Moose genomes reveal past glacial demography and the origin of modern lineages

Abstract

Background Numerous megafauna species from northern latitudes went extinct during the Pleistocene/Holocene transition as a result of climate-induced habitat changes. However, several ungulate species managed to successfully track their habitats during this period to eventually flourish and recolonise the holarctic regions. So far, the genomic impacts of these climate fluctuations on ungulates from high latitudes have been little explored. Here, we assemble a de-novo genome for the European moose (Alces alces) and analyse it together with re-sequenced nuclear genomes and ancient and modern mitogenomes from across the moose range in Eurasia and North America. Results We found that moose demographic history was greatly influenced by glacial cycles, with demographic responses to the Pleistocene/Holocene transition similar to other temperate ungulates. Our results further support that modern moose lineages trace their origin back to populations that inhabited distinct glacial refugia during the Last Glacial Maximum (LGM). Finally, we found that present day moose in Europe and North America show low to moderate inbreeding levels resulting from post-glacial bottlenecks and founder effects, but no evidence for recent inbreeding resulting from human-induced population declines. Conclusions Taken together, our results highlight the dynamic recent evolutionary history of the moose and provide an important resource for further genomic studies.

Copy number variations with adaptive potential in caribou (Rangifer tarandus): genome architecture and new annotated genome assembly

“…In terms of contiguity and accuracy (BUSCO analysis; Table 1), this new genome assembly compares very well with other recent genome assemblies for livestock species such as chicken (W. C. Warren et al 2017) or other wild species such as the grizzly bear (Taylor et al 2018) or sea otter (Jones et al 2017) and was superior to those of other Cervidae species (Dussex et al 2020;Upadhyay et al 2020). Notably, the highest quality genome assemblies (including the present one) are usually obtained when different sequencing strategies are used, including long reads and linked reads, often in combination with typical short-read sequencing (Wallberg et al 2019;Kongsstovu et al 2019).…”

Ancient Mitogenomes Suggest Stable Mitochondrial Clades of the Siberian Roe Deer

“…Similar to previous studies [ 10 , 12 , 13 , 14 ], our geographical distribution of haplogroups ( Figure 3 b) showed that Siberian roe deer from Central Europe to East Asia are highly heterogeneous without clear geographical separation of maternal lineages. This feature might be derived from substantial gene flow due to the high mobility of this species [ 9 , 56 ], as well as high levels of connectivity among populations during the Ice Age [ 33 ]. Furthermore, we found that taking Lake Baikal as the boundary, except the isolated population SKJ, the western populations mainly belong to haplogroups B and C, while all haplogroups are found in the eastern populations, suggesting a different haplogroup composition of populations on both sides of Lake Baikal.…”

Genome-wide SNP analysis of three moose subspecies at the southern range limit in the contiguous United States

“…However, considering the anthropogenic and natural threats moose currently face, future studies concerned with moose genetic diversity might benefit from the investigation of functional genetic diversity, or the measure of diversity of potentially adaptive markers, by using RNA-seq and wholegenome resequencing (WGR) methods (Brodie et al 2021). This could be accomplished using the recently sequenced European moose genome (Dussex et al 2020), or optimally, by first sequencing the nuclear genome of the North American moose, and then looking to see which SNPs/genes appear most impacted. Understanding both the genomic and transcriptomic characteristics of successful moose subpopulations, particularly in the face of increasing stressors, may highlight which genes or genomic regions are influencing their adaptive capacity.…”