Mining the LIPG allelic spectrum reveals the contribution of rare and common regulatory variants to HDL cholesterol

A Comprehensive Review of the Genetics of Dyslipidemias and Risk of Atherosclerotic Cardiovascular Disease

Dyslipidemias are often diagnosed based on an individual's lipid panel that may or may not include Lp(a) or apoB. But these values alone omit key information that can underestimate risk and misdiagnose disease, which leads to imprecise medical therapies that reduce efficacy with unnecessary adverse events. For example, knowing whether an individual's dyslipidemia is monogenic can granularly inform risk and create opportunities for precision therapeutics. This review explores the canonical and non-canonical causes of dyslipidemias and how they impact atherosclerotic cardiovascular disease (ASCVD) risk. This review emphasizes the multitude of genetic causes that cause primary hypercholesterolemia, hypertriglyceridemia, and low or elevated high-density lipoprotein (HDL)-cholesterol levels. Within each of these sections, this review will explore the evidence linking these genetic conditions with ASCVD risk. Where applicable, this review will summarize approved therapies for a particular genetic condition.

Endothelial lipase mediates efficient lipolysis of triglyceride-rich lipoproteins

Triglyceride-rich lipoproteins (TRLs) are circulating reservoirs of fatty acids used as vital energy sources for peripheral tissues. Lipoprotein lipase (LPL) is a predominant enzyme mediating triglyceride (TG) lipolysis and TRL clearance to provide fatty acids to tissues in animals. Physiological and human genetic evidence support a primary role for LPL in hydrolyzing TRL TGs. We hypothesized that endothelial lipase (EL), another extracellular lipase that primarily hydrolyzes lipoprotein phospholipids may also contribute to TRL metabolism. To explore this, we studied the impact of genetic EL loss-of-function on TRL metabolism in humans and mice. Humans carrying a loss-of-function missense variant in LIPG, p.Asn396Ser (rs77960347), demonstrated elevated plasma TGs and elevated phospholipids in TRLs, among other lipoprotein classes. Mice with germline EL deficiency challenged with excess dietary TG through refeeding or a high-fat diet exhibited elevated TGs, delayed dietary TRL clearance, and impaired TRL TG lipolysis in vivo that was rescued by EL reconstitution in the liver. Lipidomic analyses of postprandial plasma from high-fat fed Lipg-/- mice demonstrated accumulation of phospholipids and TGs harboring long-chain polyunsaturated fatty acids (PUFAs), known substrates for EL lipolysis. In vitro and in vivo, EL and LPL together promoted greater TG lipolysis than either extracellular lipase alone. Our data positions EL as a key collaborator of LPL to mediate efficient lipolysis of TRLs in humans and mice.

Hyperalphalipoproteinemia and Beyond: The Role of HDL in Cardiovascular Diseases

Hyperalphalipoproteinemia (HALP) is a lipid disorder characterized by elevated plasma high-density lipoprotein cholesterol (HDL-C) levels above the 90th percentile of the distribution of HDL-C values in the general population. Secondary non-genetic factors such as drugs, pregnancy, alcohol intake, and liver diseases might induce HDL increases. Primary forms of HALP are caused by mutations in the genes coding for cholesteryl ester transfer protein (CETP), hepatic lipase (HL), apolipoprotein C-III (apo C-III), scavenger receptor class B type I (SR-BI) and endothelial lipase (EL). However, in the last decades, genome-wide association studies (GWAS) have also suggested a polygenic inheritance of hyperalphalipoproteinemia. Epidemiological studies have suggested that HDL-C is inversely correlated with cardiovascular (CV) risk, but recent Mendelian randomization data have shown a lack of atheroprotective causal effects of HDL-C. This review will focus on primary forms of HALP, the role of polygenic inheritance on HDL-C, associated risk for cardiovascular diseases and possible treatment options.

Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of High-Density Lipoprotein Cholesterol in Humans

BACKGROUND

Genome-wide association studies have uncovered common variants at many loci influencing human complex traits, such as high-density lipoprotein cholesterol (HDL-C). However, the contribution of the identified genes is difficult to ascertain from current efforts interrogating common variants with small effects. Thus, there is a pressing need for scalable, cost-effective strategies for uncovering causal variants, many of which may be rare and noncoding.

