Sean D. Schoville
Other affiliations: University of Tsukuba, Scripps Institution of Oceanography, Centre national de la recherche scientifique ...read more
Bio: Sean D. Schoville is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Population & Colorado potato beetle. The author has an hindex of 23, co-authored 78 publications receiving 2508 citations. Previous affiliations of Sean D. Schoville include University of Tsukuba & Scripps Institution of Oceanography.
TL;DR: New algorithms based on population genetics, ecological modeling, and statistical learning techniques are proposed to screen genomes for signatures of local adaptation and demonstrate that LFMM can efficiently estimate random effects due to population history and isolation-by-distance patterns when computing gene-environment correlations.
Abstract: Adaptation to local environments often occurs through natural selection acting on a large number of loci, each having a weak phenotypic effect. One way to detect these loci is to identify genetic polymorphisms that exhibit high correlation with environmental variables used as proxies for ecological pressures. Here, we propose new algorithms based on population genetics, ecological modeling, and statistical learning techniques to screen genomes for signatures of local adaptation. Implemented in the computer program “latent factor mixed model” (LFMM), these algorithms employ an approach in which population structure is introduced using unobserved variables. These fast and computationally efficient algorithms detect correlations between environmental and genetic variation while simultaneously inferring background levels of population structure. Comparing these new algorithms with related methods provides evidence that LFMM can efficiently estimate random effects due to population history and isolation-by-distance patterns when computing gene-environment correlations, and decrease the number of false-positive associations in genome scans. We then apply these models to plant and human genetic data, identifying several genes with functions related to development that exhibit strong correlations with climatic gradients.
TL;DR: A review of the design of landscape genetics studies, the different statistical tools, some important case studies, and perspectives on how future advances in this field are likely to shed light on important processes in evolution and ecology are provided.
Abstract: There is a growing interest in identifying ecological factors that influence adaptive genetic diversity patterns in both model and nonmodel species. The emergence of large genomic and environmental data sets, as well as the increasing sophistication of population genetics methods, provides an opportunity to characterize these patterns in relation to the environment. Landscape genetics has emerged as a flexible analytical framework that connects patterns of adaptive genetic variation to environmental heterogeneity in a spatially explicit context. Recent growth in this field has led to the development of numerous spatial statistical methods, prompting a discussion of the current benefits and limitations of these approaches. Here we provide a review of the design of landscape genetics studies, the different statistical tools, some important case studies, and perspectives on how future advances in this field are likely to shed light on important processes in evolution and ecology.
University of Wisconsin-Madison1, University of Vermont2, Lund University3, University of Cincinnati4, University of Kiel5, North Dakota State University6, Ghent University7, Agricultural Research Service8, Moscow State University9, Prince of Songkla University10, Wayne State University11, University of Valencia12, Baylor College of Medicine13, National Institute of Science Education and Research14, University of Padua15, Polish Academy of Sciences16, Institut national de la recherche agronomique17, King Abdulaziz University18, Aix-Marseille University19, University of Kentucky20, Texas A&M University21, German Cancer Research Center22, Nanjing Agricultural University23, Skolkovo Institute of Science and Technology24, Illinois Institute of Technology25, University of Wisconsin–Oshkosh26, Lawrence Berkeley National Laboratory27, University of Cologne28, University of Warwick29, Max Planck Society30, University of Illinois at Urbana–Champaign31, Michigan State University32, Indiana University33, Cincinnati Children's Hospital Medical Center34, Ohio State University35
TL;DR: Surprisingly, the suite of genes involved in insecticide resistance is similar to other beetles, and duplications in the RNAi pathway might explain why Leptinotarsa decemlineata has high sensitivity to dsRNA.
Abstract: The Colorado potato beetle is one of the most challenging agricultural pests to manage. It has shown a spectacular ability to adapt to a variety of solanaceaeous plants and variable climates during its global invasion, and, notably, to rapidly evolve insecticide resistance. To examine evidence of rapid evolutionary change, and to understand the genetic basis of herbivory and insecticide resistance, we tested for structural and functional genomic changes relative to other arthropod species using genome sequencing, transcriptomics, and community annotation. Two factors that might facilitate rapid evolutionary change include transposable elements, which comprise at least 17% of the genome and are rapidly evolving compared to other Coleoptera, and high levels of nucleotide diversity in rapidly growing pest populations. Adaptations to plant feeding are evident in gene expansions and differential expression of digestive enzymes in gut tissues, as well as expansions of gustatory receptors for bitter tasting. Surprisingly, the suite of genes involved in insecticide resistance is similar to other beetles. Finally, duplications in the RNAi pathway might explain why Leptinotarsa decemlineata has high sensitivity to dsRNA. The L. decemlineata genome provides opportunities to investigate a broad range of phenotypes and to develop sustainable methods to control this widely successful pest.
