Abstract
Background:
The identification and understanding of therapeutic targets for atherosclerotic cardiovascular disease (ASCVD) is of fundamental importance given its global health and economic burden. Inhibition of angiopoietin-like 3 (ANGPTL3) has demonstrated a cardioprotective effect, showing promise for ASCVD treatment, and is currently the focus of ongoing clinical trials. Here we assessed the genetic basis of variation in ANGPTL3 levels in the San Antonio Family Heart Study.
Methods:
We assayed ANGPTL3 protein levels in ~1,000 Mexican Americans from extended pedigrees. By drawing upon existing plasma lipidome profiles and genomic data we conducted analyses to understand the genetic basis to variation in ANGPTL3 protein levels, and accordingly the correlation with the plasma lipidome.
Results:
In a variance components framework we identified that variation in ANGPTL3 was significantly heritable (h2=0.33, P=1.31×10−16). To explore the genetic basis of this heritability, we conducted a genome-wide linkage scan and identified significant linkage (LOD = 6.18) to a locus on chromosome 1 at 90 cM, corresponding to the ANGPTL3 gene location. In the genomes of 23 individuals from a single pedigree, we identified a loss of function (LoF) variant, rs398122988 (N121Kfs*2), in ANGPTL3, that was significantly associated with lower ANGPTL3 levels (β=−1.69 SDU, P=3.367×10−13), and accounted for the linkage signal at this locus. Given the known role of ANGPTL3 as an inhibitor of endothelial and lipoprotein lipase we explored the association of ANGPTL3 protein levels and rs398122988 with the plasma lipidome and related phenotypes, identifying novel associations with phosphatidylinositols.
Conclusions:
Variation in ANGPTL3 protein levels is heritable and under significant genetic control. Both ANGPTL3 levels and LoF variants in ANGPTL3 have significant associations with the plasma lipidome. These findings further our understanding of ANGPTL3 as a therapeutic target for ASCVD.
Journal Subject Terms: Biomarkers; Genetic, Association Studies; Genetics; Lipids and Cholesterol
Keywords: lipids; genetics, linkage analysis; genetics, association studies; family study; ANGPTL3
Introduction
The identification of therapeutic targets for the management and treatment of atherosclerotic cardiovascular disease (ASCVD) is a critical area of interest to the medical community. A central paradigm within the field is that cardiometabolic diseases (including ASCVD and type 2 diabetes) are intimately linked with abnormal levels of the classical lipid and lipoprotein variables of high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG) and total cholesterol1. Increased levels of total cholesterol, LDL-C and TG, and decreased levels of HDL-C are all associated with cardiometabolic disease2 and are in part determined by genetic factors.3, 4 Each of these lipid parameters, as well as ASCVD risk, have been shown previously to have a significant genetic component.5
There has been continued interest in the identification of therapeutic targets that modulate these classical lipids toward a healthy or cardioprotective profile. One target that is being actively pursued is angiopoietin-like 3 (ANGPTL3).6–8 Clinical trials targeting a cardioprotective decrease in ANGPTL3 protein levels using antibodies and antisense oligonucleotides are ongoing and to date have shown promise for the targeting of ANGPTL3 in ASCVD.6, 8, 9 Recent topline reports of an ANGPTL3 siRNA, ARO-ANG3, in a Phase 1 clinical study demonstrate safety, durability and efficacy promoted by both single and multiple injections.10–12 Phase 2 and 3 trial results of evinacumab, a monoclonal antibody against ANGPTL3, and phase 2 trial results of vupanorsen, an antisense oligonucleotide, have shown them to be safe, and effective for the treatment of homozygous (phase 3) and heterozygous (phase 2) familial hypercholesterolemia (evinacumab)13, 14 and for lowering triglycerides and atherogenic lipoproteins in patients with type 2 diabetes, hepatic steatosis and hypertriglyceridaemia (vupanorsen).9
In humans, ANGPTL3 is a 460-amino acid hepatokine that has established roles as an inhibitor of lipoprotein lipase (LPL) and endothelial lipase (EL).15, 16 ANGPTL3 was first implicated in lipid metabolism in mice17 and subsequently implicated in a common variant based genome-wide association study (GWAS) of human plasma lipids as loci associated with TG levels.18, 19 In 2009, a study of unselected individuals from the Dallas Heart Study cohort identified rare loss-of-function (LoF) variants in ANGPTL3 and other ANGPTL protein family members (ANGPTL4 and ANGPTL5) that resulted in a lowering of the circulating levels of plasma TG in variant carriers.20 Successively, multiple studies have shown that loss-of-function variants in ANGPTL3 are causal for familial combined hypolipidemia (OMIM: 605019) identifying both homozygous and compound heterozygous LoF variant carriers with clinical characteristics.21, 22 Through the inhibition of EL, ANGPTL3 regulates plasma HDL-C levels, whereas through the inhibition of LPL, ANGPTL3 regulates plasma TG levels. In ANGPTL3 LoF variant carriers, the inhibitory effects of ANGPTL3 are reduced resulting in a decrease in plasma levels of HDL-C, LDL-C and TG.20, 21, 23–25 Recent work now suggests that the mechanism by which LDL-C is decreased through ANGPTL3 inhibition occurs through the resulting derepression of EL.26, 27 These pleiotropic effects across lipids and lipoproteins, together with other non-lipid related effects underlie the rationale for which ANGPTL3 is a target for ASCVD. In addition, a joint meta-analysis of multiple cohorts, including Mendelian randomization analytical approaches, showed that carriers of LoF variants have 34–39% reduction in major adverse cardiac event (MACE) incidence, underscoring the possible therapeutic utility of targeting ANGPTL3.6, 28
Using a well characterized cohort of over 1,000 Mexican Americans from extended pedigrees in the San Antonio Family Heart Study (SAFHS), we set out to identify the genetic basis of variation in ANGPTL3 levels. Using multipoint quantitative trait linkage analysis, we conducted a 1cM genome-wide linkage scan to identify genomic regions influencing levels of circulating ANGPTL3. Then, within the linkage region identified we focused on a clear positional candidate gene and used available whole genome sequence (WGS) to identify a rare genetic variant influencing ANGPTL3. We then used plasma lipidome profiles available for this cohort to assess the relationship between ANGPTL3 and levels of individual lipid species leading to the identification of previously unknown lipid associations with ANGPTL3. Further, we show that a genetic variant identified to influence ANGPTL3 levels also affects individual lipids in the plasma lipidome. This work provides a new, deeper understanding into ANGPTL3 biology which is important for advancing the development of this clinical target.
