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Maximum likelihood resolution of multi-block genotypes

机译:多区块基因型的最大似然解析

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摘要

We present a new algorithm for the problems of genotype phasing and block partitioning. Our algorithm is based on a new stochastic model, and on the novel concept of probabilistic common haplotypes. We formulate the goals of genotype resolving and block partitioning as a maximum likelihood problem, and solve it by an EM algorithm. When applied to real biological SNP data, our algorithm outperforms two state of the art phasing algorithms. Our algorithm is also considerably more sensitive and accurate than a previous method in predicting and identifying disease association.
机译:我们提出了一种新的算法,用于基因型定相和块划分。我们的算法基于一个新的随机模型,以及基于概率常见单倍型的新概念。我们将基因型解析和块划分的目标表述为最大似然问题,并通过EM算法对其进行求解。当将其应用于实际的生物SNP数据时,我们的算法的性能优于两种先进的定相算法。在预测和识别疾病关联方面,我们的算法也比以前的方法更加灵敏和准确。

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