METHODS

Here, we used a molecular inversion probe target capture approach to resequence both coding and regulatory regions at 7 HDL-C-associated loci in 797 individuals with extremely high HDL-C versus 735 low-to-normal HDL-C controls. Our targets included protein-coding regions of GALNT2, APOA5, APOC3, SCARB1, CCDC92, ZNF664, CETP, and LIPG (>9 kb) and proximate noncoding regulatory features (>42 kb).

RESULTS

Exome-wide genotyping in 1114 of the 1532 participants yielded a >90% genotyping concordance rate with molecular inversion probe-identified variants in ≈90% of participants. This approach rediscovered nearly all established genome-wide association studies associations in GALNT2, CETP, and LIPG loci with significant and concordant associations with HDL-C from our phenotypic extremes design at 0.1% of the sample size of lipid genome-wide association studies. In addition, we identified a novel, rare, CETP noncoding variant enriched in the extreme high HDL-C group (P<0.01, score test).

CONCLUSIONS

Our targeted resequencing of individuals at the HDL-C phenotypic extremes offers a novel, efficient, and cost-effective approach for identifying rare coding and noncoding variation differences in extreme phenotypes and supports the rationale for applying this methodology to uncover rare variation-particularly noncoding variation-underlying myriad complex traits.

Association between the LIPG polymorphisms and serum lipid levels in the Maonan and Han populations

Introduction

The Maonan population is a relatively isolated minority in China. Little is known about endothelial lipase gene (LIPG) single nucleotide polymorphisms (SNPs) and serum lipid levels in the Chinese populations. The present study aimed to detect the association of several LIPG SNPs and environmental factors with serum lipid levels in the Chinese Maonan and Han populations.

Methods

In total, 773 subjects of Maonan ethnicity and 710 participants of Han ethnicity were randomly selected from our previous stratified randomized samples. Genotypes of the LIPG rs2156552, rs4939883 and rs7241918 SNPs were determined by polymerase chain reaction‐restriction fragment length polymorphism, and then confirmed by direct sequencing.

Results

The allelic (rs2156552, rs4939883 and rs7241918) and genotypic (rs2156552 and rs4939883) frequencies were different between the two ethnic groups (p < 0.05–0.01). The minor allele carriers had lower apolipoprotein (Apo)A1/ApoB ratio (rs2156552 and rs7241918), high‐density lipoprotein cholesterol (HDL‐C) and apolipoprotein (Apo)A1 (rs2156552) levels and higher ApoB levels (rs4939883) in the Han population, and lower HDL‐C (rs2156552, rs4939883 and rs7241918) levels in the Maonan minority than the minor allele non‐carriers (p < 0.0167 after Bonferroni correction). Subgroup analyses according to sex showed that the minor allele carriers had a lower ApoA1/ApoB ratio (rs2156552 and rs7241918) and higher ApoB levels (rs7241918) in Han males, and lower ApoA1 and HDL‐C levels in Maonan females than the minor allele non‐carriers (p < 0.0167–0.001).

Conclusions

The present study demonstrates the association between the LIPG polymorphsims and serum lipid levels in the two ethnic groups. These associations might have an ethnic‐ and or/sex‐specificity.

LIPG SNPs, their haplotypes and gene-environment interactions on serum lipid levels

Background

Maonan nationality is a relatively conservative and isolated minority in the Southwest of China. Little is known about the association of endothelial lipase gene (LIPG) single nucleotide polymorphisms (SNPs) and serum lipid levels in the Chinese populations.

Methods

A total of 1280 subjects of Maonan nationality and 1218 participants of Han nationality were randomly selected from our previous stratified randomized samples. Genotypes of the four LIPG SNPs were determined by polymerase chain reaction-restriction fragment length polymorphism, and then confirmed by direct sequencing.