TL;DR: This tutorial study clarifies the relationships between traditional methods based on allele frequency differentiation and EA methods and provides a unified framework for their underlying statistical tests, and demonstrates how techniques developed in the area of genomewide association studies, such as inflation factors and linear mixed models, benefit genome scan methods.
Abstract: Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on statistical models, these genomewide testing procedures have attracted considerable attention as tools to identify loci potentially targeted by natural selection. An important issue with PD and EA tests is that incorrect model specification can generate large numbers of false-positive associations. Spurious association may indeed arise when shared demographic history, patterns of isolation by distance, cryptic relatedness or genetic background are ignored. Recent works on PD and EA tests have widely focused on improvements of test corrections for those confounding effects. Despite significant algorithmic improvements, there is still a number of open questions on how to check that false discoveries are under control and implement test corrections, or how to combine statistical tests from multiple genome scan methods. This tutorial study provides a detailed answer to these questions. It clarifies the relationships between traditional methods based on allele frequency differentiation and EA methods and provides a unified framework for their underlying statistical tests. We demonstrate how techniques developed in the area of genomewide association studies, such as inflation factors and linear mixed models, benefit genome scan methods and provide guidelines for good practice while conducting statistical tests in landscape and population genomic applications. Finally, we highlight how the combination of several well-calibrated statistical tests can increase the power to reject neutrality, improving our ability to infer patterns of local adaptation in large population genomic data sets.
Seoul National University1, Swedish University of Agricultural Sciences2, University of California, Davis3, Cardiff University4, University of Burgundy5, Chinese Academy of Fishery Sciences6, Alaska Department of Fish and Game7, Nanjing Normal University8, Second Military Medical University9, Field Museum of Natural History10, Queen's University11, Spanish National Research Council12, University of Agricultural Sciences, Bangalore13, Vanderbilt University14, Uppsala University15, National Oceanic and Atmospheric Administration16, University of Windsor17, University of Ljubljana18, National Fisheries Research & Development Institute19, United States Department of Agriculture20, University of Southern Mississippi21, Ewha Womans University22, University of California, Berkeley23, Xiamen University24, University of Zagreb25, Shanghai Ocean University26, ETH Zurich27, California State University, Sacramento28, Clemson University29, National Research Council30, Tulane University31, Ocean University of China32
TL;DR: The addition of 411 microsatellite marker loci and 15 pairs of Single Nucleotide Polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database are documents.
Abstract: This article documents the addition of 411 microsatellite marker loci and 15 pairs of Single Nucleotide Polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database. Loci were developed for the following species: Acanthopagrus schlegeli, Anopheles lesteri, Aspergillus clavatus, Aspergillus flavus, Aspergillus fumigatus, Aspergillus oryzae, Aspergillus terreus, Branchiostoma japonicum, Branchiostoma belcheri, Colias behrii, Coryphopterus personatus, Cynogolssus semilaevis, Cynoglossus semilaevis, Dendrobium officinale, Dendrobium officinale, Dysoxylum malabaricum, Metrioptera roeselii, Myrmeciza exsul, Ochotona thibetana, Neosartorya fischeri, Nothofagus pumilio, Onychodactylus fischeri, Phoenicopterus roseus, Salvia officinalis L., Scylla paramamosain, Silene latifo, Sula sula, and Vulpes vulpes. These loci were cross-tested on the following species: Aspergillus giganteus, Colias pelidne, Colias interior, Colias meadii, Colias eurytheme, Coryphopterus lipernes, Coryphopterus glaucofrenum, Coryphopterus eidolon, Gnatholepis thompsoni, Elacatinus evelynae, Dendrobium loddigesii Dendrobium devonianum, Dysoxylum binectariferum, Nothofagus antarctica, Nothofagus dombeyii, Nothofagus nervosa, Nothofagus obliqua, Sula nebouxii, and Sula variegata. This article also documents the addition of 39 sequencing primer pairs and 15 allele specific primers or probes for Paralithodes camtschaticus.
28 Jul 2005
01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.
Abstract: The relationship between the two estimates of genetic variation at the DNA level, namely the number of segregating sites and the average number of nucleotide differences estimated from pairwise comparison, is investigated. It is found that the correlation between these two estimates is large when the sample size is small, and decreases slowly as the sample size increases. Using the relationship obtained, a statistical method for testing the neutral mutation hypothesis is developed. This method needs only the data of DNA polymorphism, namely the genetic variation within population at the DNA level. A simple method of computer simulation, that was used in order to obtain the distribution of a new statistic developed, is also presented. Applying this statistical method to the five regions of DNA sequences in Drosophila melanogaster, it is found that large insertion/deletion (greater than 100 bp) is deleterious. It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.
01 Jun 2012
TL;DR: SPAdes as mentioned in this paper is a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler and on popular assemblers Velvet and SoapDeNovo (for multicell data).
Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.