Genetic correlations between gut microbiota genera and Alzheimers Disease
Davis CammanniD, Yimei LuiD, Melika J. Cummings, Mark L. Zhang, Joan Manuel CueiD, Jenifer Do, Jeffrey L. EbersoleiD, Xiangning CheniD, Edwin C. T. OhiD, Jeffrey L. CummingsiD, Jingchun CheniD
Abstract
1.AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSDysbiosis of the human gut microbiota has been implicated in the development of Alzheimers disease (AD). However, the genetic correlation between gut microbiota and AD is not well addressed. MethodsUsing the largest genome-wide association study (GWAS) of gut microbiota genera from the MiBioGen consortium, we conducted the "best-fit" model from PRSice-2 to determine the genetic correlation between 119 genera and AD in a discovery sample (case/control: 1,278/1,293); we then replicated our findings in an independent sample (case/control: 799/778) and further performed meta-analyses to confirm the correlation. Finally, we conducted a linear regression to assess the correlation between the PRSs for the significant genera and the APOE genotype. ResultsIn the discovery sample, 20 gut microbiota genera were initially identified genetically associated with AD. Three genera (Eubacterium fissicatena as a protective factor, Collinsella and Veillonella as a risk factor) were validated in the replication sample. Meta-analysis confirmed nine genera to have a significant correlation with AD, three of which were significantly associated with the APOE rs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOE rs429358 risk allele in both samples. ConclusionHost genetic factors influencing the abundance of nine genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.
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