Computational lipidology: predicting lipoprotein density profiles in human blood plasma.

Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia.

Common haplotypes in five genes influence genetic variance of LDL and HDL cholesterol in the general population.

We studied the association between common haplotypes in six relevant lipid metabolism genes with plasma lipid levels. We selected single-nucleotide polymorphisms (SNPs) in the cholesterol ester transfer protein (CETP), lipoprotein lipase (LPL), hepatic triglyceride lipase (HL), low-density lipoprotein cholesterol receptor (LDLR), apolipoprotein E (ApoE) and lecithin-cholesterol acyltransferase (LCAT) genes, and studied 732 individuals from 184 German families. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL) and high-density lipoprotein cholesterol (HDL) were similar to those reported in other European and American populations. Haplotypes derived from SNP combinations resulted in more significance and of a higher degree than did single SNPs in the genotype-phenotype association analysis. Reduction of the polygenic variance attributable to haplotypes was estimated using variance components analysis. Under the biometrical genetic model, allelic association of haplotypes was highly significant for HDL, LDL and the LDL/HDL ratio. The residual kinship correlation was reduced accordingly. The ApoE gene had a strong effect on trait variation; however, the other genes also contributed substantially. An epistatic interaction could not be demonstrated in this sample. The data are consistent with the notion that common genetic variants influence common traits.

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

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.