NEW WAYS TO FIND HUMAN BMD GENES USING MOUSE QTL MAPPING

描述（由申请人提供）：群体和动物研究的结合是复杂性状遗传学的可行方法。我们在啮齿动物和人类的研究中发现了几个与骨质疏松相关表型的数量性状位点（QTL）。其中一些qtl在小鼠和人类之间显示出重叠。因此，本提案的目标是：(a)利用人类全基因组关联研究（GWA）的信息来完善小鼠模型，以识别含有QTL的合成区域的强候选基因；(b)将小鼠QTL分析的结果直接纳入人类队列研究。我们将重点关注位于小鼠染色体8、15和17上与人类16q22-q23、8q24和6p21-p12区域相同的骨矿物质密度（BMD）和相关性状的qtl，在这些qtl中我们也发现了与人类骨量相关性状的连锁。在老鼠身上发现QTL基因后，将在人类关联研究中进行测试。我们将在假设驱动的关联研究中检查人类的多态性，这比没有先验假设的关联研究测试多个基因具有统计优势。拟议的工作是创新的，因为它是基于两个主要发展：(a)新的小鼠和人力资源，使在硅比较遗传学研究成为可能；(b)加深对两个物种间同源性的详细了解。因此，本研究通过整合多个科学学科的分析优势，为缩小QTL提供了一套新的生物信息学工具，从而有可能显著推进当前的QTL研究。这种综合的生物信息学方法最终可以扩展到其他复杂疾病的基因鉴定。

DESCRIPTION (provided by applicant): Combination of population and animal studies is a viable approach for genetics of complex traits. We identified several quantitative trait loci (QTL) for osteoporosis-related phenotypes in our studies of both rodents and humans. Some of these QTLs show overlap between mice and humans. The objectives of this proposal thus are: (a) to use information from human genome-wide association study (GWA) to refine a murine model to identify strong candidate genes at the syntenic regions containing QTLs, and (b) to directly incorporate the results of the mouse QTL analysis into a human cohort study. We will focus on QTLs for bone mineral density (BMD) and related traits located on mouse chromosomes 8, 15 and 17, syntenic to human regions on 16q22-q23, 8q24, and 6p21-p12, correspondingly, in which we also found linkage with bone mass related traits in humans. After a QTL gene is identified in the mouse, it will be tested in human association studies. We will examine polymorphisms in humans in a hypothesis- driven association study, which has statistical advantages over association studies that test multiple genes with no a priori hypothesis. The proposed work is innovative since it is based on 2 major developments: (a) novel mouse and human resources that allow in-silico comparative genetic study possible; (b) growing detailed knowledge of inter-species homology between the two species. This study thus has the potential to significantly advance current QTL studies by providing a new set of bioinformatics tools for narrowing QTLs by integrating the analytical strengths of several scientific disciplines. This integrated bioinformatic approach ultimately can be extended to gene identification for other complex diseases.