Article | Open | Published:

Fine mapping the CETP region reveals a common intronic insertion associated to HDL-C

npj Aging and Mechanisms of Disease volume 1, Article number: 15011 (2015) | Download Citation

Abstract

Background:

Individuals with exceptional longevity and their offspring have significantly larger high-density lipoprotein concentrations (HDL-C) particle sizes due to the increased homozygosity for the I405V variant in the cholesteryl ester transfer protein (CETP) gene. In this study, we investigate the association of CETP and HDL-C further to identify novel, independent CETP variants associated with HDL-C in humans.

Methods:

We performed a meta-analysis of HDL-C within the CETP region using 59,432 individuals imputed with 1000 Genomes data. We performed replication in an independent sample of 47,866 individuals and validation was done by Sanger sequencing.

Results:

The meta-analysis of HDL-C within the CETP region identified five independent variants, including an exonic variant and a common intronic insertion. We replicated these 5 variants significantly in an independent sample of 47,866 individuals. Sanger sequencing of the insertion within a single family confirmed segregation of this variant. The strongest reported association between HDL-C and CETP variants, was rs3764261; however, after conditioning on the five novel variants we identified the support for rs3764261 was highly reduced (βunadjusted=3.179 mg/dl (P value=5.25×10−509), βadjusted=0.859 mg/dl (P value=9.51×10−25)), and this finding suggests that these five novel variants may partly explain the association of CETP with HDL-C. Indeed, three of the five novel variants (rs34065661, rs5817082, rs7499892) are independent of rs3764261.

Conclusions:

The causal variants in CETP that account for the association with HDL-C remain unknown. We used studies imputed to the 1000 Genomes reference panel for fine mapping of the CETP region. We identified and validated five variants within this region that may partly account for the association of the known variant (rs3764261), as well as other sources of genetic contribution to HDL-C.

Introduction

Aging is characterized by a deterioration in the maintenance of homeostatic processes over time, leading to functional decline and increased risk for disease and death.1 One of the genes linked to healthy aging and longevity is the cholesteryl ester transfer protein (CETP) gene.1,2 Homozygosity in the 405VV variants of CETP is associated with lower concentrations of CETP, higher concentrations of high-density lipoprotein concentrations (HDL-C), and greater HDL-C particle size, all associated with both protection against cardiovascular disease3 and exceptional longevity.4

Functional analyses in mice,5 hamsters,6 and rabbits7 have revealed that the protein encoded by the CETP gene mediates the transfer of cholesteryl esters from HDL-C to other lipoproteins such as atherogenic (V)LDL particle and is a key participant in the reverse transport of cholesterol from the periphery to the liver.8 Due to the function of CETP and the association of the gene with HDL-C in humans,9,10 the CETP gene is one of the targets for drug development for dyslipidemia.6,11,12 CETP-inhibition leads to an increase of HDL-C from 30 up to 140% depending on the compound used. The first drug of its class, Torcetrapib was unfortunately associated with an increased mortality and morbidity in patients receiving the CETP inhibitor in addition to atorvastatin.13,14

The estimated heritability of HDL-C levels is high in humans: 47–76%.15,​16,​17,​18,​19,​20,​21,​22,​23 Previously published whole-genome sequence data23 reported that common variants (minor allele frequency (MAF)>1%) explain up to 61.8% of the variance in HDL-C levels and that rare variants (MAF<1%) explain an additional 7.8% of the variance. Genome-wide association studies revealed that numerous variants are associated with HDL-C, among which are various common9,10 and rare24,25 variants within the CETP gene in multiple ancestries.4,8,26,​27,​28 In this paper, we investigate the association between CETP and HDL-C in humans in further detail to identify variants that are likely to be causal.

To this end, we used a meta-analysis of association studies with imputed genotypes within the CETP region. Our study consisted of data from 59,432 samples, of which the genotypes were imputed to the 1000 Genomes project reference panel (version Phase 1 integrated release v3, April 2012, all populations). By using 1000 Genomes imputed data, we expected to find more rare or low-frequent variants, as well as novel insertions and deletions.

Materials and Methods

Study descriptions

The descriptions of the participating cohorts can be found in the Supplementary Material. All studies were performed with the approval of the local medical ethics committees, and written informed consent was obtained from all participants.

Study samples and phenotypes

The total number of individuals in the discovery phase was 59,432 and in the replication phase 47,866. Of the discovery samples, 44,108 individuals (74.21%) were of European ancestry. Of the replication samples, 47,081 individuals (98.36%) were of European ancestry. A summary of the details of both the discovery and replication cohorts participating in this study can be found in Supplementary Table 1.

Genotyping and imputations

All cohorts were genotyped using commercially available Affymetrix or Illumina genotyping arrays, or custom Perlegen arrays. Quality control was performed independently for each study. To facilitate meta-analysis and replication, each discovery and replication cohort performed genotype imputation using IMPUTE229 or Minimac30 with reference to the 1000 Genomes project reference panel. The details per cohort can be found in Supplementary Table 2.

