FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2886513277> ?p ?o ?g. }

• W2886513277 endingPage "789" @default.
• W2886513277 startingPage "777" @default.
• W2886513277 abstract "Viral infections are complex traits that are influenced by viral and environmental as well as host factors. Complete knockouts of genes are rare in humans whereas natural variation at the nucleotide level is abundant. Thus, successful translation from mice to humans is more likely working with natural variation in mouse populations. The Collaborative Cross is a mouse genetic reference population that is well suited to be utilized to identify networks of host genetics key players that influence complex traits such as viral infections. Indefinitely reproducible mouse strains with fully sequenced genomes offer the chance for wide collaborations across pathogens. Additionally, it offers the ability to identify cross-pathogen susceptibility or resistance alleles. The laboratory mouse has proved an invaluable model to identify host factors that regulate the progression and outcome of virus-induced disease. The paradigm is to use single-gene knockouts in inbred mouse strains or genetic mapping studies using biparental mouse populations. However, genetic variation among these mouse strains is limited compared with the diversity seen in human populations. To address this disconnect, a multiparental mouse population has been developed to specifically dissect the multigenetic regulation of complex disease traits. The Collaborative Cross (CC) population of recombinant inbred mouse strains is a well-suited systems-genetics tool to identify susceptibility alleles that control viral and microbial infection outcomes and immune responses and to test the promise of personalized medicine. The laboratory mouse has proved an invaluable model to identify host factors that regulate the progression and outcome of virus-induced disease. The paradigm is to use single-gene knockouts in inbred mouse strains or genetic mapping studies using biparental mouse populations. However, genetic variation among these mouse strains is limited compared with the diversity seen in human populations. To address this disconnect, a multiparental mouse population has been developed to specifically dissect the multigenetic regulation of complex disease traits. The Collaborative Cross (CC) population of recombinant inbred mouse strains is a well-suited systems-genetics tool to identify susceptibility alleles that control viral and microbial infection outcomes and immune responses and to test the promise of personalized medicine. Viral infections pose a major threat to human and animal health, causing significant morbidity and mortality every year. During the past decades, the emergence of several highly pathogenic zoonotic viruses has demonstrated the fragility of the species barrier in protecting human and farm animal populations from pathogens that exist in the animal kingdom. While basic virological research offers the possibility to detect viral strains that are ‘poised’ for emergence and identify mutations that might promote pathogen emergence, accurate prognoses are confounded by our inability to predict disease severity and virulence. Importantly, viruses with identical genome sequences do not always cause the same set of clinical manifestations in humans. Moreover, the complex interplay between environmental, viral, and host genetic factors drives differences in interindividual disease progression, severity, and outcome. These factors change over the course of a lifetime and some, like individual health status, comorbidities, and environmental factors [1Gautret P. et al.Emerging viral respiratory tract infections – environmental risk factors and transmission.Lancet Infect. Dis. 2014; 14: 1113-1122Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar, 2Weiss R.A. McMichael A.J. Social and environmental risk factors in the emergence of infectious diseases.Nat. Med. 2004; 10: S70-S76Crossref PubMed Scopus (449) Google Scholar], are difficult if not impossible to control. However, perhaps one of the most important key players in the fragile balance of microbial pathogenesis centers around host genetic susceptibility alleles that dramatically influence the course of disease in different individuals. In humans, a growing number of genetic factors like entry receptors, receptor-modifying enzymes, and innate and adaptive immune-related proteins that regulate influenza virus, norovirus, rotavirus, respiratory syncytial virus (RSV), HIV, hepatitis B and C viruses, herpes virus, and other acute and chronic virus disease outcomes have been identified (Table 1; more detailed list in [3Kenney A.D. et al.Human genetic determinants of viral diseases.Annu. Rev. Genet. 