Coalescent-based association mapping and fine mapping of complex trait loci
Coalescent-based association mapping and fine mapping of complex trait loci
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DOI：
10.1534/genetics.104.031799
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发表时间：
2005-02-01
期刊：
影响因子：
3.3
通讯作者：
Pritchard, JK
中科院分区：
文献类型：
--
作者：
Zöllner, S;Pritchard, JK
我们概述了一个通用的合并框架，使用基因型数据的连锁不平衡为基础的定位研究。我们的方法统一了基因作图的两个主要目标，这两个目标在过去通常被分开处理：检测关联（即，显著性检验）和估计原因变异的位置。为了解决这个问题，我们将推理分为两个阶段。首先，我们使用马尔可夫链蒙特卡罗抽样后验分布的所有采样的染色体不考虑表型的结合系谱。然后，在整个系谱中取平均值，我们估计在未观察到的疾病位点的突变和突变率的各种模型下的表型数据的可能性。这些模型寻找的基本信号是，在一个地区存在疾病易感性变异的情况下，根据表型，树上的染色体存在非随机聚类。非随机聚类的程度由可能性捕获，并可用于构建位置的显著性检验或贝叶斯后验分布。我们的框架的新奇在于它可以自然地容纳定量数据。我们描述了应用程序的方法来模拟数据和数据从孟德尔基因座（CFTR，负责囊性纤维化），并从一个拟议的复杂的性状基因座（钙蛋白酶-10，牵连在2型糖尿病）。
We outline a general coalescent framework for using genotype data in linkage disequilibrium-based mapping studies. Our approach unifies two main goals of gene mapping that have generally been treated separately in the past: detecting association (i.e., significance testing) and estimating the location of the causative variation. To tackle the problem, we separate the inference into two stages. First, we use Markov chain Monte Carlo to sample from the posterior distribution of coalescent genealogies of all the sampled chromosomes without regard to phenotype. Then, averaging across genealogies, we estimate the likelihood of the phenotype data under various models for mutation and penetrance at an unobserved disease locus. The essential signal that these models look for is that in the presence of disease susceptibility variants in a region, there is nonrandom clustering of the chromosomes on the tree according to phenotype. The extent of nonrandom clustering is captured by the likelihood and can be used to construct significance tests or Bayesian posterior distributions for location. A novelty of our framework is that it can naturally accommodate quantitative data. We describe applications of the method to simulated data and to data from a Mendelian locus (CFTR, responsible for cystic fibrosis) and from a proposed complex trait locus (calpain-10, implicated in type 2 diabetes).