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Runs of homozygosity (ROH) are continuous homozygous segments that arise through the transmission of haplotypes that are identical by descent (IBD). The length and distribution of ROH segments provide insight into the genetic diversity of populations and are useful to detect selection signatures. Here, we analysed pooled whole-genome sequencing data from 265 Western honey bee colonies from the two subspecies Apis mellifera mellifera and Apis mellifera carnica. Integrating individual ROH patterns and admixture levels in a high-resolution population network visualization allowed us to ascertain major differences between the two subspecies. Within A. m. mellifera, we identified well-defined substructures according to the genetic origin of the colonies and a fair amount of admixed colonies, despite the current applied conservation efforts. In contrast, A. m. carnica colonies were more inbred and could not be differentiated according to the geographical origin. We identified 29 coding genes in overlapping ROH segments within the two subspecies. Genes embedded in A. m. carnica specific homozygosity islands suggested a strong selection for production and behavioural traits, whilst the identified cuticula protein-coding genes (CPR3 and CPR4) were associated with their breed-specific stripe pattern. Local adaption of the two subspecies could be confirmed by the identification of two genes involved in the response to ultraviolet (UV) light. We demonstrated that colony genotypes derived from pooled honey bee workers are reliable to unravel the population dynamics in A. mellifera and provide fundamental information to conserve native honey bees.

International audience; The identification of quantitative trait loci (QTL) through genome-wide association studies (GWAS) is a powerful method for unravelling the genetic background of selected traits and improving early-stage predictions. In honey bees (Apis mellifera), past genetic analyses have particularly focused on individual queens and workers. In this study, we used pooled wholegenome sequences to ascertain the genetic variation of the entire colony. In total, we sampled 216 Apis mellifera mellifera and 28 Apis mellifera carnica colonies. Different experts subjectively assessed the gentleness and calmness of the colonies using a standardised protocol. Conducting a GWAS for calmness on 211 purebred A. m. mellifera colonies, we identified three QTL, on chromosomes 8, 6, and 12. The two first QTL correspond to LOC409692 gene, coding for a disintegrin and metalloproteinase domain-containing protein 10, and to Abscam gene, coding for a Dscam family member Abscam protein, respectively. The last gene has been reported to be involved in the domestication of A. mellifera. The third QTL is located 13 kb upstream of LOC102655631, coding for a trehalose transporter. For gentleness, two QTL were identified on chromosomes 4 and 3. They are located within gene LOC413669, coding for a lap4 protein, and gene LOC413416, coding for a bicaudal C homolog 1-B protein, respectively. The identified positional candidate genes of both traits mainly affect the olfaction and nervous system of honey bees. Further research is needed to confirm the results and to better understand the genetic and phenotypic basis of calmness and gentleness.

Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.

Additional file 1. Comparison of G-matrices. Comparison of different methods to calculate estimated relationships between individuals and their impact on the loss of genetic diversity.

Whole genome sequencing technologies are rapidly developing. In some ways, the speed of this development has outstripped our capacity to use this type of data in selection strategies, especially in livestock diversity conservation. In this study, relationship matrices were computed for 118 Holstein bulls, key ancestors of the current population, from three different types of data: pedigree records, 50K SNP chips and whole genome sequences, considering three different scenarios (with, without or only using rare alleles). Estimates from different data were highly correlated. Rare alleles had a significant impact on relationship estimates, mostly when whole genome sequence data were used. Hence sequence data, and information from rare alleles, are potentially of use for improving relationship computation. Estimation of relationships made with this type of data may result in different individual optimal contributions and influence selection strategies and conservation decisions of livestock species

Genomic selection is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, genomic selection allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for genomic selection our findings are of major economic interest. This study aims to answer the following questions: How would decisions on the reference population affect the breeding population? How to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation and optimal contribution strategies. Optimal contribution maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over ten generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.

Background Whole-genome sequence (WGS) data give access to more complete structural genetic information of individuals, including rare variants, not fully covered by single nucleotide polymorphism chips. We used WGS to investigate the amount of genetic diversity remaining after selection using optimal contribution (OC), considering different methods to estimate the relationships used in OC. OC was applied to minimise average relatedness of the selection candidates and thus miminise the loss of genetic diversity in a conservation strategy, e.g. for establishment of gene bank collections. Furthermore, OC was used to maximise average genetic merit of the selection candidates at a given level of relatedness, similar to a genetic improvement strategy. In this study, we used data from 277 bulls from the 1000 bull genomes project. We measured genetic diversity as the number of variants still segregating after selection using WGS data, and compared strategies that targeted conservation of rare (minor allele frequency