From QTL to Gene for HDL Cholesterol

描述(申请人提供)：流行病学研究表明，即使高密度脂蛋白(高密度脂蛋白)胆固醇略有增加，也能相当好地预防心脏病。通过对小鼠和人类进行数量性状基因座(QTL)定位，已发现许多基因组区域含有影响高密度脂蛋白水平的基因。结合我们自己实验室的数据和文献中的数据，大多数高密度脂蛋白QTL已经被多次发现，最近报道的QTL只是证实了以前报道的QTL，并且在同源染色体区域发现了小鼠和人类的QTL，这表明在这些物种中决定高密度脂蛋白的是相同的基因。我们估计，高密度脂蛋白浓度的大部分群体变异是由23到30个基因引起的。识别这些基因将增加我们对哪些因素导致高密度脂蛋白水平升高的理解，并可能提供治疗靶点。使用小鼠模型，我们的目标是识别在小鼠中选择进行进一步分析的五个高密度脂蛋白QTL的基因，因为它们在人类和小鼠基因组的同源区域中被发现；它们在不同的小鼠杂交中被多次发现；并且它们的影响足够大，足以处理。对于每个QTL，我们将通过将导致高密度脂蛋白升高的等位基因转移到B6遗传背景中来测试增加的高密度脂蛋白预防动脉粥样硬化的假设。利用生物信息学资源，我们将结合几个杂交组合的统计数据，比较小鼠和人类的同源性，应用单倍型分析，并比较小鼠品系之间的序列，从而缩小每个QTL的范围。利用遗传资源，利用重组自交系、高级异交系和重叠遗传，进一步缩小每个QTL的范围。候选基因将进行表达和序列差异测试；极有可能的候选基因将通过转基因、基因敲除和其他技术进行验证。最后，我们将为我们和其他人生成的原始QTL数据建立一个公共数据库，以便新的分析技术，如确定QTL的95%可信区间，发现基因互作，以及分析组合杂交数据，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.