Genetic Determinants in Hepatic Fibrosis: From Experimental Models to Fibrogenic Gene Signatures in Humans

Genetic Determina nts in Hepatic Fibrosis : From Exp eriment al Models to Fibro genic Gene Signatures in Huma ns Susanne Weber, PhDa,*, Olav A. Gressner, MDb, Rabea Hall, MSa, Frank Grˇnhage, MDa, Frank Lammert, MDa KEYWORDS  Fibrogenesis  Cirrhosis  Quantitative trait locus  Inbred mouse strains

Hepatic fibrosis, or scarring of the liver, is a nonspecific reaction in response to chronic liver injury. The damage to hepatocytes causes activation of inflammatory cells and an abnormal deposition of extracellular matrix (ECM) in the space of Disse between hepatocytes and sinusoidal endothelia.1–4 The ECM proteins comprise mainly collagen but also glycoproteins, proteoglycans, and glycosaminoglycans, all of which are produced by activated hepatic stellate cells (HSCs or myofibroblasts) or portal fibroblasts. HSC are vitamin A–storing cells that usually remain in a quiescent stage and are activated by the infiltration of inflammatory cells releasing profibrogenic mediators such as transforming growth factor b1 (TGF-b1) and angiotensinogen. Activation of HSCs leads to a myofibroblast-like phenotype characterized by the enhanced production and secretion of ECM proteins.2,3,5 Hepatic fibrosis is commonly caused by exogenous factors such as viral hepatitis or alcohol abuse, but recent studies also indicate a genetic predisposition.4 Although some patients who have chronic liver diseases show only minor morphologic and functional alterations of the liver and are characterized by slow progression of disease with mild clinical symptoms, others develop pronounced hepatic fibrosis rapidly,

a

Department of Medicine II, Saarland University Hospital, Saarland University, Kirrberger St., 66424 Homburg/Saar, Germany b Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Aachen, Aachen University (RWTH), Aachen, Germany * Corresponding author. E-mail address: [email protected] (S. Weber). Clin Liver Dis 12 (2008) 747–757 doi:10.1016/j.cld.2008.07.012 1089-3261/08/$ – see front matter ª 2008 Elsevier Inc. All rights reserved.

liver.theclinics.com

748

Weber et al

culminating in cirrhosis, liver failure, or hepatocellular carcinoma, respectively. Hence, a distinction between ‘‘rapid fibrosers’’ and ‘‘slow fibrosers’’ has been proposed.6 These well-known differences in progression of hepatic fibrosis persist when controlling for age (at infection), gender, and exogenous factors in multivariate analysis, indicating that genetic factors might play important roles in the modulation of hepatic fibrosis and contribute to the variability in fibrosis progression.7,8 Furthermore, epidemiologic studies indicate that the prevalence of cryptogenic cirrhosis among Hispanic and African American patients is 3.1-fold higher and 3.9-fold lower, respectively, than among European Americans (Fig. 1), despite similar prevalence rates of diabetes mellitus.9 These ethnic differences support the hypothesis that fibrosis induced by nonalcoholic fatty liver disease, a common cause of cryptogenic cirrhosis, is at least partly genetically determined. In contrast to these epidemiologic studies, twin studies are a better source for dissecting the complex genetics because the interaction of genotypes and phenotypes with age, gender, and lifestyle factors can be analyzed. Comparisons of concordance rates between monozygotic and dizygotic pairs of twins can provide information on whether the familial pattern is caused by hereditary or environmental factors.10,11 However, few studies of liver diseases in twins have been reported. The large twin study of the NAS-NRC Twin Registry (N 5 15924)12 showed that the concordance rates for the development of alcohol-induced liver cirrhosis were significantly higher in monozygotic (14.6%) compared with dizygotic twins (5.4%) (Fig. 2), but this was also the case for alcohol abuse, and no further histologic analyses were provided. Maximum-likelihood modeling indicated that approximately 50% of the overall variance was caused by additive genetic effects, but only a minor portion of the genetic variance of the individual complications was independent of the genetic predisposition for alcoholism.12 Hence, further family-based studies are needed. With respect to genetic determinants of human diseases, monogenic and polygenic diseases can be distinguished. Monogenic diseases are caused by a single gene defect following Mendelian rules of inheritance. These disorders are relatively rare,

Fig. 1. Ethnic differences in the prevalence of cryptogenic cirrhosis. Prevalence rates of diabetes mellitus (DM, white bars) and cryptogenic cirrhosis (CC, black bars) in 41 patients (African Americans, European Americans, and Hispanics). (Data from Browning JD, Kumar KS, Saboorian MH, et al. Ethnic differences in the prevalence of cryptogenic cirrhosis. Am J Gastroenterol 2004;99:292–8.)

