Biology subjects

Decourtye, A.

Publications and source records attributed to Decourtye, A..

3 recordsLinked to original sources

bioRxiv2024

Sequence-based genome-wide association studies reveal the polygenic architecture of Varroa destructor resistance in Western honey bees Apis mellifera

Honey bees, Apis mellifera, have experienced the full impacts of globalisation, including the recent invasion by the parasitic mite Varroa destructor which has become one of the main causes of colony losses worldwide. Despite its lethal effects, some colonies have developed defence strategies conferring colony resistance and, assuming non-null heritability, selective breeding of naturally resistant bees could be a sustainable way to fight infestations. Here we report on the largest genome-wide association study performed on honey bees to understand the genetic basis of multiple phenotypes linked to varroa resistance. This study was performed on whole genome sequencing of more than 1,500 colonies belonging to different ancestries and combined in a meta-analysis. Results show that varroa resistance is polygenic. A total of 60 genetic markers were identified as having a significant impact in at least one of the tested populations pinpointing several regions of the honey bee genome. Our results also support strategies for genomic selection in honey bee breeding.

genetics↗

bioRxiv2021

From group to individual - Genotyping by pool sequencing eusocial colonies

BackgroundEusocial insects play a central role in many ecosystems, and particularly the important pollinator honeybee (Apis mellifera). One approach to facilitate their study in molecular genetics, is to consider whole colonies as single individuals by combining DNA of multiple individuals in a single pool sequencing experiment. Such a technique comes with the drawback of producing data requiring dedicated analytical methods to be fully exploited. Despite this limitation, pool sequencing data has been shown to be informative and cost-effective when working on random mating populations. Here, we present new statistical methods for exploiting pool sequencing data of eusocial colonies in order to reconstruct the genotype of the colony founder, the queen. This leverages the possibility to monitor genetic diversity, perform genomic-based studies or implement selective breeding. ResultsUsing simulations and honeybee real data, we show that the methods allow for a fast and accurate estimation of the genetic ancestry, with correlations of 0.9 with that obtained from individual genotyping, and for an accurate reconstruction of the queen genotype, with 2% genotyping error. We further validate the inference using experimental data on colonies with both pool sequencing and individual genotyping of drones. ConclusionIn this study we present statistical models to accurately estimate the genetic ancestry and reconstruct the genotype of the queen from pool sequencing data from workers of an eusocial colony. Such information allows to exploit pool sequencing for traditional population genetics, association studies and selective breeding. While validated in Apis mellifera, these methods are applicable to other eusocial hymenoptera species.

genomics↗

bioRxiv2020

Measuring ontogenetic shifts in central-place foraging insects: a case study with honey bees

O_LIMeasuring time-activity budgets over the complete individual lifespan is now possible for many animals with the recent advances of life-long individual monitoring devices. Although analyses of changes in the patterns of time-activity budgets have revealed ontogenetic shifts in birds or mammals, no such technique has been applied to date on insects. C_LIO_LIWe tested an automated breakpoint-based procedure to detect, assess and quantify shifts in the temporal pattern of the flight activities in honey bees. We assumed that the learning and foraging stages of honey bees will differ in several respects, to detect the age at onset of foraging (AOF). C_LIO_LIUsing an extensive dataset covering the life-long monitoring of 2,100 individuals, we compared the AOF outputs with the more conventional approaches based on arbitrary thresholds. We further evaluated the robustness of the different methods comparing the foraging time-activity budget allocations between the presumed foragers and confirmed foragers. C_LIO_LIWe revealed a clear-cut learning-foraging ontogenetic shift that differs in duration, frequency, and time of occurrence of flights. Although AOF appeared to be highly plastic among bees, the breakpoint-based procedure seems better able to detect it than arbitrary threshold-based methods that are unable to deal with inter-individual variation. C_LIO_LIWe developed the aof R-package including a broad range of examples with both simulated and empirical dataset to illustrate the simplicity of use of the procedure. This simple procedure is generic enough to be derived from any individual life-long monitoring devices recording the time-activity budgets of honey bees, and could propose new ecological applications of bio-logging to detect ontogenetic shifts in the behaviour of central-place foraging insects. C_LI

ecology↗