Randall, Joshua C.
Joshua leads the Human Genetics Informatics team, who are responsible for handling the informatics needs of the Human Genetics research programme, including processing of large-scale genetics and genomics data sets. He holds an S.B in Electrical Engineering & Computer Science from MIT and a D.Phil in Statistical Genetics from the University of Oxford and is passionate about data sharing and free open source software.
I believe the best informatics teams are comprised of skilled software developers who have developed an interest in the domain area, and that each individual's role should involve both data production and software development. Having software developers who are responsible for bioinformatics data production and bioinformaticians who are responsible for writing software means that we are all in a position to be motivated to automate our way out of manual work whenever possible. Automated computational workflows are less prone to error than human mediated data processing and are much more scalable.
We develop tools to assist with the management and operation of computational pipelines, and we generally do this development in the open on public github repositories and make it available under free and open-source software licenses. The types of projects that we contribute to includes software for NGS data processing and analysis, workflow management infrastructure, tools for storage and quota management and reporting, and internal web interfaces and backend infrastructure to interact with our services.
Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits.
PLoS genetics 2013;9;6;e1003500
Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.
Nature genetics 2010;42;11;949-60
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.
Nature genetics 2010;42;11;937-48
Hundreds of variants clustered in genomic loci and biological pathways affect human height.
Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.
PLoS genetics 2009;5;6;e1000508
Evoker: a visualization tool for genotype intensity data.
Bioinformatics (Oxford, England) 2010;26;14;1786-7