Genetic Determinants in Hepatic Fibrosis

Fig. 2. Genetic predisposition to organ-specific endpoints of alcoholism. Medical records of 15,924 twin-pairs in the National Academy of Sciences-National Research Council (NAS-NRC) twin registry aged 67 to 77 years were collected. Concordance rates for alcoholism, alcoholic psychosis, and cirrhosis for monozygotic twins (black bars) and dizygotic twins (white bars). (Data from Reed T, Page WF, Viken RJ, et al. Genetic predisposition to organ-specific endpoints of alcoholism. Alcohol Clin Exp Res 1996;20:1528–33.)

mostly severe, characterized by early onset, and usually not determined by environmental factors. In contrast, polygenic diseases such as coronary heart disease, cancer, diabetes, and osteoporosis result from the interaction of multiple gene variants, and environmental factors play a major role in their development.13 Accordingly, the current paradigm predicts that a genetic predisposition for rapid fibrosis is mediated by a combination of different variants in many genes.5 Hence, the goal is to identify genes that enhance the susceptibility to hepatic fibrosis and represent molecular targets for novel antifibrotic therapies. Furthermore, predictive gene signatures might help define the subgroup of patients who are at-risk for rapid fibrosis progression and would benefit most from antifibrotic therapies.14,15 EXPERIMENTAL GENETICS

Mouse models are of great interest for the study of human disorders, including hepatic fibrosis, because mice have similar physiology, anatomy, and metabolism to humans.16 These similarities are also reflected in the mouse genome. Almost every gene in the human genome has a counterpart in the mouse. Researchers have allied this to powerful genetic tools and developed a diverse set of inbred mouse strains with variants that mirror those seen in human genetic diseases.17,18 The generation of inbred mouse strains is a great achievement for the research of polygenic diseases. Inbred mice are the result of at least 20 brother-sister matings and are therefore homozygous for all alleles (ie, genetically identical). Several hundred inbred strains currently exist, each with a different background. The most common mouse strains that differ in their susceptibility toward fibrosis are A/J, AKR, BALB/c, C3H/He, C57BL/6, DBA/2, and FVB/N.7,19,20 Hepatic fibrosis in mice can be induced either surgically or chemically using different protocols.21 A surgical method inducing fibrosis is bile duct ligation (BDL). Here, the common bile duct is ligated and the mice display hepatic fibrosis approximately 2 to 4 weeks after surgery, which leads to stimulation of portal myofibroblasts, ductular reaction,22 and pronounced periportal and portoportal septal fibrosis.23,24 An alternative way to induce hepatic fibrosis chemically is the administration of carbon tetrachloride (CCl4),7,19 thioacetamide  ethanol,25 or dimethynitrosamine.26 Low-dose CCl4 injection leads to

749

750

Weber et al

formation of CCl3 radicals, which damage hepatocytes27 and cause necrosis, inflammation, and centrilobular fibrosis, which develops from activated HSC between the surrounding parenchyma and results in portocentral septal fibrosis. Thioacetamide in the drinking water induces fibrosis but also tumors (ie, cholangiocarcinoma),28 which must be considered when planning the experiments. Injection of dimethynitrosamine also leads to tumor formation besides fibrosis,26 and therefore CCl4 has become the most common method for fibrosis studies in mice. GENOME-WIDE QUANTITATIVE TRAIT LOCI ANALYSIS

