Human Molecular Genetics

Human Molecular Genetics Advance Access originally published online on March 25, 2004

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Human Molecular Genetics, 2004, Vol. 13, No. 10 993-1004
DOI: 10.1093/hmg/ddh119
Human Molecular Genetics, Vol. 13, No. 10 © Oxford University Press 2004; all rights reserved

Haplotypes and SNPs in 13 lipid-relevant genes explain most of the genetic variance in high-density lipoprotein and low-density lipoprotein cholesterol

Hans Knoblauch^1,, Anja Bauerfeind^3,, Mohammad Reza Toliat², Christian Becker², Tatjana Luganskaja³, Ulf Peter Günther³, Klaus Rohde³, Herbert Schuster¹, Christine Junghans¹, Friedrich C. Luft^1,*,, Peter Nürnberg^2,4, and Jens Georg Reich^3,

¹Medical Faculty of the Charité, Franz Volhard Clinic, Helios Klinikum, Berlin, Germany, ²Gene Mapping Center, ³Bioinformatics Section, Max Delbrück Center for Molecular Medicine, Berlin, Germany and ⁴Institute of Medical Genetics, Charité University Hospital, Humboldt University, Berlin, Germany

Received November 22, 2003; Revised February 17, 2004; Accepted March 15, 2004

	ABSTRACT

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS REFERENCES

 
Single nucleotide polymorphisms (SNPs) and derived haplotypes within multiple genes may explain genetic variance in complex traits; however, this hypothesis has not been rigorously tested. In an earlier study we analyzed six genes and have now expanded this investigation to include 13. We studied 250 families including 1054 individuals and measured lipid phenotypes. We focused on low-density cholesterol (LDL), high-density cholesterol (HDL) and their ratio (LDL/HDL). A component analysis of the phenotypic variance relying on a standard genetic model` showed that the genetic variance on LDL explained 26%, on HDL explained 38% and on LDL/HDL explained 28% of the total variance, respectively. Genotyping of 93 SNPs in 13 lipid-relevant genes generated 230 haplotypes. The association of haplotypes in all the genes tested explained a major fraction of the genetic phenotypic variance component. For LDL, the association with haplotypes explained 67% and for HDL 58% of the genetic variance relative to the polygenic background. We conclude that these haplotypes explain most of the genetic variance in LDL, HDL and LDL/HDL in these representative German families. An analysis of the contribution to the genetic variance at each locus showed that APOE (50%), CETP (28%), LIPC (9%), APOB (8%) and LDLR (5%) influenced variation in LDL. LIPC (53%), CETP (25%), ABCA1 (10%), LPL (6%) and LDLR (6%) influenced the HDL variance. The LDL/HDL ratio was primarily influenced by APOE (36%), CETP (27%) and LIPC (31%). This expanded analysis substantially increases the explanation of genetic variance on these complex traits.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS REFERENCES

 
Cardiovascular disease is the leading cause of death worldwide and serum low-density cholesterol (LDL) and high-density cholesterol (HDL) are primary risk factors (1). Major mutations have been described in genes coding for the low-density lipoprotein cholesterol receptor, apolipoprotein B and, more recently, LDLR adaptor protein (2,3). These Mendelian conditions are mechanistically highly informative, but are relatively infrequent or rare. In the general population, single nucleotide polymorphism (SNP) variants in known genes important to lipid metabolism probably account for most of the genetic variance. We recently modeled the interactions of six genes important to lipid metabolism mathematically by means of differential equations (4). We then selected SNPs in these genes, namely cholesteryl ester transfer protein (CETP), lipoprotein lipase (LPL), hepatic triglyceride lipase (LIPC), low density lipoprotein cholesterol receptor (LDLR), apolipoprotein E (APOE) and lecithin : cholesterol acyltransferase (LCAT) genes and studied 625 individuals from 186 German families (5). We found that allelic association of haplotypes was highly significant for HDL, LDL and the LDL/HDL ratio. Haplotypes derived from SNP combinations resulted in greater significance than did single SNPs in the genotype–phenotype association analysis. In recent studies the heritability of lipid phenotypes is around 50% (6,7). Based on lower heritability estimates (33% for LDL and 45% for HDL) from our previous study (5), we estimated that about 58% and 47% of the genetic variance for LDL and HDL, respectively, could be ‘predicted’ by haplotypes.

