Fine-scale mapping of disease loci via shattered coalescent modeling of genealogies
Fine-scale mapping of disease loci via shattered coalescent modeling of genealogies
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DOI：
10.1086/339271
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发表时间：
2002-03-01
影响因子：
9.8
通讯作者：
Balding, DJ
中科院分区：
文献类型：
--
作者：
Morris, AP;Whittaker, JC;Balding, DJ
我们提出了一个贝叶斯，马尔可夫链蒙特卡罗方法精细规模的连锁不平衡基因定位使用高密度标记地图。该方法明确建模的系谱基本的情况下，在一个假定的疾病位点附近的染色体样本，与许多现有的多点方法的星形树的假设相反。在这个建模框架内，我们可以允许缺失的标记信息和不确定性的真实基础系谱和祖先标记单倍型的组成。我们的方法的一个关键优势是纳入了破碎的合并模型的系谱，允许在疾病位点和零星病例的疾病多个创始突变。该方法的输出包括疾病位点的位置和群体标记单倍型比例的近似后验分布。此外，从算法的输出被用来构建一个分支图，以代表在疾病基因座的遗传异质性，突出集群的情况下染色体共享相同的突变。我们提出了详细的模拟，以提供证据的改进，现有的方法。此外，推断的疾病位点的位置保持稳健的建模假设。
We present a Bayesian, Markov-chain Monte Carlo method for fine-scale linkage-disequilibrium gene mapping using high-density marker maps. The method explicitly models the genealogy underlying a sample of case chromosomes in the vicinity of a putative disease locus, in contrast with the assumption of a star-shaped tree made by many existing multipoint methods. Within this modeling framework, we can allow for missing marker information and for uncertainty about the true underlying genealogy and the makeup of ancestral marker haplotypes. A crucial advantage of our method is the incorporation of the shattered coalescent model for genealogies, allowing for multiple founding mutations at the disease locus and for sporadic cases of disease. Output from the method includes approximate posterior distributions of the location of the disease locus and population-marker haplotype proportions. In addition, output from the algorithm is used to construct a cladogram to represent genetic heterogeneity at the disease locus, highlighting clusters of case chromosomes sharing the same mutation. We present detailed simulations to provide evidence of improvements over existing methodology. Furthermore, inferences about the location of the disease locus are shown to remain robust to modeling assumptions.