Analysis of polygenic diseases and identification of disease-related gene loci have become possible using quantitative trait loci (QTL) analysis.29–31 This method combines molecular techniques and classic genetic linkage analysis and has already led to the identification of many QTL for polygenic diseases.13 Experimental crossing of inbred mouse strains can be regarded as the central procedure in identifying the genetic loci that determine complex diseases. QTL are regions of DNA associated with a particular trait (eg, cancer or fibrosis). Although not necessarily genes themselves, QTL are stretches of DNA that include at least one gene that underlies the trait in question.32 Actual QTL analysis in mice comprises the following steps: first, two inbred mouse strains that are preferably clearly distinguishable with respect to their susceptibility toward is hepatic fibrosis, are mated to obtain many F2 progeny. Whereas the F1 mice are heterozygous and uniform, a recombination of the parental chromosomes may occur during meiosis of the F1 genomes and appear genotypically and phenotypically visible in the F2 progeny. All F2 mice are phenotyped according to quantitative trait characteristics, such as concentration of hepatic collagen (according to photometric measurement of the collagen-specific amino acid hydroxyproline), histologic fibrosis stages (after Picrosirius red staining), or mRNA expression profiles. The F2 progeny are genotyped for genetic markers that differ between the parental strains and that densely cover the whole genome (eg, microsatellites or single nucleotide polymorphisms). Although unlinked genetic loci segregate independently from each other in the F2 mice, markers that lie near disease-associated genes are inherited together with the specific phenotype.33 Linkage analysis of fibrosis-specific phenotypes and marker genotypes is performed using genetic mapping software such as MapManager QTX34 or R/qtl,35 which allow user-friendly identification (eg, by regression analysis or interval mapping)36 and statistical validation of linkage. The logarithm of the odds ratio (LOD) score, the traditional statistics for genetic linkage, is a measure of the strength of evidence for the presence of a quantitative trait locus. The LOD score is the odds ratio comparing the hypothesis of a quantitative trait locus at a particular location versus that of none. LOD scores of 3 or higher are generally considered to indicate significant results, although theoretic thresholds have been calculated for specific cross-designs37 and alternatively, empiric thresholds, which correct for multiple testing, can be calculated using permutation tests.38 TRANSLATIONAL STUDIES FROM MICE TO HUMANS

Hillebrandt and colleagues7 performed a systematic inbred strain survey to identify unknown susceptibility loci for hepatic fibrosis. For QTL analysis, F1 hybrids of susceptible BALB/c and resistant A/J inbred strains were intercrossed to obtain 358 F2 progeny. Linkage analysis of phenotypes and genotypes identified QTL on chromosomes 2 and 15 (Table 1) that significantly affect the histologic stage of fibrosis and

Genetic Determinants in Hepatic Fibrosis

Table 1 Fibrogenic gene loci in polygenic experimental models Mouse QTL

Chromosome

Hfib1

15

Candidate Gene(s) A1bg, Mtss1

Hfib2

2

C5 (Hc)

Hfib3

1

—

5

Cxcl9

hepatic collagen contents.7,20 Using studies with congenic and transgenic mice, the locus on chromosome 2 could be refined to the gene encoding complement factor C5. Small molecule inhibitors of the C5a receptor displayed antifibrotic effects in vivo, and common haplotype-tagging polymorphisms of the human gene C5 were associated with advanced fibrosis in a small cohort of patients who had chronic hepatitis C virus infection. Thus, the mouse quantitative trait gene analysis led to the identification of an unknown gene possibly underlying human susceptibility to hepatic fibrosis.20 Another fibrosis susceptibility locus identified through QTL analysis is located on chromosome 15. This locus, designated hepatic fibrogenic gene 1 (Hfib1), significantly affected the stage of fibrosis and hepatic collagen contents in the F2 progeny of BALB/c and A/J mice.7 In an intercross between the fibrosis-susceptible strain BALB/cJ and another resistant strain FVB/NJ, a quantitative trait locus was detected on mouse chromosome 1. According to standard nomenclature, this major quantitative trait locus with significant effects on the progression of hepatic fibrosis (LOD 3.9) was named Hfib3.39 Recently, functional variants of the chemokines CXCL9 and CCL5 (RANTES) were shown to be linked to enhanced fibrosis progression in experimental crosses of mice and knock-out mice and to be associated with the stage of fibrosis in independent cohorts of patients, providing evidence for chemokines as genetic risk factors for hepatic fibrosis.40,41 ASSOCIATION (IN SILICO) MAPPING IN MICE