An analysis of complex genetic traits presents several difficulties. The majority of synonymous changes or intronic SNPs will have no physiological impact. Nevertheless, these SNPs may serve as linkage disequilibrium (LD) markers. Strong LD between SNPs may be anticipated in our candidate genes, since they typically extend over genomic regions not larger than 30 kb (8–14). Furthermore, the LD profile along chromosomes does not decrease linearly with the physical distance. The population–genetic process of mutation and recombination has considerably stochastic scatter; population bottlenecks may complicate the picture. We have relied on a variance component model, since simple regression analysis is not valid in situations where phenotypes are correlated between family members (15–20). Moreover, each individual locus may contribute only 5–10% to the total variation. For example, APOE that has a large effect accounts for <10% of the total cholesterol variance (21,22). These difficulties caused us to expand the sample size of our earlier study (5) as we incorporated additional genes.

	RESULTS

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS REFERENCES

 
We studied 93 SNPs in 13 lipid-regulatory genes. We present the SNP identification number, position, their allele frequencies, sequence information and pseudonyms in Table 1. Twenty one SNPs had a frequency <5%, 12 SNPs a frequency between 5 and 10%, 12 SNPs a frequency between 10 and 20% and 48 SNPs a frequency >20%. About one-third was located within exons, one-third within introns and the remaining SNPs in 5'- and 3'-regions of the respective genes. In Table 2, all haplotypes are shown. Depending upon the size of the gene and the number of SNPs per gene, subhaplotypes were built. In Table 3, we provide demographic data concerning the subjects' age, total cholesterol (TC), HDL, LDL and LDL/HDL ratio. TC and LDL increased with increasing age. Men had higher TC concentrations, higher LDL values and lower HDL values than women in every generation.

View this table: [in this window] [in a new window]   Table 1. SNP characteristics including SNP identification system, localization, allele frequencies, nucleotide exchange and pseudonym are shown

 

View this table: [in this window] [in a new window]   Table 2. Haplotypes were estimated based on unrelated founder individuals using an EM-algorithm; depending upon the size of the gene, haplotypes were split into subhaplotypes

 

View this table: [in this window] [in a new window]   Table 3. Total cholesterol (TC), LDL-cholesterol (LDL), HDL-cholesterol (HDL), LDL/HDL ratio and triglycerides (TGL) are shown in mg/dl; lipid phenotypes are structured by generation and gender (mean±SD)

 
Table 4 provides the results of the variance component QTDT analysis. In the ‘null model’ (columns 2 and 3) the phenotype variance was partitioned into an environmental, family-independent (V_env) and a family-related genetic (V_poly) component. Genotype data are not yet included. We estimated that 21% of the total variance could be ascribed to genetic effects for total cholesterol, 10% for triglycerides, 26% for LDL, 38% for HDL and 28% for the LDL/HDL ratio.

View this table: [in this window] [in a new window]   Table 4. Partition of phenotypic variance into variance components

 
To test whether or not the genetic component can be attributed to the candidate genes, we extended the standard genetic model by introducing haplotypes (columns 4–8) and SNP genotypes (columns 9–13) as covariates. The result, displayed in Table 4, is a further partition of the genetic component into V_hap (attributed to the haplotypes) or V_SNP (attributed to SNP genotypes) and V_poly (polygenic and family-shared background effects, which cannot reliably be distinguished in nuclear families). The non-shared environmental component V_env (columns 5 and 10) remained approximately at its previous value. The significance of introducing SNP and haplotype effects was tested using a likelihood ratio-test (LR-test) derived from the likelihoods of the ‘null model’ (without genotypes) and the ‘full model’ (genotypes included). The partition was highly significant for LDL, HDL and LDL/HDL ratio and not significant for triglyerides. For total cholesterol, SNPs gave significant result, while haplotypes did not (columns 7 and 12). The interpretation must consider that total cholesterol is a mixture of discordant fractions and that triglycerides vary widely and are difficult to standardize. This state-of-affairs tends to increase the ‘environmental’ variance component relative to the genetic component.

The haplotype variation accounted for the major part of the genetic variance for LDL, HDL and LDL/HDL ratio. For LDL, the association with haplotypes explained 67%, for HDL 58%, and for the LDL/HDL ratio 99% of the genetic variance (column 8). Modeling of SNPs as independent variables also explained a major part of the genetic variance, except in the case of HDL where there seems to be a substantial fraction not accounted for by the measured loci (column 13). Haplotypes improved the explanation of HDL and LDL/HDL ratio variance compared to SNPs. For LDL, SNPs were the better predictor.

