NEW WAYS TO FIND HUMAN BMD GENES USING MOUSE QTL MAPPING

描述(申请人提供)：种群和动物研究相结合是复杂性状遗传学的一种可行方法。在我们对啮齿动物和人类的研究中，我们确定了几个与骨质疏松症相关的表型的数量性状基因座(QTL)。其中一些QTL显示了老鼠和人类之间的重叠。因此，这项建议的目标是：(A)使用来自人类全基因组关联研究(GWA)的信息来改进小鼠模型，以在包含QTL的同线区域识别强候选基因，以及(B)将小鼠QTL分析的结果直接纳入人类队列研究。我们将重点介绍位于小鼠8、15和17号染色体上的骨密度及相关性状的QTL，与人类同源的16q22-q23、8q24和6p21-p12区域，以及与人类骨量相关性状连锁的QTL。在小鼠身上确定了QTL基因后，它将在人类关联性研究中进行测试。我们将在假设驱动的关联研究中检查人类的多态，该研究比没有先验假设测试多个基因的关联研究具有统计学优势。拟议的工作是创新的，因为它基于两个主要发展：(A)新的老鼠和人类资源，使电子比较遗传学研究成为可能；(B)日益详细地了解两个物种之间的物种间同源性。因此，这项研究有可能通过提供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来缩小QTL的范围，从而显著推进当前的QTL研究。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.