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Advantages and pitfalls in the application of mixed-model association methods

Overview of attention for article published in Nature Genetics, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

news
1 news outlet
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19 X users
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1 patent
facebook
2 Facebook pages
wikipedia
1 Wikipedia page
googleplus
1 Google+ user
q&a
1 Q&A thread

Citations

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798 Dimensions

Readers on

mendeley
1263 Mendeley
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10 CiteULike
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Title
Advantages and pitfalls in the application of mixed-model association methods
Published in
Nature Genetics, January 2014
DOI 10.1038/ng.2876
Pubmed ID
Authors

Jian Yang, Noah A Zaitlen, Michael E Goddard, Peter M Visscher, Alkes L Price

Abstract

Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 1,263 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 26 2%
Spain 4 <1%
Australia 4 <1%
France 4 <1%
United Kingdom 4 <1%
Germany 3 <1%
Chile 2 <1%
Brazil 2 <1%
Canada 2 <1%
Other 18 1%
Unknown 1194 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 348 28%
Researcher 288 23%
Student > Master 140 11%
Student > Bachelor 85 7%
Student > Doctoral Student 64 5%
Other 194 15%
Unknown 144 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 508 40%
Biochemistry, Genetics and Molecular Biology 196 16%
Medicine and Dentistry 86 7%
Computer Science 49 4%
Mathematics 42 3%
Other 191 15%
Unknown 191 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 December 2023.
All research outputs
#1,290,622
of 25,571,620 outputs
Outputs from Nature Genetics
#2,003
of 7,593 outputs
Outputs of similar age
#14,214
of 324,288 outputs
Outputs of similar age from Nature Genetics
#29
of 61 outputs
Altmetric has tracked 25,571,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,593 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.0. This one has gotten more attention than average, scoring higher than 73% of its peers.
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 324,288 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.