Tools for monitoring and managing sustainable improvement in honeybee populations

Abstract

The Western honeybee is a species of economic importance globally, yet in recent decades it has been experiencing substantial colony losses that result in economic damage and possibly decreased genetic diversity.

This situation is highlighting the need for honeybee breeding and conservation programmes. Monitoring genetic variability is an essential component of breeding programmes to ensure genetic gain and managing both global and local genetic diversity.

One way to study breeding and conservation programmes is via stochastic simulation. Stochastic simulators are essential for rapid and low-cost testing of breeding decisions and methods, aiding in the optimization of current programmes or the establishment of new ones. These simulations provide valuable insights when they accurately model realistic populations and parameters, allowing for a deeper understanding of factors such as population genetic variability, responses to selection and levels of inbreeding.

There was, however, no existing genetics simulator that allows for a detailed simulation of individual honeybee. Therefore, the aim of this thesis was to develop a simulation tool and demonstrate concepts of honeybee relatedness to aid in the sustainable improvement of managed honeybee populations.

Chapter 1 introduces key concepts essential to the understanding of the thesis.

It’s first focus is on honeybees; describing some of their basic biology, their economic importance to humans, addressing the stressors affecting honeybee populations, and why maintaining genetic diversity within these population is so critical.

The chapter then delves into quantitative genetic methods for calculating relatedness, outlining methods such as relatedness coefficients derived from both pedigree and genome-wide data, as well as their pedigree decomposition of genetic values into parent average and Mendelian sampling deviations. The penultimate section describes stochastic simulators and their application in quantitative genetics and selective breeding.

Finally, the chapter concludes by outlining the thesis objectives, providing the necessary understanding and context as to why this thesis is relevant.

Chapter 2 of this thesis describes the implementation of SIMplyBee, a holistic simulator of honeybee populations and breeding programs that a small team and myself have developed as an R package. SIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable honeybee-specific simulations, AlphaSimR was extended by developing classes for global simulation parameters, for a honeybee single colony, and multiple colonies. Functions to address major honeybee specificities were also developed: honeybee genome, haplodiploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics at the individual- and colony-levels. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and genetic variability.

Chapter 3 demonstrates principles of genetic relatedness in honeybees by analysing the genetic and pedigree information from a SIMplyBee simulation, comparing closed and hybrid populations. Coefficients of relatedness are regularly used to measure genetic similarity within and between populations and their individuals. Although the haplodiploid inheritance of honeybees is well understood, interpreting the various types of relatedness coefficients based on pedigree and genotype data is a challenge for researchers and practitioners in honeybee breeding. I evaluated the relatedness between individuals within a colony, between queens of the same population, and between queens of different populations.

The results demonstrated an alignment of mean relatedness using different sources of information when calculated using the same founder population. Identity-by-state (IBS) relatedness varied significantly when calculated relative to different founder populations. While this result is anticipated, it highlights the need for caution when comparing values across studies that use different founder populations with varying allele frequencies. Misestimation of relatedness if interpreted incorrectly can lead to inappropriate breeding and conservation decisions. These decisions may exacerbate inbreeding, reduce a population’s genetic diversity, or compromise genetic gain. This emphasises the need for better understanding and standardising methodologies for computing relatedness coefficients, to ensure accurate comparability in relatedness studies.

In Chapter 4, I evaluated pedigree reconstruction and patriline determination in honeybees using both real and simulated data. Comparison of simulated data and real data allows researchers to identify weaknesses in established models of genetic inheritance associated methods, or errors in real data, and analyse the accuracy of outputs. In this chapter, real genotype data collected from the mating experiment "BeeConSel" was replicated using SIMplyBee-generated genotypes. Four simulated SNP array sizes were examined to assess the limitations of each software and used the actual pedigree information of the simulation to measure the precision of sire assignments. While gametic information is an important aspect of genetic analysis, gap in the literature was identified for a method to assign parent-of-origin to haplotypes derived from phased genotypes. To address this gap, I developed an R function for this task. Additionally, the information gathered from these tasks was used to develop and evaluate a method for determining the number of patrilines in a honeybee colony. My analysis demonstrated that the effectiveness of different software tools and SNP array sizes in determining paternal assignments and reconstructing pedigrees varies significantly. Real-world data showed variations in software performance, revealing that simulated results often failed to capture the full complexity of actual genetic data. Moreover, the observed deviations in haplotype assignments highlight potential issues with phasing accuracy and the need for better methods and higher-quality data in genetic studies.

Overall, my thesis explores the complexities of honeybee biology and breeding, through the development and application of the honeybee-specific simulation tool, SIMplyBee. Through the analysis of the simulation outputs using quantitative genetic methods and comparison to real data, the thesis provides insight into optimizing breeding programmes and managing the genetic diversity of honeybee populations.

The thesis concludes with Chapter 5, which reiterates the objectives and findings of Chapters 2-4 before discussing the relevance of these findings in relation to current research. This chapter also expands upon the limitations of the work and the implications of the thesis’ findings on future work.