Infectious diseases, such as HIV-AIDS, malaria, pneumonia and tuberculosis, account for 25% of global mortality and more than half of all deaths in children under the age of five years. The genetic epidemiology of these diseases can be complex, especially as they may involve several genomes, including the host, pathogen(s) and a vector. There is also a need to look beyond the genome to consider other 'omes, such as the transcriptome, in a more systems biology framework.
High throughput genotyping and sequencing technologies are providing insights into these genomes, metabolomes, transcriptomes and proteomes, thereby revolutionising genetic epidemiological studies and biomedical research. The use of SNP chips in large-scale genome-wide studies of association and genic selection has revolutionized the study of human disease susceptibility. Whole genome studies of pathogens using high throughput sequencing technologies is leading to the ability to track microbial evolution over time and space, as well as identify variants correlated with phenotypes such as anti-microbial resistance. Further, RNAseq methodologies are being used to measure transcript abundance and differential gene expression across isolates.
To take full advantage of new 'omic technologies requires the ability to analyse large amounts of data using methods from bioinformatics, population genetics and statistics - the focus of this course. Specifically, this course offers hands-on experience of processing sequencing data to construct genomes, identifying genomic variants and applying downstream methods, such as phylogenetics. Further, the course covers transcriptomic and proteomic analysis in human and pathogen settings. High profile examples, including malaria, TB and MRSA, will be used to illustrate the concepts, and there is a strong emphasis on how to implement the methods in practice, with the majority of sessions computer-based.Prerequisites
This course focuses on bioinformatic, population genetic, statistical and genetic epidemiological methods for analysing large genetic datasets. Participants would be expected have some familiarity with molecular biology, and relevant statistical concepts such as a linear regression.Course fee