Bayesian analysis of an admixture model with mutations and arbitrarily linked markers
Bayesian analysis of an admixture model with mutations and arbitrarily linked markers
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DOI：
10.1534/genetics.104.036236
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发表时间：
2005-03-01
期刊：
影响因子：
3.3
通讯作者：
Cornuet, JM
中科院分区：
文献类型：
--
作者：
Excoffier, L;Estoup, A;Cornuet, JM
我们在这里介绍了一个经典的混合模型的贝叶斯分析，其中所有的参数同时估计。我们的方法遵循近似贝叶斯计算（ABC）框架，依赖于大量的模拟和拒绝回归算法。虽然计算密集，这种方法可以很容易地处理复杂的突变模型和部分连锁基因座，它可以彻底验证，而没有太多的额外计算成本。与最近的最大似然（ML）方法相比，ABC方法在最近的混合物事件的情况下导致类似的混合物比例的准确估计，但当混合物更古老时，它被发现是上级。与ML方法不同，混合模型的所有其他参数，如亲本种群之间的分歧时间，混合时间和种群大小也得到了很好的估计。部分连锁标记的使用不会在混合物的估计中引入任何特定的偏倚，但是如果没有特别考虑连锁，则发现ML置信区间太窄。对意大利西北部一个人工混合的家蜂种群的应用表明，混合发生在最后10-40代，亲本Apis mellifiera和A. ligustica种群自末次盛冰期以来完全分离。
We introduce here a Bayesian analysis of a classical admixture model in which all parameters are simultaneously estimated. Our approach follows the approximate Bayesian Computation (ABC) framework, relying on massive simulations and a rejection-regression algorithm. Although computationally intensive, this approach can easily deal with complex mutation models and partially linked loci, and it can be thoroughly validated without much additional computation cost. Compared to a recent maximum-likelihood (ML) method, the ABC approach leads to similarly accurate estimates of admixture proportions in the case of recent admixture events, but it is found superior when the admixture is more ancient. All other parameters of the admixture model such as the divergence time between parental populations, the admixture time, and the population sizes are also well estimated, unlike the ML method. The use of partially linked markers does not introduce any particular bias in the estimation of admixture, but ML confidence intervals are found too narrow if linkage is not specifically accounted for. The application of our method to an artificially admixed domestic bee population from northwest Italy suggests that the admixture occurred in the last 10-40 generations and that the parental Apis mellifiera and A. ligustica populations were completely separated since the last glacial maximum.