Elucidation of the relationships between genetic polymorphisms and environmental exposures can provide insights into the pathways and mechanisms underlying complex traits. A new approach was used to detect G×E （...Elucidation of the relationships between genetic polymorphisms and environmental exposures can provide insights into the pathways and mechanisms underlying complex traits. A new approach was used to detect G×E （gene-environment） interactions involved in human skin pigmentation variation to better understand the adaptive evolution of skin pigmentation. Specifically, we used genetic engineering, remote UVR （ultraviolet radiation） sensing and GIS （geographic information systems） to integrate the analysis of genetic and environmental factors into a coherent biological framework. Since we expected to generate large datasets for this multidimensional analysis, we used PCA （principal components analysis） as a spatial statistical analysis technique for analyzing the G×E interactions. The results suggest that skin pigmentation may be affected by mutations induced by UVR and support the hypothesis that global variation in skin pigmentation may be the result of localized adaptation to different UVR conditions via natural selection. Analyzing the relationships between heterozygous frequencies for SNP （single nucleotide polymorphism） loci and seasonal UVR levels as the environment changes will help elucidate the selective mechanisms involved in the UVR-induced evolution of skin pigmentation. Skin pigmentation fulfills the criteria for a successful evolutionary G×E interactions model.展开更多
文摘Elucidation of the relationships between genetic polymorphisms and environmental exposures can provide insights into the pathways and mechanisms underlying complex traits. A new approach was used to detect G×E （gene-environment） interactions involved in human skin pigmentation variation to better understand the adaptive evolution of skin pigmentation. Specifically, we used genetic engineering, remote UVR （ultraviolet radiation） sensing and GIS （geographic information systems） to integrate the analysis of genetic and environmental factors into a coherent biological framework. Since we expected to generate large datasets for this multidimensional analysis, we used PCA （principal components analysis） as a spatial statistical analysis technique for analyzing the G×E interactions. The results suggest that skin pigmentation may be affected by mutations induced by UVR and support the hypothesis that global variation in skin pigmentation may be the result of localized adaptation to different UVR conditions via natural selection. Analyzing the relationships between heterozygous frequencies for SNP （single nucleotide polymorphism） loci and seasonal UVR levels as the environment changes will help elucidate the selective mechanisms involved in the UVR-induced evolution of skin pigmentation. Skin pigmentation fulfills the criteria for a successful evolutionary G×E interactions model.
