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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 (75th percentile)

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7 tweeters

Citations

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39 Dimensions

Readers on

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107 Mendeley
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3 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Mexico 1 <1%
Unknown 102 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 33%
Researcher 22 21%
Student > Master 20 19%
Student > Bachelor 6 6%
Student > Postgraduate 4 4%
Other 10 9%
Unknown 10 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 64%
Biochemistry, Genetics and Molecular Biology 17 16%
Environmental Science 2 2%
Mathematics 2 2%
Nursing and Health Professions 1 <1%
Other 5 5%
Unknown 12 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 06 June 2015.
All research outputs
#4,751,794
of 19,499,600 outputs
Outputs from BMC Genomics
#2,049
of 9,809 outputs
Outputs of similar age
#57,449
of 235,968 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 19,499,600 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,809 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 79% of its peers.
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