2017; 51: 241-263Crossref PubMed Scopus (88) Google Scholar]).Table 1Genes with Significant Associations with Viral Disease in HumansPathogenPhenotypeCausal geneRefsDengue virus (DENV)DENV shock syndromeMICB, PLCE173Khor C.C. et al.Genome-wide association study identifies susceptibility loci for dengue shock syndrome at MICB and PLCE1.Nat. Genet. 2011; 43: 1139-1141Crossref PubMed Scopus (167) Google ScholarEpstein–Barr virus (EBV)EBNA-1 IgG titerHLA-DRB1, HLA-DQB174Rubicz R. et al.A genome-wide integrative genomic study localizes genetic factors influencing antibodies against Epstein–Barr virus nuclear antigen 1 (EBNA-1).PLoS Genet. 2013; 9e1003147Crossref PubMed Scopus (80) Google ScholarHepatitis B virus (HBV)Chronic infectionHLA-DPA1, HLA-DPB175Kamatani Y. et al.A genome-wide association study identifies variants in the HLA-DP locus associated with chronic hepatitis B in Asians.Nat. Genet. 2009; 41: 591-595Crossref PubMed Scopus (439) Google ScholarPersistenceINST1076Li Y. et al.Genome-wide association study identifies 8p21.3 associated with persistent hepatitis B virus infection among Chinese.Nat. Commun. 2016; 7 (11664)Google ScholarHepatitis C virus (HCV)Spontaneous clearanceIL28B77Rauch A. et al.Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study.Gastroenterology. 2010; 138 (1345.e1–e7): 1338-1345Abstract Full Text Full Text PDF PubMed Scopus (1048) Google ScholarDevelopment of hepatocellular carcinomaTLL178Matsuura K. et al.Genome-wide association study identifies TLL1 variant associated with development of hepatocellular carcinoma after eradication of hepatitis C virus infection.Gastroenterology. 2017; 152: 1383-1394Abstract Full Text Full Text PDF PubMed Scopus (93) Google ScholarProgression to hepatocellular carcinomaDEPDC579Miki D. et al.Variation in the DEPDC5 locus is associated with progression to hepatocellular carcinoma in chronic hepatitis C virus carriers.Nat. Genet. 2011; 43: 797-800Crossref PubMed Scopus (147) Google ScholarHIV-1Viral loadHLA-B, HLA-C80Fellay J. et al.Common genetic variation and the control of HIV-1 in humans.PLoS Genet. 2009; 5e1000791Crossref PubMed Scopus (343) Google ScholarViral load controlHCP581International HIV Controllers Study et al.The major genetic determinants of HIV-1 control affect HLA class I peptide presentation.Science. 2010; 330: 1551-1557Crossref PubMed Scopus (934) Google ScholarInfluenza A virus (IAV)Reduced restriction of viral replicationIFITM382Everitt A.R. et al.IFITM3 restricts the morbidity and mortality associated with influenza.Nature. 2012; 484: 519-523Crossref PubMed Scopus (574) Google ScholarIncreased incidence and increased risk of viral pneumoniaTNF83Antonopoulou A. et al.Role of tumor necrosis factor gene single nucleotide polymorphisms in the natural course of 2009 influenza A H1N1 virus infection.Int. J. Infect. Dis. 2012; 16: e204-e208Abstract Full Text Full Text PDF PubMed Scopus (37) Google ScholarNorwalk virus (NoV)ResistanceFUT284Lindesmith L. et al.Human susceptibility and resistance to Norwalk virus infection.Nat. Med. 2003; 9: 548-553Crossref PubMed Scopus (863) Google ScholarRespiratory syncytial virus (RSV)BronchiolitisSFPA/D85Lahti M. et al.Surfactant protein D gene polymorphism associated with severe respiratory syncytial virus infection.Pediatr. Res. 2016; 51: 696-699Crossref Scopus (200) Google Scholar, 86Lofgren J. et al.Association between surfactant protein A gene locus and severe respiratory syncytial virus infection in infants.J. Infect. Dis. 2002; 185: 283-289Crossref PubMed Scopus (149) Google ScholarWest Nile virus (WNV)ResistanceCCR587Glass W.G. et al.CCR5 deficiency increases risk of symptomatic West Nile virus infection.J. Exp. Med. 2006; 203: 35-40Crossref PubMed Scopus (412) Google Scholar Open table in a new