A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care data.

Background: Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research.

Methods: We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197).

c-di-GMP inhibits rRNA methylation and impairs ribosome assembly in the presence of kanamycin.

Cyclic diguanosine monophosphate (c-di-GMP) is a ubiquitous bacterial secondary messenger with diverse functions. A previous Escherichia coli proteome microarray identified that c-di-GMP binds to the 23S rRNA methyltransferases RlmI and RlmE. Here we show that c-di-GMP inhibits RlmI activity in rRNA methylation assays, and that it modulates ribosome assembly in the presence of kanamycin.

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity from a common risk factor.

Mendelian randomization (MR) is an epidemiological approach that utilizes genetic variants as instrumental variables to estimate the causal effect of an exposure on a health outcome. This paper investigates an MR scenario in which genetic variants aggregate into clusters that identify heterogeneous causal effects. Such variant clusters are likely to emerge if they affect the exposure and outcome via distinct biological pathways.