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Approaches to the detection of recessive effects using next generation sequencing data from outbred populations

Overview of attention for article published in Advances and Applications in Bioinformatics and Chemistry : AABC, June 2013
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Title
Approaches to the detection of recessive effects using next generation sequencing data from outbred populations
Published in
Advances and Applications in Bioinformatics and Chemistry : AABC, June 2013
DOI 10.2147/aabc.s44332
Pubmed ID
Authors

David Curtis

Abstract

Conventional methods to analyze genome-wide association studies and whole exome or whole genome sequencing studies would be prone to overlook variants which might exert a recessive effect on risk of disease, either as homozygotes or compound heterozygotes. It is plausible that such effects may be common even in outbred populations. An approach is described which is based on identifying a set of variants in a gene as being potentially of interest and then testing whether there is an excess of cases who are either homozygotes or complex heterozygotes for these variants. Methods based on departure from Hardy-Weinberg equilibrium are more powerful than those which compare cases to controls. However, linkage disequilibrium between variants can be difficult to deal with if phase is unknown. A simple approach for discarding variants apparently in strong linkage disequilibrium with others is proposed. The procedure is simple and quick to apply so can be used in the context of whole genome or exome sequencing studies and is implemented in the SCOREASSOC program.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
United States 1 4%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 7 27%
Student > Master 3 12%
Student > Doctoral Student 3 12%
Other 1 4%
Other 2 8%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 35%
Medicine and Dentistry 5 19%
Biochemistry, Genetics and Molecular Biology 5 19%
Veterinary Science and Veterinary Medicine 1 4%
Unknown 6 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 April 2014.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Advances and Applications in Bioinformatics and Chemistry : AABC
#41
of 55 outputs
Outputs of similar age
#181,521
of 206,481 outputs
Outputs of similar age from Advances and Applications in Bioinformatics and Chemistry : AABC
#1
of 1 outputs
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So far Altmetric has tracked 55 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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