Single Nucleotide Polymorphisms: A Window into the Informatics of the Living Genome

Nested in the environment of the nucleus of the cell, the 23 sets of chromosomes that comprise the human genome function as one integrated whole system, orchestrating the expression of thousands of genes underlying the biological characteristics of the cell, individual and the species. The extraction of meaningful information from this complex data set depends crucially upon the lens through which the data are examined. We present a biophysical perspective on genomic information encoded in single nucleotide polymorphisms (SNPs), and introduce metrics for modeling information encoded in the genome. Information, like energy, is considered to be a conserved physical property of the universe. The information structured in SNPs describes the adaptation of a human population to a given environment. The maintained order measured by the information content is associated with entropies, energies, and other state variables for a dynamic system in homeostasis. “Genodynamics” characterizes the state variables for genomic populations that are stable under stochastic environmental stresses. The determination of allelic energies allows the parameterization of specific environmental influences upon individual alleles across populations. The environment drives population-based genome variation. From this vantage point, the genome is modeled as a complex, dynamic information system defined by patterns of SNP alleles and SNP haplotypes.

Mathematical Modeling the Biology of Single Nucleotide Polymorphisms (SNPs) in Whole Genome Adaptation

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.

Information Dynamics of Whole Genome Adaptation

The human genome is a complex, dynamic information system that encodes principles of life and living systems. These principles are incorporated in the structure of human genome sequence variation and are foundational for the continuity of life and human survival. Using first principles of thermodynamics and statistical physics, we have developed analogous “genodynamic tools” for population genomic studies. Characterizing genomic information through the lens of physics has allowed us to develop energy measures for modeling genome-environment interactions. In developing biophysical parameters for genome-environment homeostasis, we found that stable genomic free energy trades off low genomic energy (genomic conservation and increased order) and high genomic entropy (genomic variation) with an environmental potential that drives the variation. In our approach, we assert that common variants are dynamic sites in the genome of a population and that the stability of whole genome adaptation is reflected in the frequencies of maintained diversity in common variants for the population in its environment. In this paper, we address the relativity of whole genome adaptation towards homeostasis. By this we mean that adaptive forces are directly reflected in the frequency distribution of alleles and/or haplotypes of the population relative to its environment, with adaptive forces driving the genome towards homeostasis. The use of genomic energy units as a biophysical metric in DNA sequence variation analyses provides new insights into the foundations of population biology and diversity. Using our biophysical tools, population differences directly reflect the adaptive influences of the environment on populations.