Association analysis in discovery cohorts

The lipid measurements were adjusted for sex, age, and age2 in all cohorts, and if necessary also for cohort-specific covariates (Supplementary Table 1). Some cohorts included samples using lipid-lowering medication; we did not adjust for lipid-lowering medication in our analysis because HDL-C levels are only minimally influenced by lipid-lowering medication. Each discovery cohort ran association analysis for all variants within the CETP region (chromosome 16, 56.99–57.02 Mbp) with HDL-C.

Meta-analysis of discovery cohorts

The association results of all discovery cohorts for all variants within the CETP region (chromosome 16, 56.99–57.02 Mbp) were combined using inverse-variance weighting as applied by METAL.31 This tool also applies genomic control by automatically correcting the test statistics to account for small amounts of population stratification or unaccounted relatedness and the tool also allows for heterogeneity. We used the following filters for the variants: 0.3<R2 (measurement for the imputation quality)<1.0 and expected minor allele count (expMAC=2×MAF×R2×sample size)>10 prior to meta-analysis. After meta-analysis of all available variants, we excluded the variants that were not present in at least three cohorts, to prevent false positive findings.

Selection of independent variants

To select only variants that were independently associated with HDL-C, we used the Genome-wide Complex Trait Analysis (GCTA) tool, version 1.13.32 Although this tool currently supports multiple functionalities, we only used the functions for conditional and joint genome-wide association analysis. This function performs a stepwise selection procedure to select independent single nucleotide polymorphisms (SNP) associations by a conditional and joint analysis approach. It utilizes summary-level statistics from the meta-analysis and linkage disequilibrium (LD) corrections between SNPs are estimated from the 1000 Genomes (1000G Phase I Integrated Release Version 22 Haplotypes (2010–11 data freeze, 14 February 2012 haplotypes)). GCTA estimates the effective sample size and determines the effect size, the s.e., and the P value from a joint analysis of all the selected SNPs. In this way, we select the best associated variants in CETP. We subsequently checked whether these variants were in LD within the 1000 Genomes reference panel using PLINK33 software (Supplementary Table 3).

Replication of independent CETP variants

Five variants were selected for replication in a sample of 12 independent cohorts: Athero-Express, CHS, FINCAVAS, LBC1936, Lifelines, LLS, NTR-NESDA, PREVEND, PROSPER, QIMR, TRAILS, and YFS. The lipid measurements were adjusted for sex, age, and age2 in all cohorts, and if necessary also for cohort-specific covariates (Supplementary Table 1b). The details per cohort regarding variant genotyping and imputations can be found in Supplementary Table 2. The association results of all replication cohorts were combined and the s.e.-based weights were calculated by METAL.31 Since none of the five variants are in LD (Supplementary Table 3), the Bonferroni-corrected P value for multiple testing was 0.01.

Test previous published results

The meta-analysis of HDL-C as published by Teslovich et al.9 identified 38 genome-wide significant (P value<5×10−8) variants within the CETP region (chromosome 16, 56.99–57.02 Mbp). Within all discovery and replication cohorts, we tested these 38 variants, adjusting for the 5 newly identified independent variants to explore whether the new variants explain previously published results. The association results of all cohorts were combined and the s.e.-based weights were calculated by METAL.31

We used the genotypes of all 1,092 individuals of the 1000 Genomes project to calculate the correlation between the 38 variants. This correlation matrix was used by matSpDlite34 which examines the ratio of observed eigenvalue variance to its theoretical maximum to determine the number of independent variables. For these 38 genome-wide significant variants within the CETP region, the effective number of independent variables is 18 and therefore the experiment-wide significance threshold required to keep type I error rate at 5% is 2.85×10−3.

Conditional analysis of independent CETP variants

The replicated independent variants were selected for conditional analysis in both the discovery and the replication cohorts. In this analysis we adjusted for the lead SNP for this region as reported by Teslovich et al.9 (rs3764261, chromosome 16, position 56,993,324 bp). The association results of all discovery and replication cohorts were combined and the s.e. based weights were calculated by METAL.31 The Bonferroni-corrected P value for multiple testing was 0.01, since none of the five variants is in LD (Supplementary Table 3).

Validation of the new CETP insertion within a family

Within the ERF study, 3,658 individuals have been genotyped on various Illumina (Illumina, San Diego, CA, USA) and Affymetrix chips (Affymetrix, Santa Clara, CA, USA), followed by imputations with MaCH (1.0.18c) and Minimac (minimac-β-14 March 2012) to the 1000 Genomes reference panel. Based on the best guess imputed genotypes, we selected one family in which we expected the insertion to segregate.

Validation of the insertion was performed by Sanger sequencing. Genomic DNA was isolated from peripheral blood using standard protocols (salting-out). The intron 2–3 of the CETP gene (Supplementary Table 4) was amplified using PCR and the following primer sequences were used to amplify: forward; 5ʹ-tgggggactcaggtctctcc-3ʹ; reverse; 5ʹ-aaagcacctggcccacaacc-3ʹ; size 409 bp.

