Comparative study of population genomic approaches for mapping colony-level traits

Shani Inbar, Pnina Cohen, Tal Yahav, Eyal Privman

Research output: Contribution to journalArticlepeer-review


Social insect colonies exhibit colony-level phenotypes such as social immunity and task coordination, which are produced by the individual phenotypes. Mapping the genetic basis of such phenotypes requires associating the colony-level phenotype with the genotypes in the colony. In this paper, we examine alternative approaches to DNA extraction, library construction, and sequencing for genome wide association studies (GWAS) of colony-level traits using a population sample of Cataglyphis Niger ants. We evaluate the accuracy of allele frequency estimation from sequencing a pool of individuals (pool-seq) from each colony using either whole-genome sequencing or reduced representation genomic sequencing. Based on empirical measurement of the experimental noise in sequenced DNA pools, we show that reduced representation pool-seq is drastically less accurate than whole-genome pool-seq. Surprisingly, normalized pooling of samples did not result in greater accuracy than un-normalized pooling. Subsequently, we evaluate the power of the alternative approaches for detecting quantitative trait loci (QTL) of colony-level traits by using simulations that account for an environmental effect on the phenotype. Our results can inform experimental designs and enable optimizing the power of GWAS depending on budget, availability of samples and research goals. We conclude that for a given budget, sequencing un-normalized pools of individuals from each colony provides optimal QTL detection power.

Original languageEnglish
Article numbere1007653
JournalPLoS Computational Biology
Issue number3
StatePublished - Mar 2020

Bibliographical note

Publisher Copyright:
© 2020 Inbar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation


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