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Rare variants analysis using penalization methods for whole genome sequence data

Overview of attention for article published in BMC Bioinformatics, December 2015
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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1 Facebook page

Citations

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16 Dimensions

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37 Mendeley
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Title
Rare variants analysis using penalization methods for whole genome sequence data
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/s12859-015-0825-4
Pubmed ID
Authors

Akram Yazdani, Azam Yazdani, Eric Boerwinkle

Abstract

Availability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration. We introduce a new approach that applies restricted principal component analysis with convex penalization and then selects the best predictors of a phenotype by a concave penalized regression model, while estimating the impact of each genomic region on the phenotype. Using simulated data, we show that the proposed method maintains good power for association testing while keeping the false discovery rate low under a verity of genetic architectures. Illustrative data analyses reveal encouraging result of this method in comparison with other commonly applied methods for rare variants analysis. By taking into account linkage disequilibrium and sparseness of the data, the proposed method improves power and controls the false discovery rate compared to other commonly applied methods for rare variant analyses.

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X Demographics

The data shown below were collected from the profiles of 10 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 36 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 35%
Student > Master 9 24%
Student > Bachelor 3 8%
Other 2 5%
Student > Ph. D. Student 2 5%
Other 4 11%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 35%
Agricultural and Biological Sciences 10 27%
Computer Science 4 11%
Medicine and Dentistry 2 5%
Mathematics 1 3%
Other 0 0%
Unknown 7 19%
Attention Score in Context

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 02 May 2016.
All research outputs
#6,800,845
of 22,834,308 outputs
Outputs from BMC Bioinformatics
#2,585
of 7,288 outputs
Outputs of similar age
#106,429
of 387,469 outputs
Outputs of similar age from BMC Bioinformatics
#53
of 149 outputs
Altmetric has tracked 22,834,308 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% 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 387,469 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 149 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 62% of its contemporaries.