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GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans

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  • Oscar Lao
  • Fan Liu
  • Andreas Wollstein
  • Manfred Kayser

Abstract

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.Author Summary: Understanding genetic population substructure is important in evolutionary biology, behavioral ecology, medical genetics and forensic genetics, among others. Several algorithms have recently been developed for investigating genetic population substructure. However, detecting genetic population substructure can be cumbersome in humans since most of the genetic diversity present in that species exists among individuals from the same population rather than between populations. We developed a Genetic Algorithm for Genetic Ancestry (GAGA) to overcome current limitations in reliably detecting population substructure from genetic and genomic data in humans, which can also be applied to any other species. The method was validated by means of extensive demographic simulations. When applied to a real, human genome-wide SNP microarray dataset covering a reasonable proportion of the European continent, we identified previously undetected fine-scale genetic population substructure. Overall, our study thus not only introduces a new method for investigating genetic population substructure in humans and other species, but also highlights that fine population substructure can be detected among European humans.

Suggested Citation

  • Oscar Lao & Fan Liu & Andreas Wollstein & Manfred Kayser, 2014. "GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-11, February.
    • Handle: RePEc:plo:pcbi00:1003480
    DOI: 10.1371/journal.pcbi.1003480
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