Working with the needles in the genomics haystack


Genomic data can be used to try to explain complex phenotypes, ranging from diabetes to crop yield.  The success is frustratingly inadequate in some respects and spectacularly successful in others.  Broadly speaking, only a small proportion of the loci that affect such traits can be identified, even in large studies; conversely, simple whole-genome regression can predict the trait (disease susceptibility, crop yield etc) with remarkable efficiency.  The student will combine the gene-hunting (GWAS) and regression (Genomic Prediction) approaches in a Bayesian framework to improve both the identification of key loci and the accuracy of the predictions.       


Greg Gibson (2010) Hints of hidden heritability in GWAS Nature Genetics 42, 558–560


HU Jan, A Abbadi, S Lücke, RA Nichols, RJ Snowdon (2016)Genomic Prediction of Testcross Performance in Canola (Brassica napus). PloS one 11 (1), e0147769


Papageorgiou, G., Richardson, S. and Best, N. (2015). Bayesian nonparametric models for spatially indexed data of mixed type. Journal of the Royal Statistical Society - Series B, 77:973-999


Gustavo de los Campos, John M. Hickey, Ricardo Pong-Wong, Hans D. Daetwyler, Mario P. L. Calus (2013) Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding GENETICS 193: 327-345     

Biological Areas:



Genes, development and STEM approaches to biology