Table 5 shows association effects of single genes by comparing likelihoods excluding and including the SNPs and haplotypes of the respective gene. Section A shows the association of haplotypes with the lipid phenotypes. Only significant gene contributions are shown. For LDL, APOE and CETP exerted significant effects (P<0.05). For HDL, LIPC and CETP haplotypes contribute significantly. Haplotypes of APOE, CETP and LIPC significantly predicted the LDL/HDL ratio. Section B shows the association with single SNPs. Again the effect of APOE on TC, LDL and LDL/HDL is visible.

View this table: [in this window] [in a new window]   Table 5. Significance of locus contribution obtained from a likelihood-ratio-test of a full model versus a model with one locus excluded

 
Figure 1 shows a pie diagram that gives the relative contributions of haplotypes in genes contributing >0% to the genetic variance on the phenotypes LDL (Fig. 1A), HDL (Fig. 1B) and LDL/HDL ratio (Fig. 1C), even if not statistically significant. The LDL/HDL ratio is a clinically highly relevant expression. These data show the relative contributions of the various genes. Genes with a contribution of 0% were excluded. APOE and CETP mainly influenced the variation in LDL. For HDL, the LIPC and CETP genes contributed the most. For the LDL/HDL ratio, the major contributors were APOE, CETP and LIPC.

View larger version (24K): [in this window] [in a new window]   Figure 1. Locuswise contribution of haplotypes to the genetic variance of LDL and HDL variance. (A) LDL; (B) HDL; (C) LDL/HDL ratio.

 

	DISCUSSION

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS REFERENCES

 
The important finding in our study was that we were able to explain a major part of the genetic variance for LDL, HDL and especially LDL/HDL ratio in the general German population on the basis of the genetic variants identified in 13 candidate genes important to lipid metabolism. The normal population is highly relevant in terms of investigation since more than half of ‘normal’ people can expect to die of cardiovascular disease. The levels of LDL, HDL and LDL/HDL ratio are important clinical indicators of the cardiovascular risk or degree of protection for each individual. Under controlled standardized physiological conditions, these levels do not vary much in any given individual. The variation between individuals is larger. The variation between members of an out-bred human population is heritable to a degree of about 50% (6,7,12).

We aimed to determine whether or not there is an association of phenotypic variation with genotypic variation in a large number of candidate genes in individuals without signs of lipid disturbances. The genotype–phenotype relationship was established by genotyping ‘common’ SNPs (>3%) in 13 candidate genes. About one-third was located in intron sequences and therefore may have less functional relevance. However, these SNPs can serve as markers for functionally relevant genetic variants due to high LD between SNPs within the candidate genes. This finding is demonstrated by the fact that a few dominant haplotypes accounted for most of the genotypic variation. The occurrence of LD blocks is in accordance with recent investigations (8–14).

The heritability of lipid phenotypes was lower than those estimated in previous studies (6,7,23,24). This result may be due to the greater variation in the non-genetic component in our sample, compared with more standardized sampling in other studies. Heritability is a relative value that depends on environmental and genetic factors. The families sampled for this analysis were recruited from across Germany and most lipid measurements were performed in routine clinical laboratories. These laboratories have high standards; however, a single reference laboratory may have been better. We certainly do not believe that our population structure differs from the populations described in earlier studies.

We found that the association was significant between lipid phenotypes and SNPs as well as haplotypes. The haplotype association was stronger for HDL, while the phase-independent SNP association was stronger for LDL and TC. The interpretation of such differences depends on whether SNPs or haplotypes are functionally relevant or whether they are markers of functional alleles. In most cases, common SNP variants are only functionally relevant if they are exonic non-synonymous SNPs or are located in the regulatory elements of genes, for example in promoters or in splice sites. In contrast, synonymous or intronic variants may or may not be markers of functionally relevant variants. In principle, it is not possible to predict a priori, if a given SNP is functional or simply a marker. Furthermore, it remains unknown if synonymous or intron SNPs are functional or not. For this analysis, we tried to cover the whole genomic sequence of each gene with frequent SNPs and did not try to distinguish between functional and non-functional SNPs. In this scenario, the population history is of importance. Our study demonstrates that a major part of the variation of LDL, HDL and LDL/HDL ratio may be explained by variation of common SNPs or SNP-derived haplotypes. These results support the ‘common trait–common variant’ hypothesis for an important metabolic trait (25). Templeton (26) discussed the possibility of epistatic effects in complex traits, exemplified by lipid metabolism. We explored possible gene–gene interaction models by regression models and could not find considerable gene–gene interaction effects between different alleles from the same or different genes in our dataset. Presumably, such effects become distinct only when there is a significant deviation from steady-state physiology. In addition, genes not included in this analysis might account for the remaining polygenic background.

