scholarly journals GenVectors: an integrative analytical tool for spatial genetics

2018 ◽  
Author(s):  
Leandro Duarte ◽  
Jacqueline Souza Lima ◽  
Renan Maestri ◽  
Vanderlei Debastiani ◽  
Rosane Garcia Collevatti
Keyword(s):  
Genetic Divergence ◽  
R Package ◽  
Secondary Contact ◽  
Direct Inference ◽  
Environmental Correlates ◽  
Local Populations ◽  
Anthropogenic Habitat ◽  
Spatial Genetics ◽  
Alternative Processes ◽  
Analytical Tools

AbstractMetapopulations are sets of local populations connected by dispersal. While genetic turnover informs about the number of alleles shared by (meta)populations, a set of populations that do not share alleles with a second set may still show low genetic divergence to it. Recent secondary contact driven by anthropogenic habitat fragmentation and/or current climate change, for instance, may erase the historical track of genetic turnover. On the other hand, genetic turnover among sets of populations is expected to be related to the degree of genetic divergence among them if metapopulations become isolated from others due to vicariance or ancient dispersal. Yet, current analytical tools do not permit direct inference about alternative processes underlying spatial, environmental and/or biogeographic correlates of genetic turnover among populations. We introduce GenVectors, a new R package that offers flexible analytical tools that allow evaluating biogeographic or environmental correlates of genetic turnover among sets of local populations based on fuzzy set theory. Analyses implemented in GenVectors allow exploring the distribution of haplotypes or SNPs across sets of local populations. Moreover, GenVectors provides tools to analyze environmental or biogeographic correlates of haplotype or SNP turnover among sets of local populations by applying appropriate null models, which enable to discriminate history-driven genetic turnover (vicariance, ancient dispersal) from non-historical ones (recent secondary contact). Finally, we demonstrate the application of GenVectors in two empirical datasets, one based on single-locus marker (haplotypes) and other based on multi-loci marker (SNPs).

scholarly journals Null expectation of spatial correlograms under a stochastic process of genetic divergence with small sample sizes

2000 ◽  
Vol 23 (4) ◽  
pp. 739-743 ◽  
Author(s):  
Mariana Pires de Campos Telles ◽  
José Alexandre Felizola Diniz-Filho
Keyword(s):  
Stochastic Process ◽  
Genetic Divergence ◽  
Isolation By Distance ◽  
Genetic Distances ◽  
Small Sample ◽  
Group Method ◽  
Gene Frequencies ◽  
Exponential Relationship ◽  
Local Populations ◽  
Pair Group

An Ornstein-Uhlenbeck process was used to simulate the exponential relationship between genetic divergence and geographic distances, as predicted by stochastic processes of population differentiation, such as isolation-by-distance, stepping-stone or coalescence models. These simulations were based only on the spatial coordinates of the local populations that defined a spatial unweighted pair-group method using arithmetic averages (UPGMA) link among them. The simulated gene frequency surfaces were then analyzed using spatial autocorrelation procedures and Nei's genetic distances, constructed with different numbers of variables (gene frequencies). Stochastic divergence in space produced strong spatial patterns at univariate and mutivariate levels. Using a relatively small number of local populations, the correlogram profiles varied considerably, with Manhattan distances greater than those defined by other simulation studies. This method allows one to establish a range of correlogram profiles under the same stochastic process of spatial divergence, thereby avoiding the use of unnecessary explanations of genetic divergence based on other microevolutionary processes.

scholarly journals Rapid polygenic response to secondary contact in a hybrid species

2017 ◽  
Vol 284 (1853) ◽  
pp. 20170365 ◽  
Author(s):  
Glenn-Peter Sætre ◽  
Angélica Cuevas ◽  
Jo S. Hermansen ◽  
Tore O. Elgvin ◽  
Laura Piñeiro Fernández ◽  
...  
Keyword(s):  
Interspecific Competition ◽  
Neural Development ◽  
Genetic Divergence ◽  
Bird Species ◽  
Secondary Contact ◽  
Parent Species ◽  
Significant Drop ◽  
Genetic Changes ◽  
Hybrid Species ◽  
Feeding Site

Secondary contact between closely related species can have genetic consequences. Competition for essential resources may lead to divergence in heritable traits that reduces interspecific competition leading to increased rate of genetic divergence. Conversely, hybridization and backcrossing can lead to genetic convergence. Here, we study a population of a hybrid species, the Italian sparrow ( Passer italiae ), before and after it came into secondary contact with one of its parent species, the Spanish sparrow ( P. hispaniolensis ), in 2013. We demonstrate strong consequences of interspecific competition: Italian sparrows were kept away from a popular feeding site by its parent species, resulting in poorer body condition and a significant drop in population size. Although no significant morphological change could be detected, after only 3 years of sympatry, the Italian sparrows had diverged significantly from the Spanish sparrows across a set of 81 protein-coding genes. These temporal genetic changes are mirrored by genetic divergence observed in older sympatric Italian sparrow populations within the same area of contact. Compared with microallopatric birds, sympatric ones are genetically more diverged from Spanish sparrows. Six significant outlier genes in the temporal and spatial comparison (i.e. showing the greatest displacement) have all been found to be associated with learning and neural development in other bird species.

