Modeling of autosomal-dominant retinitis pigmentosa in Caenorhabditis elegans uncovers a nexus between global impaired functioning of certain splicing factors and cell type-specific apoptosis

Retinitis pigmentosa (RP) is a rare genetic disease that causes gradual blindness through retinal degeneration. Intriguingly, seven of the 24 genes identified as responsible for the autosomal-dominant form (adRP) are ubiquitous spliceosome components whose impairment causes disease only in the retina. The fact that these proteins are essential in all organisms hampers genetic, genomic, and physiological studies, but we addressed these difficulties by using RNAi in Caenorhabditis elegans. Our study of worm phenotypes produced by RNAi of splicing-related adRP (s-adRP) genes functionally distinguishes between components of U4 and U5 snRNP complexes, because knockdown of U5 proteins produces a stronger phenotype. RNA-seq analyses of worms where s-adRP genes were partially inactivated by RNAi, revealed mild intron retention in developing animals but not in adults, suggesting a positive correlation between intron retention and transcriptional activity. interestingly, RNAi of s-adRP genes produces an increase in the expression of atl-1 (homolog of human ATR), which is normally activated in response to replicative stress and certain DNA-damaging agents. The up-regulation of atl-1 correlates with the ectopic expression of the pro-apoptotic gene egl-1 and apoptosis in hypodermal cells, which produce the cuticle, but not in other cell types. Our model in C. elegans resembles s-adRP in two aspects: The phenotype caused by global knockdown of s-adRP genes is cell type-specific and associated with high transcriptional activity. Finally, along with a reduced production of mature transcripts, we propose a model in which the retina-specific cell death in s-adRP patients can be induced through genomic instability

Insights into the Genetic Architecture of Early Stage Age-Related Macular Degeneration: A Genome-Wide Association Study Meta-Analysis

10.1371/journal.pone.0053830PLoS ONE81

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants