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A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data

Overview of attention for article published in Nature Biotechnology, May 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Citations

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91 Dimensions

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210 Mendeley
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5 CiteULike
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Title
A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data
Published in
Nature Biotechnology, May 2014
DOI 10.1038/nbt.2895
Pubmed ID
Authors

Hao Hu, Jared C Roach, Hilary Coon, Stephen L Guthery, Karl V Voelkerding, Rebecca L Margraf, Jacob D Durtschi, Sean V Tavtigian, Shankaracharya, Wilfred Wu, Paul Scheet, Shuoguo Wang, Jinchuan Xing, Gustavo Glusman, Robert Hubley, Hong Li, Vidu Garg, Barry Moore, Leroy Hood, David J Galas, Deepak Srivastava, Martin G Reese, Lynn B Jorde, Mark Yandell, Chad D Huff

Abstract

High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 210 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Netherlands 1 <1%
Sweden 1 <1%
Taiwan 1 <1%
Finland 1 <1%
China 1 <1%
Belgium 1 <1%
Unknown 199 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 63 30%
Student > Ph. D. Student 48 23%
Student > Doctoral Student 15 7%
Professor > Associate Professor 14 7%
Professor 12 6%
Other 40 19%
Unknown 18 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 81 39%
Biochemistry, Genetics and Molecular Biology 56 27%
Medicine and Dentistry 23 11%
Computer Science 8 4%
Mathematics 6 3%
Other 14 7%
Unknown 22 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 59. 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 07 November 2015.
All research outputs
#697,731
of 24,981,585 outputs
Outputs from Nature Biotechnology
#1,419
of 8,695 outputs
Outputs of similar age
#6,458
of 232,983 outputs
Outputs of similar age from Nature Biotechnology
#13
of 101 outputs
Altmetric has tracked 24,981,585 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,695 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 42.8. This one has done well, scoring higher than 83% 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 232,983 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.