  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
