↓ Skip to main content

Hypothesis testing of meiotic recombination rates from population genetic data

Overview of attention for article published in BMC Genetics, November 2014
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Readers on

mendeley
10 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Hypothesis testing of meiotic recombination rates from population genetic data
Published in
BMC Genetics, November 2014
DOI 10.1186/s12863-014-0122-7
Pubmed ID
Authors

Junming Yin

Abstract

BackgroundMeiotic recombination, one of the central biological processes studied in population genetics, comes in two known forms: crossovers and gene conversions. A number of previous studies have shown that when one of these two events is nonexistent in the genealogical model, the point estimation of the corresponding recombination rate by population genetic methods tends to be inflated. Therefore, it has become necessary to obtain statistical evidence from population genetic data about whether one of the two recombination events is absent.ResultsIn this paper, we formulate this problem in a hypothesis testing framework and devise a testing procedure based on the likelihood ratio test (LRT). However, because the null value (i.e., zero) lies on the boundary of the parameter space, the regularity conditions for the large-sample approximation to the distribution of the LRT statistic do not apply. In turn, the standard chi-squared approximation is inaccurate. To address this critical issue, we propose a parametric bootstrap procedure to obtain an approximate p-value for the observed test statistic. Coalescent simulations are conducted to show that our approach yields accurate null p-values that closely follow the theoretical prediction while the estimated alternative p-values tend to concentrate closer to zero. Finally, the method is demonstrated on a real biological data set from the telomere of the X chromosome of African Drosophila melanogaster.ConclusionsOur methodology provides a necessary complement to the existing procedures of estimating meiotic recombination rates from population genetic data.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 30%
France 1 10%
Unknown 6 60%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 30%
Researcher 3 30%
Student > Bachelor 1 10%
Student > Master 1 10%
Professor 1 10%
Other 1 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 50%
Biochemistry, Genetics and Molecular Biology 1 10%
Business, Management and Accounting 1 10%
Economics, Econometrics and Finance 1 10%
Social Sciences 1 10%
Other 1 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 October 2015.
All research outputs
#8,687,830
of 11,293,566 outputs
Outputs from BMC Genetics
#487
of 776 outputs
Outputs of similar age
#156,870
of 251,570 outputs
Outputs of similar age from BMC Genetics
#26
of 41 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 776 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 31st percentile – i.e., 31% 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 251,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.