A proximity-based method to identify genomic regions correlated with a continuously varying environmental variable.
Knowledge of markers in the human genome which show spatial patterns and display extreme correlation with different environmental determinants play an important role in understanding the factors which affect the biological evolution of our species. We used the genotype data of more than half a million single nucleotide polymorphisms (SNPs) from the data set Human Genome Diversity Panel (HGDP-CEPH -CEPH) and we calculated Spearman's correlation between absolute latitude and one of the two allele frequencies of each SNP. We selected SNPs with a correlation coefficient within the upper 1% tail of the distribution. We then used a criterion of proximity between significant variants to focus on DNA regions showing a continuous signal over a portion of the genome. Based on external information and genome annotations, we demonstrated that most regions with the strongest signals also have biological relevance. We believe this proximity requirement adds an edge to our novel method compared to the existing literature, highlighting several genes (for example DTNB, DOT1L, TPCN2, RELN, MSRA, NRG3) related to body size or shape, human height, hair color, and schizophrenia. Our approach can be applied generally to any measure of association between polymorphic frequencies and continuously varying environmental variables.
['Di Gaetano|Cornelia|C|', 'Matullo|Giuseppe|G|', 'Piazza|Alberto|A|', 'Ursino|Moreno|M|', 'Gasparini|Mauro|M|']
spatial patterns latitude adaptations outlier approach point processes