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Generalized Linear Model for Mapping Discrete Trait Loci Implemented with LASSO Algorithm

Overview of attention for article published in PLoS ONE, September 2014
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  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 tweeters

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Readers on

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Title
Generalized Linear Model for Mapping Discrete Trait Loci Implemented with LASSO Algorithm
Published in
PLoS ONE, September 2014
DOI 10.1371/journal.pone.0106985
Pubmed ID
Authors

Jun Xing, Huijiang Gao, Yang Wu, Yani Wu, Hongwang Li, Runqing Yang

Abstract

Generalized estimating equation (GEE) algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS) method for continuous traits to discrete traits. In contrast to mixture model-based expectation-maximization (EM) algorithm, the GEE algorithm can well detect quantitative trait locus (QTL), especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO) is derived for generalized linear model (GLM) with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.

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

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 31%
Student > Ph. D. Student 3 23%
Researcher 3 23%
Professor 1 8%
Student > Doctoral Student 1 8%
Other 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 69%
Mathematics 1 8%
Unspecified 1 8%
Computer Science 1 8%
Environmental Science 1 8%
Other 0 0%

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 26 May 2015.
All research outputs
#3,681,018
of 7,909,943 outputs
Outputs from PLoS ONE
#53,307
of 111,194 outputs
Outputs of similar age
#75,441
of 187,946 outputs
Outputs of similar age from PLoS ONE
#1,444
of 2,629 outputs
Altmetric has tracked 7,909,943 research outputs across all sources so far. This one has received more attention than most of these and is in the 51st percentile.
So far Altmetric has tracked 111,194 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one is in the 48th percentile – i.e., 48% 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 187,946 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 56% of its contemporaries.
We're also able to compare this research output to 2,629 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.