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Reducing the exome search space for Mendelian diseases using genetic linkage analysis of exome genotypes

Overview of attention for article published in Genome Biology, September 2011
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Citations

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73 Dimensions

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206 Mendeley
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9 CiteULike
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Title
Reducing the exome search space for Mendelian diseases using genetic linkage analysis of exome genotypes
Published in
Genome Biology, September 2011
DOI 10.1186/gb-2011-12-9-r85
Pubmed ID
Authors

Katherine R Smith, Catherine J Bromhead, Michael S Hildebrand, A Eliot Shearer, Paul J Lockhart, Hossein Najmabadi, Richard J Leventer, George McGillivray, David J Amor, Richard J Smith, Melanie Bahlo

Abstract

Many exome sequencing studies of Mendelian disorders fail to optimally exploit family information. Classical genetic linkage analysis is an effective method for eliminating a large fraction of the candidate causal variants discovered, even in small families that lack a unique linkage peak. We demonstrate that accurate genetic linkage mapping can be performed using SNP genotypes extracted from exome data, removing the need for separate array-based genotyping. We provide software to facilitate such analyses.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 206 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
Italy 4 2%
United Kingdom 3 1%
Sweden 2 <1%
France 2 <1%
Germany 1 <1%
Belgium 1 <1%
Finland 1 <1%
China 1 <1%
Other 1 <1%
Unknown 183 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 74 36%
Student > Ph. D. Student 51 25%
Student > Master 13 6%
Professor 12 6%
Other 12 6%
Other 29 14%
Unknown 15 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 91 44%
Medicine and Dentistry 45 22%
Biochemistry, Genetics and Molecular Biology 29 14%
Computer Science 9 4%
Neuroscience 5 2%
Other 10 5%
Unknown 17 8%
Attention Score in Context

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 01 February 2016.
All research outputs
#15,169,543
of 25,373,627 outputs
Outputs from Genome Biology
#3,931
of 4,467 outputs
Outputs of similar age
#88,367
of 137,127 outputs
Outputs of similar age from Genome Biology
#40
of 46 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 11th percentile – i.e., 11% 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 137,127 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.