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How can polygenic inheritance be used in population screening for common diseases?

Overview of attention for article published in Genetics in Medicine, February 2013
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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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
68 X users
facebook
1 Facebook page

Citations

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48 Dimensions

Readers on

mendeley
110 Mendeley
citeulike
1 CiteULike
Title
How can polygenic inheritance be used in population screening for common diseases?
Published in
Genetics in Medicine, February 2013
DOI 10.1038/gim.2012.182
Pubmed ID
Authors

Muin J. Khoury, A. Cecile J.W. Janssens, David F. Ransohoff

Abstract

Advances in genomics have near-term impact on diagnosis and management of monogenic disorders. For common complex diseases, the use of genomic information from multiple loci (polygenic model) is generally not useful for diagnosis and individual prediction. In principle, the polygenic model could be used along with other risk factors in stratified population screening to target interventions. For example, compared to age-based criterion for breast, colorectal, and prostate cancer screening, adding polygenic risk and family history holds promise for more efficient screening with earlier start and/or increased frequency of screening for segments of the population at higher absolute risk than an established screening threshold; and later start and/or decreased frequency of screening for segments of the population at lower risks. This approach, while promising, faces formidable challenges for building its evidence base and for its implementation in practice. Currently, it is unclear whether or not polygenic risk can contribute enough discrimination to make stratified screening worthwhile. Empirical data are lacking on population-based age-specific absolute risks combining genetic and non-genetic factors, on impact of polygenic risk genes on disease natural history, as well as information on comparative balance of benefits and harms of stratified interventions. Implementation challenges include difficulties in integration of this information in the current health-care system in the United States, the setting of appropriate risk thresholds, and ethical, legal, and social issues. In an era of direct-to-consumer availability of personal genomic information, the public health and health-care systems need to prepare for an evidence-based integration of this information into population screening.

X Demographics

X Demographics

The data shown below were collected from the profiles of 68 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 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Netherlands 2 2%
Denmark 1 <1%
Switzerland 1 <1%
Unknown 103 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 24%
Student > Ph. D. Student 19 17%
Student > Master 14 13%
Student > Bachelor 10 9%
Professor > Associate Professor 7 6%
Other 22 20%
Unknown 12 11%
Readers by discipline Count As %
Medicine and Dentistry 29 26%
Biochemistry, Genetics and Molecular Biology 19 17%
Agricultural and Biological Sciences 16 15%
Social Sciences 7 6%
Psychology 5 5%
Other 16 15%
Unknown 18 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 58. 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 08 May 2019.
All research outputs
#734,636
of 25,373,627 outputs
Outputs from Genetics in Medicine
#192
of 2,943 outputs
Outputs of similar age
#5,919
of 296,584 outputs
Outputs of similar age from Genetics in Medicine
#6
of 34 outputs
Altmetric has tracked 25,373,627 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 2,943 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.0. This one has done particularly well, scoring higher than 93% 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 296,584 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 98% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.