Neural network correlates of high‐altitude adaptive genetic variants in Tibetans: A pilot, exploratory study. Issue 9 (4th March 2020)

• Record Type: Journal Article
• Authors: Guo, Zhiyue; Fan, Cunxiu; Li, Ting; Gesang, Luobu; Yin, Wu; Wang, Ningkai; Weng, Xuchu; Gong, Qiyong; Zhang, Jiaxing; Wang, Jinhui
• Is Part Of: Human brain mapping. Volume 41:Issue 9(2020)
• Page Start: 2406
• Page End: 2430
• Publication Date: 2020-03-04
• Subjects: brain network -- genetic variant -- high altitude -- MRI -- Tibetans; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24954
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype–phenotype associations remain largely unknown in the context of high‐altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci ( EGLN1, EPAS1, and PPARA ) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large‐scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single‐nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype–phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases. Abstract :