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Sparse regression models for unraveling group and individual associations in eQTL mapping

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 tweeters

Citations

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4 Dimensions

Readers on

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16 Mendeley
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Title
Sparse regression models for unraveling group and individual associations in eQTL mapping
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0986-9
Pubmed ID
Authors

Wei Cheng, Yu Shi, Xiang Zhang, Wei Wang

Abstract

As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways. To alleviate this limitation, in this paper, we propose geQTL, a sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits. geQTL can also correct the effects of potential confounders. Our method employs computationally efficient technique, thus it is able to fulfill large scale studies. Moreover, our method can automatically infer the proper number of group-wise associations. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that geQTL can effectively detect both individual and group-wise signals and outperforms the state-of-the-arts by a large margin. This paper well illustrates that decoupling individual and group-wise associations for association mapping is able to improve eQTL mapping accuracy, and inferring individual and group-wise associations.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 31%
Student > Ph. D. Student 3 19%
Other 1 6%
Student > Bachelor 1 6%
Lecturer 1 6%
Other 2 13%
Unknown 3 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 38%
Agricultural and Biological Sciences 4 25%
Computer Science 2 13%
Decision Sciences 1 6%
Unknown 3 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 March 2016.
All research outputs
#4,296,302
of 9,723,213 outputs
Outputs from BMC Bioinformatics
#2,094
of 4,146 outputs
Outputs of similar age
#107,562
of 285,339 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 124 outputs
Altmetric has tracked 9,723,213 research outputs across all sources so far. This one has received more attention than most of these and is in the 55th percentile.
So far Altmetric has tracked 4,146 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 46th percentile – i.e., 46% 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 285,339 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 61% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.