Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations

Tshela Elizabeth Mason; James Lindesay; Georgia Mae Dunston

doi:doi:10.11648/j.ajpa.20190702.15

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Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations

Tshela Elizabeth Mason, James Lindesay, Georgia Mae Dunston

Published in American Journal of Physics and Applications (Volume 7, Issue 2)

Received: 3 March 2019 Accepted: 9 May 2019 Published: 13 June 2019

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Abstract

Using genodynamics, the Howard University biophysics research and interdisciplinary development group transforms genomic sequence data into genomic energy measures to explore the science of genome variation in population diversity and human biology. Genodynamics utilizes the statistical distribution of single nucleotide polymorphism (SNP) data from the Haplotype Map project to mathematically model whole genome-environment interactions in human adaptation to environmental stressors/stimuli by functionally parameterizing the interplay between the biophysical and environmental factors in a quantifiable manner. Our double-blind computer program flagged smooth mathematical function relationships between allelic energies of two SNPs in intron one of the egl-9 family hypoxia inducible factor 1 (EGLN1) and the environmental parameter averaged ancestral annual ultraviolet radiation exposure. EGLN1 is a gene on chromosome 1 known to play an essential role in the regulation of the hypoxia inducible factor pathway. We have demonstrated that our genodynamics approach can quantify, through adaptive forces, the effects that environmental stressors/stimuli have had on patterns of common variation in the human genome and by doing so offer an alternative means of investigating the implications of SNP information dynamics on natural selection in human populations.

Published in	American Journal of Physics and Applications (Volume 7, Issue 2)
DOI	10.11648/j.ajpa.20190702.15
Page(s)	61-67
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Population Diversity, Modeling Whole Genome Adaptation, SNP Information Dynamics, Genodynamics, Natural Selection in Human Populations

References

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APA Style

Tshela Elizabeth Mason, James Lindesay, Georgia Mae Dunston. (2019). Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations. American Journal of Physics and Applications, 7(2), 61-67. https://doi.org/10.11648/j.ajpa.20190702.15

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ACS Style

Tshela Elizabeth Mason; James Lindesay; Georgia Mae Dunston. Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations. Am. J. Phys. Appl. 2019, 7(2), 61-67. doi: 10.11648/j.ajpa.20190702.15

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AMA Style

Tshela Elizabeth Mason, James Lindesay, Georgia Mae Dunston. Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations. Am J Phys Appl. 2019;7(2):61-67. doi: 10.11648/j.ajpa.20190702.15

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@article{10.11648/j.ajpa.20190702.15,
  author = {Tshela Elizabeth Mason and James Lindesay and Georgia Mae Dunston},
  title = {Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations},
  journal = {American Journal of Physics and Applications},
  volume = {7},
  number = {2},
  pages = {61-67},
  doi = {10.11648/j.ajpa.20190702.15},
  url = {https://doi.org/10.11648/j.ajpa.20190702.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpa.20190702.15},
  abstract = {Using genodynamics, the Howard University biophysics research and interdisciplinary development group transforms genomic sequence data into genomic energy measures to explore the science of genome variation in population diversity and human biology. Genodynamics utilizes the statistical distribution of single nucleotide polymorphism (SNP) data from the Haplotype Map project to mathematically model whole genome-environment interactions in human adaptation to environmental stressors/stimuli by functionally parameterizing the interplay between the biophysical and environmental factors in a quantifiable manner. Our double-blind computer program flagged smooth mathematical function relationships between allelic energies of two SNPs in intron one of the egl-9 family hypoxia inducible factor 1 (EGLN1) and the environmental parameter averaged ancestral annual ultraviolet radiation exposure. EGLN1 is a gene on chromosome 1 known to play an essential role in the regulation of the hypoxia inducible factor pathway. We have demonstrated that our genodynamics approach can quantify, through adaptive forces, the effects that environmental stressors/stimuli have had on patterns of common variation in the human genome and by doing so offer an alternative means of investigating the implications of SNP information dynamics on natural selection in human populations.},
 year = {2019}
}

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TY  - JOUR
T1  - Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations
AU  - Tshela Elizabeth Mason
AU  - James Lindesay
AU  - Georgia Mae Dunston
Y1  - 2019/06/13
PY  - 2019
N1  - https://doi.org/10.11648/j.ajpa.20190702.15
DO  - 10.11648/j.ajpa.20190702.15
T2  - American Journal of Physics and Applications
JF  - American Journal of Physics and Applications
JO  - American Journal of Physics and Applications
SP  - 61
EP  - 67
PB  - Science Publishing Group
SN  - 2330-4308
UR  - https://doi.org/10.11648/j.ajpa.20190702.15
AB  - Using genodynamics, the Howard University biophysics research and interdisciplinary development group transforms genomic sequence data into genomic energy measures to explore the science of genome variation in population diversity and human biology. Genodynamics utilizes the statistical distribution of single nucleotide polymorphism (SNP) data from the Haplotype Map project to mathematically model whole genome-environment interactions in human adaptation to environmental stressors/stimuli by functionally parameterizing the interplay between the biophysical and environmental factors in a quantifiable manner. Our double-blind computer program flagged smooth mathematical function relationships between allelic energies of two SNPs in intron one of the egl-9 family hypoxia inducible factor 1 (EGLN1) and the environmental parameter averaged ancestral annual ultraviolet radiation exposure. EGLN1 is a gene on chromosome 1 known to play an essential role in the regulation of the hypoxia inducible factor pathway. We have demonstrated that our genodynamics approach can quantify, through adaptive forces, the effects that environmental stressors/stimuli have had on patterns of common variation in the human genome and by doing so offer an alternative means of investigating the implications of SNP information dynamics on natural selection in human populations.
VL  - 7
IS  - 2
ER  -

