Genetic association mapping via evolution-based clustering of haplotypes
Genetic association mapping via evolution-based clustering of haplotypes
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DOI：
10.1371/journal.pgen.0030111
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发表时间：
2007-07-01
期刊：
影响因子：
4.5
通讯作者：
De Iorio, Maria
中科院分区：
文献类型：
--
作者：
Tachmazidou, Ioanna;Verzilli, Claudio J.;De Iorio, Maria
单核苷酸多态性单倍型的多位点分析是一种很有前途的方法来剖析复杂疾病的遗传基础。我们提出了一个基于聚结的关联映射模型，该模型可能增加在遗传关联研究中检测疾病易感性变体的能力。该方法使用贝叶斯分区模型，通过利用进化信息对具有相似疾病风险的单倍型进行聚类。我们专注于候选基因区域与密集间隔的标记和模型染色体片段在高连锁不平衡，假设一个完美的同源性。为了使这一假设更加现实，我们将感兴趣的染色体区域分成高连锁不平衡的子区域或窗口。然后将单倍型空间划分为不相交的簇，其中假定表型-单倍型关联是相同的。例如，在病例对照研究中，我们预期在共同祖先背景上携带致病变异的染色体片段在病例中比对照组更常见，从而产生两个独立的单倍型簇。我们的方法的新奇源于这样一个事实，即用于聚类单倍型的距离具有进化解释，因为单倍型是根据它们最近的共同祖先的时间聚类的。我们的方法是完全贝叶斯，我们开发了一个马尔可夫链蒙特卡罗算法，以有效地在可能的分区的空间进行采样。我们将所提出的方法与单标记分析和最近提出的多标记方法进行比较，结果表明贝叶斯分区模型在定位因果等位基因方面表现相似，同时产生较低的假阳性率。此外，该方法在计算上比其他多标记方法更快。我们提出了一个应用程序的真实的基因型数据的CYP 2D 6基因区域，这有一个确认的作用，在药物代谢，我们成功地映射的位置的易感性变异在一个小的错误。
Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.