Genetic loci associated with plasma concentration of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A1, and Apolipoprotein B among 6382 white women in genome-wide analysis with replication.
Journal: 2010/March - Circulation. Cardiovascular genetics
ISSN: 1942-3268
Abstract:
BACKGROUND
Genome-wide genetic association analysis represents an opportunity for a comprehensive survey of the genes governing lipid metabolism, potentially revealing new insights or even therapeutic strategies for cardiovascular disease and related metabolic disorders.
RESULTS
We have performed large-scale, genome-wide genetic analysis among 6382 white women with replication in 2 cohorts of 970 additional white men and women for associations between common single-nucleotide polymorphisms and low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein(Apo) A1, and ApoB. Genome-wide associations (P < 5 x 10(-8)) were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus. In addition,genome-wide associations with triglycerides at the GCKR gene confirm and extend emerging links between glucose and lipid metabolism. Still other genome-wide associations at the 1p13.3 locus are consistent with emerging biological properties for a region of the genome, possibly related to the SORT1 gene. Below genome-wide significance, our study provides confirmatory evidence for associations at 5 novel loci with low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides reported recently in separate genome-wide association studies. The total proportion of variance explained by common variation at the genome-wide candidate loci ranges from 4.3% for triglycerides to 12.6% for ApoB.
CONCLUSIONS
Genome-wide associations at the GCKR gene and near the SORT1 gene, as well as confirmatory associations at 5 additional novel loci, suggest emerging biological pathways for lipid metabolism among white women.
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Discussion board
Circ Cardiovasc Genet 1(1): 21-30

Genetic loci associated with plasma concentration of LDL-C, HDL-C, triglycerides, ApoA1, and ApoB among 6382 Caucasian women in genome-wide analysis with replication

+7 authors

Background

Genome-wide genetic association analysis represents an opportunity for comprehensive survey of genes governing lipid metabolism, potentially revealing new insights or even therapeutic strategies for cardiovascular disease and related metabolic disorders.

Methods and Results

We have performed large-scale, genome-wide genetic analysis among 6382 Caucasian women with replication in two cohorts of 970 additional Caucasian men and women for associations between common SNPs and LDL-C, HDL-C, triglycerides, apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). Genome-wide associations (P<5×10) were found at the PCSK9 gene, the APOB gene, the LPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus. In addition, genome-wide associations with triglycerides at the GCKR gene confirm and extend emerging links between glucose and lipid metabolism. Still other genome-wide associations at the 1p13.3 locus are consistent with emerging biological properties for a region of the genome, possibly related to the SORT1 gene. Below genome-wide significance, our study provides confirmatory evidence for associations at five novel loci with LDL-C, HDL-C, or triglycerides reported recently in separate genome-wide association studies. The total proportion of variance explained by common variation at the genome-wide candidate loci ranges from 4.3% for triglycerides to 12.6% for ApoB.

Conclusions

Genome-wide associations at the GCKR gene and near the SORT1 gene as well as confirmatory associations at five additional novel loci suggest emerging biological pathways for lipid metabolism among Caucasian women.

Introduction

Plasma lipid levels, which are highly variable in the population and also heritable, are major determinants of cardiovascular disease (CVD). To date, genetic dissection of Mendelian dyslipidemias and biochemical analysis of plasma lipid determinants have revealed a limited number of genes and pathways relevant to lipid metabolism 14. Nevertheless, identifying these few pathways has been crucial for understanding the pathophysiology of CVD and for developing targeted therapeutic strategies. It remains possible that additional genes and pathways may also be involved, and lead to further clues about the origins of CVD including potential differences between men and women. This possibility can be explored with genome-wide genetic analysis enabled by recent progress in understanding the human genome.

We performed genome-wide genotyping of 341,518 polymorphisms among approximately 6382 Caucasian women in the Women’s Genome Health Study 56 to evaluate the extent of common genetic influence on five plasma lipid fractions: LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglycerides, apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). The associations with genome-wide significance (P<5×10) confirmed involvement of known and emerging loci in lipid metabolism. Associations at the emerging loci and others could be replicated in smaller samples of Caucasian men and women from the PRINCE 7 and CAP 8 studies when genotype information was available from separate genome-wide scans. In these data, we further confirm multiple associations from very recently reported and independent genome-wide association studies of LDL-C, HDL-C, and triglycerides 912.

Methods

Study populations

The primary analyses were performed in the large-scale Women’s Genome Health Study (WGHS) 5 with replication in the smaller PRINCE and CAP studies 78. In brief, the WGHS is an initiative to examine genetic correlates with cardiovascular disease and other clinical outcomes among participants in the Women’s Health Study (WHS) 6, an ongoing prospective cohort of initially healthy American women aged 45 or older at baseline. Plasma levels of LDL-C, HDL-C, triglycerides, ApoA1, and ApoB were determined by direct assay and had low coefficients of variation 513. The PRINCE 7 and CAP 8 data used for replication derive from baseline blood samples in two statin trials included in the Pharmacogenomics and Risk of Cardiovascular Disease (PARC) program (http://www.pharmgkb.org/network/members/parc.jsp). Clinical characteristics of all three study populations are provided in Table 1.

