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The effects of sample size on population genomic analyses – implications for the tests of neutrality

Overview of attention for article published in BMC Genomics, February 2016
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Article details
Title
The effects of sample size on population genomic analyses – implications for the tests of neutrality
Published in
BMC Genomics, February 2016
DOI 10.1186/s12864-016-2441-8
Pubmed ID
Authors
Abstract

One of the fundamental measures of molecular genetic variation is the Watterson's estimator (θ), which is based on the number of segregating sites. The estimation of θ is unbiased only under neutrality and constant population growth. It is well known that the estimation of θ is biased when these assumptions are violated. However, the effects of sample size in modulating the bias was not well appreciated. We examined this issue in detail based on large-scale exome data and robust simulations. Our investigation revealed that sample size appreciably influences θ estimation and this effect was much higher for constrained genomic regions than that of neutral regions. For instance, θ estimated for synonymous sites using 512 human exomes was 1.9 times higher than that obtained using 16 exomes. However, this difference was 2.5 times for the nonsynonymous sites of the same data. We observed a positive correlation between the rate of increase in θ estimates (with respect to the sample size) and the magnitude of selection pressure. For example, θ estimated for the nonsynonymous sites of highly constrained genes (dN/dS < 0.1) using 512 exomes was 3.6 times higher than that estimated using 16 exomes. In contrast this difference was only 2 times for the less constrained genes (dN/dS > 0.9). The results of this study reveal the extent of underestimation owing to small sample sizes and thus emphasize the importance of sample size in estimating a number of population genomic parameters. Our results have serious implications for neutrality tests such as Tajima D, Fu-Li D and those based on the McDonald and Kreitman test: Neutrality Index and the fraction of adaptive substitutions. For instance, use of 16 exomes produced 2.4 times higher proportion of adaptive substitutions compared to that obtained using 512 exomes (24 % vs 10 %).

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 1%
United Kingdom 1 <1%
Chile 1 <1%
Brazil 1 <1%
Unknown 171 97%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 44 25%
Student > Master 30 17%
Researcher 23 13%
Student > Bachelor 18 10%
Student > Doctoral Student 8 5%
Other 17 10%
Unknown 36 20%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 89 51%
Biochemistry, Genetics and Molecular Biology 32 18%
Environmental Science 3 2%
Medicine and Dentistry 2 1%
Veterinary Science and Veterinary Medicine 1 <1%
Other 7 4%
Unknown 42 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 15 February 2021.
All research outputs
#20,009,661
of 32,836,612 outputs
Outputs from BMC Genomics
#6,409
of 12,677 outputs
Outputs of similar age
#169,789
of 328,325 outputs
Outputs of similar age from BMC Genomics
#118
of 242 outputs
Altmetric has tracked 32,836,612 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,677 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 48th percentile – i.e., 48% 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 328,325 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 242 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.