论文标题
随机血统的有效重建
Efficient Reconstruction of Stochastic Pedigrees
论文作者
论文摘要
我们介绍了一种称为{\ sc rec-gen}的新算法，用于重建现有种群的族谱或\ textit {pedigrey}，纯粹是根据其遗传数据的。当将{\ sc rec-gen}的有效性的数学证明从理想化的生成模型中应用于谱系时，我们通过给出了{\ sc rec-gen}的有效性的数学证明来证明我们的方法是合理的，该模型复制了真实世界的某些特征。我们的算法是迭代的，并提供了很大一部分血统的准确重建，同时具有相对较低的\ emph {样品复杂性}，以人口遗传序列的长度进行测量。我们将方法作为一种原型，以进一步研究血统重建问题，以将应用程序应用于现实世界的示例。因此，我们的结果与日益重要的基因组隐私问题有关。
We introduce a new algorithm called {\sc Rec-Gen} for reconstructing the genealogy or \textit{pedigree} of an extant population purely from its genetic data. We justify our approach by giving a mathematical proof of the effectiveness of {\sc Rec-Gen} when applied to pedigrees from an idealized generative model that replicates some of the features of real-world pedigrees. Our algorithm is iterative and provides an accurate reconstruction of a large fraction of the pedigree while having relatively low \emph{sample complexity}, measured in terms of the length of the genetic sequences of the population. We propose our approach as a prototype for further investigation of the pedigree reconstruction problem toward the goal of applications to real-world examples. As such, our results have some conceptual bearing on the increasingly important issue of genomic privacy.