The extended analysis leads to some changes in the conclusions from our earlier paper, while other results were confirmed and solidified (5). In general, neither the genetic variance not the environmental variance changed substantially across both studies. Therefore we conclude that the increase in the genetic variance explained is due to the additional candidate genes included in this analysis. In our previous manuscript we stated, that: ‘Haplotypes resulted in greater significance than did single SNPs in the genotype–phenotype association analysis’ (5). In this analysis, this pattern is no longer generally applicable. The effects of APOE were confirmed for TC, LDL and LDL/HDL ratio, but not for HDL and TGL. CETP effects were consistent. In addition, CETP SNPs affect TC and CETP haplotypes affect TC, TGL and LDL. LCAT did not show any association in either data set. The weak LPL effect on HDL could not be confirmed. The effect of LDLR SNPs was confirmed for the LDL/HDL ratio. In addition we now found a SNP effect on LDL and HDL, while the LDLR haplotype effect was not confirmed. We did not find a SNP effect of LIPC on the LDL/HDL ratio. The LIPC haplotype effect on LDL and the LDL/HDL ratio was consistent and additionally shown for TGL. The differences in significance levels are most likely due to a greater statistical power in the new data set. The linkage disequilibrium (LD) pattern for the additional genes is identical to the LD pattern described earlier. Between 3 and 5 frequent haplotypes accounted for about 80% of the whole sample.

New in our study is an analysis of the quantitative contributions of individual genes on the phenotypes LDL, HDL and LDL/HDL ratio. We were not surprised to find a large contribution of APOE to LDL and LDL/HDL ratio. This gene served as our positive control. Sing and Davignon (21) reported that APOE accounted for 10% of the LDL phenotypic variance, which is very much in accordance with our findings (polygenic variance of LDL is 26%; 67% of this variance is due genes analysed; 50% are contributed by APOE). This finding gives us confidence in our other estimates. CETP, which functions as a junction between LDL and HDL metabolism, had a substantial influence on both these phenotypes. ABCA1 plays a pivotal role in reverse cholesterol transport and indeed contributed considerably to the HDL phenotype.

Morabia et al. (27) recently reported on extreme blood-lipid profiles in a cohort of patients who underwent genotyping in 11 lipid metabolism relevant genes. They relied on a population-based sample (as did we) and had at their disposal numerous demographic risk parameters. In their association study, they classified 185 subjects as ‘cases’ who were in the upper third of LDL and lower third of HDL values (worst possible LDL/HDL ratio). They tested numerous SNPs and genes that were also examined in our study. The entire sample comprised 1708 persons. They then performed a logistic regression analysis and showed that a subset of nine genetic variants in seven candidate genes, coupled with five environmental risk factors, produced a model that may be relevant for future risk assessment. Genes that were in common with our study included the ABCA1, APOA5/A4/C3/A1 gene cluster, APOE, CETP, LPL, LIPC and LCAT genes. A comprehensive analysis comparing their results with those reported here would be of interest, particularly if genotypes absent to the two data sets were completed.

We believe that important perspectives can arise from our work. We need to extend our analysis to other cohorts, both within Germany and in other countries to test our SNP haplotypes in other populations. We also need to extend our analysis to populations containing significant numbers of persons who have developed heart disease. The notion that the haplotypes we describe have prognostic significance needs to be tested. If our haplotypes, alone or in combination, can indeed predict the occurrence of heart attack or stroke, the dream of using SNPs to predict human disease could be realized.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS REFERENCES

 
Study population
We selected 218 extended families from our genetic field working program according to methods outlined elsewhere (28). All subjects completed a medical questionnaire and were examined by their family physician or by us. Our university's committee on human subjects approved the protocol and written informed consent was obtained from all participants. Families with familial hypercholesterolemia, familial-combined hyperlipidemia or familial hypertriglyceridemia were excluded, as were families with patients who had known secondary causes of hyperlipidemia. We focused instead on families without known primary or secondary lipid disturbances. The persons in this analysis were either related to someone with heart disease or were recruited via the family of the spouses of a patient with heart disease. However, none had symptoms of angina pectoris or had known heart disease. The extended families were split into 250 nuclear families that contained at least two adult children. A total of 1054 subjects were included in the study.