PCR reactions was performed in 17.5 μl containing 37.5 ng DNA, 10 pmol/μl of each primer, 2.5 mM dNTPs, 10x PCR buffer with Mg+ (Roche) and 5 U/μl FastStart Taq (Roche Nederland B.V., Woerden, the Netherlands). Cycle conditions: 7 min at 94 °C; 10 cycles of 30-s denaturation at 94 °C, 30 s annealing at 70 –1 °C per cycle and 90-s extension at 72 °C; followed by 20 cycles of 30-s denaturation at 94 °C, 30 s at 60 °C, and 90 s at 72 °C; final extension 10 min at 72 °C. Sephadex G50 (Amersham Biosciences) was used to purify the sequenced PCR products. Direct sequencing of both strands was performed using Big Dye Terminator chemistry version 4 (Applied Biosystems, Bleiswijk, the Netherlands). Fragments were loaded on an ABI3100 automated sequencer and analyzed with DNA Sequencing Analysis (version 5.3) and SeqScape (version 2.6) software (Applied Biosystems). All sequence variants are numbered at the nucleotide levels according to the following references: NC_000016.10:g.56963437_56963438insA (NCBI), NM_000078.2:c.233+313_233+314insA, Human Feb. 2009 (GRCh37/hg19) Assembly.

Results

Meta-analysis in all discovery cohorts to select independent variants

The association of all variants within the CETP region (chromosome 16, 56.99–57.02 Mbp) to HDL-C was tested in all discovery cohorts. These results were combined using the inverse-variance weights as applied by METAL.31 After exclusion of the variants that were not present in at least 3 cohorts, 254 variants remained (Figure 1). A conditional and joint analysis of the 254 variants using GCTA identified 5 independent variants (Figure 2). Three variants were intronic (rs5817082, rs4587963, and rs7499892), one variant was intergenic (rs12920974) and one variant was exonic (rs34065661) (Table 1). Using PLINK software,33 we calculated the LD between the five variants based on the 1000 Genomes reference panel, and found that none are in high LD with each other (Supplementary Table 3).

Figure 1
Figure 1

Results of the meta-analysis of all discovery cohorts within the CETP region. CETP, cholesteryl ester transfer protein.

Figure 2
Figure 2

Forest plots from the discovery meta-analysis results for the five independent variants identified within the CETP region. Only cohorts in which the variants passed QC are included in the forest plot. (a) rs12920974 (chromosome 16, position 56,993,025), (b) rs34065661 (chromosome 16, position 56,995,935), (c) rs5817082 (chromosome 16, position 56,997,349), (d) rs4587963 (chromosome 16, position 56,997,369), and (e) rs7499892 (chromosome 16, position 57,006,590). CETP, cholesteryl ester transfer protein.

Table 1: The five independent variants after meta-analysis in the discovery cohorts

Replication of the independent CETP variants

The five independent variants within the CETP region were selected for replication within the following cohorts: Athero-Express, CHS, FINCAVAS, LBC1936, Lifelines, LLS, NTR-NESDA, PREVEND, PROSPER, QIMR, TRAILS, and YFS. Five variants were replicated at a P value of 2.99×10−34 (Figure 3 and Table 2).

Figure 3
Figure 3

Forest plots of the replication meta-analysis for the five independent variants within the CETP region. Only cohorts in which the variants passed QC are included in the forest plot. (a) rs12920974 (chromosome 16, position 56,993,025), (b) rs34065661 (chromosome 16, position 56,995,935), (c) rs5817082 (chromosome 16, position 56,997,349), (d) rs4587963 (chromosome 16, position 56,997,369), and (e) rs7499892 (chromosome 16, position 57,006,590). CETP, cholesteryl ester transfer protein.

Table 2: Replication of the 5 independent variants within the CETP region

Test to explain the previously published results

In each discovery and replication cohort, we tested if the five independent variants explain the associations within the CETP region (chromosome 16, 56.99–57.02 Mbp) as reported in the study by Teslovich et al.9 We tested a total of 38 genome-wide significant (P value<5×10−8) SNPs within this region identified by Teslovich et al.9 and conditioned for the five independent variants in all discovery and replication cohorts. All 38 variants were significantly (P value corrected for multiple testing<2.85×10−3) associated with HDL-C in our joint analyses without adjusting for the 5 independent variants we identified in this work, and 37 (97.37%) were genome-wide significant (P value<5×10−8) despite the fact that our sample size is about 65% of the study by Teslovich et al.9 (Table 3). When conditioning on the 5 variants identified in this work, 27 (71.05%) variants remained significant (P value<2.85×10−3), though the P values were markedly reduced (Table 3). This finding suggests that the new variants we identified may explain in part the previously reported association. Remarkably, the P value of rs3764261 which was reported as the lead SNP for this CETP region by Teslovich et al.9 was highly reduced from 5.25×10−509 to 9.51×10−25 while the β decreased from 3.179 mg/dl to 0.859 mg/dl. This variant is not in LD with any of the five new variants. Due to the lack of LD, the s.e. of rs3764261 does not change much (s.e.unadj=0.066, s.e.adj=0.084), but the effect of rs3764261 does (βunadj=3.179, βadj=0.859) and therefore the χ2 decreases as well, and that results in a higher P value. This indicates that a part of the effect of rs3764261 can be explained by the effect of the five new variants.

