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1.  HIV-1 Integrates Widely throughout the Genome of the Human Blood Fluke Schistosoma mansoni 
PLoS Pathogens  2016;12(10):e1005931.
Schistosomiasis is the most important helminthic disease of humanity in terms of morbidity and mortality. Facile manipulation of schistosomes using lentiviruses would enable advances in functional genomics in these and related neglected tropical diseases pathogens including tapeworms, and including their non-dividing cells. Such approaches have hitherto been unavailable. Blood stream forms of the human blood fluke, Schistosoma mansoni, the causative agent of the hepatointestinal schistosomiasis, were infected with the human HIV-1 isolate NL4-3 pseudotyped with vesicular stomatitis virus glycoprotein. The appearance of strong stop and positive strand cDNAs indicated that virions fused to schistosome cells, the nucleocapsid internalized and the RNA genome reverse transcribed. Anchored PCR analysis, sequencing HIV-1-specific anchored Illumina libraries and Whole Genome Sequencing (WGS) of schistosomes confirmed chromosomal integration; >8,000 integrations were mapped, distributed throughout the eight pairs of chromosomes including the sex chromosomes. The rate of integrations in the genome exceeded five per 1,000 kb and HIV-1 integrated into protein-encoding loci and elsewhere with integration bias dissimilar to that of human T cells. We estimated ~ 2,100 integrations per schistosomulum based on WGS, i.e. about two or three events per cell, comparable to integration rates in human cells. Accomplishment in schistosomes of post-entry processes essential for HIV-1replication, including integrase-catalyzed integration, was remarkable given the phylogenetic distance between schistosomes and primates, the natural hosts of the genus Lentivirus. These enigmatic findings revealed that HIV-1 was active within cells of S. mansoni, and provided the first demonstration that HIV-1 can integrate into the genome of an invertebrate.
Author Summary
Schistosomiasis is a major neglected tropical disease (NTD), which afflicts > 200 million people in developing countries. The genome sequence of the schistosome parasite has been decoded; it includes > 10,000 genes. New approaches to control this NTD are sought and genomic information may provide targets for new treatments. Methods to determine the role and importance of specific genes would facilitate these tasks. The retrovirus HIV-1, the causative agent of HIV/AIDS, has been extensively studied and modified for use in biomedical research. Using a lab-modified form of HIV-1, we manipulated the genome of Schistosoma mansoni, one of the major species of schistosomes. Lab-modified HIV-1 infected schistosomes and inserted in the chromosomes of the parasite. These chromosomal insertions were mapped using next generation sequencing and were distributed throughout the chromosomes including the sex chromosomes. The findings were notable since they revealed that HIV-1 was active within cells of S. mansoni, and they provide the first demonstration that HIV-1 can integrate into the genome of an invertebrate. They pave a route forward for investigating new therapies for schistosomiasis.
PMCID: PMC5072744  PMID: 27764257
2.  Whole genome resequencing of the human parasite Schistosoma mansoni reveals population history and effects of selection 
Scientific Reports  2016;6:20954.
Schistosoma mansoni is a parasitic fluke that infects millions of people in the developing world. This study presents the first application of population genomics to S. mansoni based on high-coverage resequencing data from 10 global isolates and an isolate of the closely-related Schistosoma rodhaini, which infects rodents. Using population genetic tests, we document genes under directional and balancing selection in S. mansoni that may facilitate adaptation to the human host. Coalescence modeling reveals the speciation of S. mansoni and S. rodhaini as 107.5–147.6KYA, a period which overlaps with the earliest archaeological evidence for fishing in Africa. Our results indicate that S. mansoni originated in East Africa and experienced a decline in effective population size 20–90KYA, before dispersing across the continent during the Holocene. In addition, we find strong evidence that S. mansoni migrated to the New World with the 16–19th Century Atlantic Slave Trade.
PMCID: PMC4754680  PMID: 26879532
3.  Susceptibility to klebsiella pneumonaie infection in collaborative cross mice is a complex trait controlled by at least three loci acting at different time points 
BMC Genomics  2014;15(1):865.
Klebsiella pneumoniae (Kp) is a bacterium causing severe pneumonia in immunocompromised hosts and is often associated with sepsis. With the rise of antibiotic resistant bacteria, there is a need for new effective and affordable control methods; understanding the genetic architecture of susceptibility to Kp will help in their development. We performed the first quantitative trait locus (QTL) mapping study of host susceptibility to Kp infection in immunocompetent Collaborative Cross mice (CC). We challenged 328 mice from 73 CC lines intraperitoneally with 104 colony forming units of Kp strain K2. Survival and body weight were monitored for 15 days post challenge. 48 of the CC lines were genotyped with 170,000 SNPs, with which we mapped QTLs.
CC lines differed significantly (P < 0.05) in mean survival time, between 1 to 15 days post infection, and broad sense heritability was 0.45. Distinct QTL were mapped at specific time points during the challenge. A QTL on chromosome 4 was found only on day 2 post infection, and QTL on chromosomes 8 and 18, only on day 8. By using the sequence variations of the eight inbred strain founders of the CC to refine QTL localization we identify several candidate genes.
Host susceptibility to Kp is a complex trait, controlled by multiple genetic factors that act sequentially during the course of infection.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-865) contains supplementary material, which is available to authorized users.
PMCID: PMC4201739  PMID: 25283706
Klebsiella pneumoniae; Mouse model; Collaborative cross mice; Host susceptibility; QTL mapping; Candidate genes
4.  Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs 
Briefings in Bioinformatics  2011;13(2):135-142.
