Lipoprotein association studies: taking stock and moving forward

Rare and common variants in LPL and APOA5 in Thai subjects with severe hypertriglyceridemia: A resequencing approach

BACKGROUND

Severe hypertriglyceridemia usually results from a combination of genetic and environmental factors. Few data exist on the genetics of severe hypertriglyceridemia in Asian populations.

OBJECTIVE

To examine the genetic variants of 3 candidate genes known to influence triglyceride metabolism, LPL, APOC2, and APOA5, which encode lipoprotein lipase, apolipoprotein C-II, and apolipoprotein A-V, respectively, in a large group of Thai subjects with severe hypertriglyceridemia.

METHODS

We identified sequence variants of LPL, APOC2, and APOA5 by sequencing exons and exon-intron junctions in 101 subjects with triglyceride levels ≥ 10 mmol/L (886 mg/dL) and compared with those of 111 normotriglyceridemic subjects.

RESULTS

Six different rare variants in LPL were found in 13 patients, 2 of which were novel (1 heterozygous missense variant: p.Arg270Gly and 1 frameshift variant: p.Asp308Glyfs*3). Four previously identified heterozygous missense variants in LPL were p.Ala98Thr, p.Leu279Val, p.Leu279Arg, and p.Arg432Thr. Collectively, these rare variants were found only in the hypertriglyceridemic group but not in the control group (13% vs 0%, P < .0001). One common variant in APOA5 (p.Gly185Cys, rs2075291) was found at a higher frequency in the hypertriglyceridemic group compared with the control group (25% vs 6%, respectively, P < .0005). Altogether, rare variants in LPL or APOA5 and/or the common APOA5 p.Gly185Cys variant were found in 37% of the hypertriglyceridemic group vs 6% in the controls (P = 3.1 × 10(-8)). No rare variant in APOC2 was identified.

CONCLUSIONS

Rare variants in LPL and a common variant in APOA5 were more commonly found in Thai subjects with severe hypertriglyceridemia. A common p.Gly185Cys APOA5 variant, in particular, was quite prevalent and potentially contributed to hypertriglyceridemia in this group of patients.

Variants identified in a GWAS meta-analysis for blood lipids are associated with the lipid response to fenofibrate

A recent large-scale meta-analysis of genome-wide studies has identified 95 loci, 59 of them novel, as statistically significant predictors of blood lipid traits; we tested whether the same loci explain the observed heterogeneity in response to lipid-lowering therapy with fenofibrate. Using data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 861) we fit linear mixed models with the genetic markers as predictors and high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, and triglyceride concentrations as outcomes. For all four traits, we analyzed both baseline levels and changes in response to treatment with fenofibrate. For the markers that were significantly associated with fenofibrate response, we fit additional models evaluating potential epistatic interactions. All models were adjusted for age, sex, and study center as fixed effects, and pedigree as a random effect. Statistically significant associations were observed between the rs964184 polymorphism near APOA1 (P-value≤0.0001) and fenofibrate response for HDL and triglycerides. The association was replicated in the Pharmacogenetics of Hypertriglyceridemia in Hispanics study (HyperTG, n = 267). Suggestive associations with fenofibrate response were observed for markers in or near PDE3A, MOSC1, FLJ36070, CETP, the APOE-APOC1-APOC4-APOC2, and CILP2. Finally, we present strong evidence for epistasis (P-value for interaction =  0.0006 in GOLDN, 0.05 in HyperTG) between rs10401969 near CILP2 and rs4420638 in the APOE-APOC1-APOC4-APOC2 cluster with total cholesterol response to fenofibrate. In conclusion, we present evidence linking several novel and biologically relevant genetic polymorphisms to lipid lowering drug response, as well as suggesting novel gene-gene interactions in fenofibrate pharmacogenetics.

Integrated approaches to functionally characterize novel factors in lipoprotein metabolism

PURPOSE OF REVIEW

To discuss if and how the combined analysis of large-scale datasets from multiple independent sources benefits the mapping of novel genetic elements with relevance to lipoprotein metabolism and allows for conclusions on underlying molecular mechanisms.

RECENT FINDINGS

Genome-wide association studies (GWAS) have identified numerous genomic loci associated with plasma lipid levels and cardiovascular disease. Yet, despite being highly successful in mapping novel loci the GWAS approach falls short to systematically extract functional information from genomic data. With the aim to complement GWAS for a better insight into disease mechanisms and identification of the most promising targets for drug development, a number of high-throughput functional genomics strategies have now been applied. These include computational approaches, consideration of gene-gene and gene-environment interactions, as well as unbiased gene-expression analyses in relevant tissues. For a limited number of loci, mechanistic insight has been gained through in-vitro and in-vivo studies by knockdown and overexpression of candidate genes.

SUMMARY

The integration of GWAS data with existing functional genomics strategies has contributed to ascertain the relevance of a number of novel factors for lipoprotein biology and disease. However, technologies are warranted that provide a more systematic insight into the molecular function and pathogenic relevance of promising candidate genes.

The Fox and the Rabbits-Environmental Variables and Population Genetics (1) Replication Problems in Association Studies and the Untapped Power of GWAS (2) Vitamin A Deficiency, Herpes Simplex Reactivation and Other Causes of Alzheimer's Disease

Classical population genetics shows that varying permutations of genes and risk factors permit or disallow the effects of causative agents, depending on circumstance. For example, genes and environment determine whether a fox kills black or white rabbits on snow or black ash covered islands. Risk promoting effects are different on each island, but obscured by meta-analysis or GWAS data from both islands, unless partitioned by different contributory factors. In Alzheimer's disease, the foxes appear to be herpes, borrelia or chlamydial infection, hypercholesterolemia, hyperhomocysteinaemia, diabetes, cerebral hypoperfusion, oestrogen depletion, or vitamin A deficiency, all of which promote beta-amyloid deposition in animal models—without the aid of gene variants. All relate to risk factors and subsets of susceptibility genes, which condition their effects. All are less prevalent in convents, where nuns appear less susceptible to the ravages of ageing. Antagonism of the antimicrobial properties of beta-amyloid by Abeta autoantibodies in the ageing population, likely generated by antibodies raised to beta-amyloid/pathogen protein homologues, may play a role in this scenario. These agents are treatable by diet and drugs, vitamin supplementation, pathogen detection and elimination, and autoantibody removal, although again, the beneficial effects of individual treatments may be tempered by genes and environment.