Identification of Novel Single Nucleotide Polymorphisms (SNPs) in Deer (Odocoileus spp.) Using the BovineSNP50 BeadChip

Single nucleotide polymorphisms (SNPs) are growing in popularity as a genetic marker for investigating evolutionary processes. A panel of SNPs is often developed by comparing large quantities of DNA sequence data across multiple individuals to identify polymorphic sites. For non-model species, this is particularly difficult, as performing the necessary large-scale genomic sequencing often exceeds the resources available for the project. In this study, we trial the Bovine SNP50 BeadChip developed in cattle (Bos taurus) for identifying polymorphic SNPs in cervids Odocoileus hemionus (mule deer and black-tailed deer) and O. virginianus (white-tailed deer) in the Pacific Northwest. We found that 38.7% of loci could be genotyped, of which 5% (n = 1068) were polymorphic. Of these 1068 polymorphic SNPs, a mixture of putatively neutral loci (n = 878) and loci under selection (n = 190) were identified with the FST-outlier method. A range of population genetic analyses were implemented using these SNPs and a panel of 10 microsatellite loci. The three types of deer could readily be distinguished with both the SNP and microsatellite datasets. This study demonstrates that commercially developed SNP chips are a viable means of SNP discovery for non-model organisms, even when used between very distantly related species (the Bovidae and Cervidae families diverged some 25.1−30.1 million years before present)

Genetic Heterogeneity in a Cyclical Forest Pest, the Southern Pine Beetle, Dendroctonus frontalis, is Differentiated Into East and West Groups in the Southeastern United States

The southern pine beetle, Dendroctonus frontalis Zimmerman (Coleoptera: Curculionidae) is an economically important pest species throughout the southeastern United States, Arizona, Mexico, and Central America. Previous research identified population structure among widely distant locations, yet failed to detect population structure among national forests in the state of Mississippi. This study uses microsatellite variation throughout the southeastern United States to compare the southern pine beetle's pattern of population structure to phylogeographic patterns in the region, and to provide information about dispersal. Bayesian clustering identified east and west genetic groups spanning multiple states. The east group had lower heterozygosity, possibly indicating greater habitat fragmentation or a more recent colonization. Significant genetic differentiation (θST = 0.01, p < 0.0001) followed an isolation-by-distance pattern (r = 0.39, p < 0.001) among samples, and a hierarchical AMOVA indicated slightly more differentiation occurred between multi-state groups. The observed population structure matches a previously identified phylogeographic pattern, division of groups along the Appalachian Mountain/Apalachicola River axis. Our results indicate that the species likely occurs as a large, stable metapopulation with considerable gene flow among subpopulations. Also, the relatively low magnitude of genetic differentiation among samples suggests that southern pine beetles may respond similarly to management across their range

Microsatellite based genetic diversity and population structure of the endangered Spanish Guadarrama goat breed

<p>Abstract</p> <p>Background</p> <p>Assessing genetic biodiversity and population structure of minor breeds through the information provided by neutral molecular markers, allows determination of their extinction risk and to design strategies for their management and conservation. Analysis of microsatellite loci is known to be highly informative in the reconstruction of the historical processes underlying the evolution and differentiation of animal populations. Guadarrama goat is a threatened Spanish breed which actual census (2008) consists of 3057 females and 203 males distributed in 22 populations more or less isolated. The aim of this work is to study the genetic status of this breed through the analysis of molecular data from 10 microsatellites typed in historic and actual live animals.</p> <p>Results</p> <p>The mean expected heterozygosity across loci within populations ranged from 0.62 to 0.77. Genetic differentiation measures were moderate, with a mean F_STof 0.074, G_STof 0.081 and R_STof 0.085. Percentages of variation among and within populations were 7.5 and 92.5, respectively. Bayesian clustering analyses pointed out a population subdivision in 16 clusters, however, no correlation between geographical distances and genetic differences was found. Management factors such as the limited exchange of animals between farmers (estimated gene flow Nm = 3.08) mostly due to sanitary and social constraints could be the major causes affecting Guadarrama goat population subdivision.</p> <p>Conclusion</p> <p>Genetic diversity measures revealed a good status of biodiversity in the Guadarrama goat breed. Since diseases are the first cause affecting the census in this breed, population subdivision would be an advantage for its conservation. However, to maintain private alleles present at low frequencies in such small populations minimizing the inbreeding rate, it would necessitate some mating designs of animals carrying such alleles among populations. The systematic use of molecular markers will facilitate the comprehensive management of these populations, which in combination with the actual breeding program to increase milk yield, will constitute a good strategy to preserve the breed.</p

Assessing population genetic structure via the maximisation of genetic distance

<p>Abstract</p> <p>Background</p> <p>The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics.</p> <p>Methods</p> <p>In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a <it>simulated annealing </it>algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set.</p> <p>Results</p> <p>The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for <it>F</it>_{<it>ST </it>}≥ 0.03, but only STRUCTURE estimates the correct number of clusters for <it>F</it>_{<it>ST </it>}as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found.</p> <p>Conclusion</p> <p>This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.</p

Going Coastal: Shared Evolutionary History between Coastal British Columbia and Southeast Alaska Wolves (Canis lupus)

Many coastal species occupying the temperate rainforests of the Pacific Northwest in North America comprise endemic populations genetically and ecologically distinct from interior continental conspecifics. Morphological variation previously identified among wolf populations resulted in recognition of multiple subspecies of wolves in the Pacific Northwest. Recently, separate genetic studies have identified diverged populations of wolves in coastal British Columbia and coastal Southeast Alaska, providing support for hypotheses of distinct coastal subspecies. These two regions are geographically and ecologically contiguous, however, there is no comprehensive analysis across all wolf populations in this coastal rainforest.By combining mitochondrial DNA datasets from throughout the Pacific Northwest, we examined the genetic relationship between coastal British Columbia and Southeast Alaska wolf populations and compared them with adjacent continental populations. Phylogenetic analysis indicates complete overlap in the genetic diversity of coastal British Columbia and Southeast Alaska wolves, but these populations are distinct from interior continental wolves. Analyses of molecular variation support the separation of all coastal wolves in a group divergent from continental populations, as predicted based on hypothesized subspecies designations. Two novel haplotypes also were uncovered in a newly assayed continental population of interior Alaska wolves.We found evidence that coastal wolves endemic to these temperate rainforests are diverged from neighbouring, interior continental wolves; a finding that necessitates new international strategies associated with the management of this species

Accurate Inference of Subtle Population Structure (and Other Genetic Discontinuities) Using Principal Coordinates

Accurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely-used methods to infer population structure are model-based, Bayesian MCMC procedures that minimize Hardy-Weinberg and linkage disequilibrium within subpopulations. These methods are useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.PCO-MC uses data from all principal coordinate axes simultaneously to calculate a multidimensional "density landscape", from which the number of subpopulations, and the membership within subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is marginally superior with few loci (e.g. 10). When presented with sufficient data, PCO-MC accurately delineated subpopulations with population F(st) values as low as 0.03 (G'(st)>0.2), whereas the limit of resolution of the Bayesian approach was F(st) = 0.05 (G'(st)>0.35).We draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, like those produced by PCO-MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case-control correction in structured association studies