Table 3: Unadjusted and conditional analysis of the Teslovich variants on the five independent variants in the combined analysis of all discovery and replication cohorts

Conditional analysis of the independent CETP variants

Next, we performed conditional analysis of the independent variants in both the discovery and replication cohorts. We conditioned on the lead SNP for the CETP region as reported by the study by Teslovich et al.9 (rs3764261, chromosome 16, position 56,993,324 bp), see Table 4 and Figure 4. This analysis showed that three out of the five variants (rs34065661, rs5817082, rs7499892) are independent of rs3764261. For all variants the P values and β’s decreased, but all P values remained significant. The effect of the single variant rs34065661, of the insertion rs5817082, and of the single variant rs7499892 were reduced by 53.20%, 38.48%, and 32.67%, respectively.

Table 4: Analysis of the independent variants within the CETP region conditioned on the lead SNP for the CETP region as reported by the study by Teslovich et al.9 (rs3764261) in the combined analysis of all discovery and replication cohorts
Figure 4
Figure 4

Forest plots of the conditional analysis in the combined discovery and replication cohorts for the five independent variants within the CETP region. Only cohorts in which the variants passed quality control (QC) are included in the forest plot. (a) rs12920974 (chromosome 16, position 56,993,025), (b) rs34065661 (chromosome 16, position 56,995,935), (c) rs5817082 (chromosome 16, position 56,997,349), (d) rs4587963 (chromosome 16, position 56,997,369), and (e) rs7499892 (chromosome 16, position 57,006,590). CETP, cholesteryl ester transfer protein.

Validation of the insertion within a family

We selected based on the best guess imputations of the ERF study, a large family of 30 individuals for Sanger sequencing of rs5817082. Using MERLIN35 we estimated that the total heritability of HDL-C within this family is 27.47%. DNA was available for 16 individuals. Figure 5 shows the results of the Sanger sequencing for rs5817082 for these 16 individuals within the family. The sequencing of the insertion confirmed the best guess results for 10 individuals (62.5%), of which 7 were heterozygous for the insertion, 1 was homozygous for the insertion, and 2 did not carry the insertion. Three individuals that are homozygous for the insertion, were predicted to be heterozygous by the best guess imputations. Three individuals that are heterozygous for the insertion were not predicted to carry the insertion by the best guess imputations. Furthermore, the Sanger sequencing showed that the insertion segregates with the outcome within this family. The proportion of variance explained by the insertion within this family is 35.50%, while the proportion explained by rs3764261, the lead SNP within the CETP region as reported by the study by Teslovich et al.9 is 14.11%.

Figure 5
Figure 5

Validation of the insertion (rs5817082) with a large family. The numbers present the dosage for rs5817082 after imputations, second row the best guess result (I is insertion, R is reference) and the third row the genotypes of the insertion from Sanger sequencing.

Discussion

We conducted an analysis to fine map the association between CETP genetic variants and HDL-C. To this end, a total of 59,432 samples were imputed to the latest version of the 1000 Genomes (version Phase 1 integrated release v3, April 2012, all populations). We identified and replicated five independent variants within the CETP region (chromosome 16, 56.99–57.02 Mbp), of which four are SNPs and one is an insertion. We validated the insertion by Sanger sequencing within a large family, as the largest effect on HDL-C comes from this insertion.

The relationship between the CETP gene and HDL-C has been known for a long time9 and genome-wide association studies have revealed many common and rare variants in this region. Although the associated genetic variants are strongly correlated with HDL-C, the causal variants have not been determined. Our study showed that when using the latest 1000 Genomes reference panel, we have more power to fine map this association. By conditional analysis of the five variants, we were able to reduce the P values of the genome-wide significant associations published before by Teslovich et al.9 Furthermore, conditional analysis showed that three out of the five variants are independent of the lead SNP for the CETP region as reported by the study by Teslovich et al.9 (rs3764261).

Several fine-mapping effort have been previously published36,37 and in all those efforts sequencing was used for the fine mapping. In our project we did not use sequencing, but imputations using the 1000 Genomes as a reference panel. This method has been widely used in the past and is much lower in cost. With new reference panels available, we were able to have a revised study of this region. The 1000 Genomes reference panel consists of 30 million variants including a million insertions and deletions. By using this reference panel for imputation, we were able to impute these insertions and deletions in 59,432 samples from various cohorts. This led to the significant association of an insertion within a known region with HDL-C. So far, no association between a structural variation and HDL-C has been found in such a large sample size. Validation of the insertion by Sanger sequencing confirms the correct imputations of this insertion in 62.5% of the individuals, of which seven heterozygous carriers, one homozygous carrier and two did not carry the insertion.

The results of this study showed that by using the 1000 Genomes reference panel, the proportion of the variance explained can be increased and that multiple common variants in the same region may be implicated in a single family of the ERF study. The insertion we identified in this study explains 35.50% of variation in the HDL-C level in a single family of the ERF study; this is in concordance with the results of the whole-genome sequence data.23 This is much higher than the proportion of the variance explained (14.11%) in the same family by rs3764261, which was reported before as the lead variant of this region. Fine mapping of various associations may help us to unravel the genetic background of various phenotypes.

