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      Genome-Wide Analysis of Genetic Diversity in Plasmodium falciparum Isolates From China–Myanmar Border

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          Abstract

          Plasmodium falciparum isolates from China–Myanmar border (CMB) have experienced regional special selective pressures and adaptive evolution. However, the genomes of P. falciparum isolates from this region to date are poorly characterized. Herein, we performed whole-genome sequencing of 34 P. falciparum isolates from CMB and a series of genome-wide sequence analyses to reveal their genetic diversity, population structures, and comparisons with the isolates from other epidemic regions (Thai–Cambodia border, Thai–Myanmar border, and West Africa). Totally 59,720 high-quality single-nucleotide polymorphisms (SNPs) were identified in the P. falciparum isolates from CMB, with average nucleotide diversity (π = 4.59 × 10 −4) and LD decay (132 bp). The Tajima’s D and Fu and Li’s D values of the CMB isolates were −0.8 ( p < 0.05) and −0.84 ( p < 0.05), respectively, suggesting a demographic history of recent population expansion or purifying selection. Moreover, 78 genes of the parasite were identified that could be under positive selection, including those genes conferring drug resistance such as pfubp1. In addition, 33 SNPs were identified for tracing the source of the parasites with a high accuracy by analysis of the most differential SNPs among the four epidemic regions. Collectively, our data demonstrated high diversity of the CMB isolates’ genomes forming a distinct population, and the identification of 33-SNP barcode provides a valuable surveillance of parasite migration among the regions.

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          Most cited references25

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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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            Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing

            Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance. 1,2 Here we describe methods for large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short term culture. Analysis of 86,158 exonic SNPs that passed genotyping quality control in 227 samples from Africa, Asia and Oceania provides genome-wide estimates of allele frequency distribution, population structure and linkage disequilibrium. By comparing the genetic diversity of individual infections with that of the local parasite population, we derive a metric of within-host diversity that is related to the level of inbreeding in the population. An open-access web application has been established for exploration of regional differences in allele frequency and of highly differentiated loci in the P. falciparum genome.
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              rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure.

              With the development of next-generation sequencing and genotyping approaches, large single nucleotide polymorphism haplotype datasets are becoming available in a growing number of both model and non-model species. Identifying genomic regions with unexpectedly high local haplotype homozygosity relatively to neutral expectation represents a powerful strategy to ascertain candidate genes responding to natural or artificial selection. To facilitate genome-wide scans of selection based on the analysis of long-range haplotypes, we developed the R package rehh. It provides a versatile tool to detect the footprints of recent or ongoing selection with several graphical functions that help visual interpretation of the results. Stable version is available from CRAN: http://cran.r-project.org/. Development version is available from the R-forge repository: http://r-forge.r-project.org/projects/rehh. Both versions can be installed directly from R. Function documentation and example data files are provided within the package and a tutorial is available as Supplementary Material. rehh is distributed under the GNU General Public Licence (GPL ≥ 2).
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                29 October 2019
                2019
                : 10
                : 1065
                Affiliations
                [1] 1Department of Tropical Diseases, Naval Medical University , Shanghai, China
                [2] 2Yunnan Institute of Parasitic Diseases , Puer, China
                Author notes

                Edited by: Jacob A. Tennessen, Harvard University, United States

                Reviewed by: Xue Li, Texas Biomedical Research Institute,United States; Kaitlin M. Bonner, St. John Fisher College, United States; Shannon Takala Harrison, University of Maryland School of Medicine, United States

                *Correspondence: Dongmei Zhang, dmzhangcn@ 123456163.com ; Weiqing Pan, wqpan0912@ 123456aliyun.com

                This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics

                †These authors have contributed equally to this work

                Article
                10.3389/fgene.2019.01065
                6830057
                31737048
                993bffbe-67eb-40eb-8ec8-ff25d1e388b3
                Copyright © 2019 Ye, Tian, Huang, Zhang, Wang, Sun, Zhou, Zhang and Pan

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 June 2019
                : 03 October 2019
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 41, Pages: 8, Words: 4347
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81601780, 81220108019
                Categories
                Genetics
                Original Research

                Genetics
                plasmodium falciparum,genomes,china–myanmar border,diversity,genetic marker
                Genetics
                plasmodium falciparum, genomes, china–myanmar border, diversity, genetic marker

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