An alternative way to identify genes or genomic regions linked to a specific phenotype is association, or in silico mapping (Fig. 3).20,42,43 In contrast to QTL (linkage) analysis in experimental crosses, the phenotypes of a set of inbred strains are correlated to dense maps of single nucleotide polymorphism (SNP) genotypes. After entry of the phenotypic information of the different inbred mouse strains, large mouse SNP databases are scanned using different algorithms to predict chromosomal regions that determine phenotypic traits.44 The trait under study and its genetic component, the number of strains, the number of genetic markers, and the algorithms used are important factors to consider.43 To illustrate the application of an integrated approach, Cervino and colleagues43 used hepatic fibrosis as an example. The study used the phenotypes determined by Hillebrandt and colleagues7,20 and performed an in silico analysis using a 140 k SNP map and a cladistic analysis of haplotypes (ie, unique combinations of SNP alleles located closely together on the same chromosome and tend to be inherited together).45 The two most likely susceptibility loci identified on chromosome 2 contained nine candidate genes, as filtered with a median threefold increase for liver expression. This list included the C5 gene identified by experimental QTL mapping, illustrating the power of the integrated in silico approach. In addition, chromosome 15 showed

751

752

Weber et al

Fig. 3. An integrated strategy to narrowing down the genome to a list of candidate genes in experimental models.43 The integrated workflow brings in information from linkage (QTL) analysis, identical by descent (IBD) blocks, in silico analysis, and gene expression to produce a short list of candidate genes.

a strong association pattern at around 68 Mb, harboring two creedal candidate genes (a-1-B glycoprotein [A1bg] and metastasis suppressor 1 [Mtss1]) (see Table 1).43 HUMAN ASSOCIATION (CASE-CONTROL) STUDIES

In contrast to mouse models, which can be studied under defined environmental conditions, identifying disease-related genes in humans is much more complicated. This complexity is because of the large variety of environmental factors and the lack of standardized noninvasive phenotyping of hepatic fibrosis in patients who have chronic liver diseases. Genetic studies of hepatic fibrosis in humans are based on associations between phenotypes and gene variants.46 In these case-control studies, the genotype frequencies in patients who have severe fibrosis are compared with patients who have normal liver tissue or mild fibrosis. The most common variants investigated are SNPs. An SNP is a DNA sequence variation, occurring in at least 1% of the human population. SNPs are generally considered to be a form of point mutation that was evolutionarily successful enough to recur in a significant proportion of humans. Once an SNP is observed at a significantly higher frequency in patients compared with controls, it is considered to be associated with the disease. For efficient association studies, the choice of SNP is essential. Completion of the International HapMap Project45,47 has made a lot of information readily available about the occurrence and frequency of polymorphisms throughout the human genome. The International HapMap Project determined the common patterns of DNA sequence variation and has published a HapMap of many millions of sequence variants, including their frequencies, linkage disequilibrium, and haplotypes in specific populations with ancestry from Africa, Asia, and Europe. Recent scholarly reviews48–50 summarized and evaluated all currently available association studies (Table 2), which investigated the role of polymorphisms in several

Genetic Determinants in Hepatic Fibrosis

Table 2 Replicated fibrosis risk genes in humans Gene

Mechanism

Liver Disease

AGT

Profibrogenic

HBV, HCV, NAFLD

APOE

Viral entry

HCV, PBC

CCL2 (RANTES)

Chemotaxis

HCV

CCR5

Chemotaxis

HCV

CD14

Endotoxin receptor

ALD, NAFLD

CTLA4

Immune response

ALD, HCV

GSTM1

Oxidative stress

ALD

IL1B

Proinflammatory

ALD, HCV, PBC

IL1RN

Proinflammatory

ALD, HCV, PBC

IL10

Anti-inflammatory

ALD, HCV

KRT8

Cytoskeleton

HCV

MPO

Immune response, oxidative stress

HCV, HHC

MTP

Lipid metabolism

HCV, NAFLD

SERPINA1 (A1AT)

