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Multivariate classification of human populations. I. Allocation of Yanomama indians to villages.

机译:人口的多元分类。 I.将亚诺玛玛印第安人分配到村庄。

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摘要

A set of 12 anthropometric measures and six genetic traits, available for 520 Yanomama Indians from 19 villages in nine clusters, were used to allocate individuals to villages. On the basis of anthropometrics alone, 36% of the individuals were allocated to the right village and 60% to the right cluster. On the basis of genetic traits alone, 16% were allocated to the right village and 26% to the right cluster. A combination of all 18 characters yielded 41% allocation to the right village and 63% to the right cluster. Of the 924 possible combinations of six anthropometric measures, only one provided poorer resolution than did the six genetic traits. We explain the better resolution of the anthropometric traits by noting that the anthropometric traits are not totally heritable and that genetic traits are not continuously distributed. Randomization studies indicated that all of the observed correct-allocation fractions are far in excess of random expectation. We infer that the village phenotype distributions overlap only partially, and that they represent real and substantial population differentiation.
机译:一组12种人体测量学方法和6种遗传特征可供9个集群中的19个村庄的520名Yanomama印第安人使用,用于将个人分配到各个村庄。仅根据人体测量学,就有36%的人被分配到正确的村庄,而60%的人被分配到正确的人群。仅根据遗传性状,将16%分配给正确的村庄,将26%分配给正确的集群。所有18个字符的组合产生了对正确村庄的41%分配和对正确集群的63​​%分配。六种人体测量学的924种可能组合中,只有一种提供的分辨力比六种遗传特征差。我们通过指出人体测量特征不是完全可遗传的并且遗传特征没有连续分布来解释人体测量特征的更好的分辨率。随机研究表明,所有观察到的正确分配分数都远远超出了随机预期。我们推断村庄的表型分布仅部分重叠,并且它们代表了真实的和大量的人口分化。

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