4,518 research outputs found

    A signature of dynamic biogeography: enclaves indicate past species replacement

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    Understanding how species have replaced each other in the past is important to predicting future species turnover. While past species replacement is difficult to detect after the fact, the process may be inferred from present-day distribution patterns. Species with abutting ranges sometimes show a characteristic distribution pattern, where a section of one species range is enveloped by that of the other. Such an enclave could indicate past species replacement: when a species is partly supplanted by a competitor, but a population endures locally while the invading species moves around and past it, an enclave forms. If the two species hybridize and backcross, the receding species is predicted to leave genetic traces within the expanding one under a scenario of species replacement. By screening dozens of genes in hybridizing crested newts, we uncover genetic remnants of the ancestral species, now inhabiting an enclave, in the range of the surrounding invading species. This independent genetic evidence supports the past distribution dynamics we predicted from the enclave. We suggest that enclaves provide a valuable tool in understanding historical species replacement, which is important because a major conservation concern arising from anthropogenic climate change is increased species replacement in the future

    Molecular footprints of the Holocene retreat of dwarf birch in Britain

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    © 2014 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

    Comparison of Population-Based Association Study Methods Correcting for Population Stratification

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    Population stratification can cause spurious associations in population–based association studies. Several statistical methods have been proposed to reduce the impact of population stratification on population–based association studies. We simulated a set of stratified populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing population–based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various population stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of population stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified populations. It may be better to apply GC in the stratified populations with low stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies

    Microsatellite analysis of populations of the endangered tree Gomortega keule suggests pre-Columbian differentiation

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    Temperate forests have been affected extensively by human activities, resulting in land cover changes and population fragmentation. However, these anthropogenic effects can be superimposed onto the natural history of species, making it difficult to determine which effect is more important for a particular species. Gomortega keule is an endangered tree that is found in one of the world’s biodiversity hotspots in central–south Chile. Human activities have significantly impacted on the original habitat in this region in recent years and are commonly considered to be the main cause of the scarcity of this species. However, aspects of the natural history of this evergreen tree may also help to explain its present-day genetic structure. In this study, we undertook microsatellite genotyping of the two southernmost populations of G. keule, which are 7.5 km apart and well isolated from other populations. We found that there was genetic differentiation between these populations, suggesting that they exhibited at least some differentiation before becoming isolated, most likely before human activities first impacted the region some two centuries ago. Molecular estimates of their divergence time supported a more ancient differentiation of the populations than would be explained by human activities alone. It is possible that their isolation may have followed the extinction of megafaunal seed dispersers around 12,000 years before present in this region, as indicated by fruit characteristics, the absence of recruitment by seedlings and the existence of clonal trees

    Bacterial genospecies that are not ecologically coherent : population genomics of Rhizobium leguminosarum

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    Biological species may remain distinct because of genetic isolation or ecological adaptation, but these two aspects do not always coincide. To establish the nature of the species boundary within a local bacterial population, we characterized a sympatric population of the bacterium Rhizobium leguminosarum by genomic sequencing of 72 isolates. Although all strains have 16S rRNA typical of R. leguminosarum, they fall into five genospecies by the criterion of average nucleotide identity (ANI). Many genes, on plasmids as well as the chromosome, support this division: recombination of core genes has been largely within genospecies. Nevertheless, variation in ecological properties, including symbiotic host range and carbon-source utilization, cuts across these genospecies, so that none of these phenotypes is diagnostic of genospecies. This phenotypic variation is conferred by mobile genes. The genospecies meet the Mayr criteria for biological species in respect of their core genes, but do not correspond to coherent ecological groups, so periodic selection may not be effective in purging variation within them. The population structure is incompatible with traditional 'polyphasic taxonomy' that requires bacterial species to have both phylogenetic coherence and distinctive phenotypes. More generally, genomics has revealed that many bacterial species share adaptive modules by horizontal gene transfer, and we envisage a more consistent taxonomic framework that explicitly recognizes this. Significant phenotypes should be recognized as 'biovars' within species that are defined by core gene phylogeny

    Relationship between conservation biology and ecology shown through machine reading of 32,000 articles

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    Conservation biology was founded on the idea that efforts to save nature depend on a scientific understanding of how it works. It sought to apply ecological principles to conservation problems. We investigated whether the relationship between these fields has changed over time through machine reading the full texts of 32,000 research articles published in 16 ecology and conservation biology journals. We examined changes in research topics in both fields and how the fields have evolved from 2000 to 2014. As conservation biology matured, its focus shifted from ecology to social and political aspects of conservation. The 2 fields diverged and now occupy distinct niches in modern science. We hypothesize this pattern resulted from increasing recognition that social, economic, and political factors are critical for successful conservation and possibly from rising skepticism about the relevance of contemporary ecological theory to practical conservation

    Detecting functional rare variants by collapsing and incorporating functional annotation in Genetic Analysis Workshop 17 mini-exome data

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    Association studies using tag SNPs have been successful in detecting disease-associated common variants. However, common variants, with rare exceptions, explain only at most 5–10% of the heritability resulting from genetic factors, which leads to the common disease/rare variants assumption. Indeed, recent studies using sequencing technologies have demonstrated that common diseases can be due to rare variants that could not be systematically studied earlier. Unfortunately, methods for common variants are not optimal if applied to rare variants. To identify rare variants that affect disease risk, several investigators have designed new approaches based on the idea of collapsing different rare variants inside the same genomic block (e.g., the same gene or pathway) to enrich the signal. Here, we consider three different collapsing methods in the multimarker regression model and compared their performance on the Genetic Analysis Workshop 17 data using the consistency of results across different simulations and the cross-validation prediction error rate. The comparison shows that the proportion collapsing method seems to outperform the other two methods and can find both truly associated rare and common variants. Moreover, we explore one way of incorporating the functional annotations for the variants in the data that collapses nonsynonymous and synonymous variants separately to allow for different penalties on them. The incorporation of functional annotations led to higher sensitivity and specificity levels when the detection results were compared with the answer sheet. The initial analysis was performed without knowledge of the simulating model

    Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data

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    In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives
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