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      Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error

      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , The CREAM Consortium, 23andMe Research Team, UK Biobank Eye and Vision Consortium
      Nature Genetics
      Springer Nature
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Refractive errors, including myopia, are the most frequent eye disorders worldwide and an increasingly common cause of blindness. This genome-wide association meta-analysis in 160,420 participants and replication in 95,505 participants, increased the established independent signals from 37 to 161 and revealed high genetic correlation between Europeans and Asians (>0.78). Expression experiments and comprehensive in silico analyses identified retinal cell physiology and light processing as prominent mechanisms, and functional contributions to refractive error development in all cell types of the neurosensory retina, retinal pigment epithelium, vascular endothelium and extracellular matrix. Newly identified genes elicited novel mechanisms such as rod and cone bipolar synaptic neurotransmission, anterior segment morphology, and angiogenesis. Thirty-one loci resided in or near regions transcribing small RNAs, suggesting a role for post-transcriptional regulation. Our results support the notion that refractive errors are caused by a light-dependent retina-to-sclera signaling cascade, and delineate potential pathobiological molecular drivers.

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          Most cited references88

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          The UCSC Known Genes.

          The University of California Santa Cruz (UCSC) Known Genes dataset is constructed by a fully automated process, based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank. The detailed steps of this process are described. Extensive cross-references from this dataset to other genomic and proteomic data were constructed. For each known gene, a details page is provided containing rich information about the gene, together with extensive links to other relevant genomic, proteomic and pathway data. As of July 2005, the UCSC Known Genes are available for human, mouse and rat genomes. The Known Genes serves as a foundation to support several key programs: the Genome Browser, Proteome Browser, Gene Sorter and Table Browser offered at the UCSC website. All the associated data files and program source code are also available. They can be accessed at http://genome.ucsc.edu. The genomic coverage of UCSC Known Genes, RefSeq, Ensembl Genes, H-Invitational and CCDS is analyzed. Although UCSC Known Genes offers the highest genomic and CDS coverage among major human and mouse gene sets, more detailed analysis suggests all of them could be further improved.
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            Population stratification and spurious allelic association.

            Great efforts and expense have been expended in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, technology for detection and scoring of single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a means for investigation of the genetic causes of complex human diseases. For many diseases, population-based studies of unrelated individuals--in which case-control and cohort studies serve as standard designs for genetic association analysis--can be the most practical and powerful approach. However, extensive debate has arisen about optimum study design, and considerable concern has been expressed that these approaches are prone to population stratification, which can lead to biased or spurious results. Over the past decade, a great shift has been noted, away from case-control and cohort studies, towards family-based association designs. These designs have fewer problems with population stratification but have greater genotyping and sampling requirements, and data can be difficult or impossible to gather. We discuss past evidence for population stratification on genotype-phenotype association studies, review methods to detect and account for it, and present suggestions for future study design and analysis.
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              Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral.

              Genetic testing for hereditary cancer syndromes contributes to the medical management of patients who may be at increased risk of one or more cancers. BRCA1 and BRCA2 testing for hereditary breast and ovarian cancer is one such widely used test. However, clinical testing methods with high sensitivity for deleterious mutations in these genes also detect many unclassified variants, primarily missense substitutions. We developed an extension of the Grantham difference, called A-GVGD, to score missense substitutions against the range of variation present at their position in a multiple sequence alignment. Combining two methods, co-occurrence of unclassified variants with clearly deleterious mutations and A-GVGD, we analysed most of the missense substitutions observed in BRCA1. A-GVGD was able to resolve known neutral and deleterious missense substitutions into distinct sets. Additionally, eight previously unclassified BRCA1 missense substitutions observed in trans with one or more deleterious mutations, and within the cross-species range of variation observed at their position in the protein, are now classified as neutral. The methods combined here can classify as neutral about 50% of missense substitutions that have been observed with two or more clearly deleterious mutations. Furthermore, odds ratios estimated for sets of substitutions grouped by A-GVGD scores are consistent with the hypothesis that most unclassified substitutions that are within the cross-species range of variation at their position in BRCA1 are also neutral. For most of these, clinical reclassification will require integrated application of other methods such as pooled family histories, segregation analysis, or validated functional assay.
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                Author and article information

                Journal
                Nature Genetics
                Nat Genet
                Springer Nature
                1061-4036
                1546-1718
                May 28 2018
                Article
                10.1038/s41588-018-0127-7
                aa967ecb-bbf1-40ad-9453-3ec39570f8ef
                © 2018

                http://www.springer.com/tdm

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