Autophagic response

ALD, CCI, HCV

SOD2

Oxidative stress

HCV, NAFLD

TGFB1

Profibrogenic

ALD, HCV, NAFLD

TNF

Proinflammatory

ALD, HCV, HHC, NAFLD, PBC

Abbreviations: ALD, alcoholic liver disease; CCI, cryptogenic cirrhosis; HBV, chronic hepatitis B virus infection; HCV, chronic hepatitis C virus infection; HHC, hereditary hemochromatosis; NAFLD, nonalcoholic liver disease; PBC, primary biliary cirrhosis. Data from Refs.48–50

candidate genes on the progression of hepatic fibrosis or development of cirrhosis in patients who had different types of chronic liver diseases. Particularly, functional SNPs of genes coding for proinflammatory modulators or profibrogenic cytokines were investigated. However, many publications on the role of certain genetic variants in Table 3 Fibrogenic gene signatures Gene Signature

Odds Ratio for Individual Gene Variant (Range)

Reference

AQP2

2.2 (1.4–3.3)

Huang et al.15

AZIN1

3.2 (1.8–6.1)

TLR4

2.1 (1.2–2.8)

TRPM5

1.9 (1.4–3.1)

rs2290351

1.9 (1.2–2.8)

rs4290029

2.4 (1.5–3.6)

rs17740066

2.8 (1.5–5.2)

APOE

1.6 (0.8–3.3)

CCR5

2.1 (0.9–4.7)

CTLA4

2.3 (1.2–4.5)

HFE

1.8 (0.9–3.6)

MTP

2.7 (1.3–5.4)

SOD2

2.5 (1.2–4.5)

Richardson et al.14

753

754

Weber et al

modulating the progression of hepatic fibrosis have been limited by small sample sizes, low minor allele frequencies, and low statistical power.49 This finding is reflected by a recent meta-analysis51 on hemochromatosis genotypes and risk for disease end points in 6969 patients who had liver diseases and 41,017 controls. This meta-analysis did not detect any association between heterozygous or compound mutations of the hemochromatosis gene HFE and hepatitis C, hepatocellular carcinoma, or nonalcoholic steatohepatitis, although several much smaller previous studies reported these associations. Only recently have genetic studies of hepatic fibrosis taken into account the polygenic nature of the disorder, which is not affected by a single major gene but combinations of several genes, all contributing to the fibrosis phenotypes (Table 3). A study from Australia14 detected associations between polymorphisms in six genes (APOE, CCR5, CTLA4, HFE, MTP, SOD2) and progressive hepatic fibrosis in chronic hepatitis C virus infection, with individual odds ratios ranging from 2.1 to 4.5.

Fig. 4. Study design for association study between genetic markers and fibrosis phenotypes in humans and building of the cirrhosis risk score (CRS) signature. (From Huang H, Shiffman ML, Friedman S, et al. A 7 gene signature identifies the risk of developing cirrhosis in patients with chronic hepatitis C. Hepatology 2007;46:297–306; with permission.)

Genetic Determinants in Hepatic Fibrosis

Using logistic regression analysis, a predictive equation could be developed and tested using a second cohort of patients who have advanced fibrosis. The odds ratio between rapidly progressing fibrosis and possession of at least 3, 4, or 5 at-risk alleles increased from 9.1 and 15.5 to 24.1, respectively,14 consistent with an additive mode of inheritance. Huang and colleagues15 used a ‘‘machine learning’’ approach to develop and validate a gene signature consisting of markers most predictive for cirrhosis risk in Caucasian patients (Fig. 4). The area under the receiver operating characteristic curves of the Cirrhosis Risk Score (see Table 3) were 0.73 to 0.75, and a better predictor than clinical factors in differentiating high risk versus low risk for cirrhosis,15 but prospective studies are needed to further validate these findings. Genome-wide association studies in large, well-characterized cohorts with chronic liver diseases are anticipated to allow the determination and validation of additional gene signatures that predict rapid fibrosis progression and help to select patients for personalized antifibrotic therapies.15,52,53 REFERENCES