scholarly journals Genetic Divergence among Local Populations of the Japanese Freshwater Crab Geothelphusa Dehaani (Decapoda, Brachyura, Potamidae) from Southern Kyushu, Japan

2000 ◽  
Vol 20 (4) ◽  
pp. 759-768 ◽  
Author(s):  
Tomokazu Okano ◽  
Hiroshi Suzuki ◽  
Tomoyuki Miura ◽  
Youichi Hiwatashi ◽  
Fumiaki Nagoshi
Keyword(s):  
Genetic Divergence ◽  
Freshwater Crab ◽  
Local Populations

scholarly journals rTASSEL: an R interface to TASSEL for association mapping of complex traits

2020 ◽  
Author(s):  
Brandon Monier ◽  
Terry M. Casstevens ◽  
Edward S. Buckler
Keyword(s):  
Complex Traits ◽  
R Package ◽  
Genomic Diversity ◽  
Reproducible Research ◽  
Data Handling ◽  
Genetics Research ◽  
Front End ◽  
Genomic Analyses ◽  
Many Core ◽  
Analytical Tools

AbstractThe need for efficient tools and applications for analyzing genomic diversity is essential for any genetics research program. One such tool, TASSEL (Trait Analysis by aSSociation, Evolution and Linkage), provides many core methods for genomic analyses. Despite its efficiency, TASSEL has limited means for reproducible research and interacting with other analytical tools. Here we present an R package rTASSEL, a front-end to connect to a variety of highly used TASSEL methods and analytical tools. The goal of this package is to create a unified scripting workflow that exploits the analytical prowess of TASSEL in conjunction with R’s popular data handling and parsing capabilities without ever having the user to switch between these two environments.

scholarly journals Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R

2018 ◽  
Vol 7 (8) ◽  
pp. 293 ◽  
Author(s):  
Binbin Lu ◽  
Huabo Sun ◽  
Paul Harris ◽  
Miaozhong Xu ◽  
Martin Charlton
Keyword(s):  
Network Analysis ◽  
Road Network ◽  
R Package ◽  
Statistical Computing ◽  
Spatial Network ◽  
Computing Environment ◽  
Vast Array ◽  
Urban Road ◽  
Analytical Tools ◽  
Statistical Functions

In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

scholarly journals Genetic Divergence Across Glacial Refugia Despite Interglacial Gene Flow in a Crested Newt

2020 ◽  
Author(s):  
Ben Wielstra ◽  
Daniele Salvi ◽  
Daniele Canestrelli
Keyword(s):  
Species Distribution ◽  
Genetic Divergence ◽  
Nuclear Dna ◽  
Southern Italy ◽  
Glacial Refugia ◽  
Northern Italy ◽  
Secondary Contact ◽  
Gene Pools ◽  
Crested Newt ◽  
The Impact

Abstract MtDNA-based phylogeography has illuminated the impact of the Pleistocene Ice Age on species distribution dynamics and the build-up of genetic divergence. The well-known shortcomings of mtDNA in biogeographical inference can be compensated by integrating multilocus data and species distribution modelling into phylogeography. We re-visit the phylogeography of the Italian crested newt (Triturus carnifex), a species distributed in two of Europe’s main glacial refugia, the Balkan and Italian Peninsulas. While a new 51 nuclear DNA marker dataset supports the existence of three lineages previously suggested by mtDNA (Balkan, northern Italy and southern Italy), the nuclear DNA dataset also provides improved resolution where these lineages have obtained secondary contact. We observe geographically restricted admixture at the contact between the Balkan and northern Italy gene pools and identify a potential mtDNA ghost lineage here. At the contact between the northern and southern Italy gene pools we find admixture over a broader area, as well as asymmetric mtDNA introgression. Our species distribution model is in agreement with a distribution restricted to distinct refugia during Pleistocene glacial cycles and postglacial expansion with secondary contact. Our study supports: (1) the relevance of the north-western Balkan Peninsula as a discrete glacial refugium; (2) the importance of north-eastern Italy and the northern Apennine as suture zones; and (3) the applicability of a refugia-within-refugia scenario within the Italian Peninsula.

scholarly journals Prius: From Differentiated Genes to Affected Pathways

2016 ◽  
Author(s):  
Shailesh Patil ◽  
Bharath Venkatesh ◽  
Randeep Singh
Keyword(s):  
Gene Expression ◽  
Pathway Analysis ◽  
Cohort Analysis ◽  
Variant Calling ◽  
Interaction Network ◽  
R Package ◽  
Pathway Data ◽  
Number Of Genes ◽  
Differential Behaviour ◽  
Analytical Tools

