Gourjon Géraud, Saliba-Serre Bérengère, Degioanni Anna
CNRS, MCC - MMSH, LAMPEA UMR 7269, Aix-Marseille Université, 5 rue du Château de l'Horloge, BP 647, 13094, Aix-en-Provence Cedex 2, France,
Genetica. 2014 Oct;142(5):473-82. doi: 10.1007/s10709-014-9792-3. Epub 2014 Sep 20.
The genetic admixture is a dynamic and diachronic process, taking place during a great number of generations. Consequently, a sole admixture rate does not represent such an event and several estimates could help to take into account its dynamics. We developed an Admixture Indicative Interval (AII) which gives a mathematical key to avoid this problem by integrating several admixture estimators and their respective accuracy into a single metric and provides a trend in genetic admixture. To illustrate AIIs interests in admixture studies, AII were calculated using seven estimators on two sets of simulated SNPs data generated under two different admixture scenarios and were then calculated from several published admixed population data: a Comorian population and several Puerto-Rican and Colombian populations for recent admixture events as well as European populations representing the Neolithic/Paleolithic admixture for an older event. Our method provides intervals taking properly the variability and accuracy of admixture estimates into account. The AII lays in the intuitive interval in all actual and simulated datasets and is not biased by divergent points by the mean of a double-weighting step. The great quantity of heterogeneous parental contributions is synthesized by a few AII, which turn out to be more manageable and meaningful than aplenty variable point estimates. This offers an improvement in admixture study, allowing a better understanding of migratory flows. Furthermore, it offers a better assessment of admixture than the arithmetic mean, and enhances comparisons between regions, samples, and between studies on same population.
基因混合是一个动态的、历时性的过程，发生在许多代人的时间里。因此，单一的混合率并不能代表这样一个事件，多种估计方法有助于考虑其动态变化。我们开发了一种混合指示区间（AII），它通过将多种混合估计方法及其各自的准确性整合到一个单一指标中，给出了一个数学方法来避免这个问题，并提供了基因混合的趋势。为了说明AII在混合研究中的作用，我们使用七种估计方法，对在两种不同混合情景下生成的两组模拟单核苷酸多态性（SNP）数据计算了AII，然后从多个已发表的混合群体数据中进行计算：一个科摩罗人群以及几个代表近期混合事件的波多黎各人和哥伦比亚人群，还有代表新石器时代/旧石器时代混合的欧洲人群，用于一个更久远的事件。我们的方法提供了适当考虑混合估计的变异性和准确性的区间。AII在所有实际和模拟数据集中都处于直观的区间内，并且通过双重加权步骤的均值，不会受到离散点的偏差影响。大量不同的亲本贡献通过少数几个AII进行综合，结果表明，这些AII比大量可变的点估计更易于管理和更有意义。这为混合研究带来了改进，有助于更好地理解迁徙流动。此外，它比算术平均值能更好地评估混合情况，并增强了不同区域、样本之间以及同一人群不同研究之间的比较。
