Millions of migratory birds occupy seasonally favourable breeding grounds in the Arctic 1 , but we know little about the formation, maintenance and future of Arctic bird migration routes and genetic determinants of migratory distance. Here, we established a continental-scale migration system, satellite tracking 56 peregrine falcons (Falco peregrinus) from six Eurasian Arctic breeding populations and resequencing 35 genomes from four of these. Different breeding populations used five migration routes across Eurasia, likely formed by longitude and latitude breeding ground shifts during the LGM-Holocene transition. Contemporary inter-route environmental divergence appears to maintain distinct migration routes. We found that the novel gene ADCY8 was associated with population-level migratory distance differences. We elucidated its regulatory mechanism and found the most likely selective agent for this divergence was long-term memory. Global warming is predicted to influence migration strategies and diminish breeding ranges of Eurasian Arctic peregrines. Harnessing ecological interactions and evolutionary processes to study climate-driven changes in migration can facilitate the conservation of migratory birds.

Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modelling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses.

The state of somatic energy stores in metazoans is communicated to the brain, which regulates key aspects of behaviour, growth, nutrient partitioning and development 1 . The central melanocortin system acts through Melanocortin-4 Receptor (MC4R) to control appetite, food intake and energy expenditure 2 . We now present evidence that the Melanocortin-3 Receptor (MC3R) regulates the timing of sexual maturation, the rate of linear growth and the accrual of lean mass, all energy-sensitive processes. We found that humans who carry loss-of-function mutations in MC3R, including a rare homozygote, have a later onset of puberty. Consistent with previous findings in mice, they also had reduced linear growth, lean mass and IGF-1 levels. Mice lacking Mc3r had delayed sexual maturation and an insensitivity of reproductive cycle length to nutritional perturbation. The expression of Mc3r is enriched in hypothalamic neurons controlling reproduction and growth and increases during post-natal development in a manner consistent with a role in regulation of sexual maturation. These findings suggest a bifurcating model of nutrient sensing by the central melanocortin pathway with signalling through MC4R controlling the acquisition and retention of calories, while MC3R primarily regulates their disposition into growth, lean mass and the timing of sexual maturation.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies, and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat colouration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backwards in time while re-weighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface for both the selection coefficient and the allele age to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and non-constant demographic histories. We apply our approach to re-analyse ancient DNA data associated with horse base coat colours. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

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Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally-spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies, and hold the promise of improving power for inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events like plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage and small samples. To address these challenges, we introduce a novel Bayesian approach for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to account for sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our method permits the reconstruction of the underlying mutant allele frequency trajectory of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and illustrate its utility with an application to the ancient horse samples genotyped at the loci for coat colouration.

With the rapid growth of the number of sequenced ancient genomes, there has been increasing interest in using this new information to study past and present adaptation.Such an additional temporal component has the promise of providing improved power for the estimation of natural selection. Over the last decade, statistical approaches