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

Abstract

BackgroundWith 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 FST, 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.ResultsWe 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.ConclusionsThese 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 (http://usegalaxy.org/r/kenyanbee) that can be applied to any model system with genomic information.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1712-0) contains supplementary material, which is available to authorized users.

Genome‐wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

“…Assessing the time and spatial scales at which selection acts is key to understand how genetic diversity is maintained or lost through adaptation (Stinchcombe and Hoekstra, 2008;Fuller et al, 2015). In plants, and especially in grasses, this question has been largely restricted to crops, which biases our understanding of evolutionary processes that have shaped genomes in their natural ancestors and extant relatives.…”

Genome-wide scans of selection highlight the impact sof biotic and abiotic constraints in natural populations of the model grassBrachypodium distachyon

“…Assessing the time and spatial scales at which selection acts is a key to understand how genetic diversity is maintained or lost through adaptation (Stinchcombe and Hoekstra 2008; Fuller et al 2015). In plants, and especially in grasses, this question has been largely restricted to crops, which biases our understanding of evolutionary processes that have shaped genomes in their natural ancestors and extant relatives.…”

Runs of homozygosity for autozygosity estimation and genomic analysis in production animals

“…More recently, research activities to identify selection signatures, based on the analysis of the frequency of ROHs, have been carried out for several species of production animals. Fuller et al (2015) identified ROHs related to adaptive characteristics of commercial bee species, observing the effect of environmental temperature on honey production. In bovine, O'Brien et al (2014), Somavilla et al (2014), and Zavarez et al (2015) identified ROHs associated to the adaptive potential and reproductive and productive characteristics of zebu breeds, whereas Kim et al (2015a) identified more than 15 regions related to milk production in Jersey herds.…”

“…Currently, SNP genotyping using DNA chips is the most consistent and low-cost solution to generate highdefinition data (Silva et al, 2015). This methodology is being used in experiments to identify and use of ROHs in production animal species, such as bovine (Zavarez et al, 2015;Zhang et al, 2015a;Mastrangelo et al, 2016;Szmatoła et al, 2016;Reverter et al, 2017), ovine (Al-Mamun et al, 2015;Beynon et al, 2015), equine (Metzger et al, 2015), swine (Gomez-Raya et al, 2015;Saura et al, 2015), poultry (Wolc et al, 2015), feline (Bertolini et al, 2016), and bees (Fuller et al, 2015). Table 1 presents a summary of the objectives and conclusions of recent studies related to different uses of ROHs in production animals.…”