Association of the Lipoprotein Receptor <i>SCARB1</i> Common Missense Variant rs4238001 with Incident Coronary Heart Disease

<div><p>Background</p><p>Previous studies in mice and humans have implicated the lipoprotein receptor <i>SCARB1</i> in association with atherosclerosis and lipid levels. In the current study, we sought to examine association of <i>SCARB1</i> missense single nucleotide polymorphism (SNP) rs4238001 with incident coronary heart disease (CHD).</p><p>Methods and Results</p><p>Genotypes for rs4238001 were imputed for 2,319 White, 1,570 African American, and 1,292 Hispanic-American MESA participants using the 1,000 Genomes reference set. Cox proportional hazards models were used to determine association of rs4238001 with incident CHD, with adjustments for age, sex, study site, principal components of ancestry, body mass index, diabetes status, serum creatinine, lipid levels, hypertension status, education and smoking exposure. Meta-analysis across race/ethnic groups within MESA showed statistically significant association of the T allele with higher risk of CHD under a consistent and formally adjudicated definition of CHD events in this contemporary cohort study (hazard ratio [HR]=1.49, 95% CI [1.04, 2.14], <i>P</i> = 0.028). Analyses combining MESA with additional population-based cohorts expanded our samples in Whites (total n = 11,957 with 871 CHD events) and African Americans (total n = 5,962 with 355 CHD events) and confirmed an increased risk of CHD overall (HR of 1.19 with 95% CI [1.04, 1.37], <i>P</i> = 0.013), in African Americans (HR of 1.49 with 95% CI [1.07, 2.06], <i>P</i> = 0.019), in males (HR of 1.29 with 95% CI [1.08, 1.54], <i>P</i> = 4.91x10^-3) and in White males (HR of 1.24 with 95% CI [1.03, 1.51], <i>P</i> = 0.026).</p><p>Conclusion</p><p><i>SCARB1</i> missense rs4238001 is statistically significantly associated with incident CHD across a large population of multiple race/ethnic groups.</p></div

Effect estimates (log hazard ratio of CHD for rs4238001 effect allele T) and corresponding 95% confidence intervals shown for Model 1 (basic), Model 2 (extended), Model 3 (Model 2 + lipid medication) and Model 4 (NMR lipids).

<p>Analyses were conducted stratified by race/ethnic group and combined by meta- analysis, for all participants as well as stratified by sex (males or female).</p

Characteristics of MESA participants across three ethnic groups.

<p>Data are presented as N (%) for binary measures or median [IQR] for continuous measure.</p><p>*Summary statistics are reported for the subset of individuals with data available for at least one of the clinical events.</p><p>†P-values are presented for statistical significance of the difference in values across race/ethnic groups according to a likelihood ratio test with 2 degrees of freedom.</p><p>Characteristics of MESA participants across three ethnic groups.</p

Summary of genetic additive effects of rs4238001 allele T on HDL-C (log mg/dL), HDL particle number (nmol/L) and HDL particle size (log nm) under a basic model (Model 1).

<p>Analyses were conducted stratified by race/ethnic group and combined by meta-analysis, for all participants as well as stratified by sex (males or female).</p

Summary of estimated genetic additive effects of rs4238001 allele T on LDL-C (mg/dL), LDL particle number (nmol/L) and LDL particle size (log nm) under a basic regression model (Model 1).

<p>Analyses were conducted stratified by race/ethnic group and combined by meta-analysis, for all participants as well as stratified by sex (males or female).</p

Enrichment for functional annotations and cell-type groups using stratified LD score regression.

<p><b>A.</b> Enrichment estimates of 24 main annotations for each of four BP traits. Annotations are ordered by size. Error bars represent jackknife standard errors around the estimates of enrichment, and stars indicate significance at P < 0.05 after Bonferroni correction for 24 hypotheses tested and four BP traits. <b>B.</b> Significance of enrichment of 10 cell-type groups for four BP traits. Dotted line and stars indicate significance at P < 0.05 after Bonferroni correction for 10 hypotheses tested and four BP traits.</p

Intelligent Forecasting of Electricity Demand

In this paper, a number of approaches to the modelling of electricity demand, on a variety of time-scales, are considered. These approaches fall under the category of 'intelligent' systems engineering, where techniques such as neural networks, fuzzy logic and genetic algorithms are employed. The paper attempts to give some motivation for the employment of such techniques, while also making some effort to be realistic about the limitations of such methods, in particular a number of important caveats that should be borne in mind when utilising these techniques within the current application domain. In general, the electricity demand data is modelled as a time series, but one application considered involves application of linguistic modelling to capture operator expertise

Plots show the individual interaction p-values based on Stage I (indicated as solid dots) or Stage I + Stage II meta-analysis (indicated as outlined dots with “+” symbol) against their genomic position for the combined cardiovascular disease (CVD) outcome for the four antihypertensive medication exposures: (a) Angiotensin-converting enzyme (ACE) inhibitors, (b) Beta-blockers, (c) Calcium Channel Blockers, and (d) Thiazide Diuretics.

<p>Within each chromosome, shown on the x-axis, the results are plotted left to right from the p-terminal end. The nearest genes are indicated for variants with an interaction p-value less than 1×10⁻⁵ in the meta-analysis.</p

Characteristics of Study Participants.

<p>Age indicates mean age. Model indicates analysis method: C, Cox proportional hazards regression; L, logistic regression. For prevalence of antihypertensive medication use ACE indicates Angiotensin-converting enzyme inhibitor (or angiotensin receptor blocker); BB, beta-blocker; CCB, calcium channel blocker; Diuretics, thiazide diuretics.For studies analyzed with logistic regression, summaries are provided separately for cases and controls.</p><p>Characteristics of Study Participants.</p

Study design schematic for discovery and replication of loci.

<p>QC, quality control; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; HTN, hypertension; eQTL, expression quantitative loci.</p