tab Accordingly, research on host genetics is a promising tool for understanding susceptibility and virulence patterns in human populations and refining pandemic-preparedness efforts. Furthermore, the discovery of innovative prophylactic or diagnostic and therapeutic treatment options for viral disease that can be leveraged across different host genetic susceptibility patterns can lead to improved personalized medicine. In this review we discuss historic and new platform strategies designed to unravel the interplay between the complex host and viral genetic determinants that regulate disease severity. Moreover, we discuss recent developments in the field of complex genetics designed to resolve quantitative trait loci (QTLs) (see Glossary) and rapidly identify single candidate genes and alleles that regulate microbial pathogenesis. Animal models offer a strategy to reduce system-wide complexity through standardizing environmental influences without losing the integrity of a functional biological system. By far, most in vivo viral pathogenesis studies are conducted in inbred mouse models. Not only are the husbandry and breeding of mice cost-efficient, but genome sequences, as well as many species-specific immunologic, molecular, and biochemical reagents, are available to the research community. However, disease spectra in inbred mouse models are narrow compared with the diverse spectra noted in outbred populations like humans. For respiratory viral infections, additional phenotypic variations must also be considered, as mice do not sneeze, cough, or develop fever following infection. Rather, they exhibit loss of body weight, reduced respiratory function, and decreased locomotive activity. As human pathogens often replicate less efficiently in mice, it is frequently necessary to use mouse-adapted viral strains selected for increased replication and disease in inbred mouse strains, which may or may not replicate disease symptoms seen in humans [4Choi W.S. et al.The significance of avian influenza virus mouse-adaptation and its application in characterizing the efficacy of new vaccines and therapeutic agents.Clin. Exp. Vaccine Res. 2017; 6: 83-94Crossref PubMed Scopus (3) Google Scholar]. The most commonly used parameters for viral infection intensity in mice are changes in body weight and survival rate. Detailed analysis of disease progression can be undertaken utilizing time-course experiments during which samples of interest are collected and disease kinetics revealed. Most recently, mouse genetic reference populations with diverse genetic backgrounds have been developed that replicate the genetic and disease outcome variability found in outbred populations like humans. These new platforms are further supported by technologies that allow targeted genetic modifications, enabling researchers to study how complex traits regulate microbial pathogenesis. Using the mouse as a model organism offers two distinct types of genetic approaches: reverse and forward genetic studies. The most commonly used application in reverse genetic approaches involves the specific ablation of a single gene, either by deleting parts of or the entire gene or by replacing coding exons. Gene trapping by comparison offers the possibility to insert reporter genes into the gene of interest, disrupting its function. Both techniques require the DNA construct of choice to be transfected into mouse embryonic stem cells (mESCs), injection of screened mESC clones into a blastocyst, and transfer of this blastocyst into the uterus of the host animal. Most recently, innovative techniques like transcription activator-like effector nucleases (TALENs) [5Sommer D. et al.TALEN-mediated genome engineering to generate targeted mice.Chromosome Res. 2015; 23: 43-55Crossref PubMed Scopus (22) Google Scholar] and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) [6Harms D.W. et al.Mouse genome editing using the CRISPR/Cas system.Curr. Protoc. Hum. Genet. 2014; 83: 15.7.1-15.7.27Crossref Scopus (97) Google Scholar] are accelerating the process of targeted genome editing, including the introduction of susceptibility alleles that improve virus replication and pathogenesis in the mouse [7Cockrell A.S. et al.A mouse model for MERS coronavirus-induced acute respiratory distress syndrome.Nat. Microbiol. 