NEW WAYS TO FIND HUMAN BMD GENES USING MOUSE QTL MAPPING

描述（申请人提供）：群体和动物研究相结合是复杂性状遗传学的可行方法。在啮齿动物和人类的研究中，我们确定了几个与狂犬病相关表型的数量性状位点（QTL）。这些QTL中的一些在小鼠和人类之间显示出重叠。因此，本建议的目的是：（a）使用来自人类全基因组关联研究（GWA）的信息来完善小鼠模型，以鉴定含有QTL的同线区域的强候选基因，以及（B）将小鼠QTL分析的结果直接并入人类队列研究。本研究将重点研究位于小鼠第8、15和17号染色体上的骨密度及相关性状的QTL，这些QTL分别位于与人类同线的16 q22-q23、8 q24和6p 21-p12区域，并且在这些区域中我们也发现了与人类骨量相关性状的连锁。在小鼠中鉴定QTL基因后，将在人类关联研究中对其进行测试。我们将在假设驱动的关联研究中检查人类的多态性，该研究比在没有先验假设的情况下测试多个基因的关联研究具有统计学优势。拟议的工作是创新性的，因为它是基于2个主要的发展：（a）新的小鼠和人类资源，使计算机比较遗传学研究成为可能;（B）不断增长的详细知识，两个物种之间的种间同源性。因此，这项研究有可能显着推进目前的QTL研究提供了一套新的生物信息学工具，通过整合几个科学学科的分析优势缩小QTL。这种集成的生物信息学方法最终可以扩展到其他复杂疾病的基因识别。

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.