利用遗传似然的多项式表达式快速计算连锁分析中的大量LOD分数

Hongling Wang, Yungui Huang, V. Vieland, Alberto Maria Segre, J. O’Connell
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引用次数: 5

摘要

本文介绍了一种新的方法来计算链接分析中的大量LOD分数。LOD评分法通常用于遗传连锁分析,将基因的功能与其在染色体上的位置联系起来。LOD分数是有关联与无关联的似然比为log 10。我们不是直接从系谱数据中计算给定遗传参数值下的连锁和无连锁的似然值,而是将连锁和无连锁的似然表达式构造为遗传参数的多项式。系谱的这些似然多项式不随参数值的不同而变化。构造似然多项式后,通过对具有特定参数值的似然多项式求值,即可计算出有关联和无关联的似然值。在构造过程中对似然多项式进行了优化,使得表达式中的重复项是共享的。此外,我们发现不同谱系的似然多项式也经常共享项。这个术语共享特性使我们得到一种求值策略,在这种策略中,共享的术语只求一次,并被共享它们的所有多项式重用。在多项式求值中重用共享项,大大减少了大量LOD分数计算中的重复计算,提高了计算效率。结果表明,该方法可将传统的遗传连锁计算速度提高10~1200倍。这种方法已应用于计算后验概率的联系(PPL),其中计算大量的LOD分数是需要的。
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Rapid computation of large numbers of LOD scores in linkage analysis through polynomial expression of genetic likelihoods
This paper introduces a new method for computing large numbers of LOD scores in linkage analysis. The LOD score method is commonly used in genetic linkage analysis to associate functionality of genes to their locations on chromosomes. A LOD score is a log 10 likelihood ratio of linkage to no linkage. Instead of calculating values of likelihoods of linkage and no linkage under given values of genetic parameters directly from pedigree data, we construct expressions for likelihoods of linkage and no linkage as polynomials of genetic parameters. These likelihood polynomials of pedigrees don't change for different parameter values. After the likelihood polynomials are constructed, the values of likelihoods of linkage and no linkage can be computed by evaluating the likelihood polynomials with specific parameter values. The likelihood polynomials are optimized during construction so that repeated terms within the expressions are shared. Moreover, we find that the likelihood polynomials of different pedigrees also often share terms. This term-sharing feature leads us to an evaluation strategy where shared terms are evaluated only once and reused by all the polynomials that share them. The reuse of shared terms in polynomial evaluation greatly decreases the re- computation in calculation of large numbers of LOD scores and improves the computing efficiency. Our results show that this approach can speed up the traditional genetic linkage computation by 10~1200 times. This approached has been applied to the computation of the posterior probability of linkage (PPL) where calculation of large numbers of LOD scores is required.
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