Methods
Data used in this paper are publicly available through dbGaP (accession numbers: phs000462.v2.p1, phs001215.v1.p1) except for the lipidome data and ANGPTL3 protein levels associated with the San Antonio Family Heart Study. Both the lipidome data and ANGPTL3 protein measurements for the San Antonio Family Heart Study can be made available to researchers from the corresponding author Dr. Joanne E. Curran (joanne.curran@utrgv.edu) via a material transfer agreement for work consistent with the informed consent.
Informed consent was obtained from all participants before sample collection. The study conformed to the Declaration of Helsinki and has been approved by the Institutional Review Boards of the University of Texas Health Sciences Center at San Antonio and the University of Texas Rio Grande Valley. The full methodology for this work is described in detail in the supplemental methods section.
Results
ANGPTL3 protein levels are heritable
ANGPTL3 protein levels were measured by ELISA in fasting plasma samples from 1,030 individuals in the San Antonio Family Heart Study. The mean concentration of ANGPTL3 protein was 218.64 ng/ml (median = 199.99, standard deviation = 101.79), with a measured minimum value of 24.59 ng/ml and a maximum of 811.53 ng/ml, in line with previously reported ranges.29
Through variance components modelling in SOLAR we assessed - for the first time to our knowledge - the heritability of ANGPTL3 plasma protein levels. In this fully adjusted model, we identified that ANGPTL3 levels were highly heritable, h2 = 0.325 (SE = 0.05, P=1.31×10−16) indicating a significant genetic component to variation in levels of this protein.
Genome-wide linkage scan identifies significant linkage on chromosome 1
To genomically localize genetic factors underlying the heritability of ANGPTL3 we conducted a 1 centimorgan (cM) genome-wide linkage scan, in an empirical kinship framework. This identified a significant linkage for ANGPTL3 on chromosome 1 at 90 cM of LOD = 6.18 (Figure 1). This locus, spanning at chr1:62151202-63714422, contains a clear positional candidate for ANGPTL3 protein levels, the ANGPTL3 structural gene itself.
Figure 1.

A 1 cM genome-wide linkage scan for ANGPTL3 identifies a significant linkage on chromosome 1 at 90 cM with LOD = 6.18. Dashed red line indicates the LOD=3 linkage significance threshold.
Measured genotype association testing of variants in ANGPTL3 identifies a rare variant associated with protein levels
Using available whole genome sequence data from individuals in the SAFHS, we conducted a multi-sample joint call of the ANGPTL3 gene, chr1:63063191-63071984 (hg19 coordinates) using freebayes to identify exonic variation in this gene. There were eight exonic variants in ANGPTL3 across the 1,222 SAFHS genomes including one 5bp deletion and seven SNVs. Among the 1,030 SAFHS individuals with available ANGPTL3 measurements, six of these variants were present. All SAFHS variant carriers were heterozygous. These variants are summarized in Supplemental Table II.
To identify variants associated with ANGPTL3 protein levels, we conducted a measured genotype association analysis of the six exonic variants. The frameshift deletion rs398122988 (N121Kfs*2), with 23 heterozygous carriers among the sample was identified as associated with a significant decrease in ANGPTL3 protein levels (β = −1.69 SDU, P = 3.367 × 10−13). This is a very large biological effect size. For carriers of rs398122988 the unadjusted mean ANGPTL3 protein concentration was 82.76 ng/ml (median = 86.36, standard deviation = 38.46), whereas for non-carriers the unadjusted mean ANGPTL3 protein concentration was 221.74 ng/ml (median = 203.34, standard deviation = 100.67). The remaining five exonic variants in ANGPTL3 were not associated with ANGPTL3 protein levels, even after controlling for rs398122988. We were unable to assess the effect of two variants, rs767910330 (E98K) and rs1196457133 (R428R), as the carriers of these variants did not have ANGPTL3 protein level measurements.
The rs398122988 variant is a rare 5bp frameshift deletion, introducing an early STOP codon into the mRNA sequence. Across all gnomAD populations (v3.1) the minor allele frequency of this variant is 0.0003 and it is most frequent in non-Finnish Europeans (MAF=0.0005) and not present in Asian populations.30 This variant is predicted to produce a truncated protein of 121 amino acids, instead of the normal 460 amino acid full length protein, and thus is classified as a loss of function variant. In our cohort this variant occurs in a single multigenerational family (Figure 2). Figure 3 shows the distribution of ANGPTL3 protein levels in carriers and non-carriers of rs398122988.