Table 1

Baseline clinical characteristics of WHS, PRINCE, and CAP populations described in this study

WGHSPRINCECAP

(N=6382)(N=671)(N=299)
Age (yrs.)52 (49–58)66 (56–75)53 (45–61)
Male (fr.)00.750.49
BMI (kg/m)25 (22–28)28 (25–32)27 (24–31)
history hypertens. (fr.)0.22--
current smoking (fr.)0.120.130.14
HRT user (fr.)0.44--
menopause (fr.)0.52--
LDL-C (mg/dl)122 (101–147)131 (111–149)132 (112–153)
HDL-C (mg/dl)54 (44–65)35 (30–41)51 (42–65)
triglycerides (mg/dl)116 (83–171)169 (117–238)107 (76–166)
ApoA1 (mg/dl)151 (135–171)--
ApoB (mg/dl)97 (82–118)--

Median (IQR) values are shown for continuously-valued clinical characteristics

Genotype datasets

Genotyping was performed using the Illumina (San Diego, CA) Infinium II assay. For the WGHS samples, either Illumina HumanHap300 Duo “+” chips or the combination of the Illumina HumanHap300 Duo and iSelect chips were used. In both cases, the custom content (“+” or iSelect content) was the same, and consisted of candidate SNPs chosen without regard to allele frequency for potential functional effects in cardiovascular disease and other clinical conditions. In all, 363,808 SNPs were represented by the combination of tag SNPs in the standard panel 14 and custom content before exclusions based-on quality control measures. In data reduction, the default genotype cluster assignments for the standard panel were used in BeadStudio v.3.1 software. For the custom content, cluster definitions were first determined by the automated procedure in BeadStudio, and then manually curated when one or more of the following conditions was met: Hardy Weinberg equilibrium deviation P-value <0.0005, call rate <95%, or GenTrain score <0.65, leading to manual review for 15,479 assays. In all, manually assigned cluster positions were used for 2264 of the 45,882 custom content SNPs with successful assays. 7635 WGHS samples passed quality control with 98% of the SNPs successfully genotyped. In addition, these samples were further validated on the basis of matching calls for a set for 44 SNPs that had already been genotyped by an independent method across the entire WGHS cohort. Among these retained samples, principal component analysis based on identity by state of genotypes in PLINK 15 for 1443 ancestry informative SNPs (Fst > 0.4 in CEU, YRB, and JPN+CHB HapMap populations 16) was used to identify a subgroup with nearly complete correspondence (99.7%) to self-reported European ancestry. The 6382 WGHS participants who comprised the final data set for analysis of lipid associations had both inferred and self-reported European ancestry, and further were free of diabetes at baseline and not using lipid lowering therapy, again by self-report. Within this final collection of subjects, 341,518 SNPs were retained on the basis of minor allele frequency >1% and deviations from Hardy-Weinberg equilibrium with significance P-value > 10 as determined by an exact method in PLINK 1517. Of the final SNPs, 307,595 derived from the HumanHap300 panel while the remaining 33,923 derived from the custom content. Of the SNPs with minor allele frequency between 1–5%, 4868 or 68% derived from the custom content.

Similarly, genotyping of the PRINCE and CAP cohorts was performed with the HumanHap300 platform but did not include the custom SNP content from the WGHS. The final data for analysis included 671 and 299 samples from PRINCE and CAP, respectively, all with self-reported European ancestry. Among 314,621 SNPs, the final PRINCE and CAP samples had at least 96.8% and 97.5% complete genotyping, respectively.

Analytic methods

Before testing for genetic associations, lipid fractions from the WGHS were adjusted by linear regression in R 18 for age, BMI, smoking status, menopausal status, and use of hormone replacement therapy. A small number of samples (<9) for each lipid fraction were excluded from further analysis based on outlying values in the residuals. In PRINCE and CAP, the adjustment included also gender (both studies) and enrollment criterion (PRINCE only, primary or secondary prevention) but not menopausal status. Analysis for triglycerides was performed on log-transformed values. SNP association tests were performed by linear regression in PLINK under the assumption of an additive relationship between the number of copies of the minor allele and the residual lipid values. With 6382 samples, there was 80% power to detect additive genetic effects explaining 0.61% of the variance in one of the lipid fractions at the 5×10 significance level. To confirm that any ancestral diversity remaining in the final data set of Caucasians did not unduly inflate the significance of the reported associations, the regression procedure was repeated on all SNPs with correction by genomic control in PLINK. Assessment of the influence of all common SNPs at the candidate loci on lipid fractions was performed by multiple linear regression on the residuals of the adjusted lipid fractions, again with additive model encoding of genetic effects. For each combination of lipid fraction and candidate locus, initial regression models included all SNPs with association P < 0.1 within a maximum of 500 kb of a candidate locus SNP with association P < 10. A minimal set of non-redundant SNPs in these models was determined by stepwise backward-forward selection based on the Bayesian information criterion (BIC) 19. Genomic context for all annotations derived from human genome build 36.1 and dbSNP build 126.

Analytic methods

The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

Study populations

The primary analyses were performed in the large-scale Women’s Genome Health Study (WGHS) 5 with replication in the smaller PRINCE and CAP studies 78. In brief, the WGHS is an initiative to examine genetic correlates with cardiovascular disease and other clinical outcomes among participants in the Women’s Health Study (WHS) 6, an ongoing prospective cohort of initially healthy American women aged 45 or older at baseline. Plasma levels of LDL-C, HDL-C, triglycerides, ApoA1, and ApoB were determined by direct assay and had low coefficients of variation 513. The PRINCE 7 and CAP 8 data used for replication derive from baseline blood samples in two statin trials included in the Pharmacogenomics and Risk of Cardiovascular Disease (PARC) program (http://www.pharmgkb.org/network/members/parc.jsp). Clinical characteristics of all three study populations are provided in Table 1.