Although rs3764261 was identified by Teslovich et al.9 to be the lead SNP of this region, other variants are used in clinical settings. Three of the classical variants are located in the promoter region of the CETP gene: −1337C/T (rs708272 or Taq1B), −971G/A, and −629C/A (rs1800775) polymorphisms.38 Carriers of the B2 allele of the common Taq1B polymorphism exhibit lower plasma CETP levels and higher HDL-C. Furthermore, a recent meta-analysis showed that the B2 allele is associated with a reduced risk for coronary heart disease.39 One more classical variant is rs5882A (405I/V), which is located outside the promoter region.40 The −1337C/T and −629C/A are in strong LD, however, they are in very low LD (r2 of 0.442 for rs708272 and 0.461 for rs1800775) with rs3764261, despite the fact that all three variant are within 3,000 bp of each other.

Large HDL-C particle sizes have been associated with exceptional longevity before and with an increased homozygosity for the I405V variant within the CETP gene.1,​2,​3,​4 Many of the studies confirm this relationship, however, all are based on genotyping of the I405V variant. Our study, however, shows that more variants within the CETP gene are associated with HDL-C levels in the blood circulation. Therefore we would suggest investigating more variants within the CETP gene for its association with longevity and healthy aging.

Some genetic variants identified in our study were published before,41,42 but so far no conditional analyses have been performed with these variants. Our study suggests that various CETP variants may be relevant for HDL-levels in the blood circulation and that these may have a substantial role in the heritability of HDL-C in specific families.

References

  1. 1.

    , , , , , . Genetic studies reveal the role of the endocrine and metabolic systems in aging. J Clin Endocrinol Metab 2010; 95: 4493–4500.

  2. 2.

    , , , . The critical role of metabolic pathways in aging. Diabetes 2012; 61: 1315–1322.

  3. 3.

    , , , , , et al. I405V polymorphism of the cholesteryl ester transfer protein (CETP) gene in young and very old people. Arch Gerontol Geriatr 2006; 43: 213–221.

  4. 4.

    , , , , , et al. Unique lipoprotein phenotype and genotype associated with exceptional longevity. JAMA 2003; 290: 2030–2040.

  5. 5.

    , , , , , et al. Decreased early atherosclerotic lesions in hypertriglyceridemic mice expressing cholesteryl ester transfer protein transgene. J Clin Invest 1995; 96: 2071–2074.

  6. 6.

    , , , , , et al. Anacetrapib and dalcetrapib differentially alters HDL metabolism and macrophage-to-feces reverse cholesterol transport at similar levels of CETP inhibition in hamsters. Eur J Pharmacol 2014; 740: 135–143.

  7. 7.

    , , , , , . Effect of inhibiting cholesteryl ester transfer protein on the kinetics of high-density lipoprotein cholesteryl ester transport in plasma: in vivo studies in rabbits. Arterioscler Thromb Vasc Biol 2006; 26: 884–890.

  8. 8.

    , , , , , et al. Increased coronary heart disease in Japanese-American men with mutation in the cholesteryl ester transfer protein gene despite increased HDL levels. J Clin Invest 1996; 97: 2917–2923.

  9. 9.

    , , , , , et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010; 466: 707–713.

  10. 10.

    Global Lipids Genetics Consortium, , , , , et al. Discovery and refinement of loci associated with lipid levels. Nat Genet 2013; 45: 1274–1283.

  11. 11.

    , , , , , et al. Effects of high-density lipoprotein elevation with cholesteryl ester transfer protein inhibition on insulin secretion. Circ Res 2013; 113: 167–175.

  12. 12.

    , , . Novel concepts in HDL pharmacology. Cardiovasc Res 2014; 103: 423–428.

  13. 13.

    , . The failure of torcetrapib: what have we learned? Br J Pharmacol 2008; 154: 1379–1381.

  14. 14.

    , , , , , et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med 2007; 357: 2109–2122.

  15. 15.

    , , . Dissecting the genetic architecture of lipids, lipoproteins, and apolipoproteins: lessons from twin studies. Arterioscler Thromb Vasc Biol 1999; 19: 2826–2834.

  16. 16.

    , , . Biological and environmental sources of variation in plasma lipids and lipoproteins: the Jerusalem Lipid Research Clinic. Hum Hered 1986; 36: 143–153.

  17. 17.

    , , , , , et al. Anthropometry, carbohydrate and lipid metabolism in the East Flanders Prospective Twin Survey: heritabilities. Diabetologia 2007; 50: 2107–2116.

  18. 18.

    , , . Heritabilities of the metabolic syndrome phenotypes and related factors in Korean twins. J Clin Endocrinol Metab 2009; 94: 4946–4952.

  19. 19.

    , , , , , et al. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 2011; 54: 2811–2819.

  20. 20.

    , , . Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet 2012; 8: e1002637.

  21. 21.

    , , . Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet 2013; 9: e1003264.

  22. 22.

    , . Identity-by-descent-based heritability analysis in the Northern Finland Birth Cohort. Hum Genet 2013; 132: 129–138.

