Computationally Efficient Composite Likelihood Statistics for Demographic Inference
Computationally Efficient Composite Likelihood Statistics for Demographic Inference
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DOI：
10.1093/molbev/msv255
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发表时间：
2016-02-01
影响因子：
10.7
通讯作者：
Gutenkunst, Ryan N.
中科院分区：
文献类型：
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作者：
Coffman, Alec J.;Hsieh, Ping Hsun;Gutenkunst, Ryan N.
许多群体遗传学工具采用复合可能性，因为完全建模基因组连锁具有挑战性。但估计参数不确定性和执行模型选择的传统方法需要完全似然，因此这些工具依赖于自举数据的计算成本昂贵的最大似然估计 (MLE)。在这里，我们证明统计理论可以应用于调整复合可能性，并在两种人口统计推断工具中执行稳健的计算有效的统计推断：偏导数、偏导数 i 和 TRACTS。在模拟数据和实际数据上，调整的执行效果与 MLE 自举相当，同时使用的计算时间要少几个数量级。
Many population genetics tools employ composite likelihoods, because fully modeling genomic linkage is challenging. But traditional approaches to estimating parameter uncertainties and performing model selection require full likelihoods, so these tools have relied on computationally expensive maximum-likelihood estimation (MLE) on bootstrapped data. Here, we demonstrate that statistical theory can be applied to adjust composite likelihoods and perform robust computationally efficient statistical inference in two demographic inference tools: partial derivative a partial derivative i and TRACTS. On both simulated and real data, the adjustments perform comparably to MLE bootstrapping while using orders of magnitude less computational time.