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Model based heritability scores for high-throughput sequencing data

Overview of attention for article published in BMC Bioinformatics, March 2017
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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 (81st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters

Citations

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5 Dimensions

Readers on

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30 Mendeley
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Title
Model based heritability scores for high-throughput sequencing data
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1539-6
Pubmed ID
Authors

Pratyaydipta Rudra, W. Jenny Shi, Brian Vestal, Pamela H. Russell, Aaron Odell, Robin D. Dowell, Richard A. Radcliffe, Laura M. Saba, Katerina Kechris

Abstract

Heritability of a phenotypic or molecular trait measures the proportion of variance that is attributable to genotypic variance. It is an important concept in breeding and genetics. Few methods are available for calculating heritability for traits derived from high-throughput sequencing. We propose several statistical models and different methods to compute and test a heritability measure for such data based on linear and generalized linear mixed effects models. We also provide methodology for hypothesis testing and interval estimation. Our analyses show that, among the methods, the negative binomial mixed model (NB-fit), compound Poisson mixed model (CP-fit), and the variance stabilizing transformed linear mixed model (VST) outperform the voom-transformed linear mixed model (voom). NB-fit and VST appear to be more robust than CP-fit for estimating and testing the heritability scores, while NB-fit is the most computationally expensive. CP-fit performed best in terms of the coverage of the confidence intervals. In addition, we applied the methods to both microRNA (miRNA) and messenger RNA (mRNA) sequencing datasets from a recombinant inbred mouse panel. We show that miRNA and mRNA expression can be a highly heritable molecular trait in mouse, and that some top heritable features coincide with expression quantitative trait loci. The models and methods we investigated in this manuscript is applicable and extendable to sequencing experiments where some biological replicates are available and the environmental variation is properly controlled. The CP-fit approach for assessing heritability was implemented for the first time to our knowledge. All the methods presented, as well as the generation of simulated sequencing data under either negative binomial or compound Poisson mixed models, are provided in the R package HeritSeq.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 27%
Student > Ph. D. Student 6 20%
Student > Master 5 17%
Student > Doctoral Student 3 10%
Other 2 7%
Other 2 7%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 30%
Biochemistry, Genetics and Molecular Biology 4 13%
Mathematics 4 13%
Engineering 2 7%
Medicine and Dentistry 2 7%
Other 3 10%
Unknown 6 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 March 2018.
All research outputs
#1,847,397
of 15,508,924 outputs
Outputs from BMC Bioinformatics
#723
of 5,662 outputs
Outputs of similar age
#47,350
of 260,701 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 24 outputs
Altmetric has tracked 15,508,924 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,662 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 87% 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 260,701 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.