High-resolution genetic mapping with pooled sequencing.
High-resolution genetic mapping with pooled sequencing.
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DOI：
10.1186/1471-2105-13-s6-s8
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发表时间：
2012-04-19
影响因子：
3
通讯作者：
Gifford DK
中科院分区：
文献类型：
--
作者：
Edwards MD;Gifford DK
高通量测序改变了现代遗传学。新的模式生物实验设计包括分析许多个体，将它们集中在一起并进行排序，以提高效率。然而，合并带来的不确定性和噪声测序数据的挑战要求更先进的计算方法。我们提出了多池，这是一种计算方法，用于通过混合基因分型分析模式生物杂交中的遗传图谱。与分析集合序列数据的其他方法不同，在估计因果变异的位置时，我们同时考虑来自所有相关染色体标记的信息。我们使用信息测序读数作为离散的动态贝叶斯网络，我们扩展了连续的近似，允许快速推理，而不依赖于池的大小。多池泛指包括生物重复和二元和数量性状的仅病例或病例对照设计。与现有方法相比，我们增加的信息共享和对相关误差源的原则性包含提高了分辨率和准确性，在几种情况下定位了与单基因的关联。多池在http://cgs.csail.mit.edu/multipool/.上免费提供
Modern genetics has been transformed by high-throughput sequencing. New experimental designs in model organisms involve analyzing many individuals, pooled and sequenced in groups for increased efficiency. However, the uncertainty from pooling and the challenge of noisy sequencing data demand advanced computational methods. We present MULTIPOOL, a computational method for genetic mapping in model organism crosses that are analyzed by pooled genotyping. Unlike other methods for the analysis of pooled sequence data, we simultaneously consider information from all linked chromosomal markers when estimating the location of a causal variant. Our use of informative sequencing reads is formulated as a discrete dynamic Bayesian network, which we extend with a continuous approximation that allows for rapid inference without a dependence on the pool size. MULTIPOOL generalizes to include biological replicates and case-only or case-control designs for binary and quantitative traits. Our increased information sharing and principled inclusion of relevant error sources improve resolution and accuracy when compared to existing methods, localizing associations to single genes in several cases. MULTIPOOL is freely available at http://cgs.csail.mit.edu/multipool/.