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Phenotypic variance explained by local ancestry in admixed African Americans

Overview of attention for article published in Frontiers in Genetics, October 2015
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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8 X users

Citations

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33 Mendeley
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Title
Phenotypic variance explained by local ancestry in admixed African Americans
Published in
Frontiers in Genetics, October 2015
DOI 10.3389/fgene.2015.00324
Pubmed ID
Authors

Daniel Shriner, Amy R. Bentley, Ayo P. Doumatey, Guanjie Chen, Jie Zhou, Adebowale Adeyemo, Charles N. Rotimi

Abstract

We surveyed 26 quantitative traits and disease outcomes to understand the proportion of phenotypic variance explained by local ancestry in admixed African Americans. After inferring local ancestry as the number of African-ancestry chromosomes at hundreds of thousands of genotyped loci across all autosomes, we used a linear mixed effects model to estimate the variance explained by local ancestry in two large independent samples of unrelated African Americans. We found that local ancestry at major and polygenic effect genes can explain up to 20 and 8% of phenotypic variance, respectively. These findings provide evidence that most but not all additive genetic variance is explained by genetic markers undifferentiated by ancestry. These results also inform the proportion of health disparities due to genetic risk factors and the magnitude of error in association studies not controlling for local ancestry.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Italy 1 3%
Unknown 31 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Researcher 4 12%
Student > Doctoral Student 3 9%
Professor 3 9%
Student > Postgraduate 3 9%
Other 6 18%
Unknown 6 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 27%
Agricultural and Biological Sciences 9 27%
Medicine and Dentistry 2 6%
Immunology and Microbiology 1 3%
Mathematics 1 3%
Other 2 6%
Unknown 9 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 29 August 2021.
All research outputs
#6,742,698
of 22,831,537 outputs
Outputs from Frontiers in Genetics
#2,032
of 11,822 outputs
Outputs of similar age
#84,571
of 284,657 outputs
Outputs of similar age from Frontiers in Genetics
#16
of 57 outputs
Altmetric has tracked 22,831,537 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 11,822 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 82% 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 284,657 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.