  23. 23.

    , , , , , et al. Whole-genome sequence-based analysis of high-density lipoprotein cholesterol. Nat Genet 2013; 45: 899–901.

  24. 24.

    , , , , , et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am J Hum Genet 2014; 94: 223–232.

  25. 25.

    , , , , , et al. Identification of four novel genes contributing to familial elevated plasma HDL cholesterol in humans. J Lipid Res 2014; 55: 1693–1701.

  26. 26.

    , . Obesity and dyslipidemia in South Asians. Nutrients 2013; 5: 2708–2733.

  27. 27.

    , , , , , et al. Gene-gene interaction between CETP and APOE polymorphisms confers higher risk for hypertriglyceridemia in oldest-old Chinese women. Exp Gerontol 2014; 55: 129–133.

  28. 28.

    , , , , , et al. Association of common genetic variants with lipid traits in the Indian population. PLoS ONE 2014; 9: e101688.

  29. 29.

    , , . A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5: e1000529.

  30. 30.

    , , , , . Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 2012; 44: 955–959.

  31. 31.

    , , . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

  32. 32.

    , , , . GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88: 76–82.

  33. 33.

    , , , , , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

  34. 34.

    , . Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb) 2005; 95: 221–227.

  35. 35.

    , , , . Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002; 30: 97–101.

  36. 36.

    , , , , , et al. Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained. PLoS Genet 2013; 9: e1003379.

  37. 37.

    , , , , , et al. Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet 2011; 7: e1002198.

  38. 38.

    , , , , , et al. A novel cholesteryl ester transfer protein promoter polymorphism (−971G/A) associated with plasma high-density lipoprotein cholesterol levels. Interaction with the TaqIB and −629C/A polymorphisms. Atherosclerosis 2002; 161: 269–279.

  39. 39.

    , , , , , et al. Cholesteryl ester transfer protein TaqIB variant, high-density lipoprotein cholesterol levels, cardiovascular risk, and efficacy of pravastatin treatment: individual patient meta-analysis of 13,677 subjects. Circulation 2005; 111: 278–287.

  40. 40.

    , , , , , et al. Common genetic variation in multiple metabolic pathways influences susceptibility to low HDL-cholesterol and coronary heart disease. J Lipid Res 2010; 51: 3524–3532.

  41. 41.

    , , , , , et al. Amerindian-specific regions under positive selection harbour new lipid variants in Latinos. Nat Commun 2014; 5: 3983.

  42. 42.

    , , , , , et al. Genetic analysis of long-lived families reveals novel variants influencing high density-lipoprotein cholesterol. Front Genet 2014; 5: 159.

Download references

Acknowledgements

We especially thank all volunteers who participated in our study. Further detailed acknowledgements are provided in the Supplementary Material. The funding sources of this project can be found in the Supplementary Material.

Author information

Affiliations

  1. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands

    • Elisabeth M van Leeuwen
    • , Aaron Isaacs
    • , Andrea JM Vermeij-Verdoold
    • , Andy ALJ van Oosterhout
    • , Jeannette M Vergeer-Drop
    • , Carolina Medina-Gomez
    • , Fernando Rivadeneira
    • , Andre G Uitterlinden
    • , Abbas Dehghan
    • , Oscar H Franco
    • , Albert Hofman
    •  & Cornelia van Duijn
  2. MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK

    • Jennifer E Huffman
    • , Pau Navarro
    • , Holly Trochet
    • , Veronique Vitart
    • , Caroline Hayward
    •  & Alan F Wright
  3. National Heart, Lung, and Blood Institute (NHLBI) Cardiovascular Epidemiology and Human Genomics Branch, Framingham Heart Study, Framingham, MA, USA

    • Jennifer E Huffman
  4. Department of Medicine, University of Washington, Seattle, WA, USA

    • Joshua C Bis
    •  & Jennifer A Brody
  5. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands

    • Monique Mulder
    • , Carolina Medina-Gomez
    • , Fernando Rivadeneira
    • , Andre G Uitterlinden
    •  & Eric J Sijbrands
  6. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    • Aniko Sabo
  7. Icelandic Heart Association, Kopavogur, Iceland

    • Albert V Smith
    •  & Vilmundur Gudnason
  8. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    • Albert V Smith
    •  & Vilmundur Gudnason
  9. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    • Serkalem Demissie
    •  & L Adrienne Cupples
  10. Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

    • Ani Manichaikul
    • , Josyf C Mychaleckyj
    • , Leslie A Lange
    •  & Stephen S Rich
  11. Department of Genetics, Washington University School of Medicine, St Louis, MO, USA

    • Mary F Feitosa
    •  & Ingrid B Borecki
  12. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    • Qing Duan
  13. Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland

    • Katharina E Schraut
    • , Peter K Joshi
    •  & James F Wilson
  14. Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Jana V van Vliet-Ostaptchouk
  15. Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    • Gu Zhu
    • , John B Whitfield
    •  & Nicholas G Martin
  16. Department of Biological Psychology, VU University Amsterdam and EMGO Institute for Health and Care Research, Amsterdam, The Netherlands

