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Accurate and equitable medical genomic analysis requires an understanding of demography and its influence on sample size and ratio

Overview of attention for article published in Genome Biology, February 2017
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Title
Accurate and equitable medical genomic analysis requires an understanding of demography and its influence on sample size and ratio
Published in
Genome Biology, February 2017
DOI 10.1186/s13059-017-1172-8
Pubmed ID
Authors

Michael D. Kessler, Timothy D. O’Connor

Abstract

In a recent study, Petrovski and Goldstein reported that (non-Finnish) Europeans have significantly fewer nonsynonymous singletons in Online Mendelian Inheritance in Man (OMIM) disease genes compared with Africans, Latinos, South Asians, East Asians, and other unassigned non-Europeans. We use simulations of Exome Aggregation Consortium (ExAC) data to show that sample size and ratio interact to influence the number of these singletons identified in a cohort. These interactions are different across ancestries and can lead to the same number of identified singletons in both Europeans and non-Europeans without an equal number of samples. We conclude that there is a need to account for the ancestry-specific influence of demography on genomic architecture and rare variant analysis in order to address inequalities in medical genomic analysis.The authors of the original article were invited to submit a response, but declined to do so. Please see related Open Letter: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1016-y.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 36%
Student > Ph. D. Student 3 21%
Unspecified 1 7%
Professor 1 7%
Lecturer 1 7%
Other 2 14%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 21%
Immunology and Microbiology 2 14%
Unspecified 1 7%
Chemical Engineering 1 7%
Philosophy 1 7%
Other 4 29%
Unknown 2 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 March 2017.
All research outputs
#16,725,651
of 25,382,440 outputs
Outputs from Genome Biology
#4,057
of 4,468 outputs
Outputs of similar age
#200,483
of 325,414 outputs
Outputs of similar age from Genome Biology
#60
of 67 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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 325,414 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.