Research Associate in the Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences

King’s College London is seeking a talented, ambitious and highly skilled postdoctoral researcher at the Department of Twin Research and Genetic Epidemiology. The ideal candidate should have a strong background in one of the following fields: microbiome analysis, genomics, statistical genetics, preferably relating to human health. The post holder will join a dynamic research group investigating the human microbiome and its relationship with diet, genetics, metabolomics, and multiple clinically relevant traits. The post holder will therefore also join the established multi-disciplinary transnational research consortium in human microbiomics.

The post is funded via a grant from a research charity investigating a novel microbiome space – the human urinary microbiome, focusing on outcomes relating to human ageing and health. The project will be a world first to explore human urinary microbial profiles, their relation to host genotype, and relate the urinary microbiome of older adults to gut microbiome, and important conditions of ageing. The researcher will work on the largest novel dataset of urinary microbiome data in the world, on subjects in the TwinsUK cohort, collaborating with Rob Knight’s group in UCSD. This work builds on previous work from the group that established the presence of host genetic impacts on the human microbiome (Goodrich et al. 2014, Cell; Goodrich et al. 2016 Cell Host Microbes), and links to medication use, clinical traits, and environmental exposures (Jackson et al. 2016, Gut, Jackson 2016 Genome Medicine).
The main research focus will be to use next generation sequencing technologies to

  1. identify and characterize human microbiome variation in urine related to host genetics, environmental factors, medication use, and and
  2. investigate how these variants contribute to the underlying mechanisms of recurrant urinary tract infection, inflammaging, frailty and cognitive decline, among other traits.

The project will utilize a unique multi-dimensional dataset collected on over 1,800 deeply phenotyped twins with available urinary and faecal microbiome 16S data, where sample subsets include individuals with metagenomics, whole genome sequence, epigenetic, transcriptomic, and metabolomic profiles. The breadth of the dataset will allow integration of multiple ‘omics, and in particular the concurrent analysis of both genetic and environmental factors.

Applicants should have a strong computational background and expertise in dealing with large genomic data sets. A background and interest in human microbiome studies would be highly advantageous.

The selection process will include a panel interview.
For an informal discussion to find out more about the role please contact Dr. Claire Steves at

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