Figure 2.

Subset of the SAFHS pedigree displaying inheritance of 23 copies of the ANGPTL3 5bp deletion variant rs398122988. This rare loss of function frameshift deletion variant was only detected in this single family. Half-filled symbols are heterozygous carriers of the rs398122988 variant, cross-hatched symbols are individuals for whom WGS data is not available, empty white symbols are individuals who do not carry the variant. Blue highlights indicate individuals with multiple spouses.
Figure 3.

Distribution of ANGPTL3 protein levels in rs398122988 carriers (0/1, n=23) and non-carriers (0/0, n=1007), shown as violin plots with internal box plots. The unadjusted mean ANGPTL3 protein concentration was 82.76 ng/ml (median = 86.36, standard deviation = 38.46) for carriers of rs398122988 and 221.74 ng/ml (median = 203.34, standard deviation = 100.67) in non-carriers.
ANGPTL3 frameshift deletion variant rs398122988 accounts for the linkage signal detected on chromosome 1
To test whether the rs398122988 variant identified as associated with ANGPTL3 levels accounts for the chromosome 1 linkage signal we conducted a 1 cM genome-wide linkage scan including this variant as a covariate in the linkage model. When incorporated into the multipoint linkage model, the rs398122988 variant completely accounts for the linkage detected at the chr1 90 cM locus (Supplemental Figure I).
The rs398122988 shows significant associations with plasma lipidome species
Previously, we measured 319 lipid species in 23 classes of lipids in blood plasma from participants of the SAFHS. For 1,020 of these individuals, whole genome sequence data are available. This includes 22 carriers of the rs398122988 variant. Given the importance of ANGPTL3 in lipid metabolism, we set out to identify whether this functional variant was associated with individual lipid species.
After appropriate adjustment for the effective number of tests in our plasma lipidome analyses, the rs398122988 variant was associated with decreases in 7 individual lipid species and one combined total lipid class measure, as shown in Table 1. The strongest association was with phosphatidylinositol (PI) species PI(36:2), where carriers of the rs398122988 variant had a 1.36 SDU decrease (SE = 0.27, P=2.77×10−7).
Table 1.
Significant lipidome associations with rs398122988
| Lipid species* | p(SNP) | β (SDU) |
|---|---|---|
| PI(36:2) | 2.77 × 10−7 | −1.36 |
| Total PI | 3.49 × 10−7 | −1.27 |
| PI(36:1) | 6.15 × 10−7 | −1.29 |
| PI(38:4) | 1.85 × 10−6 | −1.21 |
| PI(40:5) | 2.60 × 10−5 | −1.11 |
| PI(36:3) | 6.20 × 10−5 | −1.03 |
| PI(40:4) | 2.55 × 10−4 | −0.94 |
| PI(40:6) | 4.58 × 10−4 | −0.95 |
PI = phosphatidylinositol
Across the lipidome, 67 individual lipid species - in 14 classes - as well as six combined total lipid class measures had nominally significant (P<0.05) associations with rs398122988 indicating that this loss of function variant in ANGPTL3 has a broad effect on lipid biology. Figure 4 shows the effect of the rs398122988 variant across the plasma lipidome. As the majority of the phosphatidylinositol class of lipids were associated, Figure 5 shows the specific effects of rs398122988 on this class. The full list of plasma lipidome associations of rs398122988 is summarized in Supplemental Table III.
Figure 4.

Plasma lipidome associations with the ANGPTL3 5bp deletion variant rs398122988. Significant decreases in phosphatidylinositols are observed with nominally significant increases and decreases in other lipid classes.
Figure 5.

Specific phosphatidylinositol lipid associations with the ANGPTL3 5bp deletion variant rs398122988. Error bars indicate standard error of beta coefficients.
To determine whether the lipidome changes were specific to the effect of the ANGPTL3 variant or were secondary to the changes in the overall lipoprotein profile, we performed the same association analysis with PI(36:2) but corrected for traditional lipid and lipoprotein parameters. The significant effect of rs398122988 was retained (P=2.51×10−4), although the effect size was slightly reduced (β=−0.86). This shows that there is an independent effect of rs398122988 on PI that is not secondary to global changes in classical plasma lipids and proteins.
ANGPTL3 levels are associated with a broad range of plasma lipidome species
For 876 individuals with plasma lipidome profiles we have matching ANGPTL3 measures from the same visit. We considered whether ANGPTL3 protein levels associate with individual plasma lipidome species, or total lipidome class measures. After appropriate adjustment for the effective number of tests in our plasma lipidome analyses, associations with ANGPTL3 were detected in 15 classes of lipids. This included associations with 49 individual species, as well as five total lipidome class measures. Supplemental Figure II summarizes the association of ANGPTL3 protein levels across the lipidome, highlighting the additional 75 nominally significant associations and the broad relationship of ANGPTL3 with the plasma lipidome. The full list of plasma lipidome associations with ANGPTL3 is provided in Supplemental Table IV.
To determine whether the lipidomic effect of rs398122988 was due solely to its effect on ANGPTL3 protein levels, we conducted an analysis of the top associated plasma lipidome PI species from Table 1 conditional on ANGPTL3 protein levels. The association of rs398122988 with lipid species is retained, however significance and the effect size of the association are reduced ~0.1 to 0.3 SDU (depending on the lipid species). For example, the significance of the lead rs398122988 association with PI(36:2), after controlling for ANGPTL3 protein levels, decreases to P=5.10×10−4 and an effect size of β=−1.19. These conditional findings suggest that the effect of the variant on the lipidome is not solely a result of quantitative changes in ANGPTL3 protein levels and may reflect the influence of the structural alteration of the protein on downstream pathways.
