Emile Chimusa

Key areas of expertise:

• Medical population genomics, Genetics Epidemiology, Computational risk predication, medical OMICS machine learning

Emile Chimusa’s main research interests lie in developing statistical and machine learning methods for uncovering the genetic basis of human disease and the genetics underlying these methods. Areas of interest include functional components of heritability, common versus rare variant architectures, and disease-mapping in structured and admixed populations, medical population genetics and genetics epidemiology.

Selected publications: Chimusa, E.R., Beighton, P., Kumuthini, J. and Ramesar, R.S., 2019. Detecting genetic modifiers of spondyloepimetaphyseal dysplasia with joint laxity in the Caucasian Afrikaner community. Human Molecular Genetics, doi: 10.1093/hmg/ddy373.

Thami, P.K. and Chimusa ER, E.R., 2019. Population Structure and Implications on the Genetic Architecture of HIV-1 Phenotypes within Southern Africa. Frontiers in Genetics, 10, p.905.

Awany, D., Allali, I., Dalvie, S., Hemmings, S., Mwaikono, K.S., Thomford, N.E., Gomez, A., Mulder, N. and Chimusa, E.R., 2019. Host and Microbiome Genome-Wide Association Studies: Current State and Challenges. Front Genet.;9:637. doi: 10.3389/fgene.2018.00637.

Awany, D., Allali, I. and Chimusa, E.R., 2019. Tantalizing dilemma in risk prediction from disease scoring statistics. Briefings in functional genomics, Briefings in Functional Genomics, doi.org/10.1093/bfgp/ely040.

Chimusa, E.R., Defo, J., Thami, P.K., Awany, D., Mulisa, D.D., Allali, I., Ghazal, H., Moussa, A. and Mazandu, G.K., 2018. Dating admixture events is unsolved problem in multi-way admixed populations. Briefings in bioinformatics, doi: 10.1093/bib/bby112.

Chimusa, E.R., Dalvie, S., Dandara, C., Wonkam, A. and Mazandu, G.K., (2018). Post genome-wide association analysis: dissecting computational pathway/network-based approaches. Briefings in Bioinformatics, bby035, doi.org/10.1093/bib/bby035.

Geza Ephifania, Jacquiline Mugo, Nicola J. Mulder, Emile R. Chimusa and Gaston K. Mazandu (2018). A comprehensive survey of models for dissecting local ancestry deconvolution in human genome. Briefings in Bioinformatics, bby037, doi.org/14.1097/bib/bby037.

Chimusa, E. R., Mbiyavanga, M., Mazandu, G. K., and Mulder, N. J. (2015). ancGWAS: a Post Genome-wide Association Study Method for Interaction, Pathway, and Ancestry Analysis in Homogeneous and Admixed Populations. Bioinformatics, btv619.

Chimusa, E.R., Mbiyavanga, M., Masilela, V. and Kumuthini, J., (2015). “Broadband” Bioinformatics Skills Transfer with the Knowledge Transfer Programme (KTP): Educational Model for Upliftment and Sustainable Development. PLoS Comput Biol, 11(11), p.e1004512.

Chimusa, E.R., Meintjies, A., Tchanga, M., Mulder, N., Seoighe, C., Soodyall, H. and Ramesar, R., (2015). A genomic portrait of haplotype diversity and signatures of selection in indigenous southern African populations. PLoS Genet, 11(3), p.e1005052.