2016; 2 (16226)PubMed Google Scholar]. Genetically engineered mouse strains have represented the gold standard to investigate the role of specific host genes in regulating disease and immune outcomes following virus infections (e.g. [8Totura A.L. et al.Toll-like receptor 3 signaling via TRIF contributes to a protective innate immune response to severe acute respiratory syndrome coronavirus infection.MBio. 2015; 6 (e 00638-15)Crossref Scopus (332) Google Scholar, 9Schoggins J.W. et al.Pan-viral specificity of IFN-induced genes reveals new roles for cGAS in innate immunity.Nature. 2014; 505: 691-695Crossref PubMed Scopus (661) Google Scholar]). Furthermore, genetically modified strains can be used [10Majzoub J.A. Muglia L.J. Knockout mice.N. Engl. J. Med. 1996; 334: 904-907Crossref PubMed Scopus (72) Google Scholar] to understand human monogenic diseases like cystic fibrosis, polycystic kidney disease, and sickle cell disease. Although complete (biallelic) knockout of genes in humans is a relatively rare event, a recent study showed that healthy people have about 100 loss-of-function variants and 20 completely inactivated genes [11MacArthur D.G. et al.A systematic survey of loss-of-function variants in human protein-coding genes.Science. 2012; 335: 823-828Crossref PubMed Scopus (898) Google Scholar]. Consequently, the majority of human diseases are complex traits whose disease outcomes are influenced by multiple genetic factors. Human genomes are characterized by extensive natural variation, which has accumulated over time through, for example, mutation events (external damage to DNA or internal errors during replication), gene flow, and sexual reproduction, which drives phenotypic interindividual differences across populations. Differences in DNA sequence can modify transcript and protein levels by altering their functional properties, timing, level, and site of expression [12Justice M.J. et al.Technical approaches for mouse models of human disease.Dis. Models Mech. 2011; 4: 305-310Crossref PubMed Scopus (51) Google Scholar]. Natural variation can be used in forward genetic studies to identify novel genes involved in a variety of quantitative traits and diseases [13Hunter K.W. Crawford N.P. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies.Annu. Rev. Genet. 2008; 42: 131-141Crossref PubMed Scopus (62) Google Scholar]. The occurrence of spontaneous mutations in laboratory mouse strains was the first platform employed for forward genetic approaches [14Davisson M.T. et al.Discovery genetics – the history and future of spontaneous mutation research.Curr. Protoc. Mouse Biol. 2012; 2: 103-118Crossref PubMed Google Scholar]. There are various methods to increase the likelihood of mutations by treating male mice with mutagens such as N-ethyl-N-nitrosourea (ENU) [15Russell W.L. et al.Specific-locus test shows ethylnitrosourea to be the most potent mutagen in the mouse.Proc. Natl. Acad. Sci. U. S. A. 1979; 76: 5818-5819Crossref PubMed Scopus (448) Google Scholar] or chlorambucil [16Russell L.B. et al.Chlorambucil effectively induces deletion mutations in mouse germ cells.Proc. Natl. Acad. Sci. U. S. A. 1989; 86: 3704-3708Crossref PubMed Scopus (95) Google Scholar, 17Flaherty L. et al.Chlorambucil-induced mutations in mice recovered in homozygotes.Proc. Natl. Acad. Sci. U. S. A. 1992; 89: 2859-2863Crossref PubMed Scopus (40) Google Scholar], by irradiation [18Russell W.L. X-ray-induced mutations in mice.Cold Spring Harb. Symp. Quant. Biol. 1951; 16: 327-336Crossref PubMed Scopus (294) Google Scholar, 19Russell L.B. Russell W.L. An analysis of the changing radiation response of the developing mouse embryo.J. Cell. Physiol. Suppl. 