Broader phenotypic associations of ANGPTL3 protein levels and rs398122988
Beyond the plasma lipidome, ANGPTL3 protein levels and the rs398122988 variant show nominal associations with multiple phenotypes in the SAFHS (Table 2 and Table 3 respectively). Individuals who experienced a MACE including non-fatal myocardial infarction, stroke and cardiovascular death, during 25 years of follow-up visits of the San Antonio Family Heart Study, had higher ANGPTL3 protein levels. ANGPTL3 protein levels were higher for both individuals with a lifetime MACE event (including death), P=0.034 (β=0.18 SDU), and for individuals who died from MACE, P=0.004 (β=0.51 SDU). Participants who reported that they were cigarette smokers also showed increased ANGPTL3 levels (P=0.015, β=0.16 SDU).
Table 2.
Phenotypic associations with ANGPTL3 protein levels
| Phenotype | Individuals | ANGPTL3 P | ANGPTL3 β (SDU) |
|---|---|---|---|
| ApoE (mg/dL) | 992 | 3.00 × 10−6 | 0.14 |
| ApoA-II (mg/dL) | 994 | 0.001 | −0.09 |
| Cholesterol Efflux (with cAMP) | 1001 | 0.001 | 0.10 |
| Total Cholesterol Efflux | 1001 | 0.003 | 0.09 |
| MACE Death | 1030 | 0.004 | 0.51 |
| Waist Circumference (mm) | 1028 | 0.009 | 0.17 |
| Smoking Status | 1018 | 0.015 | 0.16 |
| MACE Event (including death) | 1030 | 0.034 | 0.18 |
Table 3.
rs398122988 variant associations with SAFHS phenotypes
| Phenotype | Individuals | P | β (SDU) | MAC |
|---|---|---|---|---|
| VLDL-C (calculated) | 1026 | 0.002 | −0.78 | 23 |
| Total Triglycerides | 1191 | 0.003 | −0.77 | 23 |
| ApoA-I (mg/dL) | 1156 | 0.004 | −0.85 | 18 |
| Total Serum Cholesterol (mg/dL) | 1191 | 0.005 | −0.71 | 23 |
| Cholesterol Efflux (no cAMP) | 1023 | 0.014 | −0.66 | 23 |
| Total HDL-C Levels | 1190 | 0.023 | −0.60 | 23 |
| 2 hour OGTT Glucose Levels (mg/dL) | 1151 | 0.043 | −0.30 | 23 |
Classical lipid phenotype associations were also observed; however, these were primarily with the rs398122988 variant. Variant carriers had lower cholesterol (total, calculated very-low-density lipoprotein cholesterol (VLDL-C), and HDL-C) and lower total TGs. A non-significant decrease in calculated LDL-C cholesterol was also observed in variant carriers (1026 individuals, P=0.28, β=−0.27 SDU). Both ANGPTL3 protein levels and rs398122988 were associated with cholesterol efflux measures, with a positive association observed between ANGPTL3 protein and total cholesterol efflux and cholesterol efflux with cAMP (Table 2), meanwhile rs398122988 variant carriers have lower cholesterol efflux with no cAMP (Table 3). Variant carriers also had nominally significant lower glucose levels after a fasting 2-hour oral glucose tolerance test challenge.
ANGPTL3 protein levels were positively associated with waist circumference and ApoE serum levels. Whereas negative associations were identified with ApoA-II serum levels (Table 2). Comparatively, the rs398122988 variant was associated with decreased ApoA-I serum levels (Table 3).
Discussion
Here we report the largest genomics and lipidomics based study of ANGPTL3.
Our investigation of the genetic factors underlying variation in ANGPTL3 protein levels in a Mexican American family-based study has measured for the first time the heritability of ANGPTL3, finding that around 33% of the total phenotypic variation in ANGPTL3 is due to genetic factors. We have conducted the first multipoint linkage analysis of ANGPTL3, identifying significant linkage to the ANGPTL3 gene locus. Drawing upon WGS data from our cohort, we then identified a rare LoF frameshift deletion variant in ANGPTL3, rs398122988, that was significantly associated with lower ANGPTL3 levels in 23 variant carriers from a single family. This variant completely accounted for the identified linkage signal and suggests that at least in this cohort, the primary genetic driver of variation in ANGPTL3 occurs at the structural gene locus. The rs398122988 variant has previously been identified in individuals with familial combined hypolipidemia23, 24, particularly as a homozygous variant, but has also been identified as a rare variant in multiple large population cohorts.6, 30, 31
Given the known biological role of ANGPTL3 in metabolism of the classical lipid parameters of HDL-C, LDL-C and TG and our plasma lipidome measurements for 319 lipid species available in 1,020 individuals with genotype information for the rs398122988 variant we assessed the effect of this variant on lipid profiles in this cohort. For the classical lipid parameters, rs398122988 shows nominally significant associations with total cholesterol, total HDL-C, and TG levels, with a decrease in each associated with this LoF variant. These were expected associations given the established biology of ANGPTL3. Unexpectedly, LDL-C levels were not associated with this LoF variant, however a nominally significant decrease in calculated VLDL-C was observed. Recent elegant work with mouse knock-out models has shown that the reported LDL receptor (LDLR) independent decreases in LDL-C levels associated with ANGPTL3 inhibition are driven through the derepression of EL, specifically the upstream remodeling of VLDL-C particles.26 Our nominal association with a decrease in calculated VLDL-C in LoF variant carriers is in line with this work. However, we infer from our findings that a partial loss of ANGPTL3 (as in heterozygous LoF variant carriers), due to the rs398122988 variant, is insufficient to drive enough derepression of EL to result in downstream decreases in LDL-C levels.