During a meeting of the SYSGENET working group ‘Bioinformatics’, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a ‘cloud’ should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.
PMCID: PMC3294237  PMID: 22396485
QTL mapping; database; mouse; systems genetics
5.  The effect of genome-wide association scan quality control on imputation outcome for common variants 
Imputation is an extremely valuable tool in conducting and synthesising genome-wide association studies (GWASs). Directly typed SNP quality control (QC) is thought to affect imputation quality. It is, therefore, common practise to use quality-controlled (QCed) data as an input for imputing genotypes. This study aims to determine the effect of commonly applied QC steps on imputation outcomes. We performed several iterations of imputing SNPs across chromosome 22 in a dataset consisting of 3177 samples with Illumina 610k (Illumina, San Diego, CA, USA) GWAS data, applying different QC steps each time. The imputed genotypes were compared with the directly typed genotypes. In addition, we investigated the correlation between alternatively QCed data. We also applied a series of post-imputation QC steps balancing elimination of poorly imputed SNPs and information loss. We found that the difference between the unQCed data and the fully QCed data on imputation outcome was minimal. Our study shows that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome. A minority of common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency. These findings may not generalise to the imputation of low frequency and rare variants.
PMCID: PMC3083623  PMID: 21267008
genome-wide association study; imputation; quality control; single nucleotide polymorphism
6.  A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana 
PLoS Genetics  2009;5(7):e1000551.
Identifying natural allelic variation that underlies quantitative trait variation remains a fundamental problem in genetics. Most studies have employed either simple synthetic populations with restricted allelic variation or performed association mapping on a sample of naturally occurring haplotypes. Both of these approaches have some limitations, therefore alternative resources for the genetic dissection of complex traits continue to be sought. Here we describe one such alternative, the Multiparent Advanced Generation Inter-Cross (MAGIC). This approach is expected to improve the precision with which QTL can be mapped, improving the outlook for QTL cloning. Here, we present the first panel of MAGIC lines developed: a set of 527 recombinant inbred lines (RILs) descended from a heterogeneous stock of 19 intermated accessions of the plant Arabidopsis thaliana. These lines and the 19 founders were genotyped with 1,260 single nucleotide polymorphisms and phenotyped for development-related traits. Analytical methods were developed to fine-map quantitative trait loci (QTL) in the MAGIC lines by reconstructing the genome of each line as a mosaic of the founders. We show by simulation that QTL explaining 10% of the phenotypic variance will be detected in most situations with an average mapping error of about 300 kb, and that if the number of lines were doubled the mapping error would be under 200 kb. We also show how the power to detect a QTL and the mapping accuracy vary, depending on QTL location. We demonstrate the utility of this new mapping population by mapping several known QTL with high precision and by finding novel QTL for germination data and bolting time. Our results provide strong support for similar ongoing efforts to produce MAGIC lines in other organisms.
Author Summary
Most traits of economic and evolutionary interest vary quantitatively and have multiple genes affecting their expression. Dissecting the genetic basis of such traits is crucial for the improvement of crops and management of diseases. Here, we develop a new resource to identify genes underlying such quantitative traits in Arabidopsis thaliana, a genetic model organism in plants. We show that using a large population of inbred lines derived from intercrossing 19 parents, we can localize the genes underlying quantitative traits better than with existing methods. Using these lines, we were able to replicate the identification of previously known genes that affect developmental traits in A. thaliana and identify some new ones. This paper also presents all the necessary biological and computational material necessary for the scientific community to use these lines in their own research. Our results suggest that the use of lines derived from a multiparent advanced generation inter-cross (MAGIC lines) should be very useful in other organisms.
PMCID: PMC2700969  PMID: 19593375
7.  Linkage disequilibrium mapping via cladistic analysis of phase-unknown genotypes and inferred haplotypes in the Genetic Analysis Workshop 14 simulated data 
BMC Genetics  2005;6(Suppl 1):S100.
We recently described a method for linkage disequilibrium (LD) mapping, using cladistic analysis of phased single-nucleotide polymorphism (SNP) haplotypes in a logistic regression framework. However, haplotypes are often not available and cannot be deduced with certainty from the unphased genotypes. One possible two-stage approach is to infer the phase of multilocus genotype data and analyze the resulting haplotypes as if known. Here, haplotypes are inferred using the expectation-maximization (EM) algorithm and the best-guess phase assignment for each individual analyzed. However, inferring haplotypes from phase-unknown data is prone to error and this should be taken into account in the subsequent analysis. An alternative approach is to analyze the phase-unknown multilocus genotypes themselves. Here we present a generalization of the method for phase-known haplotype data to the case of unphased SNP genotypes. Our approach is designed for high-density SNP data, so we opted to analyze the simulated dataset. The marker spacing in the initial screen was too large for our method to be effective, so we used the answers provided to request further data in regions around the disease loci and in null regions. Power to detect the disease loci, accuracy in localizing the true site of the locus, and false-positive error rates are reported for the inferred-haplotype and unphased genotype methods. For this data, analyzing inferred haplotypes outperforms analysis of genotypes. As expected, our results suggest that when there is little or no LD between a disease locus and the flanking region, there will be no chance of detecting it unless the disease variant itself is genotyped.
PMCID: PMC1866839  PMID: 16451556

Résultats 1-7 (7)