Author(s)

James Lindesay^1,2,3, Tshela E. Mason^1,2,3, William Hercules^1,2,3, Georgia M. Dunston³

¹Department of Physics & Astronomy, Howard University, Washington, USA.
²National Human Genome Center, Howard University, Washington, USA.
³Department of Microbiology, Howard University, Washington, USA.

As a living information and communications system, the genome encodes patterns in single nucleotide polymorphisms (SNPs) reflecting human adaptation that optimizes population survival in differing environments. This paper mathematically models environmentally induced adaptive forces that quantify changes in the distribution of SNP frequencies between populations. We make direct connections between biophysical methods (e.g. minimizing genomic free energy) and concepts in population genetics. Our unbiased computer program scanned a large set of SNPs in the major histocompatibility complex region and flagged an altitude dependency on a SNP associated with response to oxygen deprivation. The statistical power of our double-blind approach is demonstrated in the flagging of mathematical functional correlations of SNP information-based potentials in multiple populations with specific environmental parameters. Furthermore, our approach provides insights for new discoveries on the biology of common variants. This paper demonstrates the power of biophysical modeling of population diversity for better understanding genome-environment interactions in biological phenomenon.

Genome-Environment Interactions, Genomic Adaptation, SNP Functional Correlations

Lindesay, J. , Mason, T. , Hercules, W. and Dunston, G. (2018) Mathematical Modeling the Biology of Single Nucleotide Polymorphisms (SNPs) in Whole Genome Adaptation. Advances in Bioscience and Biotechnology, 9, 520-533. doi: 10.4236/abb.2018.910036.