Results

Several SNPs were associated with high-density lipoprotein cholesterol (rs3813082, rs2000813 and rs2097055) in the both ethnic groups; total cholesterol and apolipoprotein (Apo) A1 (rs2000813) in Han nationality; and low-density lipoprotein cholesterol, ApoB, triglyceride (rs2097055) and ApoA1 (rs3819166) in Maonan minority (P < 0.0125 for all after Bonferroni correction). The commonest haplotype was rs3813082T-rs2000813C-rs2097055T-rs3819166A (Han, 44.2% and Maonan, 48.7%). The frequencies of the T-C-T-A, T-C-T-G, T-T-C-G and G-T-C-G haplotypes were different between the Maonan and Han populations (P < 0.05–0.001). The associations between haplotypes and dyslipidemia were also different in the Han and/or Maonan populations (P < 0.05–0.001).

Conclusions

The differences in serum lipid profiles between the two ethnic groups might partly be attributed to these LIPG SNPs, their haplotypes and gene-environmental interactions.

Trial registration

Retrospectively registered.

Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci

Genome-wide association studies (GWASs) have identified thousands of loci associated with hundreds of complex diseases and traits, and progress is being made toward elucidating the causal variants and genes underlying these associations. Functional characterization of mechanisms at GWAS loci is a multi-faceted challenge. Challenges include linkage disequilibrium and allelic heterogeneity at each locus, the noncoding nature of most loci, and the time and cost needed for experimentally evaluating the potential mechanistic contributions of genes and variants. As GWAS sample sizes increase, more loci are identified, and the complexities of individual loci emerge. Loci can consist of multiple association signals, each of which can reflect the influence of multiple variants, inseparable by association analyses. Each signal within a locus can influence the same or different target genes. Experimental studies of genes and variants can differ on the basis of cell type, cellular environment, or other context-specific variables. In this review, we describe the complexity of mechanisms at GWAS loci-including multiple signals, multiple variants, and/or multiple genes-and the implications these complexities hold for experimental study design and interpretation of GWAS mechanisms.

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Large-scale deep-coverage whole-genome sequencing (WGS) is now feasible and offers potential advantages for locus discovery. We perform WGS in 16,324 participants from four ancestries at mean depth >29X and analyze genotypes with four quantitative traits-plasma total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. Common variant association yields known loci except for few variants previously poorly imputed. Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. A high 2M-SNP LDL-C polygenic score (top 5th percentile) confers similar effect size to a monogenic mutation (~30 mg/dl higher for each); however, among those with severe hypercholesterolemia, 23% have a high polygenic score and only 2% carry a monogenic mutation. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia.

HDL Cholesterol Metabolism and the Risk of CHD: New Insights from Human Genetics

PURPOSE OF REVIEW

Elevated high-density lipoprotein cholesterol levels in the blood (HDL-C) represent one of the strongest epidemiological surrogates for protection against coronary heart disease (CHD), but recent human genetic and pharmacological intervention studies have raised controversy about the causality of this relationship. Here, we review recent discoveries from human genome studies using new analytic tools as well as relevant animal studies that have both addressed, and in some cases, fueled this controversy.

RECENT FINDINGS

Methodologic developments in genotyping and sequencing, such as genome-wide association studies (GWAS), exome sequencing, and exome array genotyping, have been applied to the study of HDL-C and risk of CHD in large, multi-ethnic populations. Some of these efforts focused on population-wide variation in common variants have uncovered new polymorphisms at novel loci associated with HDL-C and, in some cases, CHD risk. Other efforts have discovered loss-of-function variants for the first time in genes previously implicated in HDL metabolism through common variant studies or animal models. These studies have allowed the genetic relationship between these pathways, HDL-C and CHD to be explored in humans for the first time through analysis tools such as Mendelian randomization. We explore these discoveries for selected key HDL-C genes CETP, LCAT, LIPG, SCARB1, and novel loci implicated from GWAS including GALNT2, KLF14, and TTC39B. Recent human genetics findings have identified new nodes regulating HDL metabolism while reshaping our current understanding of known candidate genes to HDL and CHD risk through the study of critical variants across model systems. Despite their effect on HDL-C, variants in many of the reviewed genes were found to lack any association with CHD. These data collectively indicate that HDL-C concentration, which represents a static picture of a very dynamic and heterogeneous metabolic milieu, is unlikely to be itself causally protective against CHD. In this context, human genetics represent an extremely valuable tool to further explore the biological mechanisms regulating HDL metabolism and investigate what role, if any, HDL plays in the pathogenesis of CHD.

