From QTL to Gene for HDL Cholesterol

描述（由申请人提供）：流行病学研究表明，即使高密度脂蛋白（HDL）胆固醇的少量增加也能对心脏病提供相当大的保护。通过对小鼠和人类的数量性状位点（QTL）定位，发现许多基因组区域含有影响HDL水平的基因。结合我们自己的实验室数据和文献数据，我们发现大多数HDL qtl已经被发现了很多次，最近报道的qtl只是证实了以前报道的qtl，并且小鼠和人类的qtl都发现在同源染色体区域，这表明相同的基因决定了这些物种的HDL。我们估计有23到30个基因可以解释高密度脂蛋白浓度的大多数种群差异。识别这些基因将增加我们对导致高密度脂蛋白水平升高的因素的理解，也可能提供治疗靶点。使用小鼠模型，我们的目标是确定小鼠中五个HDL qtl的基因，因为它们存在于人类和小鼠基因组的同源区域；在不同的小鼠杂交中多次发现了它们；而且它们的影响足够大。对于每个QTL，我们将通过同源菌株将导致HDL升高的等位基因移到B6遗传背景中来验证HDL升高可以预防动脉粥样硬化的假设。利用生物信息学资源，我们将通过结合多个杂交的统计数据，比较小鼠和人类的同源性，应用单倍型分析，以及比较小鼠品系之间的序列来缩小每个QTL。利用遗传资源，我们将利用重组自交系、高级杂交系和重叠同源进一步缩小每个QTL。将检测候选基因的表达和序列差异；极有可能的候选药物将通过转基因、基因敲除和其他技术来证明。最后，我们将建立一个我们和其他人已经生成的原始QTL数据的公共数据库，以便新的分析技术，如确定QTL的95%置信区间，寻找基因相互作用，以及分析组合交叉数据可以应用于已发表的数据。

DESCRIPTION (provided by applicant): Epidemiological studies show that even small increases in high-density lipoprotein (HDL) cholesterol afford considerable protection against heart disease. Through quantitative trait locus (QTL) mapping in both mice and humans, many genomic regions have been found that contain genes affecting HDL levels. Combining data from our own laboratory with that from the literature shows that most HDL QTLs have been discovered many times, that the recently reported QTLs simply confirm previously reported ones, and that mouse and human QTLs are found in homologous chromosomal regions, suggesting that the same genes determine HDL in these species. We estimate that between 23 and 30 genes account for most population variation in HDL concentrations. Identifying these genes would increase our understanding of what factors cause an increase in HDL levels and may also provide therapeutic targets. Using mouse models, we aim to identify the genes underlying five HDL QTLs in the mouse chosen for further analysis because they are found in homologous regions of the human and mouse genomes; they have been found multiple times in different mouse crosses; and their effects are large enough to work with. For each QTL, we will test the hypothesis that increased HDL protects against atherosclerosis using congenic strains moving the allele causing increased HDL into the B6 genetic background. Using bioinformatics resources, we will narrow each QTL by combining statistical data from several crosses, comparing mouse and human homologies, applying haplotype analysis, and comparing sequence among mouse strains. Using genetic resources, we will further narrow each QTL with recombinant inbred strains, advanced intercross lines, and overlapping congenics. Candidate genes will be tested for expression and sequence differences; highly probable candidates will be proven by transgenic, knockout, and other technologies. Finally, we will build a public database of the raw QTL data that we and others have generated so that new techniques of analysis, such as determining the 95% confidence interval of a QTL, finding gene interactions, and analyzing combined cross data can be applied to published data.