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Avigail Beryozkin, Lina Zelinger, Dikla Bandah-Rozenfeld, Elia Shevach, Anat Harel, Tim Storm, Michal Sagi, Dalia Eli, Saul Merin, Eyal Banin, Dror Sharon; Identification of Mutations Causing Inherited Retinal Degenerations in the Israeli and Palestinian Populations Using Homozygosity Mapping. Invest. Ophthalmol. Vis. Sci. 2014;55(2):1149-1160. doi: 10.1167/iovs.13-13625.
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© ARVO (1962-2015); The Authors (2016-present)
The Israeli and Palestinian populations are known to have a relatively high level of consanguineous marriages, leading to a relatively high frequency of autosomal recessive (AR) diseases. Our purpose was to use the homozygosity mapping approach, aiming to prioritize the set of genes and identify the molecular genetic causes underlying AR retinal degenerations in the Israeli and Palestinian populations.
Clinical analysis included family history, ocular examination, full-field electroretinography (ERG), and funduscopy. Molecular analysis included homozygosity mapping and mutation analysis of candidate genes.
We recruited for the study families with AR nonsyndromic retinal degenerations, including mainly retinitis pigmentosa (RP), cone-rod degeneration (CRD), and Leber congenital amaurosis (LCA). With the aim to identify the causative genes in these families, we performed homozygosity mapping using whole genome single nucleotide polymorphism (SNP) arrays in 125 families. The analysis revealed the identification of 14 mutations, 5 of which are novel, in 16 of the families. The mutations were identified in the following eight genes: RDH12, PROM1, MFRP, TULP1, LCA5, CEP290, NR2E3, and EYS. While most patients had a retinal disease that is compatible with the causing gene, in some cases new clinical features are evident.
Homozygosity mapping is a powerful tool to identify genetic defects underlying heterogeneous AR disorders, such as RP and LCA, in consanguineous and nonconsanguineous patients. The identification of significant and large homozygous regions, which do not include any known retinal disease genes, may be a useful tool to identify novel disease-causing genes, using next generation sequencing.
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