Parejo, M. | Search bioRxiv

bioRxiv2022

Dynamics in vertical transmission of viruses in naturally selected and traditionally managed honey bee colonies across Europe

The suppressed in-ovo virus infection trait (SOV) was the first trait applied in honey bee breeding programs aimed to increase resilience to virus infections, a major threat for colony survival. By screening drone eggs for viruses, the SOV trait scores the antiviral resistance of queens and its implications for vertical transmission. In this study, queens from both naturally surviving and traditionally managed colonies from across Europe were screened using a two-fold improved SOV phenotyping protocol. First, a gel-based RT-PCR was replaced by a RT-qPCR. This not only allowed quantification of the infection load but also increased the test sensitivity. Second, a genotype specific primer set was replaced by a primer set that covered all known deformed wing virus (DWV) genotypes, which resulted in higher virus loads and fewer false negative results. It was demonstrated that incidences of vertical transmission of DWV were more frequent in naturally surviving populations than in traditionally managed colonies, although the virus load in the eggs remained the same. Dynamics in vertical transmission were further emphasized when comparing virus infections with queen age. Interestingly, older queens showed significantly lower infection loads of DWV in both traditionally managed and naturally surviving colonies, as well as reduced DWV infection frequencies in traditionally managed colonies when compared with younger queens. Seasonal variation in vertical transmission was found with lower infection frequencies in spring compared to summer for DWV and black queen cell virus. Together, these patterns in vertical transmission suggest an adaptive antiviral response of queens aimed at reducing vertical transmission over time.

ecology↗

bioRxiv2021

Recovering high-quality host genomes from gut metagenomic data through genotype imputation

Metagenomic data sets of host-associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, if a reference panel is available, genotype imputation can be employed to reconstruct host genotypes and maximise the use of such a priori useless data. We tested the performance of a two-step strategy to input genotypes from four types of reference panels, comprised of deeply sequenced chickens to low-depth host genome (~2x coverage) data recovered from metagenomic samples of chicken intestines. The target chicken population was formed by two broiler breeds and the four reference panels employed were (i) an internal panel formed by population-specific individuals, (ii) an external panel created from a public database, (iii) a combined panel of the previous two, and (iv) a diverse panel including more distant populations. Imputation accuracy was high for all tested panels (concordance >0.90), although samples with coverage under 0.28x consistently showed the lowest accuracies. The best imputation performance was achieved by the combined panel due to the high number of imputed variants, including low-frequency ones. However, common population genetics parameters measured to characterise the chicken populations, including observed heterozygosity, nucleotide diversity, pairwise distances and kinship, were only minimally affected by panel choice, with all four panels yielding suitable results for host population characterization and comparison. Likewise, genome scans between the two studied broiler breeds using imputed data with each panel consistently identified the same sweep regions. In conclusion, we show that the applied imputation strategy enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitate the implementation of hologenomic approaches that jointly analyse host genomic and microbial metagenomic data. Author summaryWe introduce and assess a methodological approach that enables recovering animal genomes from complex mixtures of metagenomic data, and thus expand the portfolio of analyses that can be conducted from samples such as faeces and gut contents. Metagenomic data sets of host-associated microbial communities often contain DNA of the host organism. The principal drawback to use this data for host genomic characterisation is the low percentage and quality of the host DNA. In order to leverage this data, we propose a two-step imputation method, to recover high-density of variants. We tested the pipeline in a chicken metagenomic dataset, validated imputation accuracy statistics, and studied common population genetics parameters to assess how these are affected by genotype imputation and choice of reference panel. Being able to analyse both domains from the same data set could considerably reduce sampling and laboratory efforts and resources, thereby yielding more sustainable practices for future studies that embrace a hologenomic approach that jointly analyses animal genomic and microbial metagenomic features.

genomics↗

bioRxiv2021

Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones

Honey bee subspecies originate from specific geographic areas in Africa, Europe and the Middle East. The interest of beekeepers in specific phenotypes has led them to import subspecies to regions outside of their original range. The resulting admixture complicates population genetics analyses and population stratification can be a major problem for association studies. As a typical example, the case of the French population is studied here. We sequenced 870 haploid drones for SNP detection and identified nine genetic backgrounds in 629 samples. Five correspond to subspecies, two to isolated populations and two to human-mediated population management. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long on chromosome 11, representing 1.6 % of the genome and has two major haplotypes, corresponding to the two dominant genetic backgrounds identified.

genetics↗