Lukić Sergio, Hey Jody, Chen Kevin
Department of Genetics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854, USA.
Theor Popul Biol. 2011 Jun;79(4):203-19. doi: 10.1016/j.tpb.2011.02.003. Epub 2011 Mar 2.
A major challenge in the analysis of population genomics data consists of isolating signatures of natural selection from background noise caused by random drift and gene flow. Analyses of massive amounts of data from many related populations require high-performance algorithms to determine the likelihood of different demographic scenarios that could have shaped the observed neutral single nucleotide polymorphism (SNP) allele frequency spectrum. In many areas of applied mathematics, Fourier Transforms and Spectral Methods are firmly established tools to analyze spectra of signals and model their dynamics as solutions of certain Partial Differential Equations (PDEs). When spectral methods are applicable, they have excellent error properties and are the fastest possible in high dimension; see Press et al. (2007). In this paper we present an explicit numerical solution, using spectral methods, to the forward Kolmogorov equations for a Wright-Fisher process with migration of K populations, influx of mutations, and multiple population splitting events.
群体基因组学数据分析中的一个主要挑战在于,从随机漂变和基因流所产生的背景噪声中分离出自然选择的特征。对来自许多相关群体的大量数据进行分析,需要高性能算法来确定不同人口统计学情景的可能性,这些情景可能塑造了观察到的中性单核苷酸多态性(SNP)等位基因频率谱。在应用数学的许多领域,傅里叶变换和谱方法是分析信号频谱并将其动态建模为某些偏微分方程(PDE)解的成熟工具。当谱方法适用时,它们具有出色的误差特性,并且在高维情况下速度最快;见Press等人(2007年)。在本文中,我们提出了一种显式数值解,使用谱方法求解具有K个群体迁移、突变流入和多次群体分裂事件的赖特 - 费希尔过程的正向柯尔莫哥洛夫方程。