Indirect evidence for the genetic determination of short stature in African Pygmies

Am J Phys Anthropol. 2011 Jul;145(3):390-401. doi: 10.1002/ajpa.21512. Epub 2011 May 3.

Abstract

Central African Pygmy populations are known to be the shortest human populations worldwide. Many evolutionary hypotheses have been proposed to explain this short stature: adaptation to food limitations, climate, forest density, or high mortality rates. However, such hypotheses are difficult to test given the lack of long-term surveys and demographic data. Whether the short stature observed nowadays in African Pygmy populations as compared to their Non-Pygmy neighbors is determined by genetic factors remains widely unknown. Here, we study a uniquely large new anthropometrical dataset comprising more than 1,000 individuals from 10 Central African Pygmy and neighboring Non-Pygmy populations, categorized as such based on cultural criteria rather than height. We show that climate, or forest density may not play a major role in the difference in adult stature between existing Pygmies and Non-Pygmies, without ruling out the hypothesis that such factors played an important evolutionary role in the past. Furthermore, we analyzed the relationship between stature and neutral genetic variation in a subset of 213 individuals and found that the Pygmy individuals' stature was significantly positively correlated with levels of genetic similarity with the Non-Pygmy gene-pool for both men and women. Overall, we show that a Pygmy individual exhibiting a high level of genetic admixture with the neighboring Non-Pygmies is likely to be taller. These results show for the first time that the major morphological difference in stature found between Central African Pygmy and Non-Pygmy populations is likely determined by genetic factors.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Africa, Central / epidemiology
  • Analysis of Variance
  • Black People / genetics*
  • Body Height / genetics*
  • Case-Control Studies
  • Cluster Analysis
  • Computational Biology
  • Female
  • Genetics, Population*
  • Geography
  • Humans
  • Male