Bertrand Servin

The phenotypic and genetic diversity of horses worldwide today likely resulted from two main events: (i) domestication and, afterwards, (ii) breed creation. While many studies discuss the origin and domestication of horses, very few focus on the second important event, which has largely shaped the genetic architecture of horses at present. This study exhaustively examined all 18 local horse breeds from France, all of which are endangered by small census sizes, making them prone to suffer from genetic drift, loss of diversity, and inbreeding depression. While pedigrees are available, they are often incomplete or error-prone. Thus, we used molecular markers to reconstruct the history of French local breeds and to monitor the evolution of genetic diversity and its link to management practices. The study was based on 976 animals (≈50 per breed), spanning two time periods corresponding to the change in management from public stud farms to privatization. The samples were genotyped using a 10K SNP array dedicated to genetic resources characterization. French local horses can be classified not only phenotypically but also genetically into two categories: the light “territory” breeds and heavier “draft” breeds, with regular gene flow observed between the two groups that are linked to historical records. The evolution of kinship and inbreeding over the two time periods exhibited distinct patterns between the cohorts and across the breeds, reflecting various consequences of different management practices. To conclude, this study was the first to exhaustively depict the evolution of French local horses, while it also demonstrated the usefulness of genomic data in monitoring genetic diversity and evaluating the effectiveness of management programs.

Among domesticated species, sheep ( Ovis aries ) have achieved a wide geographic distribution due to their mobility, rusticity and ability to adapt to diverse environments and climatic conditions. Here, we characterise the genome-wide diversity of >1,000 sheep representing 77 autochthonous breeds spanning North Africa to Scandinavia using high-density genotype data. To provide a large-scale assessment of population vulnerability to climate change, we combine uni- and multivariate gene-environment association analyses, and quantify the deviation of the current genomic make-up of populations from that necessary for maintaining adaptation under climate change (genomic offset). We identify precipitation as a major driver of divergent selection and identify candidate adaptive loci overlapping genes with pleiotropic roles linking renal water balance and metabolic regulation, including glucose and insulin-related pathways. This coupling of osmoregulatory and metabolic functions likely underpins adaptation to variable drought regimes and extreme, fluctuating environments. Notably, genomic offset hot spots are identified in southwestern Europe, highlighting the need for targeted management to mitigate the effects of climate change on animal welfare and production in these regions. These findings shed light on the strategies for climate adaptation in sheep and provide insights for informed breeding programmes aimed at enhancing resilience in changing climate conditions.

Genomic samples collected from a single population over several generations provide direct access to the genetic diversity changes occurring within a specific time period. This provides information about both demographic and adaptive processes acting on the population during that period. A common approach to analyze such data is to model observed allele counts in finite samples using a Hidden Markov Model (HMM) where hidden states are true allele frequencies over time (i.e. a trajectory). The HMM framework allows one to compute the full likelihood of the data, while accounting both for the stochastic evolution of population allele frequencies along time and for the noise arising from sampling a limited number of individuals at possibly spread out generations. Several such HMM methods have been proposed so far, differing mainly in the way they model the transition probabilities of the Markov chain. Following Paris et al. (2019a), we consider here the Beta with Spikes approximation, which avoids the computational issues associated to the Wright-Fisher model while still including fixation probabilities, in contrast to other standard approximations of this model like the Gaussian or Beta distributions. To facilitate the analysis and exploitation of genomic time series data, we present an improved version of Paris et al. (2019a) ‘s approach, denoted SelNeTime, whose computation time is drastically reduced and which accurately estimates effective population size (assuming no selection) or the selection intensity at each locus (given a previously estimated value of N). We also evaluate the performance of this method in realistic situations where selection is present and both demography and selection need to be inferred. SelNeTime is implemented in a user friendly python package, which can also easily simulate genomic time series data under a user-defined evolutionary model and sampling design.

Meiotic recombination facilitates natural selection and is necessary for correct chromosomal segregation in most sexually reproducing species. Crossover rates vary greatly both within and among species, but the determinants of this variation are not fully understood. The honeybee Apis mellifera has extremely high recombination rates. Honeybee males (drones) are haploid, which enables the distribution of crossovers to be directly estimated from the progeny of a single reproductive female (queen). Here we map crossover events in the honeybee using whole genome sequencing of 1509 drone progeny of 184 queens. This allows us to assay intra-specific variation in recombination rate and its genetic and non-genetic determinants. We estimate the average crossover rate as 23 cM/Mb, with between 22 and 88 crossovers events detected in individual offspring. We estimate 28% of this variation is additive heritable variation among queens. There is no effect of queen age or genetic background on crossover rate. A genome-wide association study identifies variation in the gene mlh1 as associated with mean crossover rate. We estimate that variation in the gene is associated with a 10% difference in crossover rate between the two homozygous genotypes at the most significant SNP. This gene has a well-established role in recombination and variation in the gene could affect crossover rates by affecting resolution of Holliday junctions as crossovers. This is the first gene discovered to be associated with recombination rate variation in an insect. Adaptive evolution of this gene could potentially underlie the extremely high recombination rates in honeybees.

