Behavior is a complex trait that is often controlled by the interaction of many genes and the environment. Studying the genetics of behavior is an important endeavor for both understanding how genes produce behavioral phenotypes, and how genes underlying behavior evolve. Genomics has provided several new avenues to study the genetic architecture and evolution of behavior in animals. Primarily, analysis of individual genomes from populations that vary with respect to phenotype has provided a powerful way to identify mutations and genes influencing behavior. Additionally, analysis of neuro-transcriptomes of individuals performing different behaviors helps illuminate the genetic and molecular networks regulating behavior and behavioral plasticity. Here we provide a brief review of how genomic methods can be applied to study the genetics and evolution of animal behavior. We start with an introduction of the 'forward genetic' paradigm and cross-based approaches to mapping the genetics of complex traits. We then delve into the application of genome scans and association mapping of complex traits in natural populations. We also illustrate how population genomics can be used to understand the evolution of the genes underlying behavior. We then discuss current limits and knowledge gaps in behavioral genomics research. Genomics provides a very powerful framework to identify putative genes and gene networks underlying behavior in animals.

Hybrid populations of Africanized honey bees (scutellata-hybrids), notable for their defensive behaviour, have spread rapidly throughout South and North America since their unintentional introduction. Although their migration has slowed, the large-scale trade and movement of honey bee queens and colonies raise concern over the accidental importation of scutellata-hybrids to previously unoccupied areas. Therefore, developing an accurate and robust assay to detect scutellata-hybrids is an important first step toward mitigating risk. Here, we used an extensive population genomic dataset to assess the genomic composition of Apis mellifera native populations and patterns of genetic admixture in North and South American commercial honey bees. We used this dataset to develop a SNP assay, where 80 markers, combined with machine learning classification, can accurately differentiate between scutellata-hybrids and non-scutellata-hybrid commercial colonies. The assay was validated on 1263 individuals from colonies located in Canada, the United States, Australia and Brazil. Notably, we demonstrate that using a reduced SNP set of as few as 10 loci can still provide accurate results.

The yellow-banded bumblebee Bombus terricola was common in North America but has recently declined and is now on the IUCN Red List of threatened species. The causes of B. terricola's decline are not well understood. Our objectives were to create a partial genome and then use this to estimate population data of conservation interest, and to determine whether genes showing signs of recent selection suggest a specific cause of decline. First, we generated a draft partial genome (contig set) for B. terricola, sequenced using Pacific Biosciences RS II at an average depth of 35×. Second, we sequenced the individual genomes of 22 bumblebee gynes from Ontario and Quebec using Illumina HiSeq 2500, each at an average depth of 20×, which were used to improve the PacBio genome calls and for population genetic analyses. The latter revealed that several samples had long runs of homozygosity, and individuals had high inbreeding coefficient F, consistent with low effective population size. Our data suggest that B. terricola's effective population size has decreased orders of magnitude from pre-Holocene levels. We carried out tests of selection to identify genes that may have played a role in ameliorating environmental stressors underlying B. terricola's decline. Several immune-related genes have signatures of recent positive selection, which is consistent with the pathogen-spillover hypothesis for B. terricola's decline. The new B. terricola contig set can help solve the mystery of bumblebee decline by enabling functional genomics research to directly assess the health of pollinators and identify the stressors causing declines.