Dr Jack Hearn

Post-Doctoral research Associate

Post-Doctoral Research Associate Liverpool School of Tropical Medicine (October 2018 – Present). Principal investigator Prof Charles Wondji. Population genomics and bioinformatics of insecticide resistance in the major Malaria vector Anopheles funestus

Post-Doctoral Scientist University of Edinburgh (March 2016 - September 2018). Principal investigator Prof Tom Little. Transgenerational epigenetics of nutritional stress in Daphnia magna.

Research Bioinformatician University of Edinburgh (November 2013 - February 2016). Principal investigators Prof Graham Stone and Dr Konrad Lohse. Genomic approaches to inference of population history and multispecies community assembly.

PhD (2009-2013) University of Edinburgh
MSc Quantitative genetics and genome analysis (2007-2008), University of Edinburgh
BSc Zoology (2004-2007) Imperial College London

My long-term research goal is to assess how environmental factors shape genomes by adaptive selection, to achieve this I investigate genetic and epigenetic changes in genome-wide datasets. My Ph.D. work investigated the genomics and transcriptomics of oak gall induction. In my first post-PhD position, I developed pipelines for comparing variation across species and trophic levels, to test hypotheses of population splitting and admixture. I then elucidated the epigenetic changes that occur in response to diet and aging in the model crustacean Daphnia magna. In my current research, I am identifying causative mutations of insecticide resistance in the malaria vector Anopheles funestus in multiple regions across Africa to inform resistance management strategies. 


Selected publications

  • Hearn J, Chow F W-N, Barton H, Tung M, Wilson PJ, Blaxter M, Buck A, and Little TJ. Daphnia magna miRNAs respond to nutritional stress and aging. 2018. Molecular Ecology, 27: 1402-1412.

    Bunnefeld L, Hearn J, Stone GN, and Lohse KR. Whole-genome data reveal the complex history of a diverse ecological community. 2018. Proceedings of the National Academy of Sciences, 115: E6507-E6515.

    Hearn J, Stone GN, Bunnefeld L, Nicholls JA, Barton NH. and Lohse KR. Likelihood-based inference of population history from low-coverage de novo genome assemblies. 2014. Molecular Ecology, 23: 198–211