean arterial pressure (MAP) association results from 2q33.1 and 5q23.2 SNP are in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002563#pgen.1002563.s005" target="_blank">Table S2</a>.</p>*<p>Meta SBP: meta-analysis of discovery and replication cohort p-values and beta for systolic blood pressure (SBP) with the ADVANCED model. Association analyses were corrected for gender, age, BMI, smoking habits and alcohol consumption.</p><p>SE: standard error.</p

Cohort-wise effects of risk allele count on SBP.

<p>The higher median age in HBCS is reflected as higher systolic blood pressure (SBP) and less consistent association. Error bars show standard error.</p

Summary of cohort characteristics.

<p>WG: whole-genome, QC: quality control, BP: blood pressure, SBP: systolic blood pressure, DBP: diastolic blood pressure, MAP: mean arterial pressure, PP: pulse pressure, BMI: body-mass index, SD: 1 standard deviation.</p

Most significant BMI x SNP interaction terms for urate GWAMA.

<p>A1, allele for which effect (β) is reported; A2 alternate allele, fqA1 weighted average effect-allele frequency across studies meta-analyzed; s.e. standard error of the effect estimate, I² meta-analysis heterogeneity statistic. The interaction term is modelled within a linear model where standardised SU levels (after adjustment for age and sex) is regressed on BMI, SNP and their interaction. βinter is the regression coefficient for the interaction term.</p><p>Most significant BMI x SNP interaction terms for urate GWAMA.</p