scholarly journals Genomic analysis of natural intra-specific hybrids among Ethiopian isolates of Leishmania donovani

2019 ◽  
Author(s):  
James A. Cotton ◽  
Caroline Durrant ◽  
Susanne U. Franssen ◽  
Tesfaye Gelanew ◽  
Asrat Hailu ◽  
...  

AbstractParasites of the genus Leishmania (Kinetoplastida: Trypanosomatidae) cause widespread and devastating human diseases, ranging from self-healing but disfiguring cutaneous lesions to destructive mucocutaneous presentations or usually fatal visceral disease. Visceral leishmaniasis due to Leishmania donovani is endemic in Ethiopia where it has also been responsible for major epidemics. The presence of hybrid genotypes has been widely reported in surveys of natural populations, genetic variation reported in a number of Leishmania species, and the extant capacity for genetic exchange demonstrated in laboratory experiments. However, patterns of recombination and evolutionary history of admixture that produced these hybrid populations remain unclear, as most of the relevant literature examines only a limited number (typically fewer than 10) genetic loci. Here, we use whole-genome sequence data to investigate Ethiopian L. donovani isolates previously characterised as hybrids by microsatellite and multi-locus sequencing. To date there is only one previous study on a natural population of Leishmania hybrids, based on whole-genome sequence. The current findings demonstrate important differences. We propose hybrids originate from recombination between two different lineages of Ethiopian L. donovani occurring in the same region. Patterns of inheritance are more complex than previously reported with multiple, apparently independent, origins from similar parents that include backcrossing with parental types. Analysis indicates that hybrids are representative of at least three different histories. Furthermore, isolates were highly polysomic at the level of chromosomes with startling differences between parasites recovered from a recrudescent infection from a previously treated individual. The results demonstrate that recombination is a significant feature of natural populations and contributes to the growing body of evidence describing how recombination, and gene flow, shape natural populations of Leishmania.Author SummaryLeishmaniasis is a spectrum of diseases caused by the protozoan parasite Leishmania. It is transmitted by sandfly insect vectors and is responsible for an enormous burden of human suffering. In this manuscript we examine Leishmania isolates from Ethiopia that cause the most serious form of the disease, namely visceral leishmaniasis, which is usually fatal without treatment. Historically the general view was that such parasites reproduce clonally, so that their progeny are genetically identical to the founding cells. This view has changed over time and it is increasingly clear that recombination between genetically different Leishmania parasites occurs. The implication is that new biological traits such as virulence, resistance to drug treatments or the ability to infect new species of sandfly could emerge. The frequency and underlying mechanism of such recombination in natural isolates is poorly understood. Here we perform a detailed whole genome analysis on a cohort of hybrid isolates from Ethiopia together with their potential parents to assess the genetic nature of hybrids in more detail. Results reveal a complex pattern of mating and inbreeding indicative of multiple mating events that has likely shaped the epidemiology of the disease agent. We also show that some hybrids have very different relative amounts of DNA (polysomy) the implications of which are discussed. Together the results contribute to a fuller understanding of the nature of genetic recombination in natural populations of Leishmania.

2010 ◽  
Vol 36 (4) ◽  
pp. 688-694
Author(s):  
Yi-Jun WANG ◽  
Yan-Ping LÜ ◽  
Qin XIE ◽  
De-Xiang DENG ◽  
Yun-Long BIAN

2014 ◽  
Vol 40 (12) ◽  
pp. 2059
Author(s):  
Lin-Yi QIAO ◽  
Xin LI ◽  
Zhi-Jian CHANG ◽  
Xiao-Jun ZHANG ◽  
Hai-Xian ZHAN ◽  
...  

IDCases ◽  
2020 ◽  
pp. e01034
Author(s):  
Charlie Tan ◽  
Fang-I Lu ◽  
Patryk Aftanas ◽  
Kara Tsang ◽  
Samira Mubareka ◽  
...  

Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online


2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Theo Meuwissen ◽  
Irene van den Berg ◽  
Mike Goddard

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.


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