Scientific ReportsSystems BiologyPeer-reviewed journal article
Analysis of human mitochondrial genome co-occurrence networks of Asian population at varying altitudes
Rahul Kumar VermaiD, Alena KalyakulinaiD, Cristina GiulianiiD, Pramod S. ShindeiD, Ajay Deep KachhvahiD, Mikhail IvanchenkoiD, Sarika JalaniD
Abstract
Networks have been established as an extremely powerful framework to understand and predict the behavior of many large-scale complex systems. We have studied network motifs, the basic structural elements of networks, to describe the possible role of co-occurrence of genomic variations behind high altitude adaptation in the Asian human population. Mitochondrial DNA (mt-DNA) variations have been acclaimed as one of the key players in understanding the biological mechanisms behind adaptation to extreme conditions. To explore the cumulative effects and variations in the mitochondrial genome with the variation in the altitude, we investigated human mt-DNA sequences from the NCBI database at different altitudes under the co-occurrence motifs framework. Analysis of the co-occurrence motifs using similarity clusteringrevealed a clear distinction between lower and higher altitude regions. In addition, the previously known high altitude markers 3394 and 7697 (which are definitive sites of haplogroup M9a1a1c1b) were found to co-occur within their own gene complexes indicating the impact of intra-genic constraint on co-evolution of nucleotides. Furthermore, an ancestral RSRS50 variant 10398 was found to co-occur only at higher altitudes supporting the fact that a separate route of colonization at these altitudes might have taken place. Overall, our analysis revealed the presence of co-occurrence interactions specific to high altitude at a whole mitochondrial genome level. This study, combined with the classical haplogroups analysis is useful in understanding the role of co-occurrence of mitochondrial variations in high altitude adaptation.
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 11
- FWCI
- 1.41
- Field percentile
- top 21%
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