1954; 43: 103-149Crossref PubMed Scopus (131) Google Scholar], and by utilizing transposons such as the sleeping beauty [20Takeda J. et al.Germline mutagenesis mediated by Sleeping Beauty transposon system in mice.Genome Biol. 2007; 8: S14Crossref PubMed Scopus (27) Google Scholar] or piggyback [21Li L. et al.PiggyBac transposon-based polyadenylation-signal trap for genome-wide mutagenesis in mice.Sci. Rep. 2016; 6 (27788)Google Scholar] system, named according to their transposases, to insert specific DNA sequences. Breeding of those mutated mice allows selection of those with an altered phenotype in the trait of interest. This approach mimics natural genetic variation in humans in the controlled setting of the mouse model and has the power to reveal genomic variation and networks of genes influencing a phenotype rather than analyzing the effect of the absence of a single-gene product, making the translation from mouse to human more likely. Genetic mapping studies are undertaken to identify the genomic region that causes the altered phenotype of interest. Commonly, QTL analysis is used to reveal genotype–phenotype associations [22Abiola O. et al.The nature and identification of quantitative trait loci: a community’s view.Nat. Rev. Genet. 2003; 4: 911-916Crossref PubMed Scopus (367) Google Scholar]. F2 crosses between an inbred strain carrying the aberrant phenotype of interest and another inbred mouse strain lacking this particular phenotype have been widely performed. The identified chromosomal regions can be large, containing several hundreds of genes. The size of the chromosomal region exclusively depends on the number of recombination breakpoints in the cross and the genetic complexity of the region. Chromosomal locations can be narrowed using consomic, conplastic, congenic, recombinant inbred (RI), or recombinant congenic mouse strains [23Roberts A. et al.The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics.Mamm. Genome. 2007; 18: 473-481Crossref PubMed Scopus (203) Google Scholar] (Figure 1). The alternative to dealing with large chromosomal regions and the narrowing process is to use mouse genetic reference populations (GRPs). RI stains of mice are popular due to their long-term genetic stability, which helps in integrating data collected in different settings and reproducibility over a long time. The most extensively used mouse GRP is the BXD family of recombinant inbred strains. They are derived from a cross between C57BL/6J and DBA/2J mice [24Peirce J.L. et al.A new set of BXD recombinant inbred lines from advanced intercross populations in mice.BMC Genet. 2004; 5: 7Crossref PubMed Scopus (398) Google Scholar]. To obtain an F1 generation, C57BL/6J and DBA/2J mice were crossed. The resulting F1 generation is mated to achieve the F2 generation, which is subsequently brother–sister mated for at least 20 generations to generate inbred mouse strains with a fixed genetic architecture. Completely inbred strains are then called BXD strains, which are genotyped once (one animal per line, not every individual) and can be phenotyped indefinitely for every trait of interest. Currently, there are 156 BXD strains available [25Williams R.W. Williams E.G. Resources for systems genetics.Methods Mol. Biol. 2017; 1488: 3-29Crossref PubMed Scopus (24) Google Scholar]. For decades, recombinant inbred strains such as the BXD family have been used extensively as tools for genetic mapping of Mendelian and quantitative traits. To identify single genes that are responsible for the observed phenotype fine mapping, sequence analysis, expression profiling, and functional studies are typically performed [26Ermann J. Glimcher L.H. After GWAS: mice to the rescue?.Curr. Opin. Immunol. 2012; 24: 564-570Crossref PubMed Scopus (23) Google Scholar]. However, the identification of causal gene variants remains challenging due to the large size and the number of genes under the identified QTL region, coupled with the fact that the parental strains were identical by descent resulting in so-called ‘blind spots’ for genetic mapping. Wild-derived strains other than Mus musculus domesticus need to be employed to cover those spots and increase genetic variation [23Roberts A. et al.The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics.Mamm. Genome. 2007; 18: 473-481Crossref PubMed Scopus (203) Google Scholar]. Various resources have been established to address this issue, among them the heterogeneous stock (HS), which is derived from eight founder strains (A/J, AKR/J, BALBc/J, CBA/J, C3H/HeJ, C57BL/6J, DBA/2J, and LP/J) and maintained through random mating. No inbred mouse lines are created and therefore each mouse exhibits a unique combination of alleles with the goal of containing random variation similar to the human population [27Valdar W. et al.Genome-wide genetic association of complex traits in heterogeneous stock mice.Nat. Genet. 