Comparison of Single Genome and Allele Frequency Data Reveals Discordant Demographic Histories.
Comparison of Single Genome and Allele Frequency Data Reveals Discordant Demographic Histories.
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DOI：
10.1534/g3.117.300259
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发表时间：
2017-11-06
期刊：
影响因子：
--
通讯作者：
Lohmueller KE
中科院分区：
文献类型：
--
作者：
Beichman AC;Phung TN;Lohmueller KE
从遗传数据推断人口历史是模式和非模式生物群体遗传学的主要目标。基于全基因组的方法，例如成对/多个顺序马尔可夫合并方法，使用一到四个个体的基因组数据来推断整个种群的人口统计历史，而基于位点频谱（SFS）的方法使用样本中等位基因频率的分布来重建相同的历史事件。尽管这两种方法都广泛应用于实证研究中，并且在简单模型下模拟的数据上表现良好，但在更复杂和现实的环境中对它们的比较却很有限。在这里，我们使用基于三个人群（约鲁巴人、西北欧人的后裔和汉族人的后裔）数据的已发表的人口模型作为实证测试用例来研究这两种推理程序的行为。我们发现，基于全基因组的方法推断的一些人口统计历史并不能预测杂合性的全基因组分布，也不能预测经验SFS。然而，使用模拟数据，我们还发现全基因组方法可以重建基于SFS的方法推断出的复杂的人口统计模型，这表明遗传变异的不一致模式并不是由于缺乏统计能力，而是可能反映了潜在人口学中未建模的复杂性。更一般地说，我们的研究结果表明，应谨慎解释来自少数基因组的人口统计推断（非模型生物基因组研究的常规），因为这些模型无法概括其他数据摘要。
Inference of demographic history from genetic data is a primary goal of population genetics of model and nonmodel organisms. Whole genome-based approaches such as the pairwise/multiple sequentially Markovian coalescent methods use genomic data from one to four individuals to infer the demographic history of an entire population, while site frequency spectrum (SFS)-based methods use the distribution of allele frequencies in a sample to reconstruct the same historical events. Although both methods are extensively used in empirical studies and perform well on data simulated under simple models, there have been only limited comparisons of them in more complex and realistic settings. Here we use published demographic models based on data from three human populations (Yoruba, descendants of northwest-Europeans, and Han Chinese) as an empirical test case to study the behavior of both inference procedures. We find that several of the demographic histories inferred by the whole genome-based methods do not predict the genome-wide distribution of heterozygosity, nor do they predict the empirical SFS. However, using simulated data, we also find that the whole genome methods can reconstruct the complex demographic models inferred by SFS-based methods, suggesting that the discordant patterns of genetic variation are not attributable to a lack of statistical power, but may reflect unmodeled complexities in the underlying demography. More generally, our findings indicate that demographic inference from a small number of genomes, routine in genomic studies of nonmodel organisms, should be interpreted cautiously, as these models cannot recapitulate other summaries of the data.
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