Mutations in the ABCA4 gene are responsible for a wide variety of retinal degeneration phenotypes, including Stargardt disease (STGD), cone-rod dystrophy (CRD) and retinitis pigmentosa (RP). Different combinations of >900 ABCA4 mutant alleles result in distinct phenotypes in a continuum of disease manifestations. Variation in the ABCA4 locus has emerged as the most prevalent cause of Mendelian retinal disease, with an estimated 1/20 people across all populations carrying a potential disease-associated variant in this gene. Genetic analyses of ABCA4-associated retinal disease have been substantially advanced in recent years. New methods, such as direct sequencing of the entire genomic ABCA4 locus, have allowed detecting up to 80% of the disease-associated ABCA4 alleles, including 2 (both) mutations in ~65-75% of patients. Of these 75% are in the coding region and 25% in introns, more than half of which are outside of splice consensus sequences. Of the rest, 1 mutation is detected in ~20% of patients while no disease-associated alleles are found in another 10% of screened patients with phenotypes compatible with the ABCA4 disease. These data suggest that many (rare) disease-associated ABCA4 alleles are yet to be identified and, most importantly, unequivocally confirmed by adequate functional analyses. We will test the hypothesis that a combination of advanced genetic screening coupled with advanced functional analyses of ABCA4 alleles from non-coding sequences is necessary to unequivocally determine the ABCA4-associated disease load. The proposed research program, based on large, comprehensively characterized familial cohort of ABCA4 disease, is using integrated approaches of genetic analyses, quantified clinical data and functional analyses to generate a predictive model for ABCA4 disease. The ultimate goal of the proposed project is to complete the analysis of the ABCA4 locus. The research program is organized into two Specific Aims. In the first Aim, we propose a novel combinatorial pipeline utilizing our existing clinical and genetic databases, accumulated knowledge, and advanced in silico methodology to predict most disease-associated variants in the coding and non-coding sequences of the entire ABCA4 locus. In the second Aim we will confirm or reject the variants in non-coding sequences for disease association by functional testing ABCA4 RNA from mutated iPSC lines for splicing and expression defects. The outcome of these studies will substantially aid in disease diagnosis, prognosis and will serve as a platform for selecting patients for emerging clinical trials geared to delay the onset, or arrest the progression, of ABCA4-associated diseases.
Genetic variation affecting the structure and function of the ABCA4 protein gives rise to a broad collection of genetically and clinically heterogeneous diseases, known collectively as the ABCA4 disease, which are characterized by both phenotypic overlap and variable penetrance and expressivity. The proposed research program, based on large, comprehensively characterized familial cohort of ABCA4 disease, is using integrated approaches of genetic analyses, quantified clinical data and functional analyses to determine the pathogenic variation in non-coding regions of the ABCA4 locus and to generate a prediction matrix for ABCA4 disease. The outcome of these studies will substantially improve the accuracy of genetic testing and prognostic assessment of the disease, and will provide a comprehensive platform for the design and structure of clinical trials.