Table 1

Baseline clinical characteristics of WHS, PRINCE, and CAP populations described in this study

WGHSPRINCECAP

(N=6382)(N=671)(N=299)
Age (yrs.)52 (49–58)66 (56–75)53 (45–61)
Male (fr.)00.750.49
BMI (kg/m)25 (22–28)28 (25–32)27 (24–31)
history hypertens. (fr.)0.22--
current smoking (fr.)0.120.130.14
HRT user (fr.)0.44--
menopause (fr.)0.52--
LDL-C (mg/dl)122 (101–147)131 (111–149)132 (112–153)
HDL-C (mg/dl)54 (44–65)35 (30–41)51 (42–65)
triglycerides (mg/dl)116 (83–171)169 (117–238)107 (76–166)
ApoA1 (mg/dl)151 (135–171)--
ApoB (mg/dl)97 (82–118)--

Median (IQR) values are shown for continuously-valued clinical characteristics

Genotype datasets

Genotyping was performed using the Illumina (San Diego, CA) Infinium II assay. For the WGHS samples, either Illumina HumanHap300 Duo “+” chips or the combination of the Illumina HumanHap300 Duo and iSelect chips were used. In both cases, the custom content (“+” or iSelect content) was the same, and consisted of candidate SNPs chosen without regard to allele frequency for potential functional effects in cardiovascular disease and other clinical conditions. In all, 363,808 SNPs were represented by the combination of tag SNPs in the standard panel 14 and custom content before exclusions based-on quality control measures. In data reduction, the default genotype cluster assignments for the standard panel were used in BeadStudio v.3.1 software. For the custom content, cluster definitions were first determined by the automated procedure in BeadStudio, and then manually curated when one or more of the following conditions was met: Hardy Weinberg equilibrium deviation P-value <0.0005, call rate <95%, or GenTrain score <0.65, leading to manual review for 15,479 assays. In all, manually assigned cluster positions were used for 2264 of the 45,882 custom content SNPs with successful assays. 7635 WGHS samples passed quality control with 98% of the SNPs successfully genotyped. In addition, these samples were further validated on the basis of matching calls for a set for 44 SNPs that had already been genotyped by an independent method across the entire WGHS cohort. Among these retained samples, principal component analysis based on identity by state of genotypes in PLINK 15 for 1443 ancestry informative SNPs (Fst > 0.4 in CEU, YRB, and JPN+CHB HapMap populations 16) was used to identify a subgroup with nearly complete correspondence (99.7%) to self-reported European ancestry. The 6382 WGHS participants who comprised the final data set for analysis of lipid associations had both inferred and self-reported European ancestry, and further were free of diabetes at baseline and not using lipid lowering therapy, again by self-report. Within this final collection of subjects, 341,518 SNPs were retained on the basis of minor allele frequency >1% and deviations from Hardy-Weinberg equilibrium with significance P-value > 10 as determined by an exact method in PLINK 1517. Of the final SNPs, 307,595 derived from the HumanHap300 panel while the remaining 33,923 derived from the custom content. Of the SNPs with minor allele frequency between 1–5%, 4868 or 68% derived from the custom content.

Similarly, genotyping of the PRINCE and CAP cohorts was performed with the HumanHap300 platform but did not include the custom SNP content from the WGHS. The final data for analysis included 671 and 299 samples from PRINCE and CAP, respectively, all with self-reported European ancestry. Among 314,621 SNPs, the final PRINCE and CAP samples had at least 96.8% and 97.5% complete genotyping, respectively.

Analytic methods

Before testing for genetic associations, lipid fractions from the WGHS were adjusted by linear regression in R 18 for age, BMI, smoking status, menopausal status, and use of hormone replacement therapy. A small number of samples (<9) for each lipid fraction were excluded from further analysis based on outlying values in the residuals. In PRINCE and CAP, the adjustment included also gender (both studies) and enrollment criterion (PRINCE only, primary or secondary prevention) but not menopausal status. Analysis for triglycerides was performed on log-transformed values. SNP association tests were performed by linear regression in PLINK under the assumption of an additive relationship between the number of copies of the minor allele and the residual lipid values. With 6382 samples, there was 80% power to detect additive genetic effects explaining 0.61% of the variance in one of the lipid fractions at the 5×10 significance level. To confirm that any ancestral diversity remaining in the final data set of Caucasians did not unduly inflate the significance of the reported associations, the regression procedure was repeated on all SNPs with correction by genomic control in PLINK. Assessment of the influence of all common SNPs at the candidate loci on lipid fractions was performed by multiple linear regression on the residuals of the adjusted lipid fractions, again with additive model encoding of genetic effects. For each combination of lipid fraction and candidate locus, initial regression models included all SNPs with association P < 0.1 within a maximum of 500 kb of a candidate locus SNP with association P < 10. A minimal set of non-redundant SNPs in these models was determined by stepwise backward-forward selection based on the Bayesian information criterion (BIC) 19. Genomic context for all annotations derived from human genome build 36.1 and dbSNP build 126.