A particularly novel finding within our cohort has been the identification that ANGPTL3 LoF variant carriers had significantly decreased levels of several phosphatidylinositol (PI) species. Shown in Figure 5, carriers had 0.31 – 1.36 SDU decreases in PIs, with the largest effect of a 1.36 SDU decrease seen for PI(36:2). This is a completely novel association for ANGPTL3 in humans and is of significant interest in light of the clinical potential of ANGPTL3 targeting. Phosphatidylinositol species associations were reported recently in a study of the plasma lipidome effects of pravastatin treatment32, negative associations were shown with multiple species of PI and the lipid ratio PI(36:2)/PC(38:4) ratio was identified as predicting the efficacy of pravastatin therapy in secondary prevention of MACE. Together this raises the possibility that the cardioprotective effects of ANGPTL3 targeting may be mediated through the lowering of PI, in addition to the demonstrated effects on the clinical lipid parameters.
ANGPTL3 protein levels in this study showed positive associations with ApoE levels and negative associations with ApoA-II levels. ApoE is enriched in triglyceride-rich lipoprotein particles (TRLP) such as VLDL-C and in remnant lipoprotein particles, and it mediates clearance of these particles in the liver via LRP1 and LDL receptors.33 The positive relationship between ApoE and ANGPTL3 levels may be explained by decreased LPL-mediated catabolism and clearance of TRLPs when ANGPTL3 levels are elevated. ApoA-II is the second most abundant protein associated with HDL-C and interacts with ABCA1 to promote ABCA1-mediated cholesterol efflux.34 The inverse relationship between ApoA-II and ANGPTL3 protein levels is somewhat surprising since ANGPTL3 represses EL activity that mediates HDL-C catabolism. However, when studying the lipoprotein metabolism in a hypobetalipoproteinemia family harboring compound heterozygous ANGPTL3 LoF variants, the observed low HDL-C and ApoA-I levels were caused by higher ApoA-I fractional catabolic rates without significant decreases in ApoA-I production rates.35 Thus, HDL-C, and possibly ApoA-II, is likely produced at normal levels with the same or more potential to mediate peripheral cholesterol efflux and is cleared more rapidly in ANGPTL3 LoF variant carriers.
We also observed that increased ANGPTL3 protein levels were associated with increased waist circumference, and that LoF variant carriers had a nominal association with lower plasma glucose levels 2 hours after an oral glucose tolerance test. This observation, together with previous findings that a deficiency of ANGPTL3 is associated with an increased insulin sensitivity and TG lowering in mutation carriers, supports an extended role for ANGPTL3 targeting in cardiometabolic disease and diabetes.36
The recent work of Adam et al.26 with LDLR and EL knockout mouse models provides a great deal of understanding as to the lipidomic differences observed here between carriers and non-carriers of the rs398122988 LoF variant. Adam et al. established that several lipidomic changes occur as a result of the derepression of EL from ANGPTL3 inhibition using evinacumab. Further, as shown previously in mouse models where EL has been overexpressed, there is a reduction in cholesterol efflux as a result of HDL-C lowering37, which is in line with the decrease in cholesterol efflux observed in rs398122988 variant carriers and positive correlations between ANGPTL3 protein levels and cholesterol efflux. Together, these observations corroborate that the decreases in PI and phosphatidylcholine species observed here might be explained by the derepression of EL leading to increased phospholipid hydrolysis from HDL-C, whereas the observed decreases in cholesteryl esters are primarily a result of increased LPL activity. The decrease in PI may be important given that it provides a source of phosphatidylinositol phosphate (PIPs), signaling molecules that are themselves associated with multiple aspects of ASCVD.38 However, at this stage we do not know if the plasma levels of PI also reflect the cellular levels in relevant tissues and so further studies are required to define the potential protection imparted by PI lowering.
The pursuit of disease modifying and disease eradicating therapeutics for ASCVD is a priority for human medical research. ANGPTL3 has been identified and confirmed as a viable target for ASCVD therapy with clinical trials actively underway. Our findings here provide evidence that the effect of ANGPTL3 targeting extends beyond traditional lipid and lipoprotein lowering to the effect on specific species of the plasma lipidome. By joining a pedigree-based study and classical genetic analyses with whole genome sequencing and lipidome profiling, we identified a LoF ANGPTL3 variant segregating in a single Mexican American family. This facilitated direct profiling of the effect of the variant on the plasma lipidome and identified novel associations with phosphatidylinositol species, further advancing our biological understanding of ANGPTL3 in ASCVD.
Supplementary Material
Acknowledgments:
The authors thank and acknowledge the participants of the San Antonio Family Heart Study for their continued involvement in our research programs. We thank Katherine Truax and Marcelo Leandro from the South Texas Diabetes and Obesity Institute for laboratory assistance. We also thank Dr. Mark Kowala for his support of this research and Dr. Marian Mosior for scientific discussions related to biomarker and disease state correlations.
