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Whole genome sequencing of one complex pedigree illustrates challenges with genomic medicine

Overview of attention for article published in BMC Medical Genomics, February 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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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1 policy source
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39 X users

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70 Mendeley
Title
Whole genome sequencing of one complex pedigree illustrates challenges with genomic medicine
Published in
BMC Medical Genomics, February 2017
DOI 10.1186/s12920-017-0246-5
Pubmed ID
Authors

Han Fang, Yiyang Wu, Hui Yang, Margaret Yoon, Laura T. Jiménez-Barrón, David Mittelman, Reid Robison, Kai Wang, Gholson J. Lyon

Abstract

Human Phenotype Ontology (HPO) has risen as a useful tool for precision medicine by providing a standardized vocabulary of phenotypic abnormalities to describe presentations of human pathologies; however, there have been relatively few reports combining whole genome sequencing (WGS) and HPO, especially in the context of structural variants. We illustrate an integrative analysis of WGS and HPO using an extended pedigree, which involves Prader-Willi Syndrome (PWS), hereditary hemochromatosis (HH), and dysautonomia-like symptoms. A comprehensive WGS pipeline was used to ensure reliable detection of genomic variants. Beyond variant filtering, we pursued phenotypic prioritization of candidate genes using Phenolyzer. Regarding PWS, WGS confirmed a 5.5 Mb de novo deletion of the parental allele at 15q11.2 to 15q13.1. Phenolyzer successfully returned the diagnosis of PWS, and pinpointed clinically relevant genes in the deletion. Further, Phenolyzer revealed how each of the genes is linked with the phenotypes represented by HPO terms. For HH, WGS identified a known disease variant (p.C282Y) in HFE of an affected female. Analysis of HPO terms alone fails to provide a correct diagnosis, but Phenolyzer successfully revealed the phenotype-genotype relationship using a disease-centric approach. Finally, Phenolyzer also revealed the complexity behind dysautonomia-like symptoms, and seven variants that might be associated with the phenotypes were identified by manual filtering based on a dominant inheritance model. The integration of WGS and HPO can inform comprehensive molecular diagnosis for patients, eliminate false positives and reveal novel insights into undiagnosed diseases. Due to extreme heterogeneity and insufficient knowledge of human diseases, it is also important that phenotypic and genomic data are standardized and shared simultaneously.

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X Demographics

The data shown below were collected from the profiles of 39 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Researcher 13 19%
Student > Bachelor 6 9%
Student > Master 6 9%
Professor 5 7%
Other 12 17%
Unknown 15 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 23%
Medicine and Dentistry 14 20%
Agricultural and Biological Sciences 12 17%
Computer Science 2 3%
Immunology and Microbiology 2 3%
Other 8 11%
Unknown 16 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 22 October 2019.
All research outputs
#1,546,009
of 26,119,990 outputs
Outputs from BMC Medical Genomics
#57
of 2,474 outputs
Outputs of similar age
#29,425
of 327,707 outputs
Outputs of similar age from BMC Medical Genomics
#6
of 36 outputs
Altmetric has tracked 26,119,990 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,474 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 97% 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 327,707 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 91% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.