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 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

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

Venn diagrams of overlap in significant co-expression modules and functional categories between diseases and ethnicities.

<p>A) Count of module overlaps by disease based on Meta-MSEA; B) Count of module overlaps for each disease by ethnicity based on MSEA of individual studies. Co-expression modules captured in CARDIoGRAMplusC4D and DIAGRAM were not counted due to uncertain ethnic origin; C) Count of independent functional category overlaps by disease based on results from Meta-MSEA in panel A.</p

Summary of 41 independent functional categories enriched in both CVD and T2D co-expression modules (Bonferroni-corrected p< 0.05 based on Fisher’s exact test, number of direct overlapping genes > 5).

<p>Independent functional categories were defined as the categories with pair-wise overlapping ratio < 10%. Red and blue block indicates that the significant CVD or T2D co-expression modules identified from the study and ethnicity origin are enriched for the particular functional category term. CAR+C4D: CARDIoGRAMplusC4D; M: mixed ethnicities; AA: African Americans; HA: Hispanic Americans; EA: European Americans.</p

Summary of top co-expression modules associated with CVD or T2D (FDR < 1% in Meta-MSEA, in column FDR_meta).

<p>Summary of top co-expression modules associated with CVD or T2D (FDR < 1% in Meta-MSEA, in column FDR_meta).</p

Framework of network-driven integrative genomics analyses.

<p>(A) Integration of genetics and functional genomics datasets to identify CVD and T2D associated co-expression modules. The GWAS studies for CVD and T2D were derived from three independent cohorts representing three ethnic populations: WHI (AA, EA, HA), FHS (EA), and JHS (AA). These independent datasets were supplemented with GWAS of coronary artery disease from CARDIoGRAMplusC4D and T2D from DIAGRAM to increase power. We also curated a comprehensive list of tissue-specific functional genomics datasets, including 2672 co-expression modules, human eQTLs of various tissues, and ENCODE based variants annotation. The significant modules were identified by MSEA and Meta-MSEA, and then annotated to reveal shared pathways for CVD and T2D. In MSEA, the co-expression modules were used to define data-driven gene sets each containing functionally related genes, tissue-specificity was determined based on the tissue-origins of the human eQTLs, and ethnic specificity was determined based on the ethnicity of each GWAS cohort. (B) Identification of disease key drivers and subnetworks. We utilized multi-tissue graphical networks to capture key drivers for disease associated co-expression modules using wKDA, then prioritized KDs based on consistency and disease relevance of the subnetworks. (C) Validation of the top key drivers and their subnetworks via intersection with known human CVD and T2D genes from DisGeNET and GWAS catalog, in vitro adipocyte siRNA experiments, and cross-validation at both transcriptomic and genomic levels in the hybrid mouse diversity panels (HMDP).</p

Subnetworks of the top 15 shared KDs orchestrate known genes for CVD, T2D, obesity and lipids.

<p>A) Fold enrichment of KD subnetwork genes for known genes related to cardiometabolic traits reported in DisGeNET. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. B) Top KD subnetworks with GWAS hits (p < 1e-5 as reported in GWAS Catalog) for cardiometabolic traits. KDs are large nodes. Edge color denotes tissue-origin. Only high-confidence edges (those with weight score in the top 20%) are visualized.</p