Sources of Funding:
This work was supported in part by National Institutes of Health (NIH) grants P01 HL045522 (SAFHS data collection), R01 HL113323 (whole genome sequencing and genotyping), R01 HL140681 (lipid and CVD analysis), R37 MH059490 (analytical methods and software used), R01 EB015611 (analytical methods and software used), and T2D-GENES Consortium grants [U01 DK085524 (San Antonio Mexican American Family Studies), U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545 (whole genome sequencing)]. This work was conducted in part in facilities constructed under the support of NIH grant C06 RR020547. P.J.M. is supported by a senior research fellowship from the National Health and Medical Research Council of Australia (APP1042095). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Nonstandard Abbreviations and Acronyms
- ABCA1
ATP binding cassette subfamily A member 1
- ApoA
apolipoprotein A
- ApoE
apolipoprotein E
- ASCVD
atherosclerotic cardiovascular disease
- ANGPTL3
angiopoietin-like 3
- β
beta coefficient
- cAMP
cyclic adenosine monophosphate
- chr
chromosome
- cM
centimorgan
- dbGaP
database of genotypes and phenotypes
- EL
endothelial lipase
- ELISA
enzyme-linked immunosorbent assay
- GWAS
genome-wide association study
- h2
narrow-sense heritability
- LDLR
low-density lipoprotein receptor
- LOD
logarithm of odds
- LoF
loss of function
- LPL
lipoprotein lipase
- LRP1
low-density lipoprotein receptor related protein 1
- MACE
major adverse cardiac event
- MAF
minor allele frequency
- OMIM
Online Mendelian Inheritance in Man
- PC
phosphatidylcholine
- PI
phosphatidylinositol
- PIP
phosphatidylinositol phosphate
- SAFHS
San Antonio Family Heart Study
- SDU
standard deviation units
- SE
standard error
- siRNA
short interfering ribonucleic acid
- SNV
single nucleotide variant
- SOLAR
Sequential Oligogenetic Linkage Analysis Routines software
- WGS
whole genome sequence
Footnotes
Disclosures: L.F.M. and M.B. are employees of Eli Lilly and company. No additional financial interests to declare.
Supplemental Materials:
References:
- 1.Sacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, Miller M, Rimm EB, Rudel LL, Robinson JG, et al. Dietary Fats and Cardiovascular Disease: A Presidential Advisory From the American Heart Association. Circulation. 2017;136:e1–e23. [DOI] [PubMed] [Google Scholar]
- 2.Emerging Risk Factors C, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, Wood AM, Lewington S, Sattar N, et al. Major lipids, apolipoproteins, and risk of vascular disease. Jama. 2009;302:1993–2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Kathiresan S, Manning AK, Demissie S, D’Agostino RB, Surti A, Guiducci C, Gianniny L, Burtt NP, Melander O, Orho-Melander M, et al. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study. Bmc Med Genet. 2007;8 Suppl 1:S17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Natarajan P, Peloso GM, Zekavat SM, Montasser M, Ganna A, Chaffin M, Khera AV, Zhou W, Bloom JM, Engreitz JM, et al. Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nat Commun. 2018;9:3391. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Biorxiv. 2019:563866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dewey FE, Gusarova V, Dunbar RL, O’Dushlaine C, Schurmann C, Gottesman O, McCarthy S, Van Hout CV, Bruse S, Dansky HM, et al. Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease. N Engl J Med. 2017;377:211–221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Gaudet D, Gipe DA, Pordy R, Ahmad Z, Cuchel M, Shah PK, Chyu KY, Sasiela WJ, Chan KC, Brisson D, et al. ANGPTL3 Inhibition in Homozygous Familial Hypercholesterolemia. N Engl J Med. 2017;377:296–297. [DOI] [PubMed] [Google Scholar]
- 8.Graham MJ, Lee RG, Brandt TA, Tai LJ, Fu W, Peralta R, Yu R, Hurh E, Paz E, McEvoy BW, et al. Cardiovascular and Metabolic Effects of ANGPTL3 Antisense Oligonucleotides. N Engl J Med. 2017;377:222–232. [DOI] [PubMed] [Google Scholar]
- 9.Gaudet D, Karwatowska-Prokopczuk E, Baum SJ, Hurh E, Kingsbury J, Bartlett VJ, Figueroa AL, Piscitelli P, Singleton W, Witztum JL, et al. Vupanorsen, an N-acetyl galactosamine-conjugated antisense drug to ANGPTL3 mRNA, lowers triglycerides and atherogenic lipoproteins in patients with diabetes, hepatic steatosis, and hypertriglyceridaemia. Eur Heart J. 2020;41:3936–3945. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Given BD. Abstract: Treating hypertriglyceridemic states with RNA interference – emergence of an exciting new modality to treat cardiovascular diseases. Paper presented at: Global Summit On Cardiology & Heart Diseases; 2019; Dubai, UAE. [Google Scholar]
- 11.Watts GF, Schwabe C, Scott R, Gladding P, Sullivan DR, Baker J, Clifton P, Hamilton J, Given B, Melquist S, et al. Abstract: RNA Interference Targeting Hepatic Angiopoietin-Like Protein 3 Results in Prolonged Reductions in Plasma Triglycerides and LDL-C in Human Subjects. Circulation. 2019;140:E987–E988. [Google Scholar]
