An increased burden of common and rare lipid-associated risk alleles contributes to the phenotypic spectrum of hypertriglyceridemia.

OBJECTIVE: Earlier studies have suggested that a common genetic architecture underlies the clinically heterogeneous polygenic Fredrickson hyperlipoproteinemia (HLP) phenotypes defined by hypertriglyceridemia (HTG). Here, we comprehensively analyzed 504 HLP-HTG patients and 1213 normotriglyceridemic controls and confirmed that a spectrum of common and rare lipid-associated variants underlies this heterogeneity. METHODS AND RESULTS: First, we demonstrated that genetic determinants of plasma lipids and lipoproteins, including common variants associated with plasma triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) from the Global Lipids Genetics Consortium were associated with multiple HLP-HTG phenotypes. Second, we demonstrated that weighted risk scores composed of common TG-associated variants were distinctly increased across all HLP-HTG phenotypes compared with controls; weighted HDL-C and LDL-C risk scores were also increased, although to a less pronounced degree with some HLP-HTG phenotypes. Interestingly, decomposition of HDL-C and LDL-C risk scores revealed that pleiotropic variants (those jointly associated with TG) accounted for the greatest difference in HDL-C and LDL-C risk scores. The APOE E2/E2 genotype was significantly overrepresented in HLP type 3 versus other phenotypes. Finally, rare variants in 4 genes accumulated equally across HLP-HTG phenotypes. CONCLUSIONS: HTG susceptibility and phenotypic heterogeneity are both influenced by accumulation of common and rare TG-associated variants.

Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia.

Genome-wide association studies (GWAS) have identified multiple loci associated with plasma lipid concentrations. Common variants at these loci together explain <10% of variation in each lipid trait. Rare variants with large individual effects may also contribute to the heritability of lipid traits; however, the extent to which rare variants affect lipid phenotypes remains to be determined. Here we show an accumulation of rare variants, or a mutation skew, in GWAS-identified genes in individuals with hypertriglyceridemia (HTG). Through GWAS, we identified common variants in APOA5, GCKR, LPL and APOB associated with HTG. Resequencing of these genes revealed a significant burden of 154 rare missense or nonsense variants in 438 individuals with HTG, compared to 53 variants in 327 controls (P = 6.2 x 10(-8)), corresponding to a carrier frequency of 28.1% of affected individuals and 15.3% of controls (P = 2.6 x 10(-5)). Considering rare variants in these genes incrementally increased the proportion of genetic variation contributing to HTG.

Phenomics: expanding the role of clinical evaluation in genomic studies.

With advances in high-throughput genotyping technologies, the rate-limiting step of large-scale genetic investigations has become the collection of sensitive and specific phenotype information in large samples of study participants. Clinicians play a pivotal role for successful genetic studies because sound clinical acumen can substantially increase study power by reducing measurement error and improving diagnostic precision for translational research. Phenomics is the systematic measurement and analysis of qualitative and quantitative traits, including clinical, biochemical, and imaging methods, for the refinement and characterization of a phenotype. Phenomics requires deep phenotyping, the collection of a wide breadth of phenotypes with fine resolution, and phenomic analysis, composed of constructing heat maps, cluster analysis, text mining, and pathway analysis. In this article, we review the components of phenomics and provide examples of their application to genomic studies, specifically for implicating novel disease processes, reducing sample heterogeneity, hypothesis generation, integration of multiple types of data, and as an extension of Mendelian randomization studies.