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How do SNP ascertainment schemes and population demographics affect inferences about population history?

Overview of attention for article published in BMC Genomics, April 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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1 policy source
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5 X users

Citations

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124 Mendeley
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Title
How do SNP ascertainment schemes and population demographics affect inferences about population history?
Published in
BMC Genomics, April 2015
DOI 10.1186/s12864-015-1469-5
Pubmed ID
Authors

Emily Jane McTavish, David M Hillis

Abstract

The selection of variable sites for inclusion in genomic analyses can influence results, especially when exemplar populations are used to determine polymorphic sites. We tested the impact of ascertainment bias on the inference of population genetic parameters using empirical and simulated data representing the three major continental groups of cattle: European, African, and Indian. We simulated data under three demographic models. Each simulated data set was subjected to three ascertainment schemes: (I) random selection; (II) geographically biased selection; and (III) selection biased toward loci polymorphic in multiple groups. Empirical data comprised samples of 25 individuals representing each continental group. These cattle were genotyped for 47,506 loci from the bovine 50 K SNP panel. We compared the inference of population histories for the empirical and simulated data sets across different ascertainment conditions using F ST and principal components analysis (PCA). Bias toward shared polymorphism across continental groups is apparent in the empirical SNP data. Bias toward uneven levels of within-group polymorphism decreases estimates of F ST between groups. Subpopulation-biased selection of SNPs changes the weighting of principal component axes and can affect inferences about proportions of admixture and population histories using PCA. PCA-based inferences of population relationships are largely congruent across types of ascertainment bias, even when ascertainment bias is strong. Analyses of ascertainment bias in genomic data have largely been conducted on human data. As genomic analyses are being applied to non-model organisms, and across taxa with deeper divergences, care must be taken to consider the potential for bias in ascertainment of variation to affect inferences. Estimates of F ST , time of separation, and population divergence as estimated by principal components analysis can be misleading if this bias is not taken into account.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Mexico 1 <1%
Unknown 119 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 29%
Researcher 26 21%
Student > Master 22 18%
Student > Bachelor 6 5%
Professor 4 3%
Other 15 12%
Unknown 15 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 76 61%
Biochemistry, Genetics and Molecular Biology 19 15%
Environmental Science 2 2%
Mathematics 2 2%
Unspecified 1 <1%
Other 7 6%
Unknown 17 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 01 January 2023.
All research outputs
#4,831,769
of 25,402,528 outputs
Outputs from BMC Genomics
#1,871
of 11,253 outputs
Outputs of similar age
#56,943
of 279,375 outputs
Outputs of similar age from BMC Genomics
#55
of 274 outputs
Altmetric has tracked 25,402,528 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,253 research outputs from this source. They receive a mean Attention Score of 4.8. 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 279,375 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 274 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.