Nathan Skene is an Edmond and Lily Safra – UK DRI Fellow at Imperial College's Dementia Research Institute. His interests lie in using human genetics to gain insight into the neurobiology of brain disorders and cognitive traits. A core part of his work has am focused on identifying the causal cell types underlying complex genetic traits.
His postdoctoral research was done at the Karolinska Institutet (KI) where he worked with Jens Hjerling-Leffler as part of the Functional Neuromics project. At KI he was involved in developing large scale single cell RNA-seq atlases of brain cell types, and using these datasets to gain insight into genetic disorders. Using this approach he was able to show that multiple cell types play a role in the etiology of schizophrenia, while only microglia appear to be influenced by the common genetic factors influencing Alzheimers. He developed the EWCE and MAGMA_Celltyping R packages to facilitate these analyses.
He gained an undergraduate degree in Artificial Intelligence and Cybernetics from the University of Reading in 2008, followed by an MPhil at the University of Cambridge in Computational Biology in 2009. He went on to do a PhD in Molecular Biology at the Wellcome Trust Sanger Institute working with Prof Seth Grant. During this time he worked on the Genes to Cognition programme, analysing the transcriptomic changes seen in a mice carrying a wide range of synaptic mutations. Later, while working between the University of Edinburgh and UCL, he studied how postnatal gene expression changes influence the onset of psychiatric disorders.
et al., 2019, Conditional GWAS analysis identifies putative disorder-specific SNPs for psychiatric disorders
et al., 2019, Genetic analysis identifies molecular systems and biological pathways associated with household income
et al., 2019, Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk, Nature Genetics, Vol:51, ISSN:1061-4036, Pages:404-+
et al., 2019, Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways, Nature Genetics, Vol:51, ISSN:1061-4036, Pages:394-+
et al., 2019, Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals, Molecular Psychiatry, Vol:24, ISSN:1359-4184, Pages:182-197