Association of the CETP Taq1B and LIPG Thr111Ile Polymorphisms with Glycated Hemoglobin and Blood Lipids in Newly Diagnosed Hyperlipidemic Patients

OBJECTIVE

To examine the association of 2 common polymorphisms in high-density lipoprotein (HDL)-related genes, namely, cholesterol ester transfer protein CETP Taq1B (rs708272) and endothelial lipase LIPG Thr111Ile (rs2000813), with glycated hemoglobin (A1C), blood lipid levels and the risk for type 2 diabetes in a group of hyperlipidemic patients from northern Greece.

METHODS

We categorized 175 patients with hyperlipidemia into 2 subgroups according to the presence or absence of type 2 diabetes, defined as a recent diagnosis, A1C >6.5% and/or fasting glucose >126 mg/dL. Genotypes for the 2 polymorphisms studied were determined by polymerase chain reaction-restriction fragment length polymorphism. Both polymorphisms were analyzed by multivariate and univariate analyses of baseline A1C levels and plasma lipids. The genotype and allele frequencies of the 2 subgroups were compared.

RESULTS

The CETP Taq1B polymorphism was associated with HDL-cholesterol (HDL-C) and A1C levels, but this association was affected by type 2 diabetes; the association with A1C levels was significant only in type 2 diabetes (p=0.005), whereas the association with HDL-C occurred only in the subgroup without type 2 diabetes (p<0.001). LIPG Thr111Ile did not affect plasma HDL-C or A1C levels independently but appeared to modulate their association with CETP Taq1B, and LIPG 111IleIle homozygotes tended to be present at a higher frequency in the hyperlipidemic patients with type 2 diabetes compared to the hyperlipidemic patients without type 2 diabetes (p=0.056).

CONCLUSIONS

In hyperlipidemic patients, apart from its known association with HDL-C, CETP Taq1B is also associated with A1C levels, and both associations are modified by type 2 diabetes and LIPG Thr111Ile.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

The widely used statistical methods test interaction for single phenotype. However, we often observe pleotropic genetic interaction effects. The simultaneous gene-gene (GxG) interaction analysis of multiple complementary traits will increase statistical power to detect GxG interactions. Although GxG interactions play an important role in uncovering the genetic structure of complex traits, the statistical methods for detecting GxG interactions in multiple phenotypes remains less developed owing to its potential complexity. Therefore, we extend functional regression model from single variate to multivariate for simultaneous GxG interaction analysis of multiple correlated phenotypes. Large-scale simulations are conducted to evaluate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare power with traditional multivariate pair-wise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for interaction analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic GxG interactions. 267 pairs of genes that formed a genetic interaction network showed significant evidence of interactions influencing five traits.

Targeted next-generation sequencing to diagnose disorders of HDL cholesterol

A low level of HDL cholesterol (HDL-C) is a common clinical scenario and an important marker for increased cardiovascular risk. Many patients with very low or very high HDL-C have a rare mutation in one of several genes, but identification of the molecular abnormality in patients with extreme HDL-C is rarely performed in clinical practice. We investigated the accuracy and diagnostic yield of a targeted next-generation sequencing (NGS) assay for extreme levels of HDL-C. We developed a targeted NGS panel to capture the exons, intron/exon boundaries, and untranslated regions of 26 genes with highly penetrant effects on plasma lipid levels. We sequenced 141 patients with extreme HDL-C levels and prioritized variants in accordance with medical genetics guidelines. We identified 35 pathogenic and probably pathogenic variants in HDL genes, including 21 novel variants, and performed functional validation on a subset of these. Overall, a molecular diagnosis was established in 35.9% of patients with low HDL-C and 5.2% with high HDL-C, and all prioritized variants identified by NGS were confirmed by Sanger sequencing. Our results suggest that a molecular diagnosis can be identified in a substantial proportion of patients with low HDL-C using targeted NGS.

