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A probabilistic method for identifying rare variants underlying complex traits

Overview of attention for article published in BMC Genomics, January 2013
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1 tweeter

Citations

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Title
A probabilistic method for identifying rare variants underlying complex traits
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-s1-s11
Pubmed ID
Authors

Jiayin Wang, Zhongmeng Zhao, Zhi Cao, Aiyuan Yang, Jin Zhang

Abstract

Identifying the genetic variants that contribute to disease susceptibilities is important both for developing methodologies and for studying complex diseases in molecular biology. It has been demonstrated that the spectrum of minor allelic frequencies (MAFs) of risk genetic variants ranges from common to rare. Although association studies are shifting to incorporate rare variants (RVs) affecting complex traits, existing approaches do not show a high degree of success, and more efforts should be considered.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
Hong Kong 1 5%
United States 1 5%
Sweden 1 5%
Unknown 17 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 33%
Student > Ph. D. Student 5 24%
Student > Doctoral Student 2 10%
Student > Master 2 10%
Professor 1 5%
Other 3 14%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 48%
Biochemistry, Genetics and Molecular Biology 4 19%
Medicine and Dentistry 2 10%
Sports and Recreations 1 5%
Decision Sciences 1 5%
Other 1 5%
Unknown 2 10%

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 06 March 2013.
All research outputs
#9,906,320
of 12,373,620 outputs
Outputs from BMC Genomics
#5,683
of 7,313 outputs
Outputs of similar age
#99,277
of 142,263 outputs
Outputs of similar age from BMC Genomics
#30
of 40 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 12th percentile – i.e., 12% 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 142,263 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.