10 research outputs found

    A Pentanucleotide ATTTC Repeat Insertion in the Non-coding Region of DAB1, Mapping to SCA37, Causes Spinocerebellar Ataxia

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    Advances in human genetics in recent years have largely been driven by next-generation sequencing (NGS); however, the discovery of disease-related gene mutations has been biased toward the exome because the large and very repetitive regions that characterize the non-coding genome remain difficult to reach by that technology. For autosomal-dominant spinocerebellar ataxias (SCAs), 28 genes have been identified, but only five SCAs originate from non-coding mutations. Over half of SCA-affected families, however, remain without a genetic diagnosis. We used genome-wide linkage analysis, NGS, and repeat analysis to identify an (ATTTC)n insertion in a polymorphic ATTTT repeat in DAB1 in chromosomal region 1p32.2 as the cause of autosomal-dominant SCA; this region has been previously linked to SCA37. The non-pathogenic and pathogenic alleles have the configurations [(ATTTT)7-400] and [(ATTTT)60-79(ATTTC)31-75(ATTTT)58-90], respectively. (ATTTC)n insertions are present on a distinct haplotype and show an inverse correlation between size and age of onset. In the DAB1-oriented strand, (ATTTC)n is located in 5' UTR introns of cerebellar-specific transcripts arising mostly during human fetal brain development from the usage of alternative promoters, but it is maintained in the adult cerebellum. Overexpression of the transfected (ATTTC)58 insertion, but not (ATTTT)n, leads to abnormal nuclear RNA accumulation. Zebrafish embryos injected with RNA of the (AUUUC)58 insertion, but not (AUUUU)n, showed lethal developmental malformations. Together, these results establish an unstable repeat insertion in DAB1 as a cause of cerebellar degeneration; on the basis of the genetic and phenotypic evidence, we propose this mutation as the molecular basis for SCA37.info:eu-repo/semantics/publishedVersio

    A pentanucleotide ATTTC repeat insertion in the non-coding region of DAB1, mapping to SCA37, causes spinocerebellar ataxia.

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    Advances in human genetics in recent years have largely been driven by next-generation sequencing (NGS); however, the discovery of disease-related gene mutations has been biased toward the exome because the large and very repetitive regions that characterize the non-coding genome remain difficult to reach by that technology. For autosomal-dominant spinocerebellar ataxias (SCAs), 28 genes have been identified, but only five SCAs originate from non-coding mutations. Over half of SCA-affected families, however, remain without a genetic diagnosis. We used genome-wide linkage analysis, NGS, and repeat analysis to identify an (ATTTC)n insertion in a polymorphic ATTTT repeat in DAB1 in chromosomal region 1p32.2 as the cause of autosomal-dominant SCA; this region has been previously linked to SCA37. The non-pathogenic and pathogenic alleles have the configurations [(ATTTT)7-400] and [(ATTTT)60-79(ATTTC)31-75(ATTTT)58-90], respectively. (ATTTC)n insertions are present on a distinct haplotype and show an inverse correlation between size and age of onset. In the DAB1-oriented strand, (ATTTC)n is located in 5' UTR introns of cerebellar-specific transcripts arising mostly during human fetal brain development from the usage of alternative promoters, but it is maintained in the adult cerebellum. Overexpression of the transfected (ATTTC)58 insertion, but not (ATTTT)n, leads to abnormal nuclear RNA accumulation. Zebrafish embryos injected with RNA of the (AUUUC)58 insertion, but not (AUUUU)n, showed lethal developmental malformations. Together, these results establish an unstable repeat insertion in DAB1 as a cause of cerebellar degeneration; on the basis of the genetic and phenotypic evidence, we propose this mutation as the molecular basis for SCA37.We thank the families who participated in this study. We are grateful to Goncalo Abecasis, Miguel Costa, Tito Vieira, and Andre Torres for help with MERLIN analysis; Beatriz Sobrino, Jorge Amigo, and Pilar Cacheiro for next-generation sequencing analysis, performed at the Santiago de Compostela node of the Spanish National Genotyping Center; Nuno Santarem and Anabela Cordeiro-da-Silva for assistance with cloning; Antonio Amorim, Laura Vilarinho, and Paula Jorge for samples from the Portuguese population; and Paula Magalhaes from the Institute for Molecular and Cell Biology Cell Culture and Genotyping Core for DNA extraction. This work was financed by Fundo Europeu de Desenvolvimento Regional (FEDER) funds through the COMPETE 2020 Operational Program for Competitiveness and Internationalization (POCI) of Portugal 2020 and by Portuguese funds through the Fundacao para a Ciencia e a Tecnologia (FCT) and Ministerio da Ciencia, Tecnologia, e Inovacao in the framework of the project "Institute for Research and Innovation in Health Sciences" (POCI-01-0145-FEDER-007274); and by FCT grant PTDC/SAU-GMG/098305/2008 to I.S. A. I.S. was the recipient of an FCT scholarship (SFRH/BD/30702/2006). J.R.L. was supported by scholarships from PEst-C/SAU/LA0002/2013 and the European Molecular Biology Organization (ASTF494-2015). C.L.O. was supported by a scholarship from PEst-C/SAU/LA0002/2013. This work was also financed by the Porto Neurosciences and Neurologic Disease Research Initiative at the Instituto de Investigacao e Inovacao em Saude (Norte-01-0145-FEDER-000008), supported by Norte Portugal Regional Operational Programme (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement through FEDER, and by the Fondo de Investigacion Sanitaria of the Instituto de Salud Carlos III (grant PI12/00742)

    Medical genomics: The intricate path from genetic variant identification to clinical interpretation

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    The field of medical genomics involves translating high throughput genetic methods to the clinic, in order to improve diagnostic efficiency and treatment decision making. Technical questions related to sample enrichment, sequencing methodologies and variant identification and calling algorithms, still need careful investigation in order to validate the analytical step of next generation sequencing techniques for clinical applications. However, the main foreseeable challenge will be interpreting the clinical significance of the variants observed in a given patient, as well as their significance for family members and for other patients. Every step in the variant interpretation process has limitations and difficulties, and its quote of contribution to false positive and false negative results. There is no single piece of evidence enough on its own to make firm conclusions on the pathogenicity and disease causality of a given variant. A plethora of automated analysis software tools is being developed that will enhance efficiency and accuracy. However a risk of misinterpretation could derive from biased biorepository content, facilitated by annotation of variant functional consequences using previous datasets stored in the same or linked repositories. In order to improve variant interpretation and avoid an exponential accumulation of confounding noise in the medical literature, the use of terms in a standard way should be sought and requested when reporting genetic variants and their consequences. Generally, stepwise and linear interpretation processes are likely to overrate some pieces of evidence while underscoring others. Algorithms are needed that allow a multidimensional, parallel analysis of diverse lines of evidence to be carried out by expert teams for specific genes, cellular pathways or disorders
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