Analytic methods

The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

Results

Among the 6382 Caucasian WGHS participants (Table 1), we identified 13 primary SNPs at ten loci on seven chromosomes that were associated with at least one adjusted lipid fraction and association P-value smaller than 5×10, generally considered sufficient for genome-wide significance among Caucasians (Table 2) 20. Many of the SNPs with genome-wide association for one lipid fraction are also strongly associated with a second, sometimes correlated, lipid fraction, e.g. rs506585 with LDL-C and ApoB. The same ten primary loci were identified at genomewide significance without adjustment of the lipid fractions for standard clinical covariates. Essentially identical results were also found for analysis that adjusted association statistics in the Caucasian sample by genomic control (parameter range 1.02–1.03), suggesting that residual population stratification was not responsible for the associations (supplementary Table 1). A full listing of all associations with P < 10 including median lipid values by genotype and additional statistics is provided in the supplement (supplementary Table 2).

Table 2

Primary associations in the WGHS with five lipid fractions

Traits for which SNP has smallest genome-wide
Primary SNPMAFP-valueLocusPositionCandidate genesLDL-C*HDL-C*ln trig.*ApoA1*ApoB*

P beta (mg/dl)
P beta (mg/dl)
P beta
P beta (mg/dl)
P beta (mg/dl)
rs115911470.016ApoB1p32.355278235PCSK91.6×10
−12.8
---1.8×10
−11.2

rs6467760.216LDL-C, ApoB1p13.3109620053CELSR2
PSRC1
SORT1
4.9×10
−6.8
---3.5×10
−5.6

rs5065850.199LDL-C, ApoB2p24.121250687APOB9.3×10
−4.6
---7.7×10
−4.3

rs12603260.412triglycerides2p23.327584444GCKR--1.3×10
0.07
9.9×10
2.1
1.6×10
2.2

rs3280.107triglycerides8p21.319864004LPL--4.7×10
−0.09
--

rs3310.275ApoA18p21.319864685LPL-9.1×10
1.5
1.7×10
−0.06
4.0×10
2.6
-

rs31355060.060ApoB11q23.3116167617APOA5-APOA1--5.5×10
0.13
-8.4×10
5.3

rs6627990.064triglycerides11q23.3116168917APOA5-APOA1--2.9×10
0.14
-3.5×10
4.8

rs122252300.176ApoA111q23.3116233840APOA5-APOA1-5.3×10
1.5
-4.0×10
3.3
-

rs15320850.370HDL-C, ApoA115q21.356470658LIPC-1.3×10
1.8
-9.0×10
3.3
-

rs37642610.310HDL-C, ApoA116q1355550825CETP-1.0×10
4.0
-1.1×10
5.6
-

rs65117200.119LDL-C, ApoB19p13.211063306LDLR5.2×10
−7.7
---7.0×10
−6.1

rs48037500.066LDL-C, ApoB19q13.3149939467APOE-APOC3.6×10
−9.6
---8.7×10
−8.1

Shaded regions indicate associations with P <10; “-“ indicates P >10−4

Standard deviations: 36.6 mg/dl (LDL-C), 16.6 mg/dl (HDL-C), 0.53 (ln triglycerides), 26.2 mg/dl (ApoA1), 27.1 mg/dl (ApoB)
Significance (P) and estimate (beta) of regression coefficient for change in lipid (or ln transformed triglycerides) with each additional copy of the minor allele.

Eight of the loci include genes known to have allelic variation associated with one or more of the lipid fractions (See supplementary Figures 1A–H). For example, SNPs at or near the CETP gene were associated with HDL-C (P < 10), SNPs at or near the APOA5 gene were associated with triglycerides (P < 10), and SNPs at or near the LDLR gene were associated with LDL-C (P < 10). For the loci that included recognized lipid metabolic genes, non-synonymous SNPs with P < 10 were found in PCSK9 (rs11591147), APOB (rs693), LPL (rs328), APOA5 (rs3135506), and CETP (rs5880, rs5882) while a synonymous substitution was found for LDLR (rs2228671), thereby reinforcing the candidacy of these genes in determining the lipid fractions. The genome-wide association for ApoA1 at 11q23.3 encoded a potential non-synonymous change in a predicted gene from the UCSC catalog (QSK gene, similar to serine/threonine kinases, not in Refseq) adjacent to the APOA1 gene. The most significant SNP for HDL-C at 15q21.3 is somewhat remote (~41 kb) from the presumed functional gene LIPC, but HDL-C associations at this locus are correlated with associations for ApoA1, and within 0.5 kB of the LIPC gene, rs1800588 is highly significant for both lipid fractions (P=2.4×10 [ApoA1], P=1.2×10 [HDL]). Similarly, at 19p13.31, the most significant association with LDL-C is far from the APOE gene (rs4803750, >160 kb, P < 10), but the second most significant SNP at the locus for the related ApoB fraction (rs769449) maps within the APOE gene itself and is also strongly associated with LDL-C (P=4.7×10; supplementary figure 1H, supplementary Table 1). Genotypes in a subset of WGHS Caucasians from a separate study 21 showed moderate linkage disequilibrium (r=0.25) between rs4803750 and the non-synonymous SNP (rs7412, R158C) in the APOE coding region that distinguishes the E2 and E3 alleles with known differences in lipid levels. Nevertheless, it is impossible to exclude additional functional contributions to the genetic effects at the APOE locus from neighboring genes including three that encode other apolipoproteins, APOC1, APOC2, and APOC4.

