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Weighted selective collapsing strategy for detecting rare and common variants in genetic association study

Overview of attention for article published in BMC Genetics, January 2012
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2 tweeters

Citations

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6 Dimensions

Readers on

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20 Mendeley
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Title
Weighted selective collapsing strategy for detecting rare and common variants in genetic association study
Published in
BMC Genetics, January 2012
DOI 10.1186/1471-2156-13-7
Pubmed ID
Authors

Yilin Dai, Renfang Jiang, Jianping Dong

Abstract

Genome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability. With the development of next-generation sequencing technologies, researchers find more and more evidence to support the role played by rare variants in heritable variability. However, rare and common variants are often studied separately. The objective of this paper is to develop a robust strategy to analyze association between complex traits and genetic regions using both common and rare variants.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 45%
Student > Ph. D. Student 4 20%
Professor > Associate Professor 2 10%
Professor 1 5%
Student > Doctoral Student 1 5%
Other 1 5%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 55%
Biochemistry, Genetics and Molecular Biology 6 30%
Social Sciences 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 10 February 2012.
All research outputs
#2,978,979
of 4,505,992 outputs
Outputs from BMC Genetics
#299
of 477 outputs
Outputs of similar age
#139,726
of 234,198 outputs
Outputs of similar age from BMC Genetics
#11
of 17 outputs
Altmetric has tracked 4,505,992 research outputs across all sources so far. This one is in the 31st percentile – i.e., 31% of other outputs scored the same or lower than it.
So far Altmetric has tracked 477 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.