A rare functional noncoding variant at the GWAS-implicated MIR137/MIR2682 locus might confer risk to schizophrenia and bipolar disorder

Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in >100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans MIR137 (miR137) and MIR2682 (miR2682), two microRNA genes important for neuronal function. We sequenced ∼6.9 kb MIR137/MIR2682 and upstream regulatory sequences in 2,610 SZ cases and 2,611 controls of European ancestry. We identified 133 rare variants with minor allele frequency (MAF) <0.5%. The rare variant burden in promoters and enhancers, but not insulators, was associated with SZ (p = 0.021 for MAF < 0.5%, p = 0.003 for MAF < 0.1%). A rare enhancer SNP, 1:g.98515539A>T, presented exclusively in 11 SZ cases (nominal p = 4.8 × 10(-4)). We further identified its risk allele T in 2 of 2,434 additional SZ cases, 11 of 4,339 bipolar (BP) cases, and 3 of 3,572 SZ/BP study controls and 1,688 population controls; yielding combined p values of 0.0007, 0.0013, and 0.0001 for SZ, BP, and SZ/BP, respectively. The risk allele T of 1:g.98515539A>T reduced enhancer activity of its flanking sequence by >50% in human neuroblastoma cells, predicting lower expression of MIR137/MIR2682. Both empirical and computational analyses showed weaker transcription factor (YY1) binding by the risk allele. Chromatin conformation capture (3C) assay further indicated that 1:g.98515539A>T influenced MIR137/MIR2682, but not the nearby DPYD or LOC729987. Our results suggest that rare noncoding risk variants are associated with SZ and BP at MIR137/MIR2682 locus, with risk alleles decreasing MIR137/MIR2682 expression.

A roadmap to investigate the genetic basis of bicuspid aortic valve and its complications: insights from the International BAVCon (Bicuspid Aortic Valve Consortium)

Bicuspid aortic valve (BAV) is the most common adult congenital heart defect and is found in 0.5% to 2.0% of the general population. The term "BAV" refers to a heterogeneous group of disorders characterized by diverse aortic valve malformations with associated aortopathy, congenital heart defects, and genetic syndromes. Even after decades of investigation, the genetic determinants of BAV and its complications remain largely undefined. Just as BAV phenotypes are highly variable, the genetic etiologies of BAV are equally diverse and vary from complex inheritance in families to sporadic cases without any evidence of inheritance. In this paper, the authors discuss current concepts in BAV genetics and propose a roadmap for unraveling unanswered questions about BAV through the integrated analysis of genetic and clinical data.

Genetics of lipid traits: Genome-wide approaches yield new biology and clues to causality in coronary artery disease

A wealth of novel lipid loci have been identified through a variety of approaches focused on common and low-frequency variation and collaborative metaanalyses in multiethnic populations. Despite progress in identification of loci, the task of determining causal variants remains challenging. This work will undoubtedly be enhanced by improved understanding of regulatory DNA at a genomewide level as well as new methodologies for interrogating the relationships between noncoding SNPs and regulatory regions. Equally challenging is the identification of causal genes at novel loci. Some progress has been made for a handful of genes and comprehensive testing of candidate genes using multiple model systems is underway. Additional insights will be gleaned from focusing on low frequency and rare coding variation at candidate loci in large populations. This article is part of a Special Issue entitled: From Genome to Function.

Mining the genome for lipid genes

Mining of the genome for lipid genes has since the early 1970s helped to shape our understanding of how triglycerides are packaged (in chylomicrons), repackaged (in very low density lipoproteins; VLDL), and hydrolyzed, and also how remnant and low-density lipoproteins (LDL) are cleared from the circulation. Gene discoveries have also provided insights into high-density lipoprotein (HDL) biogenesis and remodeling. Interestingly, at least half of these key molecular genetic studies were initiated with the benefit of prior knowledge of relevant proteins. In addition, multiple important findings originated from studies in mouse, and from other types of non-genetic approaches. Although it appears by now that the main lipid pathways have been uncovered, and that only modulators or adaptor proteins such as those encoded by LDLRAP1, APOA5, ANGPLT3/4, and PCSK9 are currently being discovered, genome wide association studies (GWAS) in particular have implicated many new loci based on statistical analyses; these may prove to have equally large impacts on lipoprotein traits as gene products that are already known. On the other hand, since 2004 - and particularly since 2010 when massively parallel sequencing has become de rigeur - no major new insights into genes governing lipid metabolism have been reported. This is probably because the etiologies of true Mendelian lipid disorders with overt clinical complications have been largely resolved. In the meantime, it has become clear that proving the importance of new candidate genes is challenging. This could be due to very low frequencies of large impact variants in the population. It must further be emphasized that functional genetic studies, while necessary, are often difficult to accomplish, making it hazardous to upgrade a variant that is simply associated to being definitively causative. Also, it is clear that applying a monogenic approach to dissect complex lipid traits that are mostly of polygenic origin is the wrong way to proceed. The hope is that large-scale data acquisition combined with sophisticated computerized analyses will help to prioritize and select the most promising candidate genes for future research. We suggest that at this point in time, investment in sequence technology driven candidate gene discovery could be recalibrated by refocusing efforts on direct functional analysis of the genes that have already been discovered. This article is part of a Special Issue entitled: From Genome to Function.