An external file that holds a picture, illustration, etc.
Object name is nihms60633f1.jpg

Annotated representation of the human genome at two loci associated with emerging roles in lipid metabolism. Left (A) Locus 2p23.3 including the gene for glucokinase regulatory protein (GCKR) and single SNP associations with plasma triglycerides. Right (B) Locus 1p13.3 including the SORT1, CELSR2, and PSRC1 genes and single SNP associations with plasma ApoB (red) or LDL-C (blue). Upper panel in both shows a single representative transcript for locus Refseq (release 25) genes. Middle panel shows P-values for single SNP associations transformed by −log10 (red horizontal line corresponds to P=5×10), as well as recombination hot spots (vertical shading) 36. Gray curve shows estimated genetic distance from the most significantly associated SNP at each locus. Lower panel shows linkage disequlibrium relationships between pairs of locus SNPs (r (lower left), D′ (upper right)).

The highly significant associations at the APOA1-APOA5 locus included not only ApoA1 and triglycerides but also ApoB. The association with ApoB appears related to the triglyceride association in as much as adjusting ApoB for (log-transformed) triglyceride level eliminated the ApoB genetic associations but not vice versa. In contrast, neither adjustment of ApoA1 for triglyceride level nor adjustment of triglycerides for ApoA1 level had a large effect on the genetic associations for either of these two lipid fractions. The SNPs associated with ApoA1 are nearer the APOA1 gene than the APOA5 gene, known for its influence on triglycerides (supplementary Figure 1D).

The remaining associations correspond to emerging loci for genetic effects on lipids. The SNPs at 2p23.3 that are associated with triglycerides coincide with the glucokinase regulatory protein gene (GCKR) (Figure 1A). The most significant SNP here, rs1260326, encodes a non-synonymous change (P446L) and is also associated with ApoA1 (P=9.9×10) and ApoB (P=1.6×10), although with less significance than the association with triglycerides (P < 10). At 1p13.3, the association of rs646776 with ApoB (P < 10) and LDL-C (P < 10) maps near the genes for the cadherin EGF LAG seven-pass G-type receptor 2 (CELSR2) and the proline/serine-rich coiled-coil protein 1 (PSRC1), separated from the sortilin 1 (SORT1) gene by a site of elevated recombination frequency (Figure 1B).

Characteristics of the most significant SNP at each candidate locus within the WGHS were examined in greater detail. Among all of these primary SNPs, the minor alleles ranged in frequency from 1.7–41.4%. Some of the effects of the minor alleles were consistent with increased cardiovascular risk while others were consistent with decreased risk. To compare the shifts in mean lipid level with genotype across all lipid fractions at once, we scaled the additive model regression beta coefficients by the lipid fraction standard deviations (standard deviations are in the legend to Table 2). Among all primary associations, the absolute magnitude of this standardized shift per allele ranged from a minimum of 0.06 s.d. corresponding to an increase of 5.3 mg/dl in ApoB with each additional copy of the minor allele of rs3135506 at 11q23.3 (APOA5-APOA1), to a maximum of 0.43 s.d. corresponding to a decrease of 11.2 mg/dl in ApoB for minor allele of rs11591147 at 1p32.3 (PCSK9). The most significant primary association (P=1.05×10) involved rs3764261 at 16q13 (CETP) with minor allele frequency 0.37 and was characterized by an increase of 4.0 mg/dl for the mean value of HDL-C for each additional copy of the minor allele. The effects of the single most significant SNP at each locus explained a maximum of 3% of the residual lipid variance (for HDL-C and rs3764261 at CETP), and a minimum of 0.5% (for ApoB and rs3135506 at APOA5-APOA1). Among the primary associations, there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an additional degree of freedom.

Associations for LDL-C, HDL-C, and triglycerides could be confirmed at the P < 0.05 significance level in at least one of two independent and much smaller samples of Caucasian men and women from the PRINCE (N=671) and CAP (N=299) studies, for which genotypes at six of the primary SNPs from the candidate loci were available as a result of parallel genome-wide scans (Table 3). These replications were consistent with the larger WGHS population in terms of minor allele frequency, the estimated magnitude and direction of the influence of the minor allele on lipid levels, and the proportion of variance explained; and they included the associations at both the established loci (2p24.1, 15q21.3, 16q13, 19q13.31) and at the emerging loci (1p13.3 and 2p23.3). Of the two WGHS associations that were not significant in one of the replication cohorts, the rs4803750 association that maps near the APOE gene differed in PRINCE in the magnitude but not the sign of the effect estimate compared with the WGHS sample, while the rs506585 (APOB) association with LDL-C in CAP had an effect estimate that was comparable to the effect in the WGHS in both magnitude and sign. In addition, all of the associations with P < 10 in the whole WGHS sample had consistent effect estimates in two internal subsets of 4693 and 2058 individuals corresponding to consecutive and separate genotyping batches within the WGHS. Had these subsets been viewed as conventional discovery and replication cohorts, all of the candidate loci except PCSK9 would have had a strongest association with P-value smaller than 10 in the first sample and at least nominal significance in the second sample after Bonferroni correction for all SNPs carried forward for replication from the first sample (supplementary Table 2).