Goats were among the earliest managed animals, making them a natural model to explore the genetic consequences of domestication. However, a challenge in ancient genomic analysis is the relatively low genome coverage for most samples, limiting analysis to pseudohaploid genotypes. Genotype imputation offers potential to alleviate this limitation by improving information content and accuracy in low coverage genomes. To test this, we used published high coverage (>8✕) goat palaeogenomes, imputing downsampled genomes using the VarGoats dataset (1,372 individuals) as a reference panel. Measuring concordance between imputed and high coverage genotypes, we find high concordance after filtering for common (>5%), high confidence variants, with 0.5✕ genomes reaching >0.97 concordance. There is a trade-off between coverage, genotype probability (GP) thresholds, and genotype recovery, where higher coverage and more lenient GP thresholds result in higher recovery, and a reduction in heterozygous false-positive rates with stricter thresholds. We then imputed 36 goat palaeogenomes with ≥0.5✕ coverage to examine runs-of-homozygosity (ROH) and identity-by-descent (IBD) patterns. Using a novel approach combining ROH profiles across tools, we find that among Neolithic goats, ROH increases with distance from the Zagros Mountains, suggesting a large effect of the initial dispersal of managed herds. Inbreeding levels decrease across Southwest Asia in more recent periods. IBD mirrored this pattern, with less relatedness in the early herding site of Ganj Dareh compared to higher relatedness in goats from later in the dispersal process. These findings provide insights into the genetic consequences of early goat management on demography, and confirm the utility of imputation in leveraging low coverage palaeogenomes.

Background: Recombination is a fundamental biological process, both in participating to the creation of viable gametes and as a driver of genetic diversity. Characterising recombination is therefore of strong interest in breeding populations. In this study, we used ~ 50 K genotyped data and pedigree from two French populations (Alpine and Saanen) of domestic goats ( Capra hircus ) to build sex-specific recombination maps, and to explore the genetic basis of two recombination phenotypes: genome-wide recombination rate (GRR) and intra-chromosomal shuffling. Results: Sex-specific recombination maps showed higher recombination in males than females for both breeds (Alpine autosomal map size = 35.1 M in males and 30.5 M in females; Saanen map size = 34.0 M in males and 29.0 M in females). Heterochiasmy is particularly notable on small chromosomes, and at both ends of the chromosomes. Yet, no difference in shuffling has been found between populations. Genetic parameters on recombination phenotypes could only be estimated in males, due to lack of data in females. Both phenotypes are significantly heritable (h ² = 0.12 (0.03) for GRR and h ² = 0.034 (0.015) for shuffling, when pooling breeds). GWAS on male GRR identified several significant loci, including RNF212 , RNF212B and SSH1 , the last one being a novel locus for this phenotype. Correlation of SNP effects between breeds is low for both recombination phenotypes (0.25 for GRR and 0.04 for shuffling), indicating different genetic determinants in the two breeds. Conclusions: This study contributes to the understanding of recombination evolution in ruminants, both between breeds and species.

Background: Recombination is a fundamental biological process, both in participating to the creation of viable gametes and as a driver of genetic diversity. Characterising recombination is therefore of strong interest in breeding populations. In this study, we used ∼50K genotyped data and pedigree from two French populations (Alpine and Saanen) of domestic goats ( Capra hircus ) to build sex-specific recombination maps, and to explore the genetic basis of two recombination phenotypes: genome-wide recombination rate (GRR) and intra-chromosomal shuffling. Results: Sex-specific recombination maps showed higher recombination in males than females for both breeds (Alpine autosomal map size = 35.1M in males and 30.5M in females; Saanen map size = 34.0M in males and 29.0M in females). Heterochiasmy is particularly notable on small chromosomes, and at both ends of the chromosomes. Yet, no difference in shuffling has been found between populations. Genetic parameters on recombination phenotypes could only be estimated in males, due to lack of data in females. Both phenotypes are significantly heritable (h²=0.12 (0.03) for GRR and h²=0.034 (0.015) for shuffling, when pooling breeds). GWAS on male GRR identified several significant loci, including RNF212 , RNF212B and SSH1 , the last one being a novel locus for this phenotype. Correlation of SNP effects between breeds is low for both recombination phenotypes (0.25 for GRR and 0.04 for shuffling), indicating different genetic determinants in the two breeds. Conclusions: This study contributes to the understanding of recombination evolution in ruminants, both between breeds and species.

