A HIERARCHICAL DIRICHLET PROCESS MIXTURE MODEL FOR HAPLOTYPE RECONSTRUCTION FROM MULTI-POPULATION DATA
A HIERARCHICAL DIRICHLET PROCESS MIXTURE MODEL FOR HAPLOTYPE RECONSTRUCTION FROM MULTI-POPULATION DATA
复制标题
多群体数据单倍型重建的分层狄利克雷过程混合模型
DOI：
10.1214/08-aoas225
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
E. Xing
中科院分区：
文献类型：
--
作者：
Kyung;E. Xing
“有多少集群？”是一个长期存在的问题。仍然是数据挖掘和机器学习社区广泛关注的一个问题，并且在群体基因组数据等大型数据集中变得尤为突出，其中集群的数量需要相对较大且开放。在共聚类场景中，这个问题变得更加复杂，在共聚类场景中，由于来自不同多聚类样本（例如，不同人类亚群）的聚类（例如，可能来自某个祖先）共享共同质心（例如祖先），因此需要同时解决多个聚类问题。在本文中，我们提出了一种分层非参数贝叶斯模型来解决多群体单倍型推断背景下的这个问题。揭示单核苷酸多态性的单倍型对于许多生物和医学应用至关重要。虽然从多个种族不同人群中汇集基因型数据的情况并不常见，但很少有现有程序明确利用个体种族信息进行单倍型推断。在本文中，我们提出了一种新的单倍型推断程序 Haploi，它利用了这些信息，并且很容易适用于来自异质群体的具有数千个 SNP 的基因分型序列，与最先进的程序相比，其速度和准确性非常高，有时甚至更高。 Haploi 的基础是一种新的单倍型分布模型，该模型基于非参数贝叶斯形式主义（称为分层狄利克雷过程），它代表了合并过程的易于处理的替代方案。所提出的模型是可交换的、无界的，并且能够耦合不同人群的人口统计信息。它为个体单倍型的后验推断、单倍型祖先库的大小和配置以及给定基因型数据的其他感兴趣参数提供了一个基础良好的统计框架。
The perennial problem of "how many clusters?" remains an issue of substantial interest in data mining and machine learning communities, and becomes particularly salient in large data sets such as populational genomic data where the number of clusters needs to be relatively large and open-ended. This problem gets further complicated in a co-clustering scenario in which one needs to solve multiple clustering problems simultaneously because of the presence of common centroids (e.g., ancestors) shared by clusters (e.g., possible descents from a certain ancestor) from different multiple-cluster samples (e.g., different human subpopulations). In this paper we present a hierarchical nonparametric Bayesian model to address this problem in the context of multi-population haplotype inference. Uncovering the haplotypes of single nucleotide polymorphisms is essential for many biological and medical applications. While it is uncommon for the genotype data to be pooled from multiple ethnically distinct populations, few existing programs have explicitly leveraged the individual ethnic information for haplotype inference. In this paper we present a new haplotype inference program, Haploi, which makes use of such information and is readily applicable to genotype sequences with thousands of SNPs from heterogeneous populations, with competent and sometimes superior speed and accuracy comparing to the state-of-the-art programs. Underlying Haploi is a new haplotype distribution model based on a nonparametric Bayesian formalism known as the hierarchical Dirichlet process, which represents a tractable surrogate to the coalescent process. The proposed model is exchangeable, unbounded, and capable of coupling demographic information of different populations. It offers a well-founded statistical framework for posterior inference of individual haplotypes, the size and configuration of haplotype ancestor pools, and other parameters of interest given genotype data.
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