    • Hamdi Mbarek
    • , Gonneke Willemsen
    • , Eco J de Geus
    • , Jouke-Jan Hottenga
    •  & Dorret I Boomsma
  17. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands

    • Stella Trompet
    •  & J Wouter Jukema
  18. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands

    • Stella Trompet
    •  & Anton JM de Craen
  19. Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Niek Verweij
    •  & Pim van der Harst
  20. Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Tampere, Finland

    • Leo-Pekka Lyytikäinen
    •  & Terho Lehtimäki
  21. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

    • Joris Deelen
    •  & P Eline Slagboom
  22. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Ilja M Nolte
    •  & Harold Snieder
  23. Department of Experimental Cardiology, UMC Utrecht, Utrecht, The Netherlands

    • Sander W van der Laan
  24. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK

    • Gail Davies
    •  & Ian J Deary
  25. Department of Psychology, University of Edinburgh, Edinburgh, UK

    • Gail Davies
    •  & Ian J Deary
  26. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Dan E Arking
  27. Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA

    • Charles C White
  28. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    • Charles C White
  29. Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA

    • Charles C White
    •  & Gina M Peloso
  30. Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA

    • Gina M Peloso
  31. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    • Gina M Peloso
  32. Harvard Medical School, Boston, MA, USA

    • Gina M Peloso
  33. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Morris A Swertz
  34. Department of Psychiatry, VU University Medical Center Amsterdam/GGZinGeest and EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, Amsterdam, The Netherlands

    • Yuri Milaneschi
    •  & Brenda WJH Penninx
  35. Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK

    • Ian Ford
  36. Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland

    • Brendan M Buckley
    •  & John M Starr
  37. Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK

    • Brendan M Buckley
    •  & John M Starr
  38. Laboratory of Clinical Chemistry and Hematology, Division Laboratories & Pharmacy, UMC Utrecht, Utrecht, the Netherlands

    • Gerard Pasterkamp
  39. Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Albertine J Oldehinkel
  40. Department of Cardiology, Heart Centre, Tampere University Hospital and University of Tampere School of Medicine, Tampere, Finland

    • Kjell Nikus
  41. Department of Clinical Physiology, Tampere University Hospital and University of Tampere School of Medicine, Tampere, Finland

    • Mika Kähönen
  42. Division of Medicine, Turku University Hospital, and Department of Medicine, University of Turku, Turku, Finland

    • Jorma S Viikari
  43. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland

    • Olli T Raitakari
    •  & Grant Montgomery
  44. Molecular Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    • Olli T Raitakari
    •  & Grant Montgomery
  45. Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia

    • Ozren Polasek
    •  & Ivana Kolcic
  46. Centre for Population Health Sciences, Medical School, University of Edinburgh, Edinburgh, UK

    • Igor Rudan
  47. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA

    • James G Wilson
  48. National Institute on Aging, National Institute of Health, Bethesda, MD, USA

    • Tamar B Harris
  49. Human Genetics Center, The University of Texas School of Public Health, Houston, TX, USA

    • Alanna C Morrison
    •  & Eric Boerwinkle
  50. Division of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK

    • Sandosh Padmanabhan
  51. Department of Medicine, Epidemiology & Health Services, University of Washington, Seattle, WA, USA

    • Bruce M Psaty
  52. Group Health Research Institute, Group Health cooperative, Seattle, WA, USA

    • Bruce M Psaty
  53. Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA

    • Jerome I Rotter
  54. Division of Genomic Outcomes, Departments of Pediatrics and Medicine, Harbor-UCLA Medical Center, Torrance, CA, USA

    • Jerome I Rotter
  55. Departments of Pediatrics, Medicine, and Human Genetics, UCLA, Los Angeles, CA, USA

    • Jerome I Rotter
  56. Medical Research Institute, University of Dundee, Dundee, UK

    • Blair H Smith
  57. Framingham Heart Study, Framingham, MA, USA

    • L Adrienne Cupples

Consortia

  1. Generation Scotland

    A Collaboration between the University Medical Schools and NHS, Aberdeen, Dundee, Edinburgh and Glasgow, UK

  2. LifeLines Cohort Study

    See Supplementary Information.

  3. CHARGE Lipids Working Group

    See Supplementary Information.

