Human Genome Variation and the concept of Genotype Networks

TL;DR

This paper adapts genotype network analysis to human genome variation, revealing insights into genetic heterogeneity and mutation stability across different genomic regions using the 1000 Genomes dataset.

Contribution

It introduces a novel application of genotype networks to population genetics data, specifically analyzing human genome variation with a focus on coding and non-coding regions.

Findings

Genes involved in immunity have highly connected genotype networks.

Coding regions show greater diversity and stability compared to non-coding regions.

Genotype networks can effectively characterize genetic heterogeneity in human populations.

Abstract

Genotype networks are a method used in systems biology to study the "innovability" of a set of genotypes having the same phenotype. In the past they have been applied to determine the genetic heterogeneity, and stability to mutations, of systems such as metabolic networks and RNA folds. Recently, they have been the base for re-conciliating the two neutralist and selectionist schools on evolution. Here, we adapted the concept of genotype networks to the study of population genetics data, applying them to the 1000 Genomes dataset. We used networks composed of short haplotypes of Single Nucleotide Variants (SNV), and defined phenotypes as the presence or absence of a haplotype in a human population. We used coalescent simulations to determine if the number of samples in the 1000 Genomes dataset is large enough to represent the genetic variation of real populations. The result is a scan of how properties related to the genetic heterogeneity and stability to mutations are distributed along the human genome. We found that genes involved in acquired immunity, such as some HLA and MHC genes, tend to have the most heterogeneous and connected networks; and we have also found that there is a small, but significant difference between networks of coding regions and those of non-coding regions, suggesting that coding regions are both richer in genotype diversity, and more stable to mutations. Together, the work presented here may constitute a starting point for applying genotype networks to study genome variation, as larger datasets of next-generation data will become availa