Dataset Information

Characterization of Genomic Variants Associated with Scout and Recruit Behavioral Castes in Honey Bees Using Whole-Genome Sequencing.

ABSTRACT: Among forager honey bees, scouts seek new resources and return to the colony, enlisting recruits to collect these resources. Differentially expressed genes between these behaviors and genetic variability in scouting phenotypes have been reported. Whole-genome sequencing of 44 Apis mellifera scouts and recruits was undertaken to detect variants and further understand the genetic architecture underlying the behavioral differences between scouts and recruits. The median coverage depth in recruits and scouts was 10.01 and 10.7 X, respectively. Representation of bacterial species among the unmapped reads reflected a more diverse microbiome in scouts than recruits. Overall, 1,412,705 polymorphic positions were analyzed for associations with scouting behavior, and 212 significant (p-value < 0.0001) associations with scouting corresponding to 137 positions were detected. Most frequent putative transcription factor binding sites proximal to significant variants included Broad-complex 4, Broad-complex 1, Hunchback, and CF2-II. Three variants associated with scouting were located within coding regions of ncRNAs including one codon change (LOC102653644) and 2 frameshift indels (LOC102654879 and LOC102655256). Significant variants were also identified on the 5'UTR of membrin, and 3'UTRs of laccase 2 and diacylglycerol kinase theta. The 60 significant variants located within introns corresponded to 39 genes and most of these positions were > 1000 bp apart from each other. A number of these variants were mapped to ncRNA LOC100578102, solute carrier family 12 member 6-like gene, and LOC100576965 (meprin and TRAF-C homology domain containing gene). Functional categories represented among the genes corresponding to significant variants included: neuronal function, exoskeleton, immune response, salivary gland development, and enzymatic food processing. These categories offer a glimpse into the molecular support to the behaviors of scouts and recruits. The level of association between genomic variants and scouting behavior observed in this study may be linked to the honey bee's genomic plasticity and fluidity of transition between castes.

SUBMITTER: Southey BR

PROVIDER: S-EPMC4718678 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Characterization of Genomic Variants Associated with Scout and Recruit Behavioral Castes in Honey Bees Using Whole-Genome Sequencing.

Southey Bruce R BR Zhu Ping P Carr-Markell Morgan K MK Liang Zhengzheng S ZS Zayed Amro A Li Ruiqiang R Robinson Gene E GE Rodriguez-Zas Sandra L SL

PloS one 20160119 1

Among forager honey bees, scouts seek new resources and return to the colony, enlisting recruits to collect these resources. Differentially expressed genes between these behaviors and genetic variability in scouting phenotypes have been reported. Whole-genome sequencing of 44 Apis mellifera scouts and recruits was undertaken to detect variants and further understand the genetic architecture underlying the behavioral differences between scouts and recruits. The median coverage depth in recruits a ...[more]

PMID: 26784945

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Project description:BackgroundWith the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees.MethodsStochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ.ResultsIncluding the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10-20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required.ConclusionsThis study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

Project description:The information from multiple microarray experiments can be integrated in an objective manner via meta-analysis. However, multiple meta-analysis approaches are available and their relative strengths have not been directly compared using experimental data in the context of different gene expression scenarios and studies with different degrees of relationship. This study investigates the complementary advantages of meta-analysis approaches to integrate information across studies, and further mine the transcriptome for genes that are associated with complex processes such as behavioral maturation in honey bees. Behavioral maturation and division of labor in honey bees are related to changes in the expression of hundreds of genes in the brain. The information from various microarray studies comparing the expression of genes at different maturation stages in honey bee brains was integrated using complementary meta-analysis approaches.Comparison of lists of genes with significant differential expression across studies failed to identify genes with consistent patterns of expression that were below the selected significance threshold, or identified genes with significant yet inconsistent patterns. The meta-analytical framework supported the identification of genes with consistent overall expression patterns and eliminated genes that exhibited contradictory expression patterns across studies. Sample-level meta-analysis of normalized gene-expression can detect more differentially expressed genes than the study-level meta-analysis of estimates for genes that were well described by similar model parameter estimates across studies and had small variation across studies. Furthermore, study-level meta-analysis was well suited for genes that exhibit consistent patterns across studies, genes that had substantial variation across studies, and genes that did not conform to the assumptions of the sample-level meta-analysis. Meta-analyses confirmed previously reported genes and helped identify genes (e.g. Tomosyn, Chitinase 5, Adar, Innexin 2, Transferrin 1, Sick, Oatp26F) and Gene Ontology categories (e.g. purine nucleotide binding) not previously associated with maturation in honey bees.This study demonstrated that a combination of meta-analytical approaches best addresses the highly dimensional nature of genome-wide microarray studies. As expected, the integration of gene expression information from microarray studies using meta-analysis enhanced the characterization of the transcriptome of complex biological processes.

Dataset Information

Characterization of Genomic Variants Associated with Scout and Recruit Behavioral Castes in Honey Bees Using Whole-Genome Sequencing.

Publications

Characterization of Genomic Variants Associated with Scout and Recruit Behavioral Castes in Honey Bees Using Whole-Genome Sequencing.

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