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Testing the utility of dental morphological trait combinations for inferring human neutral genetic variation.

Hannes RathmannHugo Reyes-Centeno
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Researchers commonly rely on human dental morphological features in order to reconstruct genetic affinities among past individuals and populations, particularly since teeth are often the best preserved part of a human skeleton. Tooth form is considered to be highly heritable and selectively neutral and, therefore, to be an excellent proxy for DNA when none is available. However, until today, it remains poorly understood whether certain dental traits or trait combinations preserve neutral genomic signatures to a greater degree than others. Here, we address this long-standing research gap by systematically testing the utility of 27 common dental traits and >134 million possible trait combinations in reflecting neutral genomic variation in a worldwide sample of modern human populations. Our analyses reveal that not all traits are equally well-suited for reconstructing population affinities. Whereas some traits largely reflect neutral variation and therefore evolved primarily as a result of genetic drift, others can be linked to nonstochastic processes such as natural selection or hominin admixture. We also demonstrate that reconstructions of population affinity based on many traits are not necessarily more reliable than those based on only a few traits. Importantly, we find a set of highly diagnostic trait combinations that preserve neutral genetic signals best (up to [Formula: see text] r = 0.580; 95% r range = 0.293 to 0.758; P = 0.001). We propose that these trait combinations should be prioritized in future research, as they allow for more accurate inferences about past human population dynamics when using dental morphology as a proxy for DNA.
Keyphrases
  • genome wide
  • endothelial cells
  • dna methylation
  • copy number
  • induced pluripotent stem cells
  • oral health
  • pluripotent stem cells
  • gene expression
  • high resolution
  • single molecule
  • nucleic acid