Robust demographic inference from genomic and SNP data.
Robust demographic inference from genomic and SNP data.
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DOI：
10.1371/journal.pgen.1003905
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发表时间：
2013-10
期刊：
影响因子：
4.5
通讯作者：
Foll M
中科院分区：
文献类型：
--
作者：
Excoffier L;Dupanloup I;Huerta-Sánchez E;Sousa VC;Foll M
我们引入了一个灵活而强大的基于模拟的框架，从大型基因组数据集上计算的站点频谱（SFS）中推断人口统计参数。我们表明，我们的复合似然方法允许研究任意复杂性的进化模型，这是目前其他基于似然的方法无法解决的。对于简单的情况下，我们的方法比较有利的准确性和速度方面，目前在该领域的参考，同时表现出更好的收敛性能复杂的模型。我们首先将我们的方法应用于来自四个人群的非编码基因组SNP数据。为了推断他们的人口统计历史，我们比较了日益复杂的中性进化模型，包括未抽样的人口。我们进一步展示了我们的框架的多功能性，通过将其扩展到已知确定的SNP芯片的人口统计参数的推断，例如最近发布的Affyestry研究人类起源。而以前的处理确定的SNP的方法要么局限于一个单一的人口或只允许一对人口之间的分歧时间的推断，我们的框架可以正确地推断更复杂的模型，包括几个人口的分歧，瓶颈和迁移的参数。我们应用这种方法重建非洲人口使用两个不同的确定人类SNP面板下两个进化模型的研究。两个SNP小组导致全球非常相似的估计和置信区间，并表明约鲁巴人和桑人之间的古老分歧（>110 Ky）。我们的方法似乎非常适合从大型基因组数据集的复杂场景的研究。我们提出了一种新的基于似然的方法来推断一组人口的过去人口从大型基因组数据集。我们的方法可以应用于任意复杂的模型，因为可能性是通过合并模拟来估计的。在简单的情况下，我们的方法的行为类似于一个广泛使用的基于扩散的方法，同时表现出更好的收敛性能。此外，我们的方法可以应用于非常复杂的模型，包括多达十几个人口，仍然在合理的时间内非常准确地检索参数。我们应用我们的方法来估计过去的人口四个人的非编码全基因组多样性是可用的，估计欧洲混合的西南非洲裔美国人的人口和肯尼亚人口与未抽样的东非人口的程度。我们还显示了我们的框架的多功能性，通过从SNP芯片数据推断非洲人口的人口统计历史与已知的确定性偏差，并找到一个非常古老的分歧时间（>110 Ky）之间的Yorubas从西非和Sans从南部非洲。
We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets. We present a new likelihood-based method to infer the past demography of a set of populations from large genomic datasets. Our method can be applied to arbitrarily complex models as the likelihood is estimated by coalescent simulations. Under simple scenarios, our method behaves similarly to a widely used diffusion-based method while showing better convergence properties. In addition, our approach can be applied to very complex models including as many as a dozen populations, and still retrieve parameters very accurately in a reasonable time. We apply our approach to estimate the past demography of four human populations for which non-coding whole genome diversity is available, estimating the degree of European admixture of a southwest African American population and that of a Kenyan population with an unsampled East African population. We also show the versatility of our framework by inferring the demographic history of African populations from SNP chip data with known ascertainment bias, and find a very old divergence time (>110 Ky) between Yorubas from Western Africa and Sans from Southern Africa.
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影响因子：
4.5
作者：
Andrieu, Christophe;Roberts, Gareth O.
通讯作者：
Roberts, Gareth O.
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
影响因子：
3.3
作者：
Excoffier, L;Estoup, A;Cornuet, JM
通讯作者：
Cornuet, JM
DOI：
10.1073/pnas.081068098
发表时间：
2001-04-10
影响因子：
11.1
作者：
Beerli, P;Felsenstein, J
通讯作者：
Felsenstein, J
影响因子：
3.3
作者：
Adams, AM;Hudson, RR
通讯作者：
Hudson, RR