- 12.Watts GF, Schwabe C, Scott R, Gladding P, Sullivan D, Baker J, Clifton P, Hamilton J, Given B, San Martin J. Abstract: Pharmacodynamic Effect of ARO-ANG3, an Investigational RNA Interference Targeting Hepatic Angiopoietin-like Protein 3, in Patients With Hypercholesterolemia. Circulation. 2020;142:A15751–A15751. [Google Scholar]
- 13.Raal FJ, Rosenson RS, Reeskamp LF, Hovingh GK, Kastelein JJP, Rubba P, Ali S, Banerjee P, Chan KC, Gipe DA, et al. Evinacumab for Homozygous Familial Hypercholesterolemia. N Engl J Med. 2020;383:711–720. [DOI] [PubMed] [Google Scholar]
- 14.Rosenson RS, Burgess LJ, Ebenbichler CF, Baum SJ, Stroes ESG, Ali S, Khilla N, Hamlin R, Pordy R, Dong Y, et al. Evinacumab in Patients with Refractory Hypercholesterolemia. N Engl J Med. 2020;383:2307–2319. [DOI] [PubMed] [Google Scholar]
- 15.Shimizugawa T, Ono M, Shimamura M, Yoshida K, Ando Y, Koishi R, Ueda K, Inaba T, Minekura H, Kohama T, et al. ANGPTL3 decreases very low density lipoprotein triglyceride clearance by inhibition of lipoprotein lipase. J Biol Chem. 2002;277:33742 33748. [DOI] [PubMed] [Google Scholar]
- 16.Shimamura M, Matsuda M, Yasumo H, Okazaki M, Fujimoto K, Kono K, Shimizugawa T, Ando Y, Koishi R, Kohama T, et al. Angiopoietin-like protein3 regulates plasma HDL cholesterol through suppression of endothelial lipase. Arteriosclerosis Thrombosis Vasc Biology. 2007;27:366 372. [DOI] [PubMed] [Google Scholar]
- 17.Koishi R, Ando Y, Ono M, Shimamura M, Yasumo H, Fujiwara T, Horikoshi H, Furukawa H. Angptl3 regulates lipid metabolism in mice. Nat Genet. 2002;30:151–7. [DOI] [PubMed] [Google Scholar]
- 18.Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008;40:189–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161 169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Romeo S, Yin W, Kozlitina J, Pennacchio LA, Boerwinkle E, Hobbs HH, Cohen JC. Rare loss-of-function mutations in ANGPTL family members contribute to plasma triglyceride levels in humans. J Clin Invest. 2009;119:70–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Musunuru K, Pirruccello JP, Do R, Peloso GM, Guiducci C, Sougnez C, Garimella KV, Fisher S, Abreu J, Barry AJ, et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N Engl J Med. 2010;363:2220–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Calandra S, Tarugi P, Averna M, Bertolini S. Familial combined hypolipidemia due to mutations in the ANGPTL3 gene. Clinical Lipidology. 2013;8:81–95. [Google Scholar]
- 23.Martin-Campos JM, Roig R, Mayoral C, Martinez S, Marti G, Arroyo JA, Julve J, Blanco-Vaca F. Identification of a novel mutation in the ANGPTL3 gene in two families diagnosed of familial hypobetalipoproteinemia without APOB mutation. Clin Chim Acta. 2012;413:552–5. [DOI] [PubMed] [Google Scholar]
- 24.Noto D, Cefalu AB, Valenti V, Fayer F, Pinotti E, Ditta M, Spina R, Vigna G, Yue P, Kathiresan S, et al. Prevalence of ANGPTL3 and APOB gene mutations in subjects with combined hypolipidemia. Arterioscler Thromb Vasc Biol. 2012;32:805–9. [DOI] [PubMed] [Google Scholar]
- 25.Pisciotta L, Favari E, Magnolo L, Simonelli S, Adorni MP, Sallo R, Fancello T, Zavaroni I, Ardigo D, Bernini F, et al. Characterization of three kindreds with familial combined hypolipidemia caused by loss-of-function mutations of ANGPTL3. Circ Cardiovasc Genet. 2012;5:42–50. [DOI] [PubMed] [Google Scholar]
- 26.Adam RC, Mintah IJ, Alexa-Braun CA, Shihanian LM, Lee JS, Banerjee P, Hamon SC, Kim HI, Cohen JC, Hobbs HH, et al. Angiopoietin-like protein 3 governs LDL-cholesterol levels through endothelial lipase-dependent VLDL clearance. J Lipid Res. 2020;61:1271–1286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Wu L, Soundarapandian MM, Castoreno AB, Millar JS, Rader DJ. LDL-Cholesterol Reduction by ANGPTL3 Inhibition in Mice Is Dependent on Endothelial Lipase. Circ Res. 2020;127:1112–1114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Stitziel NO, Khera AV, Wang X, Bierhals AJ, Vourakis AC, Sperry AE, Natarajan P, Klarin D, Emdin CA, Zekavat SM, et al. ANGPTL3 Deficiency and Protection Against Coronary Artery Disease. J Am Coll Cardiol. 2017;69:2054–2063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Mehta N, Qamar A, Qu L, Qasim AN, Mehta NN, Reilly MP, Rader DJ. Differential association of plasma angiopoietin-like proteins 3 and 4 with lipid and metabolic traits. Arterioscler Thromb Vasc Biol. 2014;34:1057–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Hansen SEJ, Madsen CM, Varbo A, Tybjaerg-Hansen A, Nordestgaard BG. Genetic Variants Associated With Increased Plasma Levels of Triglycerides, via Effects on the Lipoprotein Lipase Pathway, Increase Risk of Acute Pancreatitis. Clin Gastroenterol Hepatol. 2020. [DOI] [PubMed] [Google Scholar]