Monogenic causes of elevated HDL cholesterol and implications for development of new therapeutics

Identification of the CETP, LIPG (encoding endothelial lipase) and APOC3 genes, and ana lysis of rare genetic variants in them, have allowed researchers to increase understanding of HDL metabolism significantly. However, development of cardiovascular risk-reducing therapeutics targeting the proteins encoded by these genes has been less straightforward. The failure of two CETP inhibitors is complex but illustrates a possible over-reliance on HDL cholesterol as a marker of therapeutic efficacy. The case of endothelial lipase exemplifies the importance of utilizing population-wide genetic studies of rare variants in potential therapeutic targets to gain information on cardiovascular disease end points. Similar population-wide studies of cardiovascular end points make apoC-III a potentially attractive target for lipid-related drug discovery. These three cases illustrate the positives and negatives of single-gene studies relating to HDL-related cardiovascular drug discovery; such studies should focus not only on HDL cholesterol and other components of the lipid profile, but also on the effect genetic variants have on cardiovascular end points.

Genetic and structure-function studies of missense mutations in human endothelial lipase

Endothelial lipase (EL) plays a pivotal role in HDL metabolism. We sought to characterize EL and its interaction with HDL as well as its natural variants genetically, functionally and structurally. We screened our biethnic population sample (n = 802) for selected missense mutations (n = 5) and identified T111I as the only common variant. Multiple linear regression analyses in Hispanic subjects revealed an unexpected association between T111I and elevated LDL-C (p-value = 0.012) and total cholesterol (p-value = 0.004). We examined lipase activity of selected missense mutants (n = 10) and found different impacts on EL function, ranging from normal to complete loss of activity. EL-HDL lipidomic analyses indicated that EL has a defined remodeling of HDL without exhaustion of the substrate and a distinct and preference for several fatty acids that are lipid mediators and known for their potent pro- and anti-inflammatory properties. Structural studies using homology modeling revealed a novel α/β motif in the C-domain, unique to EL. The EL dimer was found to have the flexibility to expand and to bind various sizes of HDL particles. The likely impact of the all known missense mutations (n = 18) on the structure of EL was examined using molecular modeling and the impact they may have on EL lipase activity using a novel structure-function slope based on their structural free energy differences. The results of this multidisciplinary approach delineated the impact of EL and its variants on HDL. Moreover, the results suggested EL to have the capacity to modulate vascular health through its role in fatty acid-based signaling pathways.

Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained

Genome-wide association studies (GWAS) have identified ∼100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1×10⁻⁴ in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.

Lipid traits are heritable, but many of the DNA variants that influence lipid levels remain unknown. In a genomic region, more than one variant may affect gene expression or function, and the frequencies of these variants can differ across populations. Genotyping densely spaced variants in individuals with different ancestries may increase the chance of identifying variants that affect gene expression or function. We analyzed high-density genotyped variants for association with TG, HDL-C, and LDL-C in African Americans, East Asians, and Europeans. At several genomic regions, we provide evidence that two or more variants can influence lipid traits; across loci, these additional signals increase the proportion of trait variation that can be explained by genes. At some association signals shared across populations, combining data from individuals of different ancestries narrowed the set of likely functional variants. At PCSK9 and APOA5, the data suggest that different variants influence trait levels in different populations. Variants previously reported to alter gene expression or function frequently exhibited the strongest association at those signals. The multiple signals and population-specific characteristics of the loci described here may be shared by genetic loci for other complex traits.