Measurement of lipid traits and genotyping
SNPs were selected from the literature and from public databases (http://www.ncbi.nlm.nih.gov/SNP/). SNP genotyping was performed by DNA sequencing and pyrosequencing (29). TC, HDL and TGL were measured in serum by automated methods and LDL was calculated by the Friedewald formula. Further analysis required a Gaussian normal distribution of phenotypes. Therefore, we took the logarithms of the original lipid measurements and transformed these values to a sample mean of zero and a sample variance of unity. The logarithmic transformation resulted in a fairly satisfactory Gaussian distribution. No correction for age and gender was done to avoid loss of age and gender-dependent genetic effects on the phenotypic variance. We also inspected the demographic data and assured ourselves that these results were similar to known population values.

Statistical genetic analysis
All SNPs were in Hardy–Weinberg equilibrium. We constructed haplotypes in all nuclear families using an E–M algorithm, which uses the nuclear family information (30). Complete haplotypes could be assigned to 625 subjects. The presence or absence of haplotypes was coded as absent (=0), present once (=1) or twice (=2).

Variance component analysis
To take advantage of the family structures, we performed a variance component analysis for family data. The analysis uses the QTDT program: http://www.well.ox.ac.uk/asthma/QTDT (19,20). First, a separation into an environmental (V_env) and a polygenic variance (V_poly) component was performed. This analysis compares variance with covariance weighted by the kinship coefficient between individuals based on phenotype data only (null model of Table 4).

Partition of polygenic and locus-associated additive variance components
The total variance (=1) was partitioned into V_env, V_poly and, in the full model, V_hap (variance attributed to haplotypes due to association) or V_SNP (variance attributed to SNP variables), respectively. The contribution of SNPs or SNP haplotypes may be modeled as fixed effects in a multivariate-normal-distribution genetic model as specified by Fulker et al. (17) and extended to arbitrary pedigrees by Abecasis et al. (20). Here, the association is measured as a regression parameter, whereas the genetic influence at non-measured loci (polygenic variance) is modeled as a residual covariance term proportional to the kinship between two individuals (=0 if unrelated, >0 in a family). Significance of the variance-component-model after estimating the multiple fixed effects was tested as ²-test derived from likelihood ratio (LR) (16–20).

The non-zero covariance necessitated the use of a non-linear maximum-likelihood estimator, although the difference to an independent-error model (least squares estimation) is only slight in our data. We found that the number of independent haplotype parameters (193 out of 625 degrees of freedom) was too large to permit the convergence of the ML optimization procedure of the QTDT software. We therefore considered only frequent (>5%) haplotypes as separate independent variables and pooled rare haplotypes into one group. For the calculation of percentages of explained genetic variance we considered V_hap or V_SNP relative to V_hap or V_SNP+V_poly in haplotype- or SNP-based multiple models. The possibility of population admixture was tested by splitting the individual genotype into a between-family and a within-family component (19,20). We did not find significant indications of admixture in our ethnically homogenous sample.

Relative contribution of genes to phenotypic variance
Significance of the variance component attributable to a locus was tested by a likelihood ratio test between the likelihood of the full multiple SNP or haplotype model and the likelihood of the model with exclusion of the locus tested (Table 5). The relative contribution attributable to each gene locus was assessed by calculating the extra-variance due to regression on a tested locus, again by including/excluding the respective variables from the multiple model. This value is biased (31–33). However, the expected value of the bias (estimate of mean residual variance multiplied by the number of parameters at the tested locus) can be deducted from the variance estimate. Resulting negative variance estimates were interpreted to be zero. Estimated relative contributions between 5 and 10% are not statistically robust. The resulting relative values of the corrected extra-variance in the haplotype model were combined into the pie charts of Figure 1.

	ACKNOWLEDGEMENTS

 
This study was supported in part by grants-in-aid from the German Human Genome Project/National Genome Research Network and by the Deutsche Forschungsgemeinschaft.

	FOOTNOTES

 
^* To whom correspondence should be addressed at: Franz Volhard Clinic, Wiltberg Strasse 50, 13125 Berlin, Germany. Tel: +49 3094172202; Fax: +49 3094172206; Email: luft{at}fvk-berlin.de

These authors contributed equally to this work.

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