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Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

Overview of attention for article published in BMC Genetics, January 2012
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  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

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9 tweeters

Citations

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69 Dimensions

Readers on

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117 Mendeley
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4 CiteULike
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Title
Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability
Published in
BMC Genetics, January 2012
DOI 10.1186/1471-2156-13-63
Pubmed ID
Authors

Lars Rönnegård, William Valdar

Abstract

A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
United Kingdom 2 2%
Sweden 1 <1%
Brazil 1 <1%
India 1 <1%
Norway 1 <1%
Spain 1 <1%
Germany 1 <1%
Unknown 106 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 32%
Student > Ph. D. Student 33 28%
Student > Master 12 10%
Professor > Associate Professor 6 5%
Student > Doctoral Student 6 5%
Other 12 10%
Unknown 11 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 80 68%
Biochemistry, Genetics and Molecular Biology 11 9%
Medicine and Dentistry 3 3%
Mathematics 2 2%
Engineering 2 2%
Other 6 5%
Unknown 13 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 September 2020.
All research outputs
#4,440,762
of 16,081,025 outputs
Outputs from BMC Genetics
#178
of 999 outputs
Outputs of similar age
#33,624
of 128,669 outputs
Outputs of similar age from BMC Genetics
#1
of 1 outputs
Altmetric has tracked 16,081,025 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 999 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 81% 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 128,669 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them