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Robust methods for population stratification in genome wide association studies

Overview of attention for article published in BMC Bioinformatics, April 2013
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Title
Robust methods for population stratification in genome wide association studies
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-132
Pubmed ID
Authors

Li Liu, Donghui Zhang, Hong Liu, Christopher Arendt

Abstract

Genome-wide association studies can provide novel insights into diseases of interest, as well as to the responsiveness of an individual to specific treatments. In such studies, it is very important to correct for population stratification, which refers to allele frequency differences between cases and controls due to systematic ancestry differences. Population stratification can cause spurious associations if not adjusted properly. The principal component analysis (PCA) method has been relied upon as a highly useful methodology to adjust for population stratification in these types of large-scale studies. Recently, the linear mixed model (LMM) has also been proposed to account for family structure or cryptic relatedness. However, neither of these approaches may be optimal in properly correcting for sample structures in the presence of subject outliers.

X Demographics

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The data shown below were collected from the profiles of 5 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 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Brazil 1 <1%
Belgium 1 <1%
Thailand 1 <1%
United States 1 <1%
Unknown 119 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 25%
Researcher 32 25%
Student > Master 18 14%
Student > Bachelor 15 12%
Student > Doctoral Student 6 5%
Other 13 10%
Unknown 10 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 40%
Biochemistry, Genetics and Molecular Biology 20 16%
Medicine and Dentistry 13 10%
Computer Science 10 8%
Mathematics 6 5%
Other 14 11%
Unknown 13 10%
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 23 August 2015.
All research outputs
#13,383,307
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#4,191
of 7,255 outputs
Outputs of similar age
#105,696
of 197,527 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 125 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 197,527 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.