2006; 38: 879-887Crossref PubMed Scopus (438) Google Scholar]. The caveat of this mouse population is that every individual mouse needs to be genotyped, which might be too expensive for some researchers. However, genotyping technologies are evolving constantly and costs are decreasing rapidly. Another mouse GRP that includes other M. musculus subspecies is the CC Mouse Resource (Figure 2), which has already been used to successfully identify highly promising candidate genes that are influencing susceptibility or resistance to viral infections (Table 2).Table 2QTLs of CC Studies Using Different VirusesPathogenPhenotypeQTL region% VariationNumber of genes under QTLRefsSARS-CoVVascular cuffingHrS1: Chr. 3: 18286790–2666841426%26 [narrowed to one (Trim55)]40Gralinski L.E. et al.Genome wide identification of SARS-CoV susceptibility loci using the Collaborative Cross.PLoS Genet. 2015; 11e1005504Crossref PubMed Scopus (103) Google ScholarViral titerHrS2: Chr. 16: 31583769–3671999722%92 (narrowed to 48)Eosinophil infiltrationHrS3: Chr. 15: 72103120–7580341426%63 (narrowed to 25 – functional change only in Bai1)Vascular cuffingHrS4: Chr. 13: 52822984–5494628621%30 (narrowed to nine – Cdhr2)D3% weightHrS5: Chr. 18: 27108062 – 586940056.6%158 [narrowed to one (Ticam2)]61Gralinski L.E. et al.Allelic variation in the Toll-like receptor adaptor protein Ticam2 contributes to SARS-coronavirus pathogenesis in mice.G3 (Bethesda). 2017; 7: 1653-1663Crossref PubMed Scopus (54) Google ScholarD4% weightHrS5: Chr. 18: 27108062–586940058.5%Log titerHrS5: Chr. 18: 27108062–5869400512.9%HemorrhageHrS5: Chr. 18: 24762824–78296346%D3% weightHrS6: Chr. 9: 116476207–telomere7%–Log titerHrS7: Chr. 7: 55169841–1172235812.3%–Log titerHrS8: Chr. 12: 81649471–1085291095.4%–HemorrhageHrS9: Chr. 15: centromere–64.4300019.1%–WNVFrequency of CD73+ TregsHI1: Chr. X: 166 Mb–telomere–43 (narrowed to 22)88Graham J.B. et al.Extensive homeostatic T cell phenotypic variation within the Collaborative Cross.Cell Rep. 2016; 21: 2313-2325Abstract Full Text Full Text PDF Scopus (28) Google ScholarDecreased frequency of CXCR3+ Tregs, CXCR3+ CD4+, and CD8+ T cellsHI2: Chr. X: 100–106 Mb–42 (narrowed to 26)Increased frequency of ICOS+ Tregs in spleenHI3: Chr. X: 140–145 Mb–18 (narrowed to 11)IAVD4 weight, log titer, IHC score, D3 clinical, airway inflammation, airway damageHrI1: Chr. 16: 97.5 Mb–98.2 Mb41.67%Ten (including Mx1)39Ferris M.T. et al.Modeling host genetic regulation of influenza pathogenesis in the Collaborative Cross.PLoS Pathog. 2013; 9e1003196Crossref PubMed Scopus (142) Google ScholarD4 weightHrI2: Chr. 7: 89.1 Mb–96.7 Mb9.7%69Pulmonary edemaHrI3: Chr. 1: 21.7 Mb–29 Mb29.73%24Airway neutrophilsHrI4: Chr. 15: 77.4 Mb–86.6 Mb22.7%206 Open table in a new tab To expand genetic variation in GRPs, an innovative strategy for a multiparent population (MPP) of mice was conceptualized in the early 21st century and developed over the next decade [28Threadgill D.W. et al.Genetic dissection of complex and quantitative traits: from fantasy to reality via a community effort.Mamm. Genome. 2002; 13: 175-178Crossref PubMed Scopus (170) Google Scholar]. Use of an octoparental crossing scheme between genetically distinct mouse strains was proposed and modifications through the research community were integrated. Breeding of this novel GRP specifically designed for complex genetics started in 2002 [28Threadgill D.W. et al.Genetic dissection of complex and quantitative traits: from fantasy to reality via a community effort.Mamm. Genome. 2002; 13: 175-178Crossref PubMed Scopus (170) Google Scholar]. The eight founder strains of the CC include three classical laboratory strains (A/J, C57BL/6J, and 129S1/SvImJ), which have been used extensively in biological research and build the genetic backbone on which most of the knockout mouse strains are generated. Moreover, two mouse models for common human diseases were included (NOD/ShiLtJ for type 1 diabetes and NZO/HlLtJ for obesity) to address research questions of comorbidities. The addition of three wild-derived mouse strains (CAST/EiJ, PWK/PhJ, and WSB/EiJ) not only increased the genetic diversity by adding new alleles that are not present among the classical inbred and disease model strains, but also covered different phylogenetic origins of the mouse species (CAST/EiJ – Mus musculus castaneous, PWK/PhJ – Mus musculus musculus) to encompass 90% of genetic variation present in the M. musculus species [23Roberts A. et al.The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics.Mamm. Genome. 