Table 3

Replication of primary associations in the PRINCE and CAP studies

PRINCE
CAP
LipidSNPlocuscandidate genesMAFP*beta*MAFP*beta*
LDL-Crs6467761p13.3CELSR2, PSRC1, SORT10.1970.0252−4.20.2110.0175−7.0
rs5065852p24.1APOB0.1970.0402−3.90.1940.3773−2.7
rs480375019q13.31APOE0.0600.7271−1.10.0600.0118−12.7
HDL-Crs153208515q21.3LIPC0.3880.03091.20.3890.02822.6
rs376426116q13CETP0.325<0.00012.90.3180.04732.3
ln trig.rs12603262p23.3GCKR0.4290.00070.10.4500.03650.1
Significance (P) and estimate (beta) of regression coefficient for change in lipid (or ln transformed triglycerides) with each additional copy of the minor allele.

To estimate the contribution of common variation within the WGHS sample at each of the candidate loci to the lipid fractions, we constructed multiple regression models that initially included the most significant SNP at each candidate locus (P < 10) as well as neighboring SNPs with association P-value < 0.1. For each locus and initial set of SNPs, a non-redundant final set of SNPs was chosen with backwards-forwards step selection using the BIC (supplementary Table 3). As with the primary associations, the additional SNPs retained in the final models varied widely in both minor allele frequency and the direction of the effect of the minor alleles. The most explanatory loci within the WGHS for the residuals of the lipid fractions were APOE for LDL-C and ApoB, CETP for HDL-C and ApoA1, and APOA5-APOA1 for triglycerides (Table 4). The proportion of the residual variance explained for the common variation any single locus ranged from a minimum of 0.50% for locus 8p21.3 (LPL) and ApoA1 to a maximum of 5.87% for locus 19q13.31 (APOE) and ApoB.

Table 4

Proportion of variance in the adjusted lipid fractions explained by candidate loci

LDL-CHDL-Cln. trig.ApoA1ApoB
Locus% (fr.)% (fr.)% (fr.)% (fr.)% (fr.)
1p32.3----0.83 (0.07)
1p13.31.29 (0.18)---1.70 (0.14)
2p24.10.89 (0.12)---1.70 (0.14)
2p23.3--1.06 (0.24)--
8p21.3--1.05 (0.24)0.50 (0.08)-
11q23.3--2.23 (0.51)0.58 (0.09)1.18 (0.09)
15q21.3-1.56 (0.27)-2.27 (0.35)-
16q13-4.13 (0.73)-3.07 (0.48)-
19p13.21.19 (0.16)---1.28 (0.10)
19q13.313.94 (0.54)---5.87 (0.47)

Total proportion7.315.694.346.4112.56

Similarly, the total proportion of the variance explained for each residual lipid fraction in the WGHS by all of the significant common variation was estimated by summing the individual contributions from each of the candidate loci. As much as 12.56% of the residual variance in ApoB was explained by common variation at the candidate loci while as little as 4.34% was explained for triglycerides (Table 4). At the same time, after further adjusting the residual lipid fractions for the significant genetic variation at the all of the candidate loci at once, essentially none of the remaining genetic variation across the genome met a formal standard for genome-wide significance with any of the lipid fractions (supplementary Figure 2).

While this work was in progress, four reports describing genome-wide association studies of LDL-C, HDL-C, and triglycerides in Caucasian men and women also confirmed associations at previously established loci and identified additional novel loci 912. One of the novel associations is equivalent to our association with LDL-C and ApoB near the SORT1 gene at 1p13.3. Among the loci in the recent reports that did not reach genome-wide significance in our study, five SNPs were represented on our genotyping platform explicitly (Table 5A). Associations at the ANGPTL3/DOC7/ATG4C locus (rs12130333) with LDL-C and at the LCAT locus (rs255052) with HDL-C were confirmed in our data. Unexpectedly, in the WGHS sample, the reported association of rs12130333 with triglycerides is less significant than the association of this SNP with LDL-C. At the LIPG/ACAA2 locus the reported association with HDL-C was marginal in two-sided testing in the WGHS (P=0.05), but the same variant was highly significant for association with the related lipid fraction ApoA1 (P=1.9×10). Neither the reported association involving rs2228603 at the CILP2/PBX4/NCAN/SF4 locus nor rs4149268 at the ABCA1 locus was significant in our data.

Table 5

Replication of associations from recent genomewide analysis of LDL-C, HDL-C, and triglycerides 911

