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Assessment of linkage disequilibrium by the decay of haplotype sharing with application to fine-scale genetic mapping.

机译:通过单倍型共享的衰退评估连锁不平衡并应用于精细尺度的遗传作图。

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摘要

Linkage disequilibrium (LD) is of great interest for gene mapping and the study of population history. We propose a multilocus model for LD, based on the decay of haplotype sharing (DHS). The DHS model is most appropriate when the LD in which one is interested is due to the introduction of a variant on an ancestral haplotype, with recombinations in succeeding generations resulting in preservation of only a small region of the ancestral haplotype around the variant. This is generally the scenario of interest for gene mapping by LD. The DHS parameter is a measure of LD that can be interpreted as the expected genetic distance to which the ancestral haplotype is preserved, or, equivalently, 1/(time in generations to the ancestral haplotype). The method allows for multiple origins of alleles and for mutations, and it takes into account missing observations and ambiguities in haplotype determination, via a hidden Markov model. Whereas most commonly used measures of LD apply to pairs of loci, the DHS measure is designed for application to the densely mapped haplotype data that are increasingly available. The DHS method explicitly models the dependence among multiple tightly linked loci on a chromosome. When the assumptions about population structure are sufficiently tractable, the estimate of LD is obtained by maximum likelihood. For more-complicated models of population history, we find means and covariances based on the model and solve a quasi-score estimating equation. Simulations show that this approach works extremely well both for estimation of LD and for fine mapping. We apply the DHS method to published data sets for cystic fibrosis and progressive myoclonus epilepsy.
机译:连锁不平衡(LD)对于基因作图和研究人口史非常重要。我们基于单倍型共享(DHS)的衰减提出了LD的多基因座模型。当感兴趣的LD是由于在祖先单倍体上引入了一个变异体,而后代的重组导致该变异体周围只保留了一小部分祖先单倍体时,DHS模型最为合适。通常,这是LD基因定位所关注的场景。 DHS参数是LD的量度,可以解释为保留祖先单倍型的预期遗传距离,或者等效地为1 /(祖先单倍型的世代时间)。该方法允许等位基因的多个起源和突变,并且它通过隐马尔可夫模型考虑了单倍型确定中缺失的观察结果和歧义。尽管最常用的LD量度适用于成对的基因座,但DHS量度是专为应用于越来越多的密集映射的单倍型数据而设计的。 DHS方法显式地模拟了染色体上多个紧密链接的基因座之间的依赖性。当关于人口结构的假设足够容易处理时,LD的估计是通过最大似然获得的。对于更复杂的人口历史模型,我们在模型的基础上找到均值和协方差,并求解准得分估计方程。仿真表明,该方法对于LD估计和精细映射都非常有效。我们将DHS方法应用于已发布的囊性纤维化和进行性肌阵挛性癫痫的数据集。

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