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      Two novel loci underlie natural differences in Caenorhabditis elegans abamectin responses

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          Abstract

          Parasitic nematodes cause a massive worldwide burden on human health along with a loss of livestock and agriculture productivity. Anthelmintics have been widely successful in treating parasitic nematodes. However, resistance is increasing, and little is known about the molecular and genetic causes of resistance for most of these drugs. The free-living roundworm Caenorhabditis elegans provides a tractable model to identify genes that underlie resistance. Unlike parasitic nematodes, C. elegans is easy to maintain in the laboratory, has a complete and well annotated genome, and has many genetic tools. Using a combination of wild isolates and a panel of recombinant inbred lines constructed from crosses of two genetically and phenotypically divergent strains, we identified three genomic regions on chromosome V that underlie natural differences in response to the macrocyclic lactone (ML) abamectin. One locus was identified previously and encodes an alpha subunit of a glutamate-gated chloride channel ( glc-1). Here, we validate and narrow two novel loci using near-isogenic lines. Additionally, we generate a list of prioritized candidate genes identified in C. elegans and in the parasite Haemonchus contortus by comparison of ML resistance loci. These genes could represent previously unidentified resistance genes shared across nematode species and should be evaluated in the future. Our work highlights the advantages of using C. elegans as a model to better understand ML resistance in parasitic nematodes.

          Author summary

          Parasitic nematodes infect plants, animals, and humans, causing major health and economic burdens worldwide. Parasitic nematode infections are generally treated efficiently with a class of drugs named anthelmintics. However, resistance to many of these anthelmintic drugs, including macrocyclic lactones (MLs), is rampant and increasing. Therefore, it is essential that we understand how these drugs act against parasitic nematodes and, conversely, how nematodes gain resistance in order to better treat these infections in the future. Here, we used the non-parasitic nematode Caenorhabditis elegans as a model organism to study ML resistance. We leveraged natural genetic variation between strains of C. elegans with differential responses to abamectin to identify three genomic regions on chromosome V, each containing one or more genes that contribute to the ML response. Two of these loci have not been previously discovered and likely represent novel resistance mechanisms. We also compared the genes in these two novel loci to the genes found within genomic regions linked to ML resistance in the parasite Haemonchus contortus and found several cases of overlap between the two species. Overall, this study highlights the advantages of using C. elegans to understand anthelmintic resistance in parasitic nematodes.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Investigation
                Role: Data curationRole: Investigation
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Investigation
                Role: Data curationRole: Investigation
                Role: Data curationRole: Investigation
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                15 March 2021
                March 2021
                : 17
                : 3
                : e1009297
                Affiliations
                [1 ] Molecular Biosciences, Northwestern University, Evanston, Illinois, United States of America
                [2 ] Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, Illinois, United States of America
                National Institutes of Health, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                [¤a]

                Current Address: Tree of Life, Wellcome Sanger Institute, Cambridge, United Kingdom

                [¤b]

                Current Address: Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, Wisconsin, United States of America

                Author information
                https://orcid.org/0000-0002-1388-8155
                https://orcid.org/0000-0002-3116-744X
                https://orcid.org/0000-0002-6075-8273
                https://orcid.org/0000-0002-5282-0815
                https://orcid.org/0000-0002-7158-1637
                https://orcid.org/0000-0001-9233-1760
                https://orcid.org/0000-0002-6938-0211
                https://orcid.org/0000-0002-7253-8390
                https://orcid.org/0000-0002-0611-3909
                https://orcid.org/0000-0003-0229-9651
                Article
                PPATHOGENS-D-21-00076
                10.1371/journal.ppat.1009297
                7993787
                33720993
                d66b8a6e-e38b-4759-ba60-4922e9e64627
                © 2021 Evans et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 January 2021
                : 2 March 2021
                Page count
                Figures: 5, Tables: 1, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: AI121836
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: AI153088
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: SFARI 597491-RWC
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1764421
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T32 GM008061
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: HA 8449/1-1
                Award Recipient :
                This work was supported by National Institutes of Health NIAID grants R21 AI121836 and R01 AI153088 to E.C.A. K.S.E. was supported by the NSF-Simons Center for Quantitative Biology ( https://www.quantitativebiology.northwestern.edu/) at Northwestern University (awards Simons Foundation/SFARI 597491-RWC ( https://www.simonsfoundation.org/) and the National Science Foundation ( https://www.nsf.gov/) 1764421) and the Cell and Molecular Basis of Disease Training grant ( https://sites.northwestern.edu/cmbd/) (T32-GM008061). S.R.H. was funded by a DFG fellowship (HA 8449/1-1) from the Deutsche Forschungsgemeinschaft. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Quantitative Trait Loci
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Caenorhabditis Elegans
                Research and Analysis Methods
                Model Organisms
                Caenorhabditis Elegans
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Animal Models
                Caenorhabditis Elegans
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Nematoda
                Caenorhabditis
                Caenorhabditis Elegans
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Nematoda
                Caenorhabditis
                Caenorhabditis Elegans
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Gene Mapping
                Linkage Mapping
                Research and Analysis Methods
                Molecular Biology Techniques
                Gene Mapping
                Linkage Mapping
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Genetics
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Genetic Polymorphism
                Medicine and Health Sciences
                Medical Conditions
                Parasitic Diseases
                Nematode Infections
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-03-25
                All relevant data are within the manuscript and its Supporting Information files. Additionally, All data and scripts to generate figures can be found at https://github.com/AndersenLab/abamectin_manuscript.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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