- 32.Jayawardana KS, Mundra PA, Giles C, Barlow CK, Nestel PJ, Barnes EH, Kirby A, Thompson P, Sullivan DR, Alshehry ZH, et al. Changes in plasma lipids predict pravastatin efficacy in secondary prevention. Jci Insight. 2019;4:e128438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Phillips MC. Apolipoprotein E isoforms and lipoprotein metabolism. Iubmb Life. 2014;66:616–23. [DOI] [PubMed] [Google Scholar]
- 34.Smith LE, Segrest JP, Davidson WS. Helical domains that mediate lipid solubilization and ABCA1-specific cholesterol efflux in apolipoproteins C-I and A-II. J Lipid Res. 2013;54:1939–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Elias N, Patterson BW, Schonfeld G. In Vivo Metabolism of ApoB, ApoA-I, and VLDL Triglycerides in a Form of Hypobetalipoproteinemia Not Linked to the ApoB Gene. Arteriosclerosis Thrombosis Vasc Biology. 2000;20:1309–1315. [DOI] [PubMed] [Google Scholar]
- 36.Robciuc MR, Maranghi M, Lahikainen A, Rader D, Bensadoun A, Öörni K, Metso J, Minicocci I, Ciociola E, Ceci F, et al. Angptl3 deficiency is associated with increased insulin sensitivity, lipoprotein lipase activity, and decreased serum free fatty acids. Arteriosclerosis Thrombosis Vasc Biology. 2013;33:1706 1713. [DOI] [PubMed] [Google Scholar]
- 37.Schilcher I, Kern S, Hrzenjak A, Eichmann TO, Stojakovic T, Scharnagl H, Duta-Mare M, Kratky D, Marsche G, Frank S. Impact of Endothelial Lipase on Cholesterol Efflux Capacity of Serum and High-density Lipoprotein. Sci Rep. 2017;7:12485. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Krajnik A, Brazzo JA 3rd, Vaidyanathan K, Das T, Redondo-Munoz J, Bae Y. Phosphoinositide Signaling and Mechanotransduction in Cardiovascular Biology and Disease. Front Cell Dev Biol. 2020;8:595849. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Mitchell BD, Kammerer CM, Blangero J, Mahaney MC, Rainwater DL, Dyke B, Hixson JE, Henkel RD, Sharp RM, Comuzzie AG, et al. Genetic and environmental contributions to cardiovascular risk factors in Mexican Americans. The San Antonio Family Heart Study. Circulation. 1996;94:2159–70. [DOI] [PubMed] [Google Scholar]
- 40.Olvera RL, Bearden CE, Velligan DI, Almasy L, Carless MA, Curran JE, Williamson DE, Duggirala R, Blangero J and Glahn DC. Common genetic influences on depression, alcohol, and substance use disorders in Mexican-American families. Am J Medical Genetics Part B Neuropsychiatric Genetics. 2011;156B:561 568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, Comuzzie AG, Mahaney MC, Jowett JB, Shaw J, et al. Plasma lipid profiling in a large population-based cohort. J Lipid Res. 2013;54:2898–908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Bellis C, Kulkarni H, Mamtani M, Kent JW Jr., Wong G, Weir JM, Barlow CK, Diego V, Almeida M, Dyer TD, et al. Human plasma lipidome is pleiotropically associated with cardiovascular risk factors and death. Circ Cardiovasc Genet. 2014;7:854–863. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Knowles EEM, Curran JE, Meikle PJ, Huynh K, Mathias SR, Göring HHH, VandeBerg JL, Mahaney MC, Jalbrzikowski M, Mosior MK, et al. Disentangling the genetic overlap between cholesterol and suicide risk. Neuropsychopharmacol. 2018;43:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Blackburn NB, Michael LF, Meikle PJ, Peralta JM, Mosior M, McAhren S, Bui HH, Bellinger MA, Giles C, Kumar S, et al. Rare DEGS1 variant significantly alters de novo ceramide synthesis pathway. J Lipid Res. 2019;60:1630–1639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Purcell SM, Chang CC. PLINK v1.90b3m.
- 47.Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Han L, Abney M. Identity by descent estimation with dense genome-wide genotype data. Genet Epidemiol. 2011;35:557–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Peralta JM, Blackburn NB, Porto A, Blangero J, Charlesworth J. Genome-wide linkage scan for loci influencing plasma triglyceride levels. BMC Proc. 2018;12:52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Higham NJ. Computing the nearest correlation matrix - a problem from finance. Ima Journal of Numerical Analysis. 2002;22:329–343. [Google Scholar]
- 51.Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62:1198–211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXivorg. 2012. [Google Scholar]
- 53.Chang X, Wang K. wANNOVAR: annotating genetic variants for personal genomes via the web. J Med Genet. 2012;49:433–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Robinson JT, Thorvaldsdottir H, Wenger AM, Zehir A, Mesirov JP. Variant Review with the Integrative Genomics Viewer. Cancer Res. 2017;77:e31–e34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Wickham H ggplot2: Springer-Verlag; New York; 2016. [Google Scholar]
- 56.Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005;95:221–227. [DOI] [PubMed] [Google Scholar]
- 57.Wen SH, Lu ZS. Factors affecting the effective number of tests in genetic association studies: a comparative study of three PCA-based methods. J Hum Genet. 2011;56:428–35. [DOI] [PubMed] [Google Scholar]
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