Gavin Band

Gavin Band serves as an Associate Professor and Head of Statistical Genomics at the Wellcome Centre for Human Genetics, University of Oxford, where he leads a research group focused on human and pathogen genomics with particular emphasis on Plasmodium falciparum malaria. His position as Group Leader and teaching co-lead for the GMS DPhil Programme demonstrates his significant role within the institution.

Band's research interests center on statistical genomics and the complex interplay between human and parasite genetics in malaria infections. He uses advanced genetic approaches to detect and dissect signals of interaction between malaria parasites and host immunity factors, including protective mutations and vaccine interventions. His work aims to unravel the underlying biological functions and evolutionary consequences of these host-pathogen interactions, with significant implications for malaria prevention and treatment strategies.

His recent publication record (2023-2025) reveals a strong focus on malaria genetics, particularly examining human genetic variants associated with malaria susceptibility and the co-evolutionary dynamics between human hosts and malaria parasites. His research extends to other infectious diseases including COVID-19, demonstrating methodological expertise applicable across multiple disease contexts.

As an educator, Band is deeply involved in computational and statistical genomics training for postgraduate programs. He serves as teaching co-lead for the GMS DPhil Programme and has supervised students including Jia-Yuan Zhang, who contributed significantly to their de novo assembly research, and Annie Forster.

Band is a notable contributor to genomic software development, having created several widely-used tools including the BGEN format (a compressed binary format for genotype data), QCTOOL (for quality control and analysis of GWAS datasets), HPTEST (for host-pathogen association testing), LDBIRD (for computing linkage disequilibrium metrics), and BINGWA (for model-based meta-analysis in GWAS studies). These contributions have significantly impacted the field of statistical genetics and genomic analysis.