1. Gressner AM. The cell biology of liver fibrogenesis—an imbalance of proliferation, growth arrest and apoptosis of myofibroblasts. Cell Tissue Res 1998;292:447–52. 2. Friedman SL. Mechanisms of disease: mechanisms of hepatic fibrosis and therapeutic implications. Nat Clin Pract Gastroenterol Hepatol 2004;1: 98–105. 3. Gressner AM, Weiskirchen R. Modern pathogenetic concepts of liver fibrosis suggest stellate cells and TGF-beta as major players and therapeutic targets. J Cell Mol Med 2006;10:76–99. 4. Schupan D, Afdhal NH. Liver cirrhosis. Lancet 2008;371:838–51. 5. Bataller R, Brenner DA. Liver fibrosis. J Clin Invest 2005;115:209–18. 6. Poynard T, Bedossa P, Opolon P. Natural history of liver fibrosis progression in patients with chronic hepatitis C. Lancet 1997;349:825–32. 7. Hillebrandt S, Goos C, Matern S, et al. Genome-wide analysis of hepatic fibrosis in inbred mice identifies the susceptibility locus Hfib1 on chromosome 15. Gastroenterology 2002;123:2041–51. 8. Lammert F. Genetic determinants of complex liver diseases: mouse models and s J, Benhamou JP, Blei A, et al, editors. quantitative trait locus analysis. In: Rode Textbook of hepatology: from basic science to clinical practice. Oxford (UK): Blackwell Publishing; 2007. p. 371–83. 9. Browning JD, Kumar KS, Saboorian MH, et al. Ethnic differences in the prevalence of cryptogenic cirrhosis. Am J Gastroenterol 2004;99:292–8. 10. Neale MC, Cardon LR. Methodology for genetic studies of twins and families Dordrecht: Kluwer Academic Publishers; 1992. 11. Perola M, Sammalisto S, Hiekkalinna T, et al. Combined genome scans for body stature in 6,602 European twins: evidence for common Caucasian loci. PLoS Genet 2007;3:e97. 12. Reed T, Page WF, Viken RJ, et al. Genetic predisposition to organ-specific endpoints of alcoholism. Alcohol Clin Exp Res 1996;20:1528–33. 13. Korstanje R, Paigen B. From QTL to gene: the harvest begins. Nat Genet 2002; 31:235–6. 14. Richardson MM, Powell EE, Barrie HD, et al. A combination of genetic polymorphisms increases the risk of progressive disease in chronic hepatitis C. J Med Genet 2005;42:e45.

755

756

Weber et al

15. Huang H, Shiffman ML, Friedman S, et al. A 7 gene signature identifies the risk of developing cirrhosis in patients with chronic hepatitis C. Hepatology 2007;46: 297–306. 16. Paigen K. A miracle enough: the power of mice. Nat Med 1995;1:215–20. 17. Mouse Genome Sequencing Consortium. Initial sequencing and comparative analysis of the mouse genome. Nature 2002;420:520–62. 18. Bogue M. Mouse phenome project: understanding human biology through mouse genetics and genomics. J Appl Physiol 2003;95:1335–7. 19. Shi Z, Wakil AE, Rockey DC. Strain-specific differences in mouse hepatic wound healing are mediated by divergent T helper cytokine responses. Proc Natl Acad Sci USA 1997;94:10663–8. 20. Hillebrandt S, Wasmuth HE, Weiskirchen R, et al. Complement factor 5 is a quantitative trait gene that modifies liver fibrogenesis in mice and humans. Nat Genet 2005;37:835–43. 21. Iredale JP. Models of liver fibrosis: exploring the dynamic nature of inflammation and repair in a solid organ. J Clin Invest 2007;117:539–48. 22. Roskams TA, Theise ND, Balabaud C, et al. Nomenclature of the finer branches of the biliary tree: canals, ductules, and ductular reactions in human livers. Hepatology 2004;39:1739–45. 23. Accatino L, Contreras A, Fernandez S, et al. The effect of complete biliary obstruction on bile flow and bile acid excretion: postcholestatic choleresis in the rat. J Lab Clin Med 1979;93:706–17. 24. Chang ML, Yeh CT, Chang PY, et al. Comparison of murine cirrhosis models induced by hepatotoxin administration and common bile duct ligation. World J Gastroenterol 2005;11:4167–72. 25. Kornek M, Raskopf E, Guetgemann I, et al. Combination of systemic thioacetamide (TAA) injections and ethanol feeding accelerates hepatic fibrosis in C3H/ He mice and is associated with intrahepatic up regulation of MMP-2, VEGF and ICAM-1. J Hepatol 2006;45:370–6. 26. Weiler-Normann C, Herkel J, Lohse AW. Mouse models of liver fibrosis. Z Gastroenterol 2007;45:43–50. 27. Muriel P, Escobar Y. Kupffer cells are responsible for liver cirrhosis induced by carbon tetrachloride. J Appl Toxicol 2003;23:103–8. 28. Yeh CN, Maitra A, Lee KF, et al. Thioacetamide-induced intestinal-type cholangiocarcinoma in rat: an animal model recapitulating the multi-stage progression of human cholangiocarcinoma. Carcinogenesis 2004;25:631–6. 29. Darvasi A. Experimental strategies for the genetic dissection of complex traits in animal models. Nat Genet 1998;18:19–24. 30. Flint J, Valdar W, Shifman S, et al. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet 2005;6:271–86. 31. Peters LL, Robledo RF, Bult CJ, et al. The mouse as a model for human biology: a resource guide for complex trait analysis. Nat Rev Genet 2007;8:58–69. 32. The Complex Trait Consortium. The nature and identification of quantitative trait loci: a community’s view. Nat Rev Genet 2003;4:911–6. 33. Hillebrandt S, Matern S, Lammert F. Mouse models for genetic dissection of polygenic gastrointestinal diseases. Eur J Clin Invest 2003;33:155–60. 34. Manly KF, Cudmore RH Jr, Meer JM. Map Manager QTX, cross-platform software for genetic mapping. Mamm Genome 2001;12:930–2. 35. Broman KW. Mapping expression in randomized rodent genomes. Nat Genet 2005;37:209–10.