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Author Information

Tshela Elizabeth Mason

The National Human Genome Center, Howard University, Washington, USA
James Lindesay

The National Human Genome Center, Howard University, Washington, USA; Computational Physics Laboratory, Department of Physics and Astronomy, Howard University, Washington, USA
Georgia Mae Dunston

The National Human Genome Center, Howard University, Washington, USA; Computational Physics Laboratory, Department of Physics and Astronomy, Howard University, Washington, USA

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Plain Text BibTeX RIS

APA Style

Tshela Elizabeth Mason, James Lindesay, Georgia Mae Dunston. (2019). Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations. American Journal of Physics and Applications, 7(2), 61-67. https://doi.org/10.11648/j.ajpa.20190702.15

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ACS Style

Tshela Elizabeth Mason; James Lindesay; Georgia Mae Dunston. Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations. Am. J. Phys. Appl. 2019, 7(2), 61-67. doi: 10.11648/j.ajpa.20190702.15

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AMA Style

Tshela Elizabeth Mason, James Lindesay, Georgia Mae Dunston. Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations. Am J Phys Appl. 2019;7(2):61-67. doi: 10.11648/j.ajpa.20190702.15

Copy | Download

@article{10.11648/j.ajpa.20190702.15,
  author = {Tshela Elizabeth Mason and James Lindesay and Georgia Mae Dunston},
  title = {Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations},
  journal = {American Journal of Physics and Applications},
  volume = {7},
  number = {2},
  pages = {61-67},
  doi = {10.11648/j.ajpa.20190702.15},
  url = {https://doi.org/10.11648/j.ajpa.20190702.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpa.20190702.15},
  abstract = {Using genodynamics, the Howard University biophysics research and interdisciplinary development group transforms genomic sequence data into genomic energy measures to explore the science of genome variation in population diversity and human biology. Genodynamics utilizes the statistical distribution of single nucleotide polymorphism (SNP) data from the Haplotype Map project to mathematically model whole genome-environment interactions in human adaptation to environmental stressors/stimuli by functionally parameterizing the interplay between the biophysical and environmental factors in a quantifiable manner. Our double-blind computer program flagged smooth mathematical function relationships between allelic energies of two SNPs in intron one of the egl-9 family hypoxia inducible factor 1 (EGLN1) and the environmental parameter averaged ancestral annual ultraviolet radiation exposure. EGLN1 is a gene on chromosome 1 known to play an essential role in the regulation of the hypoxia inducible factor pathway. We have demonstrated that our genodynamics approach can quantify, through adaptive forces, the effects that environmental stressors/stimuli have had on patterns of common variation in the human genome and by doing so offer an alternative means of investigating the implications of SNP information dynamics on natural selection in human populations.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations
AU  - Tshela Elizabeth Mason
AU  - James Lindesay
AU  - Georgia Mae Dunston
Y1  - 2019/06/13
PY  - 2019
N1  - https://doi.org/10.11648/j.ajpa.20190702.15
DO  - 10.11648/j.ajpa.20190702.15
T2  - American Journal of Physics and Applications
JF  - American Journal of Physics and Applications
JO  - American Journal of Physics and Applications
SP  - 61
EP  - 67
PB  - Science Publishing Group
SN  - 2330-4308
UR  - https://doi.org/10.11648/j.ajpa.20190702.15
AB  - Using genodynamics, the Howard University biophysics research and interdisciplinary development group transforms genomic sequence data into genomic energy measures to explore the science of genome variation in population diversity and human biology. Genodynamics utilizes the statistical distribution of single nucleotide polymorphism (SNP) data from the Haplotype Map project to mathematically model whole genome-environment interactions in human adaptation to environmental stressors/stimuli by functionally parameterizing the interplay between the biophysical and environmental factors in a quantifiable manner. Our double-blind computer program flagged smooth mathematical function relationships between allelic energies of two SNPs in intron one of the egl-9 family hypoxia inducible factor 1 (EGLN1) and the environmental parameter averaged ancestral annual ultraviolet radiation exposure. EGLN1 is a gene on chromosome 1 known to play an essential role in the regulation of the hypoxia inducible factor pathway. We have demonstrated that our genodynamics approach can quantify, through adaptive forces, the effects that environmental stressors/stimuli have had on patterns of common variation in the human genome and by doing so offer an alternative means of investigating the implications of SNP information dynamics on natural selection in human populations.
VL  - 7
IS  - 2
ER  -

Copy | Download