Xiong M, Chen H J, Prade R A, Wang Y, Griffith J, Timberlake W E, Arnold J
Department of Mathematics and Molecular Biology, University of Southern California, Los Angeles 90089, USA.
Genetics. 1996 Jan;142(1):267-84. doi: 10.1093/genetics/142.1.267.
During recent years considerable effort has been invested in creating physical maps for a variety of organisms as part of the Human Genome Project and in creating various methods for physical mapping. The statistical consistency of a physical mapping method to reconstruct a chromosome, however, has not been investigated. In this paper, we first establish that a model of physical mapping by binary fingerprinting of DNA fragments is identifiable using the key assumption-for a large randomly generated recombinant DNA library, there exists a staircase of DNA fragments across the chromosomal region of interest. Then we briefly introduce epi-convergence theory of variational analysis and transform the physical mapping problem into a constrained stochastic optimization problem. By doing so, we prove epi-convergence of the physical mapping model and epi-convergence of the physical mapping method. Combining the identifiability of our physical mapping model and the epi-convergence of a physical mapping method, finally we establish strong consistency of a physical mapping method.
近年来,作为人类基因组计划的一部分,人们投入了大量精力为各种生物体创建物理图谱,并开发了各种物理图谱构建方法。然而,尚未对用于重建染色体的物理图谱方法的统计一致性进行研究。在本文中,我们首先证明,基于DNA片段二元指纹识别的物理图谱模型在关键假设下是可识别的——对于一个大型随机生成的重组DNA文库,在感兴趣的染色体区域存在一个DNA片段阶梯。然后,我们简要介绍变分分析的外延收敛理论,并将物理图谱问题转化为一个约束随机优化问题。通过这样做,我们证明了物理图谱模型的外延收敛和物理图谱方法的外延收敛。结合我们物理图谱模型的可识别性和物理图谱方法的外延收敛,最终我们建立了物理图谱方法的强一致性。