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The role of gene variants in the pathogenesis of neurodegenerative disorders as revealed by next generation sequencing studies: a review

Overview of attention for article published in Translational Neurodegeneration, October 2017
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4 tweeters

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82 Mendeley
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Title
The role of gene variants in the pathogenesis of neurodegenerative disorders as revealed by next generation sequencing studies: a review
Published in
Translational Neurodegeneration, October 2017
DOI 10.1186/s40035-017-0098-0
Pubmed ID
Authors

Shirley Yin-Yu Pang, Kay-Cheong Teo, Jacob Shujui Hsu, Richard Shek-Kwan Chang, Miaoxin Li, Pak-Chung Sham, Shu-Leong Ho

Abstract

The clinical diagnosis of neurodegenerative disorders based on phenotype is difficult in heterogeneous conditions with overlapping symptoms. It does not take into account the disease etiology or the highly variable clinical course even amongst patients diagnosed with the same disorder. The advent of next generation sequencing (NGS) has allowed for a system-wide, unbiased approach to identify all gene variants in the genome simultaneously. With the plethora of new genes being identified, genetic rather than phenotype-based classification of Mendelian diseases such as spinocerebellar ataxia (SCA), hereditary spastic paraplegia (HSP) and Charcot-Marie-Tooth disease (CMT) has become widely accepted. It has also become clear that gene variants play a role in common and predominantly sporadic neurodegenerative diseases such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). The observation of pleiotropy has emerged, with mutations in the same gene giving rise to diverse phenotypes, which further increases the complexity of phenotype-genotype correlation. Possible mechanisms of pleiotropy include different downstream effects of different mutations in the same gene, presence of modifier genes, and oligogenic inheritance. Future directions include development of bioinformatics tools and establishment of more extensive public genotype/phenotype databases to better distinguish deleterious gene variants from benign polymorphisms, translation of genetic findings into pathogenic mechanisms through in-vitro and in-vivo studies, and ultimately finding disease-modifying therapies for neurodegenerative disorders.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 21 26%
Student > Ph. D. Student 16 20%
Student > Master 9 11%
Researcher 5 6%
Student > Doctoral Student 3 4%
Other 11 13%
Unknown 17 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 27%
Medicine and Dentistry 13 16%
Neuroscience 10 12%
Agricultural and Biological Sciences 7 9%
Computer Science 3 4%
Other 7 9%
Unknown 20 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 October 2017.
All research outputs
#6,914,178
of 12,019,430 outputs
Outputs from Translational Neurodegeneration
#79
of 116 outputs
Outputs of similar age
#131,788
of 273,850 outputs
Outputs of similar age from Translational Neurodegeneration
#1
of 2 outputs
Altmetric has tracked 12,019,430 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 116 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 273,850 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them