Segregation of LIPG, CETP, and GALNT2 mutations in Caucasian families with extremely high HDL cholesterol

To date, few mutations are described to underlie highly-elevated HDLc levels in families. Here we sequenced the coding regions and adjacent sequence of the LIPG, CETP, and GALNT2 genes in 171 unrelated Dutch Caucasian probands with HDLc≥90th percentile and analyzed segregation of mutations with lipid phenotypes in family members. In these probands, mutations were most frequent in LIPG (12.9%) followed by GALNT2 (2.3%) and CETP (0.6%). A total of 6 of 10 mutations in these three genes were novel (60.0%), and mutations segregated with elevated HDLc in families. Interestingly, the LIPG mutations N396S and R476W, which usually result in elevated HDLc, were unexpectedly found in 6 probands with low HDLc (i.e., ≤10th percentile). However, 5 of these probands also carried mutations in ABCA1, LCAT, or LPL. Finally, no CETP and GALNT2 mutations were found in 136 unrelated probands with low HDLc. Taken together, we show that rare coding and splicing mutations in LIPG, CETP, and GALNT2 are enriched in persons with hyperalphalipoproteinemia and segregate with elevated HDLc in families. Moreover, LIPG mutations do not overcome low HDLc in individuals with ABCA1 and possibly LCAT and LPL mutations, indicating that LIPG affects HDLc levels downstream of these proteins.

Population-Based Resequencing of LIPG and ZNF202 Genes in Subjects with Extreme HDL Levels

Endothelial lipase (LIPG) and zinc finger protein 202 (ZNF202) are two pivotal genes in high density lipoprotein (HDL metabolism). We sought to determine their genetic contribution to variation in HDL-cholesterol levels by comprehensive resequencing of both genes in 235 individuals with high or low HDL-C levels. The selected subjects were 141 Whites (High HDL Group: n = 68, [Formula: see text] Low HDL Group: n = 73, [Formula: see text]) and 94 Hispanics (High HDL Group: n = 46, [Formula: see text] Low HDL Group: n = 48, [Formula: see text]). We identified a total of 185 and 122 sequence variants in LIPG and ZNF202, respectively. We found only two missense variants in LIPG (T111I and N396S) and two in ZNF202 (A154V and K259E). In both genes, there were several variants unique to either the low or high HDL group. For LIPG, the proportion of unique variants differed between the high and low HDL groups in both Whites (p = 0.022) and Hispanics (p = 0.017), but for ZNF202 this difference was observed only in Hispanics (p = 0.021). We also identified a common haplotype in ZNF202 among Whites that was significantly associated with the high HDL group (p = 0.013). These findings provide insights into the genetics of LIPG and ZNF202, and suggest that sequence variants occurring with high frequency in non-exonic regions may play a prominent role in modulating HDL-C levels in the general population.

Finding genes and variants for lipid levels after genome-wide association analysis

PURPOSE OF REVIEW

We review the main findings from genome-wide association studies (GWAS) for levels of HDL-cholesterol, LDL-cholesterol and triglycerides, including approaches to identify the functional variant(s) or gene(s). We discuss study design and challenges related to whole genome or exome sequencing to identify novel genes and variants.

RECENT FINDINGS

GWAS have detected approximately 100 loci associated with one or more lipid trait. Fine mapping of several loci for LDL-cholesterol demonstrated that the trait variance explained may double when the functional variants responsible for the association signals are identified. Experimental follow-up of three loci identified by GWAS has identified functional genes GALNT2, TRIB1, and SORT1, and a functional variant at SORT1.

SUMMARY

The goal of genetic studies for lipid levels is to improve treatment and ultimately reduce the prevalence of heart disease. Many signals identified by GWAS have modest effect sizes, useful for identifying novel biologically relevant genes, but less useful for personalized medicine. Whole genome or exome sequencing studies may fill this gap by identifying rare variants of larger effect associated with lipid levels and heart disease.