2007; 18: 473-481Crossref PubMed Scopus (203) Google Scholar]. Accordingly, the CC population reaches a level of genetic diversity comparable with the diversity found in the human population. To guarantee equal contributions of all eight founder strains to each of the resulting CC strains, a specific breeding funnel was elaborated with around 135 unique recombination events and segregating polymorphisms every 100–200 bp [28Threadgill D.W. et al.Genetic dissection of complex and quantitative traits: from fantasy to reality via a community effort.Mamm. Genome. 2002; 13: 175-178Crossref PubMed Scopus (170) Google Scholar]. In this way susceptibility alleles are scrambled in new ways, allowing novel allelic combinations leading to an extension of phenotypic range beyond the scope observed in the parental strains. Breeding was performed at three different locations: Oak Ridge National Laboratory in Oak Ridge, TN, which moved to the University of North Carolina at Chapel Hill [29Chesler E.J. et al.The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics.Mamm. Genome. 2008; 19: 382-389Crossref PubMed Scopus (206) Google Scholar]; the International Livestock Research Institute in Nairobi, Kenya, which moved to the Tel Aviv University in Tel Aviv, Israel [30Iraqi F.A. et al.The Collaborative Cross, developing a resource for mammalian systems genetics: a status report of the Wellcome Trust cohort.Mamm. Genome. 2008; 19: 379-381Crossref PubMed Scopus (97) Google Scholar]; and the Western Australian Institute for Medical Research/Geniad Ltd in Perth, Australia [31Morahan G. et al.Establishment of “The Gene Mine”: a resource for rapid identification of complex trait genes.Mamm. Genome. 2008; 19: 390-393Crossref PubMed Scopus (73) Google Scholar]. Although the theoretical plan was perfectly elaborated, hundreds of CC strains became extinct, almost half from problems in male infertility [32Shorter J.R. et al.Male infertility is responsible for nearly half of the extinction observed in the mouse Collaborative Cross.Genetics. 2017; 206: 557-572Crossref PubMed Scopus (47) Google Scholar]. Prior to the development of the final CC resource, incipient CC lines that were not fully inbred yet (pre-CC lines) were used in genetic mapping studies to provide proof of concept and to show the potential of this newly designed GRP (Box 1). Candidate genes for various phenotypes, such as susceptibility to Aspergillus fumigatus infections [33Durrant C. et al.Collaborative Cross mice and their power to map host susceptibility to Aspergillus fumigatus infection.Genome Res. 2011; 21: 1239-1248Crossref PubMed Scopus (121) Google Scholar], energy balance traits [34Mathes W.F. et al.Architecture of energy balance traits in emerging lines of the Collaborative Cross.Am. J. Physiol. Endocrinol. Metab. 2011; 300: e1124-e1134Crossref PubMed Scopus (55) Google Scholar], differences in hematological parameters [35Kelada S.N. et al.Genetic analysis of hematological parameters in incipient lines of the Collaborative Cross.G3 (Bethesda). 2012; 2: 157-165Crossref PubMed Scopus (72) Google Scholar], susceptibility to Klebsiella pneumoniae [36Vered K. et al.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: 865Crossref PubMed Scopus (48) Google Scholar], and neutrophilic inflammation due to house dust mite-induced asthma [37Rutledge H. et al.Gene" @default.
• W2886513277 created "2018-08-22" @default.
• W2886513277 creator A5000944816 @default.
• W2886513277 creator A5052239466 @default.
• W2886513277 date "2018-10-01" @default.
• W2886513277 modified "2023-10-18" @default.
• W2886513277 title "Giving the Genes a Shuffle: Using Natural Variation to Understand Host Genetic Contributions to Viral Infections" @default.
• W2886513277 cites W1582652668 @default.
• W2886513277 cites W1584910068 @default.
• W2886513277 cites W1900943334 @default.
• W2886513277 cites W192579163 @default.
• W2886513277 cites W195118571 @default.
