The problem of inferring haplotypes from genotypes of single nucleotide polymorphisms (SNPs) is essential for the understanding of genetic variation within and among populations, with important applications to the genetic analysis of disease propensities and other complex traits. The problem can be formulated as a mixture model, where the mixture components correspond to the pool of haplotypes in the population. The size of this pool is unknown; indeed, knowing the size of the pool would correspond to knowing something significant about the genome and its history. Thus methods for fitting the genotype mixture must crucially address the problem of estimating a mixture with an unknown number of mixture components. In this paper we present a Bayesian approach to this problem based on a nonparametric prior known as the Dirichlet process. The model also incorporates a likelihood that captures statistical errors in the haplotype/genotype relationship trading off these errors against the size of the pool of haplotypes. We describe an algorithm based on Markov chain Monte Carlo for posterior inference in our model. The overall result is a flexible Bayesian method, referred to as DP-Haplotyper, that is reminiscent of parsimony methods in its preference for small haplotype pools. We further generalize the model to treat pedigree relationships (e.g., trios) between the population's genotypes. We apply DP-Haplotyper to the analysis of both simulated and real genotype data, and compare to extant methods.

译文

从单核苷酸多态性 (snp) 的基因型推断单倍型的问题对于理解种群内部和种群之间的遗传变异至关重要,对于疾病倾向和其他复杂性状的遗传分析具有重要应用。可以将问题表述为混合模型,其中混合成分对应于种群中的单倍型库。这个池的大小是未知的; 实际上,知道池的大小将对应于了解基因组及其历史的重要信息。因此,用于拟合基因型混合物的方法必须至关重要地解决估计具有未知数量的混合物成分的混合物的问题。在本文中,我们基于称为Dirichlet过程的非参数先验,提出了一种解决此问题的贝叶斯方法。该模型还结合了一种可能性,该可能性捕获了单倍型/基因型关系中的统计误差,将这些误差与单倍型池的大小进行了权衡。我们在模型中描述了一种基于马尔可夫链蒙特卡罗的后验推理算法。总体结果是一种灵活的贝叶斯方法,称为DP-Haplotyper,它使人想起了对小型单体型池的偏爱。我们进一步推广了该模型,以处理人群基因型之间的血统关系 (例如三重奏)。我们将DP-Haplotyper应用于模拟和真实基因型数据的分析,并与现有方法进行比较。

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