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      Deep sampling of Hawaiian Caenorhabditis elegans reveals high genetic diversity and admixture with global populations

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          Abstract

          Hawaiian isolates of the nematode species Caenorhabditis elegans have long been known to harbor genetic diversity greater than the rest of the worldwide population, but this observation was supported by only a small number of wild strains. To better characterize the niche and genetic diversity of Hawaiian C. elegans and other Caenorhabditis species, we sampled different substrates and niches across the Hawaiian islands. We identified hundreds of new Caenorhabditis strains from known species and a new species, Caenorhabditis oiwi. Hawaiian C. elegans are found in cooler climates at high elevations but are not associated with any specific substrate, as compared to other Caenorhabditis species. Surprisingly, admixture analysis revealed evidence of shared ancestry between some Hawaiian and non-Hawaiian C. elegans strains. We suggest that the deep diversity we observed in Hawaii might represent patterns of ancestral genetic diversity in the C. elegans species before human influence.

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R

            Although many computer programs can perform population genetics calculations, they are typically limited in the analyses and data input formats they offer; few applications can process the large data sets produced by whole-genome resequencing projects. Furthermore, there is no coherent framework for the easy integration of new statistics into existing pipelines, hindering the development and application of new population genetics and genomics approaches. Here, we present PopGenome, a population genomics package for the R software environment (a de facto standard for statistical analyses). PopGenome can efficiently process genome-scale data as well as large sets of individual loci. It reads DNA alignments and single-nucleotide polymorphism (SNP) data sets in most common formats, including those used by the HapMap, 1000 human genomes, and 1001 Arabidopsis genomes projects. PopGenome also reads associated annotation files in GFF format, enabling users to easily define regions or classify SNPs based on their annotation; all analyses can also be applied to sliding windows. PopGenome offers a wide range of diverse population genetics analyses, including neutrality tests as well as statistics for population differentiation, linkage disequilibrium, and recombination. PopGenome is linked to Hudson’s MS and Ewing’s MSMS programs to assess statistical significance based on coalescent simulations. PopGenome’s integration in R facilitates effortless and reproducible downstream analyses as well as the production of publication-quality graphics. Developers can easily incorporate new analyses methods into the PopGenome framework. PopGenome and R are freely available from CRAN (http://cran.r-project.org/) for all major operating systems under the GNU General Public License.
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              Genome evolution across 1,011 Saccharomyces cerevisiae isolates

              Large-scale population genomic surveys are essential to explore the phenotypic diversity of natural populations. Here we report the whole-genome sequencing and phenotyping of 1,011 Saccharomyces cerevisiae isolates, which together provide an accurate evolutionary picture of the genomic variants that shape the species-wide phenotypic landscape of this yeast. Genomic analyses support a single ‘out-of-China’ origin for this species, followed by several independent domestication events. Although domesticated isolates exhibit high variation in ploidy, aneuploidy and genome content, genome evolution in wild isolates is mainly driven by the accumulation of single nucleotide polymorphisms. A common feature is the extensive loss of heterozygosity, which represents an essential source of inter-individual variation in this mainly asexual species. Most of the single nucleotide polymorphisms, including experimentally identified functional polymorphisms, are present at very low frequencies. The largest numbers of variants identified by genome-wide association are copy-number changes, which have a greater phenotypic effect than do single nucleotide polymorphisms. This resource will guide future population genomics and genotype–phenotype studies in this classic model system.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                03 December 2019
                2019
                : 8
                : e50465
                Affiliations
                [1 ]deptDepartment of Molecular Biosciences Northwestern University EvanstonUnited States
                [2 ]deptInterdisciplinary Biological Sciences Program Northwestern University EvanstonUnited States
                [3 ]deptDepartment of Biology New York University New YorkUnited States
                University of California, Davis United States
                Max-Planck Institute for Evolutionary Biology Germany
                University of California, Davis United States
                University of Toronto
                Author information
                https://orcid.org/0000-0002-5645-4154
                http://orcid.org/0000-0003-2883-4616
                http://orcid.org/0000-0003-1588-4884
                https://orcid.org/0000-0003-0229-9651
                Article
                50465
                10.7554/eLife.50465
                6927746
                31793880
                1cdea668-d599-4b8c-a8fd-5ee9d5c3c69a
                © 2019, Crombie et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 23 July 2019
                : 02 December 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB 0922012
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Evolutionary Biology
                Genetics and Genomics
                Custom metadata
                Hawaiian Caenorhabditis elegans harbor high levels of genetic diversity that might represent the complex patterns of ancestral diversity in the species prior to human influence.

                Life sciences
                caenorhabditis elegans,hawaii,caenorhabditis oiwi,genetic diversity,niche,admixture,other
                Life sciences
                caenorhabditis elegans, hawaii, caenorhabditis oiwi, genetic diversity, niche, admixture, other

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