• W2886513277 cites W1964761690 @default.
• W2886513277 cites W1966807310 @default.
• W2886513277 cites W1969296977 @default.
• W2886513277 cites W1970726947 @default.
• W2886513277 cites W1976501195 @default.
• W2886513277 cites W1976604752 @default.
• W2886513277 cites W1980508395 @default.
• W2886513277 cites W1986492410 @default.
• W2886513277 cites W1988001018 @default.
• W2886513277 cites W1989733951 @default.
• W2886513277 cites W2000186580 @default.
• W2886513277 cites W2002106319 @default.
• W2886513277 cites W2002919058 @default.
• W2886513277 cites W2003515985 @default.
• W2886513277 cites W2008713419 @default.
• W2886513277 cites W2009998721 @default.
• W2886513277 cites W2011144168 @default.
• W2886513277 cites W2017013905 @default.
• W2886513277 cites W2022325653 @default.
• W2886513277 cites W2030704316 @default.
• W2886513277 cites W2041102163 @default.
• W2886513277 cites W2051103136 @default.
• W2886513277 cites W2051616601 @default.
• W2886513277 cites W2052501164 @default.
• W2886513277 cites W2054076321 @default.
• W2886513277 cites W2058277625 @default.
• W2886513277 cites W2058792460 @default.
• W2886513277 cites W2067376187 @default.
• W2886513277 cites W2068115215 @default.
• W2886513277 cites W2073140713 @default.
• W2886513277 cites W2077218648 @default.
• W2886513277 cites W2085237946 @default.
• W2886513277 cites W2086508545 @default.
• W2886513277 cites W2090072184 @default.
• W2886513277 cites W2094397048 @default.
• W2886513277 cites W2096176011 @default.
• W2886513277 cites W2101762508 @default.
• W2886513277 cites W2103771764 @default.
• W2886513277 cites W2103802172 @default.
• W2886513277 cites W2109130469 @default.
• W2886513277 cites W2112822002 @default.
• W2886513277 cites W2112939100 @default.
• W2886513277 cites W2114123557 @default.
• W2886513277 cites W2114421191 @default.
• W2886513277 cites W2124561494 @default.
• W2886513277 cites W2134844770 @default.
• W2886513277 cites W2136214127 @default.
• W2886513277 cites W2137287646 @default.
• W2886513277 cites W2137341544 @default.
• W2886513277 cites W2139651996 @default.
• W2886513277 cites W2143602768 @default.
• W2886513277 cites W2152968293 @default.
• W2886513277 cites W2153778605 @default.
• W2886513277 cites W2155286781 @default.
• W2886513277 cites W2158726740 @default.
• W2886513277 cites W2161771297 @default.
• W2886513277 cites W2162683818 @default.
• W2886513277 cites W2164013535 @default.
• W2886513277 cites W2171314628 @default.
• W2886513277 cites W2277726453 @default.
• W2886513277 cites W2314463797 @default.
• W2886513277 cites W2398367993 @default.
• W2886513277 cites W2426000962 @default.
• W2886513277 cites W2518689855 @default.
• W2886513277 cites W2537828271 @default.
• W2886513277 cites W2546597874 @default.
• W2886513277 cites W2559337285 @default.
• W2886513277 cites W2560146445 @default.
• W2886513277 cites W2560195165 @default.
• W2886513277 cites W2583273198 @default.
• W2886513277 cites W2612033696 @default.
• W2886513277 cites W2621775737 @default.
• W2886513277 cites W2622148497 @default.
• W2886513277 cites W2623111036 @default.
• W2886513277 cites W2624657184 @default.
• W2886513277 cites W2626985543 @default.
• W2886513277 cites W2740566705 @default.
• W2886513277 cites W2754218272 @default.
• W2886513277 cites W2767907857 @default.
• W2886513277 cites W2769678419 @default.
• W2886513277 cites W4250796669 @default.
• W2886513277 cites W778206086 @default.
• W2886513277 doi "https://doi.org/10.1016/j.tig.2018.07.005" @default.
• W2886513277 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7114642" @default.
• W2886513277 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30131185" @default.
• W2886513277 hasPublicationYear "2018" @default.

• next