Search bioRxiv⌕ Search

bioRxiv · 10.1101/2023.05.22.541688

Uncertainty in the mating strategy causes bias and inaccuracy in estimates of genetic parameters in honeybees

Abstract

BackgroundWith the increased number of honeybee breeding plans worldwide, records from queens with diversified mating strategies need to be considered. Breeding queens might be inseminated with drones produced by a single drone-producing queen (DPQ), or by a group of sister-DPQs. Often, only the dam of DPQ(s) is reported in the pedigree. Furthermore, datasets might include colony phenotypes from DPQs that were open mated in different locations. Using simulation, we investigated the impact of the mating strategy on estimates of genetic parameters and breeding values, when the DPQs were treated in different ways in the statistical evaluation model. We quantify the bias and standard error of estimates when breeding queens are mated to a single or a group of DPQs, assuming that this information is either known or not. We also investigated two alternative strategies to accommodate phenotypes of open-mated DPQs in the genetic evaluation, adding either a dummy pseudo sire in the pedigree, or a non-genetic effect to the statistical evaluation model to account for the origin of the DPQs mates. ResultsWhen breeding queens were inseminated with semen from drones of a single DPQ and this was known, estimates of genetic parameters and genetic trends were more precise. If they were inseminated using drones from a single or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in the estimates. For colony phenotypes of open-mated DPQs, adding a dummy pseudo sire in the pedigree for each mating location led to considerable overestimation of genetic variances, while correcting for the mating area by adding a non-genetic effect in the evaluation model gave unbiased estimates. ConclusionsKnowing only the dam of the DPQ(s) in the mating may lead to erroneous assumptions on how DPQs were used and cause severe biases in estimates of genetic parameters and genetic trends. Therefore, keeping track in the pedigree of which DPQ(s), and not only which dam of DPQ(s) are used, is recommended. Records from DPQ colonies with queens open mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kistler, T., Brascamp, E. W., Basso, B., Bijma, P., Phocas, F.. 2023-05-24. Uncertainty in the mating strategy causes bias and inaccuracy in estimates of genetic parameters in honeybees. https://doi.org/10.1101/2023.05.22.541688

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Generation of a transgenic cephalopod

Coleoid cephalopods (cuttlefish, octopus, and squid) are marine mollusks with elaborate nervous systems that support a diverse repertoire of complex behaviors. These include the neural control of the color, pattern, and texture of the skin, facilitating both adaptive camouflage and innate patterning that may reflect internal state. The development of transgenic cephalopods expressing fluorescent proteins, optogenetic actuators, and reporters of neural activity would contribute a new and important technology to cephalopod biology. The generation of transgenic cephalopods, however, has remained a major challenge. Here, we report the development of stable transgenic dwarf cuttlefish (Ascarosepion bandense) expressing ubiquitous nuclear-localized mScarlet, a red fluorescent protein. We evaluated multiple strategies for transgenesis, and established cuttlefish lines using both CRISPR and the transposons Sleeping Beauty and Minos. The stable expression of transgenes enabled live imaging of cell dynamics during embryonic development. The Minos transposon emerged as the most efficient transgenesis strategy and is adaptable to promoters and transgenes of choice. These strategies now enable the generation of diverse genetic tools for mechanistic studies of cephalopod biology.

genetics↗

Large language model-based bibliometric evaluation of population descriptors in human genetics

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

genetics↗

Mitigating biases of rescaling in forward-in-time population genetic simulations

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

genetics↗