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Inherited retinal and corneal dystrophies represent a group of disorders with great genetic heterogeneity. Over 250 genes are associated with retinal diseases and 16 genes are causative of corneal dystrophies. This thesis is focused on finding the genetic causes of corneal dystrophy, Leber congenital amaurosis (LCA), Stargardt disease and retinitis pigmentosa in families from northern Sweden.  By whole exome sequencing a novel mutation, c.2816C>T, p.Thr939Ile, in Collagen Type XVII, Alpha 1 chain, COL17A1, gene was identified in several families with epithelial recurrent erosion dystrophy (ERED). We showed that the COL17A1 protein is expressed in the basement membrane of the cornea, explaining the mutation involvement in the corneal symptoms. We could link all the families in this study to a couple born in the late 1700s confirming a founder mutation in northern Sweden. Our finding highlights role of COL17A1 in ERED and suggests screening of this gene in patients with similar phenotype worldwide. Furthermore the genetic causes in several retinal degenerations were identified. In one family with two recessive disorders, LCA and Stargardt disease, a novel stop mutation, c.2557C>T, p.Gln853Stop, was detected in all LCA patients. In the Stargardt patients two intronic variants, the novel c.4773+3A>G and c.5461-10T>C, were detected in the ABCA4 gene. One individual was homozygous for the known variant c.5461-10T>C and the other one was compound heterozygote with both variants present. Both variants, c.4773+3A>G and c.5461-10T>C caused exon skipping in HEK293T cells demonstrated by in vitro splice assay, proving their pathogenicity in Stargardt disease. Finally, in recessive retinitis pigmentosa, Bothnia Dystrophy (BD), we identified a second mutation in the RLBP1 gene, c.677T>A, p.Met226Lys. Thus, BD is caused not only by common c.700C>T variant but also by homozygosity of c.677T>A or compound heterozygosity. Notably, known variant, c.40C>T, p.R14W in the CAIV gene associated with a dominant retinal dystrophy RP17 was detected in one of the compound BD heterozygote and his unaffected mother. This variant appears to be a benign variant in the population of northern Sweden.

In conclusion, novel genetic causes of retinal dystrophies in northern Sweden were found demonstrating the heterogeneity and complexity of retinal diseases. Identification of the genetic defect in COL17A1 in the corneal dystrophy contributes to understanding ERED pathogenesis and encourages refinement of IC3D classification. Our results provide valuable information for future molecular testing and genetic counselling of the families.

Understanding how the genetic variations can affect characteristics and function of organisms can help researchers and medical doctors to detect genetic alterations that cause disease and reveal genes that causes antibiotic resistance. The opportunities and progress associated with such data come however with challenges related to statistical analysis. It is only by using properly designed and employed tools, that we can extract the information about hidden patterns. In this thesis we present three types of such analysis. First, the genetic variant in the gene COL17A1 that causes corneal dystrophy with recurrent erosions is reveled. By studying Next-generation sequencing data, the order of the nucleotides in the DNAsequence was be obtained, which enabled us to detect interesting variants in the genome. Further, we present results of an experimental design study with the aim to make the best selection from a family that is affected by an inherited disease. In second part of the work, we analyzed a novel antibiotic resistance Staphylococcus epidermidis clone that is only found in northern Europe. By investigating its genetic data, we revealed similarities to a world known antibiotic resistance clone. As a result, the antibiotic resistance profile is established from the DNA sequences. Finally, we also focus on the challenges related to the abundance of genetic data from different sources. The increasing number of public gene expression datasets gives us opportunity to increase our understanding by using information from multiple sources simultaneously. Naturally, this requires merging independent datasets together. However, when doing so, the technical and biological variation in the joined data increases. We present a pre-processing method to construct gene co-expression networks from a large diverse gene-expression dataset.