Evaluation of methods for estimating coalescence times using ancestral recombination graphs.

The ancestral recombination graph is a structure that describes the joint genealogies of sampled DNA sequences along the genome. Recent computational methods have made impressive progress toward scalably estimating whole-genome genealogies. In addition to inferring the ancestral recombination graph, some of these methods can also provide ancestral recombination graphs sampled from a defined posterior distribution. Obtaining good samples of ancestral recombination graphs is crucial for quantifying statistical uncertainty and for estimating population genetic parameters such as effective population size, mutation rate, and allele age. Here, we use standard neutral coalescent simulations to benchmark the estimates of pairwise coalescence times from 3 popular ancestral recombination graph inference programs: ARGweaver, Relate, and tsinfer+tsdate. We compare (1) the true coalescence times to the inferred times at each locus; (2) the distribution of coalescence times across all loci to the expected exponential distribution; (3) whether the sampled coalescence times have the properties expected of a valid posterior distribution. We find that inferred coalescence times at each locus are most accurate in ARGweaver, and often more accurate in Relate than in tsinfer+tsdate. However, all 3 methods tend to overestimate small coalescence times and underestimate large ones. Lastly, the posterior distribution of ARGweaver is closer to the expected posterior distribution than Relate's, but this higher accuracy comes at a substantial trade-off in scalability. The best choice of method will depend on the number and length of input sequences and on the goal of downstream analyses, and we provide guidelines for the best practices.

Population Genomics of Variegated Toad-Headed Lizard Phrynocephalus versicolor and Its Adaptation to the Colorful Sand of the Gobi Desert.

The variegated toad-headed agama, Phrynocephalus versicolor, lives in the arid landscape of the Chinese Gobi Desert. We analyzed populations from three different locations which vary in substrate color and altitude: Heishankou (HSK), Guazhou County (GZ), and Ejin Banner (EJN). The substrate color is either light-yellow (GZ-y), yellow (EJN-y), or black (HSK-b); the corresponding lizard population colors largely match their substrate in the degree of melanism. We assembled the P. versicolor genome and sequenced over 90 individuals from the three different populations. Genetic divergence between populations corresponds to their geographic distribution. We inferred the genetic relationships among these populations and used selection scans and differential expression to identify genes that show signatures of selection. Slc2a11 and akap12, among other genes, are highly differentiated and may be responsible for pigment adaptation to substrate color in P. versicolor.

Elevation as a selective force on mitochondrial respiratory chain complexes of the Phrynocephalus lizards in the Tibetan plateau.

Animals living in extremely high elevations have to adapt to low temperatures and low oxygen availability (hypoxia), but the underlying genetic mechanisms associated with these adaptations are still unclear. The mitochondrial respiratory chain can provide >95% of the ATP in animal cells, and its efficiency is influenced by temperature and oxygen availability. Therefore, the respiratory chain complexes (RCCs) could be important molecular targets for positive selection associated with respiratory adaptation in high-altitude environments. Here, we investigated positive selection in 5 RCCs and their assembly factors by analyzing sequences of 106 genes obtained through RNA-seq of all 15 Chinese Phrynocephalus lizard species, which are distributed from lowlands to the Tibetan plateau (average elevation >4,500 m). Our results indicate that evidence of positive selection on RCC genes is not significantly different from assembly factors, and we found no difference in selective pressures among the 5 complexes. We specifically looked for positive selection in lineages where changes in habitat elevation happened. The group of lineages evolving from low to high altitude show stronger signals of positive selection than lineages evolving from high to low elevations. Lineages evolving from low to high elevation also have more shared codons under positive selection, though the changes are not equivalent at the amino acid level. This study advances our understanding of the genetic basis of animal respiratory metabolism evolution in extreme high environments and provides candidate genes for further confirmation with functional analyses.