Mammalian biogeography and the Ebola virus in Africa

Ebola virus is responsible for the fatal Ebola-virus disease (EVD). Links between EVD outbreaks in Africa and 3 chiropteran species, presumed to be reservoirs for the Ebola virus, have been suggested, but discussions are still on-going. There is also evidence of significant virus spillover among mammal species not suspected to be natural hosts (e.g. chimpanzees, gorillas and duikers). We mapped the potential distribution of the Ebola virus in Africa based on both environmental and zoogeographic descriptors. We employed distribution modelling using the Favourability Function, alongside a complement of biogeographic approaches including chorotype analysis. We obtained a significantly well-calibrated model defining the distribution of environmentally favourable areas for the presence of Ebola virus, which was outperformed by a model determining favourable areas according to mammalian biogeography. Finally, we built a model in which the combined landscape and mammalian distribution types better explained the distribution of Ebola virus independent of human-to-human transmissions. Our findings show that the core area for the virus is associated with infections of known animal origin, but surrounded by areas where human infections of unknown source were found. This difference in the association between human and animals and the virus may offer further insights to better understand how EVD can spread, as well as providing the basis for an early warning system based on where human contact with multiple animal species may occur. We propose a biogeographically-justified list of at least 64 mammal species whose link with Ebola virus is worth investigating

Algorithms for Hierarchical Clustering: An Overview, II

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm. This review adds to the earlier version, Murtagh and Contreras (2012)

Comparison of Similarity Coefficients used for Cluster Analysis with Amplified Fragment Length Polymorphism Markers in the Silkworm, Bombyx mori

Establishing accurate genetic similarity and dissimilarity between individuals is an essential and decisive point for clustering and analyzing inter and intra population diversity because different similarity and dissimilarity indices may yield contradictory outcomes. We assessed the variations caused by three commonly used similarity coefficients including Jaccard, Sorensen-Dice and Simple matching in the clustering and ordination of seven Iranian native silkworm, Bombyx mori L. (Lepidoptera: Bombycidae), strains analyzed by amplified fragment length polymorphism markers. Comparisons among the similarity coefficients were made using the Spearman correlation analysis, dendrogram evaluation (visual inspection and consensus fork index - CIC), projection efficiency in a two-dimensional space, and groups formed by the Tocher optimization procedure. The results demonstrated that for almost all methodologies, the Jaccard and Sorensen-Dice coefficients revealed extremely close results, because both of them exclude negative co-occurrences. Due to the fact that there is no guarantee that the DNA regions with negative cooccurrences between two strains are indeed identical, the use of coefficients such as Jaccard and Sorensen-Dice that do not include negative co-occurrences was imperative for closely related organisms

Regionalização para o cultivo do feijão no Rio Grande do Sul com base na interação genótipo x ambiente¹.

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Genetic variation of wild and hatchery populations of the catla Indian major carp (Catla catla Hamilton 1822: Cypriniformes, Cyprinidae) revealed by RAPD markers

Genetic variation is a key component for improving a stock through selective breeding programs. Randomly amplified polymorphic DNA (RAPD) markers were used to assess genetic variation in three wild population of the catla carp (Catla catla Hamilton 1822) in the Halda, Jamuna and Padma rivers and one hatchery population in Bangladesh. Five decamer random primers were used to amplify RAPD markers from 30 fish from each population. Thirty of the 55 scorable bands were polymorphic, indicating some degree of genetic variation in all the populations. The proportion of polymorphic loci and gene diversity values reflected a relatively higher level of genetic variation in the Halda population. Sixteen of the 30 polymorphic loci showed a significant (p < 0.05, p < 0.01, p < 0.001) departure from homogeneity and the FST values in the different populations indicated some degree of genetic differentiation in the population pairs. Estimated genetic distances between populations were directly correlated with geographical distances. The unweighted pair group method with averages (UPGMA) dendrogram showed two clusters, the Halda population forming one cluster and the other populations the second cluster. Genetic variation of C. catla is a useful trait for developing a good management strategy for maintaining genetic quality of the species

History and structure of the closed pedigreed population of Icelandic Sheepdogs

<p>Abstract</p> <p>Background</p> <p>Dog breeds lose genetic diversity because of high selection pressure. Breeding policies aim to minimize kinship and therefore maintain genetic diversity. However, policies like mean kinship and optimal contributions, might be impractical. Cluster analysis of kinship can elucidate the population structure, since this method divides the population in clusters of related individuals. Kinship-based analyses have been carried out on the entire Icelandic Sheepdog population, a sheep-herding breed.</p> <p>Results</p> <p>Analyses showed that despite increasing population size and deliberately transferring dogs, considerable genetic diversity has been lost. When cluster analysis was based on kinships calculated seven generation backwards, as performed in previous studies, results differ markedly from those based on calculations going back to the founder-population, and thus invalidate recommendations based on previous research. When calculated back to the founder-population, kinship-based clustering reveals the distribution of genetic diversity, similarly to strategies using mean kinship.</p> <p>Conclusion</p> <p>Although the base population consisted of 36 Icelandic Sheepdog founders, the current diversity is equivalent to that of only 2.2 equally contributing founders with no loss of founder alleles in descendants. The maximum attainable diversity is 4.7, unlikely achievable in a non-supervised breeding population like the Icelandic Sheepdog. Cluster analysis of kinship coefficients can provide a supporting tool to assess the distribution of available genetic diversity for captive population management.</p

New resampling method for evaluating stability of clusters

<p>Abstract</p> <p>Background</p> <p>Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in clustering procedures. Statistical methods are required to distinguish between real and random clusters. Several methods for assessing cluster stability have been published, including resampling methods such as the bootstrap.</p> <p>We propose a new resampling method based on continuous weights to assess the stability of clusters in hierarchical clustering. While in bootstrapping approximately one third of the original items is lost, continuous weights avoid zero elements and instead allow non integer diagonal elements, which leads to retention of the full dimensionality of space, i.e. each variable of the original data set is represented in the resampling sample.</p> <p>Results</p> <p>Comparison of continuous weights and bootstrapping using real datasets and simulation studies reveals the advantage of continuous weights especially when the dataset has only few observations, few differentially expressed genes and the fold change of differentially expressed genes is low.</p> <p>Conclusion</p> <p>We recommend the use of continuous weights in small as well as in large datasets, because according to our results they produce at least the same results as conventional bootstrapping and in some cases they surpass it.</p