| First Author | Hall RA | Year | 2016 |
| Journal | Mamm Genome | Volume | 27 |
| Issue | 1-2 | Pages | 70-80 |
| PubMed ID | 26547557 | Mgi Jnum | J:237571 |
| Mgi Id | MGI:5816187 | Doi | 10.1007/s00335-015-9609-4 |
| Citation | Hall RA, et al. (2016) Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross. Mamm Genome 27(1-2):70-80 |
| abstractText | Most common diseases are attributed to multiple genetic variants, and the feasibility of identifying inherited risk factors is often restricted to the identification of alleles with high or intermediate effect sizes. In our previous studies, we identified single loci associated with hepatic fibrosis (Hfib1-Hfib4). Recent advances in analysis tools allowed us to model loci interactions for liver fibrosis. We analysed 322 F2 progeny from an intercross of the fibrosis-susceptible strain BALB/cJ and the resistant strain FVB/NJ. The mice were challenged with carbon tetrachloride (CCl4) for 6 weeks to induce chronic hepatic injury and fibrosis. Fibrosis progression was quantified by determining histological fibrosis stages and hepatic collagen contents. Phenotypic data were correlated to genome-wide markers to identify quantitative trait loci (QTL). Thirteen susceptibility loci were identified by single and composite interval mapping, and were included in the subsequent multiple QTL model (MQM) testing. Models provided evidence for susceptibility loci with strongest association to collagen contents (chromosomes 1, 2, 8 and 13) or fibrosis stages (chromosomes 1, 2, 12 and 14). These loci contained the known fibrosis risk genes Hc, Fasl and Foxa2 and were incorporated in a fibrosis network. Interestingly the hepatic fibrosis locus on chromosome 1 (Hfib5) connects both phenotype networks, strengthening its role as a potential modifier locus. Including multiple QTL mapping to association studies adds valuable information on gene-gene interactions in experimental crosses and human cohorts. This study presents an initial step towards a refined understanding of profibrogenic gene networks. |