AbstractExpression analysis and variant calling workflows are employed to identify genes that either exhibit a differential behaviour or have a significant functional impact of mutations. This is always followed by pathway analysis which provides greater insights and simplifies explanation of observed phenotype. The current techniques used towards this purpose have some serious limitations. Only a small number of genes which satisfy certain thresholds are used for pathway analysis. All the shortlisted genes are treated as equal ignoring the differences in p-values and fold changes. These genes are treated as independent entities and interactions among them are ignored for statistical pathway analysis. Hence, there is serious disconnect between the techniques employed and networked nature of the data. Various Pathway data ases have great degree of discordance on structure of pathway graphs. Many of the pathways are still far from complete. Current algorithms do not take into account this uncertainty. In this paper, we propose a theoretical framework Prius to overcome many limitations of current techniques. Prius perturbs the gene expression fold changes through interaction network and generates an ordered list of affected pathways. Thus, it integrates the networked nature of the data and provides facility to weigh each gene differently and in the process we do away with the need of arbitrary cut-offs. This framework is designed to be modular and provides the researchers with flexibility to plug analytical tools of their choice for every component. We also demonstrate effectiveness of our approach for personalized and cohort analysis of cancer gene expression samples with PageRank as one of the modules in the framework. The R package for Prius is available on GitHub.

scholarly journals Future-proofing code: Principles of coding for plant ecophysiology with {photosynthesis} as a case study

2020 ◽  
Author(s):  
Joseph R. Stinziano ◽  
Cassaundra Roback ◽  
Demi Gamble ◽  
Bridget K. Murphy ◽  
Patrick J. Hudson ◽  
...  
Keyword(s):  
Programming Languages ◽  
Software Maintenance ◽  
R Package ◽  
List Type ◽  
Plant Ecophysiology ◽  
Plant Physiological Ecology ◽  
Code Maintenance ◽  
Principles And Standards ◽  
Analytical Tools ◽  
Physical And Chemical

SummaryPlant physiological ecology is founded on a rich body of physical and chemical theory, but it is challenging to connect theory with data in unambiguous, analytically rigorous, and reproducible ways. Custom scripts written in computer programming languages (coding) enable plant ecophysiologists to model plant processes and fit models to data reproducibly using advanced statistical techniques. Since most ecophysiologists lack formal programming education, we have yet to adopt a unified set of coding principles and standards that could make coding easier to learn, use, and modify.We outline principles and standards for coding in plant ecophysiology to develop: 1) standardized nomenclature, 2) consistency in style, 3) increased modularity/extensibility for easier editing and understanding; 4) code scalability for application to large datasets, 5) documented contingencies for code maintenance; 6) documentation to facilitate user understanding; and 7) extensive tutorials for biologists new to coding to rapidly become proficient with software.We illustrate these principles using a new R package, {photosynthesis}, designed to provide a set of analytical tools for plant ecophysiology.Our goal with these principles is to future-proof coding efforts to ensure new advances and analytical tools can be rapidly incorporated into the field, while ensuring software maintenance across scientific generations.

scholarly journals Exploratory movement analysis and report building with R package stmove

2019 ◽  
Author(s):  
Dana P. Seidel ◽  
Eric R. Dougherty ◽  
Wayne M. Getz
Keyword(s):  
Best Practice ◽  
Animal Movement ◽  
Movement Ecology ◽  
Movement Analysis ◽  
R Package ◽  
Movement Data ◽  
Starting Point ◽  
Terrestrial Movement ◽  
Using Data ◽  
Analytical Tools

AbstractBackgroundAs GPS tags and data loggers have become lighter, cheaper, and longer-lasting, there has been a growing influx of data on animal movement. Simultaneously, methods of analyses and software to apply such methods to movement data have expanded dramatically. Even so, for many interdisciplinary researchers and managers without familiarity with the field of movement ecology and the open-source tools that have been developed, the analysis of movement data has remained an overwhelming challenge.DescriptionHere we present stmove, an R package designed to take individual relocation data and generate a visually rich report containing a set of preliminary results that ecologists and managers can use to guide further exploration of their data. Not only does this package make report building and exploratory data analysis (EDA) simple for users who may not be familiar with the extent of available analytical tools, but it sets forth a framework of best practice analyses, which offers a common starting point for the interpretation of terrestrial movement data.ResultsUsing data from African elephants (Loxodonta africana) collected in southern Africa, we demonstrate stmove’s report building function through the main analyses included: path visualization, primary statistic calculation, summary in space and time, and space-use construction.ConclusionsThe stmove package provides consistency and increased accessibility to managers and researchers who are interested in movement analysis but who may be unfamiliar with the full scope of movement packages and analytical tools. If widely adopted, the package will promote comparability of results across movement ecology studies.

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