A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome.
Xiaomin LiuXin TongLeying ZouYanmei JuMingliang LiuMo HanHaorong LuHuanming YangJian WangYang ZongWeibin LiuXue LiuXin JinLiang XiaoHuijue JiaRuijin GuoTao ZhangPublished in: Communications biology (2024)
The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10 -10 ): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.
Keyphrases
- genome wide
- chronic rhinosinusitis
- dna methylation
- genome wide association study
- human health
- signaling pathway
- endothelial cells
- risk assessment
- copy number
- climate change
- epithelial mesenchymal transition
- microbial community
- machine learning
- gene expression
- induced pluripotent stem cells
- electronic health record
- transcription factor
- deep learning
- drug induced
- big data