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Background: Longevity is one of the most complex phenotypes, and its genetic basis remains unclear. This study aimed to explore the genetic correlation and potential causal association between gut microbiota and longevity. Results: Linkage disequilibrium score (LDSC) regression analysis and a bi-directional two-sample Mendelian Randomization (MR) analysis were performed to analyze gut microbiota and longevity-related traits. LDSC analysis detected four candidate genetic correlations, including Veillonella (genetic correlation = 0.5578, P = 4.67 × 10− 2) and Roseburia (genetic correlation = 0.4491, P = 2.67 × 10− 2) for longevity, Collinsella (genetic correlation = 0.3144, P = 4.07 × 10− 2) for parental lifespan and Sporobacter (genetic correlation = 0.2092, P = 3.53 × 10− 2) for healthspan. Further MR analysis observed suggestive causation between Collinsella and parental longevity (father's age at death) (weighted median: b = 1.79 × 10− 3, P = 3.52 × 10− 2). Reverse MR analysis also detected several causal effects of longevity-related traits on gut microbiota, such as longevity and Sporobacter (IVW: b = 7.02 × 10− 1, P = 4.21 × 10− 25). Statistical insignificance of the heterogeneity test and pleiotropy test supported the validity of the MR study. Conclusion: Our study found evidence that gut microbiota is causally associated with longevity, or vice versa, providing novel clues for understanding the roles of gut microbiota in aging development. [ABSTRACT FROM AUTHOR]

Background: Gut microbiota is closely associated with host health and disease occurrence. Host genetic factor plays an important role in shaping gut microbial communities. The specific mechanism of host-regulated gene expression affecting gut microbiota has not been elucidated yet. Here we conducted a transcriptome-wide association study (TWAS) for gut microbiota by leveraging expression imputation from large-scale GWAS data sets. Results: TWAS detected multiple tissue-specific candidate genes for gut microbiota, such as FUT2 for genus Bifidobacterium in transverse colon (PPERM.ANL = 1.68 × 10–3) and SFTPD for an unclassified genus of Proteobacteria in transverse colon (PPERM.ANL = 5.69 × 10–3). Fine mapping replicated 3 candidate genes in TWAS, such as HELLS for Streptococcus (PIP = 0.685) in sigmoid colon, ANO7 for Erysipelotrichaceae (PIP = 0.449) in sigmoid colon. Functional analyses detected 94 significant GO terms and 11 pathways for various taxa in total, such as GO_NUCLEOSIDE_DIPHOSPHATASE_ACTIVITY for Butyrivibrio (FDR P = 1.30 × 10–4), KEGG_RENIN_ANGIOTENSIN_SYSTEM for Anaerostipes (FDR P = 3.16 × 10–2). Literature search results showed 12 genes prioritized by TWAS were associated with 12 diseases. For instance, SFTPD for an unclassified genus of Proteobacteria was related to atherosclerosis, and FUT2 for Bifidobacterium was associated with Crohn's disease. Conclusions: Our study results provided novel insights for understanding the genetic mechanism of gut microbiota, and attempted to provide clues for revealing the influence of genetic factors on gut microbiota for the occurrence and development of diseases. [ABSTRACT FROM AUTHOR]