Characterization of mutations in ATP8B1 associated with hereditary cholestasis.

Progressive familial intrahepatic cholestasis (PFIC) and benign recurrent intrahepatic cholestasis (BRIC) are clinically distinct hereditary disorders. PFIC patients suffer from chronic cholestasis and develop liver fibrosis. BRIC patients experience intermittent attacks of cholestasis that resolve spontaneously. Mutations in ATP8B1 (previously FIC1) may result in PFIC or BRIC. We report the genomic organization of ATP8B1 and mutation analyses of 180 families with PFIC or BRIC that identified 54 distinct disease mutations, including 10 mutations predicted to disrupt splicing, 6 nonsense mutations, 11 small insertion or deletion mutations predicted to induce frameshifts, 1 large genomic deletion, 2 small inframe deletions, and 24 missense mutations. Most mutations are rare, occurring in 1-3 families, or are limited to specific populations. Many patients are compound heterozygous for 2 mutations. Mutation type or location correlates overall with clinical severity: missense mutations are more common in BRIC (58% vs. 38% in PFIC), while nonsense, frameshifting, and large deletion mutations are more common in PFIC (41% vs. 16% in BRIC). Some mutations, however, lead to a wide range of phenotypes, from PFIC to BRIC or even no clinical disease. ATP8B1 mutations were detected in 30% and 41%, respectively, of the PFIC and BRIC patients screened.

SNP analysis and presentation in the Pharmacogenetics of Membrane Transporters Project.

The multidisciplinary UCSF Pharmacogenetics of Membrane Transporters project seeks to systematically identify sequence variants in transporters and to determine the functional significance of these variants through evaluation of relevant cellular and clinical phenotypes. The project is structured around four interacting cores: genomics, cellular phenotyping, clinical phenotyping, and bioinformatics. The bioinformatics core is responsible for collecting, storing, and analyzing the information obtained by the other cores and for presenting the results, in particular, for the genomic data. Most of this process is automated using locally developed software written in Python, an open source language well suited for rapid, modular development that meets requirements that are themselves constantly evolving. Here we present the details of transforming ABI trace file data into useful information for project investigators and a description of the types of data analysis and display that we have developed.