Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations
American Journal of Physics and Applications
Volume 7, Issue 2, March 2019, Pages: 61-67
Received: Mar. 3, 2019; Accepted: May 9, 2019; Published: Jun. 13, 2019
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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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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.
Population Diversity, Modeling Whole Genome Adaptation, SNP Information Dynamics, Genodynamics, Natural Selection in Human Populations
To cite this article
Tshela Elizabeth Mason, James Lindesay, Georgia Mae Dunston, Genodynamics: A New Biophysical Approach to Modeling Adaptation in Human Populations, American Journal of Physics and Applications. Vol. 7, No. 2, 2019, pp. 61-67. doi: 10.11648/j.ajpa.20190702.15
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bigham, A. W., and F. S. Lee (2014). Human high altitude adaptation: forward genetics meets the HIF pathway. Genes Dev. 28, 2189-2204.
Fan, S., M. E. Hansen, Y. Lo, S. A. Tishkoff (2016) Going global by adapting local: A review of recent human adaptation. Science 354, 54-59.
Barreiro, L. B., and L. Quintana-Murci (2010) From evolutionary genetics to human immunology: how selection shapes host defence genes. Nat. Rev. Genet. 11, 17-30.
Akey, J. M. (2009) Constructing genomic maps of positive selection in humans: where do we go from here? Genome Res. 19, 711-722.
Quintana-Murci, L. (2016) Genetic and epigenetic variation of human populations: An adaptive tale. C. R. Biol. 339, 278-283.
Hoban, S., et al. (2016) Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions. Am. Nat. 188, 379-397.
Lindesay, J., T. E. Mason, W. Hercules, G. M. Dunston (2018) Mathematical modeling the biology of single nucleotide polymorphisms (SNPs) in whole genome adaptation. ABB 9, 520-533.
International HapMap Consortium (2003) The International HapMap project. Nature 426, 789-796.
MacArthur, J., et al. (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896-D901.
Li, M. J., L. Y. Wang, Z. Xia, M. P. Wong, P. C. Sham, J. Wang (2014) dbPSHP: a database of recent positive selection across human populations. Nucleic Acids Res. 42, D910-S916.
Herman J. R., N. Krotkov, E. Celarier, D. Larko, G. Labow (1999) Distribution of UV radiation at the Earth’s surface from TOMS-measured UV-backscattered radiances. J. Geophys. Res. Atmospheres 104, 12059-12076.
Globe Task Team (1999) The Global Land One-Kilometer Base Elevation (GLOBE) Digital Elevation Model, version 1.0.
World Health Organization (2008) World Malaria Report 2008. Geneva World Health Organization, Switzerland.
Lindesay, J., T. E. Mason, W. Hercules, G. M. Dunston (2014) Development of genodynamics metrics for exploring the biophysics of DNA polymorphisms. J. Comput. Biol. Bioinform. Res. 6, 1-14.
Bigham, A. W., et al. (2009) Identifying positive selection candidate loci for high-altitude adaptation in Andean populations. Hum. Genomics 4, 79-90.
Bigham, A., et al. (2010) Identifying signatures of natural selection in Tibetan and Andean populations using dense genome scan data. PLoS Genet. 6, e1001116.
Peng, Y., et al. (2011) Genetic variations in Tibetan populations and high-altitude adaptation at the Himalayas. Mol. Biol. Evol. 28, 1075-1081.
Simonson, T. S., et al. (2010) Genetic evidence for high-altitude adaptation in Tibet. Science 329, 72-75.
Yi, X., et al. (2010) Sequencing of 50 humans exomes reveals adaptation to high altitude. Science 329, 75-78.
Semenza, G. L. (1999) Perspectives on oxygen sensing. Cell 98, 281-284.
Ivan, M., et al. (2001) HIF targeted for VHL-mediated Destruction by Proline Hydroxylation: Implications for O2 Sensing. Science 292, 464-468.
Jaakkola, P., et al. (2001) Targeting of HIF to the von- Hippel-Landau Ubiquitylation Complex by O2-regulated Proyl Hydroxylation. Science 292, 468-472.
Bacon, N. C., et al. (1998) Regulation of the Drosophila bHLH-PAS protein Sima by hypoxia: functional evidence for homology with mammalian HIF-1 alpha. Biochem. Biophys. Res. Commun. 249, 811-816.
Rezvani, H. R., A. N. Nissen, G. Harfouche, H. deVerneuil, A. Taieb, F. Mazurier (2011) HIF-1a in epidermis: oxygen sensing, cutaneous angiogenesis, cancer, and non-cancer disorders. J. Invest. Dermatol. 131, 1793-1805.
Rezvani, H. R., et al. (2011) Loss of epidermis hypoxia- inducible factor-1 alpha accelerates epidermal aging and affects re-epithelialization in human and mouse. J. Cell Sci. 154, 4172-4183.
Rosenberger, C., et al. (2007) Upregulation of hypoxia- inducible factors in normal and psoriatic skin. J. Investig. Dermatol. 127, 2445-2452.
Bedgoni, B. and M. B. Powell (2009) Hypoxia, melanocytes and melanoma – survival and tumor development in the permissive microenvironment of the skin. Pigment Cell Melanoma Res. 22, 166-174.
Giatromanolaki, A. and A. L. Harris (2001) Tumor hypoxia signaling pathways and hypoxia inducible factor expression in human cancer. Anticancer Res. 21, 4317-4324.
Gorlach, A. (2014) “Hypoxia and Reactive Oxygen Species” Hypoxia and Cancer: Biological Implications and Therapeutic Opportunities. Ed. G. Meililo. New York: Springer Science + Business Media, 65-90.
Rezvani, H. R., et al., (2007) Hypoxia-inducible factor-1, a key factor in the keratinocyte response to UVB Exposure. J. Biol. Chem. 282, 16413-16422.
Kietzmann, T. and A. Gorlach (2005) Reactive oxygen species in control of hypoxia-inducible factor-mediated gene expression. Semin. Cell Dev. Biol. 15, 474-486.
Michiels, C., E. Minet, D. Mottet, M. Raes (2002) Regulation of gene expression by oxygen: NF-kappaB and HIF-1, two extremes. Free Radic. Biol. Med. 33, 1231-1242.
Gerald, D., et al. (2004) JunD reduces tumor angiogenesis by protecting cells from oxidative stress. Cell 118, 781-794.
Bell, E. L., B. M. Emerling, N. S. Chandel (2005) Mitochondrial regulation of oxygen sensing. Mitochondrion 5, 322-332.
Kwon, S. J., J. J. Song, Y. J. Lee (2005) Signal pathway of hypoxia-inducible factor-1 alpha phosphorylation and its interaction with von Hippel-Landau tumor suppressor protein during ischemia in MiaPaCa-2 pancreatic cancer cells. Clin. Cancer Res. 11, 7607-7613.
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