Use of Genome Information-Based Potentials to Characterize Human Adaptation

TL;DR

This paper introduces a biophysical modeling approach to analyze genome SNP data, revealing how genetic variations correlate with environmental factors like altitude, enhancing understanding of human adaptation.

Contribution

It develops a novel mathematical framework linking biophysical methods to population genetics, and applies it to identify environment-dependent genetic variations.

Findings

Identified altitude-dependent SNPs in the major histocompatibility complex.

Demonstrated statistical power in linking SNP potentials to environmental parameters.

Provided insights into genome-environment interactions in human adaptation.

Abstract

As a living information and communications system, the genome encodes patterns in single nucleotide polymorphisms (SNPs) reflecting human adaption 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.