Authors

  1. Search for Elisabeth M van Leeuwen in:

  2. Search for Jennifer E Huffman in:

  3. Search for Joshua C Bis in:

  4. Search for Aaron Isaacs in:

  5. Search for Monique Mulder in:

  6. Search for Aniko Sabo in:

  7. Search for Albert V Smith in:

  8. Search for Serkalem Demissie in:

  9. Search for Ani Manichaikul in:

  10. Search for Jennifer A Brody in:

  11. Search for Mary F Feitosa in:

  12. Search for Qing Duan in:

  13. Search for Katharina E Schraut in:

  14. Search for Pau Navarro in:

  15. Search for Jana V van Vliet-Ostaptchouk in:

  16. Search for Gu Zhu in:

  17. Search for Hamdi Mbarek in:

  18. Search for Stella Trompet in:

  19. Search for Niek Verweij in:

  20. Search for Leo-Pekka Lyytikäinen in:

  21. Search for Joris Deelen in:

  22. Search for Ilja M Nolte in:

  23. Search for Sander W van der Laan in:

  24. Search for Gail Davies in:

  25. Search for Andrea JM Vermeij-Verdoold in:

  26. Search for Andy ALJ van Oosterhout in:

  27. Search for Jeannette M Vergeer-Drop in:

  28. Search for Dan E Arking in:

  29. Search for Holly Trochet in:

  30. Search for Carolina Medina-Gomez in:

  31. Search for Fernando Rivadeneira in:

  32. Search for Andre G Uitterlinden in:

  33. Search for Abbas Dehghan in:

  34. Search for Oscar H Franco in:

  35. Search for Eric J Sijbrands in:

  36. Search for Albert Hofman in:

  37. Search for Charles C White in:

  38. Search for Josyf C Mychaleckyj in:

  39. Search for Gina M Peloso in:

  40. Search for Morris A Swertz in:

  41. Search for Gonneke Willemsen in:

  42. Search for Eco J de Geus in:

  43. Search for Yuri Milaneschi in:

  44. Search for Brenda WJH Penninx in:

  45. Search for Ian Ford in:

  46. Search for Brendan M Buckley in:

  47. Search for Anton JM de Craen in:

  48. Search for John M Starr in:

  49. Search for Ian J Deary in:

  50. Search for Gerard Pasterkamp in:

  51. Search for Albertine J Oldehinkel in:

  52. Search for Harold Snieder in:

  53. Search for P Eline Slagboom in:

  54. Search for Kjell Nikus in:

  55. Search for Mika Kähönen in:

  56. Search for Terho Lehtimäki in:

  57. Search for Jorma S Viikari in:

  58. Search for Olli T Raitakari in:

  59. Search for Pim van der Harst in:

  60. Search for J Wouter Jukema in:

  61. Search for Jouke-Jan Hottenga in:

  62. Search for Dorret I Boomsma in:

  63. Search for John B Whitfield in:

  64. Search for Grant Montgomery in:

  65. Search for Nicholas G Martin in:

  66. Search for Ozren Polasek in:

  67. Search for Veronique Vitart in:

  68. Search for Caroline Hayward in:

  69. Search for Ivana Kolcic in:

  70. Search for Alan F Wright in:

  71. Search for Igor Rudan in:

  72. Search for Peter K Joshi in:

  73. Search for James F Wilson in:

  74. Search for Leslie A Lange in:

  75. Search for James G Wilson in:

  76. Search for Vilmundur Gudnason in:

  77. Search for Tamar B Harris in:

  78. Search for Alanna C Morrison in:

  79. Search for Ingrid B Borecki in:

  80. Search for Stephen S Rich in:

  81. Search for Sandosh Padmanabhan in:

  82. Search for Bruce M Psaty in:

  83. Search for Jerome I Rotter in:

  84. Search for Blair H Smith in:

  85. Search for Eric Boerwinkle in:

  86. Search for L Adrienne Cupples in:

  87. Search for Cornelia van Duijn in:

Contributions

EMvL organized the study and designed the study with substantial input from AI, LAC and CMvD. EMvL drafted the manuscript with substantial input from SSR, CvD, BMP, SWvdL, ST, JAB, JBW, GMP, AS, JVvV, DIB, GD, HS, L-PL, JEH and DEA. All authors had the opportunity to comment on the manuscript. Data collection, GWAS and statistical analysis were done by SWvdL, GP (AEGS); AVS, VG, TBH (AGES); AVS, DEA, ACM, EB (ARIC); JCB, JAB, BMP (CHS); AI, EMvL, CMvD (ERF); MFF, IBB (FamHS); SD, CCW, LAC (FHS); KN, L-PL, MK, TL (FINCAVAS and YFS); HT, SP, BHS (GS); QD, GMP, LAL, JGW (JHS); JEH, CH, IK (CROATIA Korcula); GD, JMS, IJD (LBC1936); JVvV, MAS (Lifelines); JD, AJMdC, PES (LLS); AM, JCM, SSR, JIR (MESA); HM, GW, EJdG, YM, BWJHP, J-JH, DIB (NTR-NESDA); KES, PKJ, JFW (ORCADES); NV, PvdH (PREVEND); ST, IF, BMB, JWJ (PROSPER); GZ, GW, NGM (QIMR); EMvL, MM, CM-G, FR, AGU, AD, OHF, EJS, AH, CMvD (RS); OTR, VV (CROATIA Split); IMN, AJO, HS (TRAILS); PN, AFW, IR (CROATIA Vis); JVvV-O and OTR (YFS). The Sanger sequencing was done by AJMV-V, AALJvO, JMV-D. EMvL performed the meta-analysis and all follow-up steps. Biological association of loci and bioinformatics were carried out by EMvL and CMvD.

Competing interests

PSM serves on the DSMB of a clinical trial of a device funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. SWvL is a former employee of Cavadis B.V. GP is a founder and stockholder of Cavadis B.V.

Corresponding author

Correspondence to Cornelia van Duijn.

Supplementary information

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/npjamd.2015.11

Further reading