Evidence map›Paper›PMID 25990798›Full record

ArticleEuropean journal of human genetics : EJHG2016

Transfer of genetic therapy across human populations: molecular targets for increasing patient coverage in repeat expansion diseases.

Miguel A Varela, Helen J Curtis, Andrew G L Douglas, Suzan M Hammond, Aisling J O'Loughlin, Maria J Sobrido, Janine Scholefield, Matthew J A Wood

Abstract read
In one paragraph

Article in European journal of human genetics : EJHG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Miguel A VarelaDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Helen J CurtisDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Andrew G L DouglasDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Suzan M HammondDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Aisling J O'LoughlinDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Maria J SobridoFundacion Pública Galega de Medicina Xenómica, Clinical Hospital of Santiago, Travesía da Choupana s/n, Santiago de Compostela, Spain.
Janine ScholefieldDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Matthew J A WoodDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.

Funding

Medical Research Council
6 · The paper itself

Abstract

Allele-specific gene therapy aims to silence expression of mutant alleles through targeting of disease-linked single-nucleotide polymorphisms (SNPs). However, SNP linkage to disease varies between populations, making such molecular therapies applicable only to a subset of patients. Moreover, not all SNPs have the molecular features necessary for potent gene silencing. Here we provide knowledge to allow the maximisation of patient coverage by building a comprehensive understanding of SNPs ranked according to their predicted suitability toward allele-specific silencing in 14 repeat expansion diseases: amyotrophic lateral sclerosis and frontotemporal dementia, dentatorubral-pallidoluysian atrophy, myotonic dystrophy 1, myotonic dystrophy 2, Huntington's disease and several spinocerebellar ataxias. Our systematic analysis of DNA sequence variation shows that most annotated SNPs are not suitable for potent allele-specific silencing across populations because of suboptimal sequence features and low variability (>97% in HD). We suggest maximising patient coverage by selecting SNPs with high heterozygosity across populations, and preferentially targeting SNPs that lead to purine:purine mismatches in wild-type alleles to obtain potent allele-specific silencing. We therefore provide fundamental knowledge on strategies for optimising patient coverage of therapeutics for microsatellite expansion disorders by linking analysis of population genetic variation to the selection of molecular targets.

Indexed as

Genetic TherapyMolecular Targeted TherapyAllelesDNA Repeat ExpansionGene SilencingGenetic Diseases, InbornGenetics, PopulationHeterozygoteHumansPolymorphism, Single Nucleotide

Identifiers

PMID25990798
PMCPMC4717213

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.