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Mathematical modelling for variations of inbreeding populations fitness with single and polygenic traits

Overview of attention for article published in BMC Genomics, March 2017
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Title
Mathematical modelling for variations of inbreeding populations fitness with single and polygenic traits
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3492-1
Pubmed ID
Authors

Shuhao Sun, Fima Klebaner, Tianhai Tian

Abstract

Inbreeding mating has been widely accepted as the key mechanism to enhance homozygosity which normally will decrease the fitness of the population. Although this result has been validated by a large amount of biological data from the natural populations, a mathematical proof of these experimental discoveries is still not complete. A related question is whether we can extend the well-established result regarding the mean fitness from a randomly mating population to inbreeding populations. A confirmative answer may provide insights into the frequent occurrence of self-fertilization populations. This work presents a theoretic proof of the result that, for a large inbreeding population with directional relative genotype fitness, the mean fitness of population increases monotonically. However, it cannot be extended to the case with over-dominant genotype fitness. In addition, by employing multiplicative intersection hypothesis, we prove that inbreeding mating does decrease the mean fitness of polygenic population in general, but does not decrease the mean fitness with mixed dominant-recessive genotypes. We also prove a novel result that inbreeding depression depends on not only the mating pattern but also genetic structure of population. For natural inbreeding populations without serious inbreeding depression, our theoretical analysis suggests the majority of its genotypes should be additive or dominant-recessive genotypes. This result gives a reason to explain why many hermaphroditism populations do not show severe inbreeding depression. In addition, the calculated purging rate shows that inbreeding mating purges the deleterious mutants more efficiently than randomly mating does.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 33%
Researcher 1 17%
Unknown 3 50%
Readers by discipline Count As %
Environmental Science 1 17%
Biochemistry, Genetics and Molecular Biology 1 17%
Agricultural and Biological Sciences 1 17%
Unknown 3 50%

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 12 September 2017.
All research outputs
#9,026,143
of 11,753,826 outputs
Outputs from BMC Genomics
#5,075
of 6,985 outputs
Outputs of similar age
#175,607
of 265,127 outputs
Outputs of similar age from BMC Genomics
#76
of 96 outputs
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