Genome-wide analysis of signatures of selection in populations of African honey bees (Apis mellifera) using new web-based tools

Zachary L. Fuller, Elina L. Niño, Harland M. Patch, Oscar C. Bedoya-Reina, Tracey Baumgarten, Elliud Muli, Fiona Mumoki, Aakrosh Ratan, John McGraw, Maryann Frazier, Daniel Masiga, Stephen Schuster, Christina M. Grozinger, Webb Miller

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Background: With the development of inexpensive, high-throughput sequencing technologies, it has become feasible to examine questions related to population genetics and molecular evolution of non-model species in their ecological contexts on a genome-wide scale. Here, we employed a newly developed suite of integrated, web-based programs to examine population dynamics and signatures of selection across the genome using several well-established tests, including F ST, pN/pS, and McDonald-Kreitman. We applied these techniques to study populations of honey bees (Apis mellifera) in East Africa. In Kenya, there are several described A. mellifera subspecies, which are thought to be localized to distinct ecological regions. Results: We performed whole genome sequencing of 11 worker honey bees from apiaries distributed throughout Kenya and identified 3.6 million putative single-nucleotide polymorphisms. The dense coverage allowed us to apply several computational procedures to study population structure and the evolutionary relationships among the populations, and to detect signs of adaptive evolution across the genome. While there is considerable gene flow among the sampled populations, there are clear distinctions between populations from the northern desert region and those from the temperate, savannah region. We identified several genes showing population genetic patterns consistent with positive selection within African bee populations, and between these populations and European A. mellifera or Asian Apis florea. Conclusions: These results lay the groundwork for future studies of adaptive ecological evolution in honey bees, and demonstrate the use of new, freely available web-based tools and workflows ( ) that can be applied to any model system with genomic information.

Original languageEnglish (US)
Article number518
JournalBMC genomics
Issue number1
StatePublished - Jul 10 2015

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics


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