27 research outputs found
Chromosome-level assembly of the Atlantic silverside genome reveals extreme levels of sequence diversity and structural genetic variation [preprint]
The levels and distribution of standing genetic variation in a genome can provide a wealth of insights about the adaptive potential, demographic history, and genome structure of a population or species. As structural variants are increasingly associated with traits important for adaptation and speciation, investigating both sequence and structural variation is essential for wholly tapping this potential. Using a combination of shotgun sequencing, 10X Genomics linked reads and proximity-ligation data (Chicago and Hi-C), we produced and annotated a chromosome-level genome assembly for the Atlantic silverside (Menidia menidia) - an established ecological model for studying the phenotypic effects of natural and artificial selection - and examined patterns of genomic variation across two individuals sampled from different populations with divergent local adaptations. Levels of diversity varied substantially across each chromosome, consistently being highly elevated near the ends (presumably near telomeric regions) and dipping to near zero around putative centromeres. Overall, our estimate of the genome-wide average heterozygosity in the Atlantic silverside is the highest reported for a fish, or any vertebrate, to date (1.32-1.76% depending on inference method and sample). Furthermore, we also found extreme levels of structural variation, affecting ~23% of the total genome sequence, including multiple large inversions (\u3e 1 Mb and up to 12.6 Mb) associated with previously identified haploblocks showing strong differentiation between locally adapted populations. These extreme levels of standing genetic variation are likely associated with large effective population sizes and may help explain the remarkable adaptive divergence among populations of the Atlantic silverside
The contribution of historical processes to contemporary extinction risk in placental mammals.
Data from: Population genetic structure of a common host predicts the spread of white-nose syndrome, an emerging infectious disease in bats
Landscape complexity influences patterns of animal dispersal, which in turn may affect both gene flow and the spread of pathogens. White-nose syndrome (WNS) is an introduced fungal disease that has spread rapidly throughout eastern North America, causing massive mortality in bat populations. We tested for a relationship between the population genetic structure of the most common host, the little brown myotis (Myotis lucifugus), and the geographic spread of WNS to date by evaluating logistic regression models of WNS risk among hibernating colonies in eastern North America. We hypothesized that risk of WNS to susceptible host colonies should increase with both geographic proximity and genetic similarity, reflecting historical connectivity, to infected colonies. Consistent with this hypothesis, inclusion of genetic distance between infected and susceptible colonies significantly improved models of disease spread, capturing heterogeneity in the spatial expansion of WNS despite low levels of genetic differentiation among eastern populations. Expanding our genetic analysis to the continental range of little brown myotis reveals strongly contrasting patterns of population structure between eastern and western North America. Genetic structure increases markedly moving westward into the northern Great Plains, beyond the current distribution of WNS. In western North America, genetic differentiation of geographically proximate populations often exceeds levels observed across the entire eastern region, suggesting infrequent and/or locally restricted dispersal, and thus relatively limited opportunities for pathogen introduction in western North America. Taken together, our analyses suggest a possibly slower future rate of spread of the WNS pathogen, at least as mediated by little brown myotis
A beginner's guide to low‐coverage whole genome sequencing for population genomics
Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and nonmodel species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analysed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency, genetic diversity, and linkage disequilibrium estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in nonmodel species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption
Cytb sequences from hibernating Myotis lucifugus colonies
Cytb sequences (fasta format) from hibernating colonies of Myotis lucifugus used to estimate PhiST for risk models of disease spread. Header indicates sample ID and sampling locality
Genetic and geographic distance matrix for cytb among hibernacula
Pairwise PhiST of cytb (above diagonal) and geographic distance (km; below diagonal) between hibernating colonies of Myotis lucifugus used in risk models of disease spread. Column 1 gives sample size (N) and column 2 gives sampling locality (Site)
Genetic and geographic distance matrix from range-wide ddRAD-seq
Pairwise PhiST of ddRAD-seq (above diagonal) and geographic distance (km; below diagonal) between localities of Myotis lucifugus sampled across their range. The first four columns indicate Region (E=eastern, W=western), Site-type (M=maternity colony, H=hibernaculum, F=foraging site), sample size (N) and sampling locality (Site)
Myotis cytb parsimony tree
Parsimony tree used to create Myotis lucifugus haplotype network, including outgroups and other Myotis lucifugus sequences downloaded from GenBank. See Key to cytb haplotypes for identification of individual sequences belonging to each haplotype
Genetic and geographic distance matrix for ddRAD-seq among hibernacula
Pairwise PhiST of ddRAD-seq (above diagonal) and geographic distance (km; below diagonal) between hibernating colonies of Myotis lucifugus used in risk models of disease spread. Column 1 gives sample size (N) and column 2 gives sampling locality (Site)
ddRAD-seq from hibernating Myotis lucifugus colonies
ddRAD-seq data (nexus format) used to estimate PhiST among hibernating colonies of Myotis lucifugus for risk models of disease spread. Sequences represent unphased, concatenated RAD sequences. Headers indicate the sample name and locality. Only loci with genotype calls for all samples were included