The majority of apolipoproteins known to play a major role in lipid metabolism were identified over 20 years ago, and nine of them (APOA1, -A2, -A4, -B48, -B100, -C1, -C2, -C3 and -E) have long been known to be most relevant to the regulation of lipoproteins. Polymorphisms of genes encoding apolipoproteins influence plasma levels of high-density lipoproteins (HDL), very-low-density lipoprotein (VLDL), low-density lipoprotein (LDL) chylomicrons or triglycerides. Familial hypercholesterolemia (FH), an autosomal dominant disorder, is caused by mutations mainly located in the low-density lipoprotein receptor (LDLR) gene, or more rarely within the apolipoprotein B-100 gene or the gene encoding a secreted proteinase PSCK9. FH is characterized by elevated concentrations of LDL, deposition of LDL-derived cholesterol in tendons, skin xanthomas, and premature coronary artery disease. The frequency of heterozygotes is approximately one in 500 persons, placing FH among the most common inborn errors of metabolism. The risk of cardiovascular disease in these patients is influenced not only by the type of the mutations they carry, but also by the haplotype of lipid modifier genes, as is the case of apolipoproteins. In this review, we present current information that demonstrates the impact of apolipoprotein polymorphisms on the FH phenotype.
"While environmental factors, such as diet, are important determinants of circulating lipid concentrations, heritabilities of lipid profiles have been well elucidated in twin studies [3–6]. Because of the well-established association between circulating cholesterol levels and cardiovascular diseases, genes involved in cholesterol synthesis and transport have been more extensively studied than the others involved in long-chain fatty acids (LCFAs) and phospholipids [2, 7–11]. "
[Show abstract][Hide abstract] ABSTRACT: Dysfunctional lipid metabolism plays a central role in pathogenesis of major chronic diseases, and genetic factors are important determinants of individual lipid profiles. We analyzed the associations of two well-established functional polymorphisms (FABP2 A54T and APOE isoforms) with past and family histories of 1492 population samples. FABP2-T54 allele was associated with an increased risk of past history of myocardial infarction (odds ratio (OR) = 1.51). Likewise, the subjects with APOE4, compared with E2 and E3, had a significantly increased risk of past history myocardial infarction (OR = 1.89). The OR associated with APOE4 was specifically increased in women for past history of myocardial infarction but decreased for gallstone disease. Interactions between gender and APOE isoforms were also significant or marginally significant for these two conditions. FABP2-T54 allele may be a potential genetic marker for myocardial infarction, and APOE4 may exert sex-dependent effects on myocardial infarction and gallbladder disease.
"The type of LDLR mutation has been shown to correlate with the response to statin therapy . Polymorphisms in lipid modifier genes, such as apolipoproteins, particularly ApoE, can significantly affect the FH phenotype . Conventional risk factors for atherosclerosis such as smoking, diet, hypertension, and diabetes are also additive in FH [48,49]. "
[Show abstract][Hide abstract] ABSTRACT: Familial Hypercholesterolemia (FH) is a common cause of premature cardiovascular disease and is often undiagnosed in young people. Although the disease is diagnosed clinically by high LDL cholesterol levels and family history, to date there are no single internationally accepted criteria for the diagnosis of FH. Several genes have been shown to be involved in FH; yet determining the implications of the different mutations on the phenotype remains a hard task. The polygenetic nature of FH is being enhanced by the discovery of new genes that serve as modifiers. Nevertheless, the picture is still unclear and many unknown genes contributing to the phenotype are most likely involved. Because of this evolving polygenetic nature, the diagnosis of FH by genetic testing is hampered by its cost and effectiveness.
In this review, we reconsider the clinical versus genetic nomenclature of FH in the literature. After we describe each of the genetic causes of FH, we summarize the known correlation with phenotypic measures so far for each genetic defect. We then discuss studies from different populations on the genetic and clinical diagnoses of FH to draw helpful conclusions on cost-effectiveness and suggestions for diagnosis.
"The data set includes 8 pedigrees consisting of 216 individuals, of whom 163 have complete HDL trait, age and genotype for one multiallelic marker in the APOC3 gene. Since APOC3 is part of a tight cluster of apolipoprotein genes, for which there is evidence of functional variation associated with lipid levels [Hayden et al. 1987; Dedoussis 2007], including HDL [Devlin et al. 1998; Gagnon et al. 2003], there may be multiple alleles defined by haplotypes or multiple loci across the region, in addition to locus heterogeneity. In this data set, APOC3 has a high heterozygosity of 0.94, so that the information contained in the inheritance vectors should approximate that of a dense set of SNPs at that region. "
[Show abstract][Hide abstract] ABSTRACT: Identification of the genetic basis of common traits may be hindered by underlying complex genetic architectures that are inadequately captured by existing models, including both multiallelic and multilocus modes of inheritance (MOI). One useful approach for localizing genes underlying continuous complex traits is the joint oligogenic linkage and segregation analysis implemented in the package Loki. The method uses reversible jump Markov chain Monte Carlo to eliminate the need to prespecify the number of quantitative trait loci (QTLs) in the trait model, thus providing posterior distributions for the number of QTLs in a Bayesian framework. The current implementation assumes QTLs are diallelic, and therefore can overestimate the number of linked QTLs in the presence of a multiallelic QTL. To address the possibility of multiple alleles, we extended the QTL model to allow for a variable number of additive alleles at each locus. Application to simulated data shows that, under a diallelic MOI, the multiallelic and diallelic analysis models give similar results. Under a multiallelic MOI, the multiallelic analysis model provides better mixing and improved convergence, and leads to a more accurate estimate of the underlying trait MOI and model parameter values, than does the diallelic model. Application to real data shows the multiallelic model results in fewer estimated linked QTLs and that the predominant QTL model is similar to one of two predominant models estimated from the diallelic analysis. Our results indicate that use of a multiallelic analysis model can lead to better understanding of the genetic architecture underlying complex traits.
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