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Choice of population structure informative principal components for adjustment in a case-control study

Overview of attention for article published in BMC Genetics, January 2011
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Citations

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Title
Choice of population structure informative principal components for adjustment in a case-control study
Published in
BMC Genetics, January 2011
DOI 10.1186/1471-2156-12-64
Pubmed ID
Authors

Gina M Peloso, Kathryn L Lunetta

Abstract

There are many ways to perform adjustment for population structure. It remains unclear what the optimal approach is and whether the optimal approach varies by the type of samples and substructure present. The simplest and most straightforward approach is to adjust for the continuous principal components (PCs) that capture ancestry. Through simulation, we explored the issue of which ancestry informative PCs should be adjusted for in an association model to control for the confounding nature of population structure while maintaining maximum power. A thorough examination of selecting PCs for adjustment in a case-control study across the possible structure scenarios that could occur in a genome-wide association study has not been previously reported.

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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 26%
Researcher 6 22%
Student > Bachelor 3 11%
Student > Postgraduate 2 7%
Unspecified 2 7%
Other 7 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 41%
Medicine and Dentistry 5 19%
Unspecified 3 11%
Biochemistry, Genetics and Molecular Biology 3 11%
Computer Science 1 4%
Other 4 15%

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 11 September 2012.
All research outputs
#9,905,284
of 12,372,276 outputs
Outputs from BMC Genetics
#564
of 828 outputs
Outputs of similar age
#67,843
of 86,141 outputs
Outputs of similar age from BMC Genetics
#3
of 5 outputs
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So far Altmetric has tracked 828 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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