Mapping trait loci by use of inferred ancestral recombination graphs
Mapping trait loci by use of inferred ancestral recombination graphs
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DOI：
10.1086/508901
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发表时间：
2006-11-01
影响因子：
9.8
通讯作者：
Durbin, Richard
中科院分区：
文献类型：
--
作者：
Minichiello, Mark J.;Durbin, Richard
正在进行大规模的关联研究，以期揭示复杂疾病的遗传决定因素。我们描述了一种从群体基因型数据推断谱系的计算效率方法，并展示了如何使用这些谱系来精细绘制疾病位点和解释关联信号。这些家谱采用祖先重组图（ARG）的形式。ARG为每个位点定义了一个谱系树，当一个人沿着染色体移动时，连续树的拓扑结构会根据历史重组事件的影响而改变。我们的分析分为两个阶段。首先，我们使用启发式算法推断合理的arg，该算法可以处理未分阶段和缺失的数据，并且足够快，可以应用于大规模研究。其次，我们测试了每个位点的谱系树，在一个分支下的疾病病例聚类，表明在该分支上发生了致病突变。因为真正的ARG是未知的，所以我们把这个分析平均在推断ARG的集合上。我们已经在广泛的模拟疾病模型中描述了我们的方法的性能。与简单的检测方法相比，我们的方法在定位非分型致病基因位点方面提高了准确性，也可用于估计非分型致病等位基因的频率。我们已经将我们的方法应用于Ueda等人的CTLA4与Graves病的关联研究，展示了如何使用它来剖析关联信号，给出了等位基因异质性和相互作用的潜在有趣结果。使用我们的方法推断出的arg集合的类似分析方法可能适用于从群体基因型数据推断的许多其他问题。
Large-scale association studies are being undertaken with the hope of uncovering the genetic determinants of complex disease. We describe a computationally efficient method for inferring genealogies from population genotype data and show how these genealogies can be used to fine map disease loci and interpret association signals. These genealogies take the form of the ancestral recombination graph (ARG). The ARG defines a genealogical tree for each locus, and, as one moves along the chromosome, the topologies of consecutive trees shift according to the impact of historical recombination events. There are two stages to our analysis. First, we infer plausible ARGs, using a heuristic algorithm, which can handle unphased and missing data and is fast enough to be applied to large-scale studies. Second, we test the genealogical tree at each locus for a clustering of the disease cases beneath a branch, suggesting that a causative mutation occurred on that branch. Since the true ARG is unknown, we average this analysis over an ensemble of inferred ARGs. We have characterized the performance of our method across a wide range of simulated disease models. Compared with simpler tests, our method gives increased accuracy in positioning untyped causative loci and can also be used to estimate the frequencies of untyped causative alleles. We have applied our method to Ueda et al.'s association study of CTLA4 and Graves disease, showing how it can be used to dissect the association signal, giving potentially interesting results of allelic heterogeneity and interaction. Similar approaches analyzing an ensemble of ARGs inferred using our method may be applicable to many other problems of inference from population genotype data.