Table 5A. Candidate SNPs
Candidate locus genescandidate snpLDL-C (P/beta)HDL-C (P/beta)ln trig. (P/beta)ApoA1 (P/beta)ApoB (P/beta)
ANGPTL3/DOCK7/ATG4Crs121303330.0068
−2.1
0.22
−0.4
0.011
−0.03
0.18
−0.7
0.015
−1.4
LCATrs2550520.080
1.6
0.00028
1.4
0.66
−0.02
0.0018
1.9
0.47
0.5
LIPG/ACAA2rs49398830.068
−1.6
0.051
−0.7
0.20
−0.01
1.90×10
−2.4
0.024
−1.4
CILP2/PBX4/NCAN/SF4rs22286030.88
−0.2
0.07
−1.0
0.094
−0.03
0.11
−1.3
0.70
0.3
ABCA1rs41492680.65
−0.3
0.69
−0.1
0.11
−0.01
0.49
−0.3
0.83
−0.1
Table 5B. Candidate loci strongly associated with one or more lipid fractions
Candidate locus genesN locus SNPsLDL-C (SNP/P/beta)HDL-C (SNP/P/beta)ln trig. (SNP/P/beta)ApoA1 (SNP/P/beta)ApoB (SNP/P/beta)
ANGPTL3/DOCK7/ATG4C57rs10889353
0.00014
−2.6
-rs11207993
0.0014
−0.04
-rs11207993
0.00057
−2.1
B3GALT481rs2269346
0.05
2.5
-rs1547387
0.00022
−0.05
--
BCL7B/TBL2/MLXIPL18--rs11974409
5.1×10
−0.05
--
HMGCR15rs3846662
2.2×10
2.7
----
LIPG/ACAA281-rs506696
0.0014
−0.9
-rs4939883
1.9×10−
−2.4
rs4939887
0.00035
1.7
MVK/MMAB36-rs4766613
0.0020
1.1
-rs2241206
0.0003
2.0
-
TRIB134rs4518686
0.0011
2.2
rs10808546
3.6×10
1.3
rs10808546
0.00038
−0.03
-rs10808546
1.7×10
−2.2

Shaded cells indicate associations from recent reports

Uncorrected significance (P) and estimate (beta) of SNP effect assuming an additivity in regression models.
Loci presented have at least one association meeting nominal significance after Bonferroni correction for the number of locus SNPs. Shaded cells indicate lipid fractions and associated loci from recent reports. Associations that did not meet the significance criteria or were not specified by the recent reports are ndicated by “-“.

However, on a locus-wide basis, alternative SNPs from our data could confirm associations at seven loci from the recent reports that did not have genome-wide significance in our primary analysis (Table 5B). Thus, SNPs with nominally significant association P-values after Bonferroni correction for all locus SNPs were identified at B3GALT4, BCL7B/TBL2/MLXIPL, HMGCR, MVK/MMAB, and TRIB1 loci, as well as at the ANGPTL3/DOC7/ATG4C and LIPC/ACAA2 loci mentioned above. As with associations between rs12130333 and LDL-C or triglycerides (Table 5A), the association at four of the seven loci with the lipid fraction specified in the recent reports was weaker than the best locus association with a second fraction from among LDL-C, HDL-C, and triglycerides. The reported lipid fraction represented the strongest locus association in the WGHS only for HMGCR with LDL-C, BCL7B/TBL2/MLXIPL with triglycerides, and MVK/MMAB with HDL-C, suggesting possible population dependent or environmental interaction effects on lipid metabolism at the other loci. Tables 5A and 5B also provide summaries of the associations at the candidate loci with the ApoA1 and ApoB lipid fractions that were not evaluated in the recent reports.

Discussion

In this study of a primary cohort with 6382 Caucasian women, we found genome-wide associations with plasma lipid fractions at ten loci, six of which could be replicated in smaller cohorts totaling 970 Caucasian men and women when genotypes were available. The majority of the associations are consistent with known pathways for lipid metabolism while others suggest emerging pathways. Below the genome-wide significance threshold, an additional nine loci also including known and emerging links to lipid metabolism could be confirmed by replicating associations from recent genome-wide association studies that were reported while our analysis was being completed 912. Differences between our results and the recent reports, including the inability to confirm some associations in the WGHS, may be related to differences in the populations including possible effects related to the exclusively female gender in the WGHS.

The genome-wide association of rs1260326 and other SNPs at the GCKR locus with triglycerides replicate recent novel findings 22. At this locus, our study found associations also with ApoA1 and ApoB fractions at the approximate P=10 level that extend the connection between glucose and lipid metabolism but were not detected in the previous report. The major function of the GCKR protein is post-translastional regulation of glucokinase, and thus is intimately linked to glucose metabolism. Consistent with the effects of overexpression of glucokinase in animal models 2324, the levels of triglycerides, ApoA1, and ApoB are all increased by the minor allele of rs1260326 in the WGHS, trends that would suggest decreased cardiovascular disease risk from the ApoA1 effects but increased disease risk from the triglyceride and ApoB effects. Separate results from the WGHS described elsewhere implicate GCKR also in baseline plasma levels of C-reactive protein, a component of innate immunity, with a direction of effects suggesting decreased risk among minor allele carriers (submitted). While resolving the clinical consequences of the differences in metabolic profile associated with GCKR variation remains a priority, it is noteworthy that the minor allele of rs1260326 is nearly completely linked to the minor allele of another SNP (rs780094) that may trend toward lower diabetes risk in preliminary analysis 22.

Genome-wide associations of rs646776 and others at 1p13.3 with LDL-C and ApoB may be consistent with the reported roles of the SORT1 protein in vesicular transport, including interactions with lipoprotein lipase, specialized storage vesicles associated with insulin-responsive glucose transport, and a receptor associated protein (RAP) that binds LDL receptor family members 2528. Nevertheless, more analysis will be required to exclude causal roles for other genes (CELSR2, PSRC1) that map closer to the strongest associations across a neighboring recombination hotspot from SORT1. The association at 1p13.3 is remarkably strong. The primary SNP represents the most significant association for LDL-C and ApoB in the entire genome scan. Similarly, the sampled variation at the locus as a whole is second only to variation near the APOE locus for explaining variance in the lipids due to genetics. In an unrelated study, the association with ApoB could be confirmed at just below genome-wide significance 22. And, in yet another separate study, a second SNP from the locus (rs599839) was associated with coronary artery disease (CAD) at the genome-wide level in analysis combining two cohorts 29. Based on HapMap linkage disequilibrium estimates 16, the trend of the rs599839 association could be inferred to be consistent with the effects of rs646776 on LDL-C and ApoB in our study.