Goats were among the earliest managed animals, making them a natural model to explore the genetic consequences of domestication. However, a challenge in ancient genomic analysis is the relatively low genome coverage for most samples, limiting analysis to pseudohaploid genotypes. Genotype imputation offers potential to alleviate this limitation by improving information content and accuracy in low coverage genomes. To test this we used published high coverage (>8✕) goat palaeogenomes, imputing downsampled genomes using the VarGoats dataset (1,372 individuals) as a reference panel. Measuring concordance between imputed and high coverage genotypes, we find high concordance after filtering for common (>5%), high confidence variants, with 0.5✕ genomes reaching >0.97 concordance. There is a trade-off between coverage, genotype probability (GP) thresholds, and genotype recovery, where higher coverage and more lenient GP thresholds result in higher recovery, and a reduction in heterozygous false-positive rates with stricter thresholds. We then imputed 36 goat palaeogenomes with ≥0.5✕ coverage to examine runs-of-homozygosity (ROH) and identity-by-descent (IBD) patterns. Using a novel approach combining ROH profiles across tools, we find that among Neolithic goats, ROH increases with distance from the Zagros Mountains, suggesting a large effect of the initial dispersal of managed herds. Inbreeding levels decrease across Southwest Asia in more recent periods. IBD mirrored this pattern, with less relatedness in the early herding site of Ganj Dareh compared to higher relatedness in goats from later in the dispersal process. These findings provide insights into the genetic consequences of early goat management on demography, and confirm the utility of imputation in leveraging low coverage palaeogenomes. Significance: Paleogenomics offers crucial insight into how animals were domesticated, but poor DNA preservation in ancient remains often limits the reach of genetic analyses. We utilise a cutting-edge technique, genotype imputation, to recover missing genetic information from ancient low coverage goat genomes and shine light on their domestication process. Early domestic goats showed low overall runs-of-homozygosity (ROH) and relatedness. We find that during the Neolithic, runs-of-homozygosity (ROH) and relatedness among goats increased, likely a consequence of the movement of herds beyond their natural range by humans. Inbreeding levels decline in more recent periods, potentially due to expanded herd sizes, animal trade networks, or improved husbandry practices. These findings challenge long-standing assumptions about domestication bottlenecks and highlight how ancient DNA can be used to uncover complex evolutionary histories, even from low coverage ancient samples.

Honeybees, Apis mellifera, have experienced the full impacts of globalisation, including the recent invasion by the parasitic mite Varroa destructor, now one of the main causes of colony losses worldwide. The strong selection pressure it exerts has led some colonies to develop defence strategies conferring some degree of resistance to the parasite. Assuming these traits are partly heritable, selective breeding of naturally resistant bees could be a sustainable strategy for fighting infestations. To characterise the genetic determinism of varroa resistance, we conducted the largest genome wide association study performed to date on whole genome sequencing of more than 1500 colonies on multiple phenotypes linked to varroa resistance of honeybees. To take into account some genetic diversity of honeybees, colonies belonging to different ancestries representing the main honeybee subspecies in Western Europe were included and analysed both as separate populations and combined in a meta‐analysis. The results show that varroa resistance is substantially heritable and polygenic: while 60 significant associations were identified, none explain a substantial part of the trait genetic variance. Overall our study highlights that genomic selection for varroa resistance is promising but that it will not be based on managing a few strong effect mutations and rather use approaches that leverage the genome wide diversity of honeybee populations. From a broader perspective, these results point the way towards understanding the genetic adaptation of eusocial insects to parasite load.

Summary: Genomic samples collected from a single population over several generations provide direct access to the genetic diversity changes occurring within a specific time period. This provides information about both demographic and adaptive processes acting on the population during that period. A common approach to analyze such data is to model observed allele counts in finite samples using a Hidden Markov Model (HMM) where hidden states are true allele frequencies over time (i.e. a trajectory). The HMM framework allows one to compute the full likelihood of the data, while accounting both for the stochastic evolution of population allele frequencies along time and for the noise arising from sampling a limited number of individuals at possibly spread out generations. Several such HMM methods have been proposed so far, differing mainly in the way they model the transition probabilities of the Markov chain. Following Paris et al. (2019a), we consider here the Beta with Spikes approximation, which avoids the computational issues associated to the Wright-Fisher model while still including fixation probabilities, in contrast to other standard approximations of this model like the Gaussian or Beta distributions. To facilitate the analysis and exploitation of genomic time series data, we present an improved version of Paris et al. (2019a) ‘s approach, denoted SelNeTime, whose computation time is drastically reduced and which accurately estimates effective population size (assuming no selection) or the selection intensity at each locus (given a previously estimated value of N). We also evaluate the performance of this method in realistic situations where selection is present and both demography and selection need to be inferred. SelNeTime is implemented in a user friendly python package, which can also easily simulate genomic time series data under a user-defined evolutionary model and sampling design.