Molecular Basis of High Density Lipoprotein Deficiency

描述（由申请人提供）：低水平的高密度脂蛋白胆固醇（HDL-C）是冠心病（CHD）的独立危险因素。据估计，人类HDL-C水平的变化有50%以上是由基因决定的。常见的遗传变异导致了对常见疾病易感性的遗传差异，这一概念引起了人们的极大兴趣。解决这一问题的一个有希望的方法是在基于人群的关联研究中将DNA序列的变异与患者特征联系起来。我们建议使用来自退伍军人事务HDL干预试验（VA-HIT，病例）的样本来识别与低HDL特征相关的等位基因变异，该试验旨在探索在患有低HDL- c (<40 mg/dL)，正常LDL-C （<140 mg/dL）和已知冠心病的男性中使用吉非西齐提高HDL的益处，以及弗雷明汉后代研究（FOS，对照组）。我们的主要目的是确定：1)低HDL特征的易感性位点，2)与载脂蛋白a -l-含HDL亚种水平相关的等位基因变异，以及3)与VA-HIT患者对吉非弗齐反应相关的等位基因变异。对于目标1，我们将检查生物学（n=38）和位置（n=3）候选人。前者将包括与高密度脂蛋白代谢、胰岛素抵抗和炎症有关的基因，而后者将根据与FOS中HDL- c水平相关的数量性状位点的全基因组连锁扫描结果进行选择。对于每个候选基因，我们将使用HapMap数据来选择一组信息量最大的snp (tagsnp)，这将使我们能够解析80%的单倍型。基于该算法，我们将在每个候选基因/区域中每2500 bp进行1个SNP的基因型分析。为了解决人口分层问题，我们将采用结构化关联方法，使用一组250个能够检测适度分层的标记。这项工作的结果将为了解等位基因变异在HDL代谢、炎症和胰岛素抵抗途径中对低HDL- c复杂表型的贡献提供重要见解。

DESCRIPTION (provided by applicant): A low level of high density lipoprotein cholesterol (HDL-C) is an independent risk factor for coronary heart disease (CHD). It is estimated that more than 50% of the variation in HDL-C levels in humans is genetically determined. There is great interest in the concept that common genetic variants contribute to inherited differences in the susceptibility to common diseases. One promising approach to address this issue is to relate variation in DNA sequence with patient characteristics in population-based association studies. We are proposing to identify allelic variants associated with the low HDL trait, using samples from the Veterans Affairs HDL Intervention Trial (VA-HIT, cases), a study designed to explore the benefits of HDL-raising with gemfibrozil in men having low HDL-C (<40 mg/dL), normal LDL-C (<140 mg/dL) and known CHD, and the Framingham Offspring Study (FOS, controls). Our primary aims are to identify: 1) susceptibility loci for the low HDL trait, 2) allelic variants associated with levels of apolipoproteinA-l-containing HDL subspecies, and 3) allelic variants associated with response to gemfibrozil in VA-HIT. For Aim 1, we will examine biological (n=38) and positional (n=3) candidates. The former will include genes involved in HDL metabolism, insulin resistance and inflammation, while the latter will be selected on the basis of results from genome-wide linkage scans for quantitative trait loci associated with HDL-C levels in FOS. For each candidate, we will use HapMap data in order to select a maximally informative set of SNPs (tagSNPs), which will allow us to resolve >80% of all haplotypes. Based on this algorithm, we will genotype 1 SNP per 2500 bp across each candidate gene/region. To address the issue of population stratification, we will employ a structured association approach, using a set of 250 markers that has the ability to detect modest amounts of stratification. The results of this work will provide important insight into the contribution of allelic variation in the pathways of HDL metabolism, inflammation, and insulin resistance to the complex phenotype of low HDL-C.