The sampled variation at the candidate loci accounts for only part of the heritability in lipid traits estimated in other Caucasian populations 3034. Even after adjustment for clinical and environmental factors, 7.3%, 5.7%, and 5.0% of the variance in LDL-C, HDL-C, and triglycerides, respectively, is explained by the common variation at the candidate loci. Without adjustment the values are slightly smaller: 6.1%, 5.0%, and 3.4%. It is possible that common variation at loci that were not thoroughly sampled by the SNP panel could account for some of the remaining variance, but it would be surprising if any of the missing SNPs was as strongly associated as the most significant variants reported here. Even tagging with linkage disequilibrium to causal SNPs in the range of r=0.2–0.4, i.e. considerably less than the r=0.7–0.8 used to design the genotyping panel, would have been adequate to identify the genome-wide associations typified by the CETP influences on HDL-C or the SORT1 influences on ApoB in our study with at least 80% power.

Identifying the sources of this discrepancy is crucial to the larger goal of elucidating the complete set of genes and pathways contributing to lipid metabolism. In part, the remaining genetic effects may be explained by less common alleles in the candidate loci. For example, the association of LDL-C and ApoB with PCSK9 involved a SNP (rs11591147) that was included in the custom content of our SNP panel and had minor allele frequency 1.6%. However, a multiplicity of both common and rare variants at loci with less pronounced associations (either due to sparse coverage of the SNP panel or relatively less existing functional variation) as well as structural variation that may not be measured by conventional SNP genotyping 35 likely contribute as well. Similarly, the current analysis did not consider interaction between genetic variation and itself or the environment, two potential components of heritability that remain relatively unexplored on a genome-wide basis. Capturing these alternative contributions with certainty will require the statistical power of large cohorts or combinations of smaller cohorts with precise phenotype information as exemplified by the WGHS.

Acknowledgments

Funding Sources

Supported by grants from the National Heart, Lung, and Blood Institute (HL 043851, HL69757) and the National Cancer Institute (CA 047988) (both Bethesda, MD), the Donald W. Reynolds Foundation (Las Vegas, NV), the Doris Duke Charitable Foundation, the Fondation Leducq (Paris, FR), and the Fonds de la Recherche en Santé du Quebec (to GP). Collaborative scientific support and funding for genotyping provided by Amgen, Inc.

Center for Cardiovascular Disease Prevention, Harvard Medical School, Boston, MA
Donald W Reynolds Center for Cardiovascular Research, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
Amgen, Inc, Cambridge, MA
Department of Genome Sciences, University of Washington, Department of Genome Sciences Seattle, WA
Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA
Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA
Children’s Hospital Oakland Research Institute, Oakland, CA
Correspondence to Daniel I. Chasman, Center for Cardiovascular Disease Prevention, Brigham and Women’s Hospital, 900 Commonwealth Avenue East, Boston, MA 02215. Tel 617-278-0821; FAX 617-232-3541; ude.dravrah.hwb.scir@namsahcd
These authors made equal contributions

Abstract

Background

Genome-wide genetic association analysis represents an opportunity for comprehensive survey of genes governing lipid metabolism, potentially revealing new insights or even therapeutic strategies for cardiovascular disease and related metabolic disorders.

Methods and Results

We have performed large-scale, genome-wide genetic analysis among 6382 Caucasian women with replication in two cohorts of 970 additional Caucasian men and women for associations between common SNPs and LDL-C, HDL-C, triglycerides, apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). Genome-wide associations (P<5×10) were found at the PCSK9 gene, the APOB gene, the LPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus. In addition, genome-wide associations with triglycerides at the GCKR gene confirm and extend emerging links between glucose and lipid metabolism. Still other genome-wide associations at the 1p13.3 locus are consistent with emerging biological properties for a region of the genome, possibly related to the SORT1 gene. Below genome-wide significance, our study provides confirmatory evidence for associations at five novel loci with LDL-C, HDL-C, or triglycerides reported recently in separate genome-wide association studies. The total proportion of variance explained by common variation at the genome-wide candidate loci ranges from 4.3% for triglycerides to 12.6% for ApoB.

Conclusions

Genome-wide associations at the GCKR gene and near the SORT1 gene as well as confirmatory associations at five additional novel loci suggest emerging biological pathways for lipid metabolism among Caucasian women.

Keywords: lipoprotein, lipid, GWAS, cardiovascular disease
Abstract

Footnotes

Disclosures

Collaborative scientific support and funding for genotyping was provided by Amgen, Inc. ANP and JPM are employees of Amgen, Inc. DIC, PTW, MJR, JIR, DAN, and RMK receive support from NHLBI (HL69757, Krauss PI) for genetic analysis of lipid lowering with statin therapy. RMK receives research support from Merck, Inc. PMR and RYZ receive research support for genotyping from Roche Diagnostics, Inc. NRC, JEB, DJK, LMR, and JDS have nothing to disclose.

Footnotes

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