Genetic Determinants in Hepatic Fibrosis

36. Lander ES, Botstein D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 1989;121:185–99. 37. Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 1995;11:241–7. 38. Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics 1994;138:963–71. 39. Hillebrandt S, Sauerbruch T, Lammert F. Identification of multiple interacting quantitative trait loci (QTL) for hepatic fibrosis in mice. Gastroenterology 2006; 130:A427. 40. Wasmuth HE, Wiederholt T, Dahl E, et al. The chemokine CXCL9 (MIG) is a genetic modifier of liver fibrogenesis in chronic hepatitis C. Hepatology 2006; 44:568A. 41. Berres ML, Ru¨land A, Moreno Zaldivar M, et al. The CC chemokine RANTES (CCL5) mediates liver fibrosis in mice and humans. Z Gastroenterol 2008;46. 42. Pletcher MT, McClurg P, Batalov S, et al. Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse. PLoS Biol 2004;2:e393. 43. Cervino AC, Darvasi A, Fallahi M, et al. An integrated in silico gene mapping strategy in inbred mice. Genetics 2007;175:321–33. 44. Grupe A, Germer S, Usuka J, et al. In silico mapping of complex disease-related traits in mice. Science 2001;292:1915–8. 45. Wasmuth HE, Matern S, Lammert F. From genotypes to haplotypes in hepatobiliary diseases: one plus one equals (sometimes) more than two. Hepatology 2004; 39:604–7. 46. Day CP. Genetic studies to identify hepatic fibrosis genes and SNPs in human populations. Methods Mol Med 2005;117:315–31. 47. The International HapMap Consortium. The International HapMap Project. Nature 2003;426:789–96. 48. Wasmuth HE, Lammert F, Matern S. Genetische Risikofaktoren der Fibrogenese bei chronischen Lebererkrankungen. Med Klin 2003;98:754–62. ¨ sterreicher CH, Stickel F, Brenner DA. Genomics of liver fibrosis and cirrhosis. 49. O Semin Liver Dis 2007;27:28–43. 50. Gressner OA, Weiskirchen R, Gressner AM. Biomarkers of hepatic fibrosis, fibrogenesis and genetic pre-disposition pending between fiction and reality. J Cell Mol Med 2007;11:1031–51. 51. Ellervik C, Birgens H, Tybjaerg-Hansen A, et al. Hemochromatosis genotypes and risk of 31 disease endpoints: meta-analyses including 66,000 cases and 226,000 controls. Hepatology 2007;46:1071–80. 52. Hellerbrand C, Wasmuth HE. Genomewide genetic association studies in hepatology: the end of searching for the needle in the haystack? Hepatology 2007; 46:1661–3. 53. Kruglyak L. The road to genome-wide association studies. Nat Rev Genet 2008;9: 314–8.

757