Towards deep learning in genome-wide association interaction studies
MetadataShow full item record
The complexity of phenotype-genotype mapping are characterised by non-linear interactions between gene-gene and gene-environmental factors. These interaction studies provide better understanding of underlying biological architecture of complex disease traits. A number of statistical and machine learning approaches have been proposed to identify multi-locus interactions between genetic variants and their association to a disease. However, the challenges hindering these approaches are missing heritability, curse of dimensionality, and computational limitations. Despite abundant computational methods and tools available to discover interactions, there have been no breakthrough methods that can demonstrate replicable results. In this paper, a deep feedforward neural network is trained to identify two-locus interacting genetic variants responsible for a disease risk. The method is evaluated on number of simulated datasets to predict the performance of the model. The results are encouraging with replicable results. Hence, the model is further evaluated to confirm its findings on a published genome-wide association dataset. The experimental results demonstrated significant improvements in the prediction accuracy over the previous approaches. The result ranks top 20 interactions among 35 polymorphisms associated with the disease.
Showing items related by title, author, creator and subject.
Modelling the co-occurence of Streptococcus pneumoniae with other bacterial and viral pathogens in the upper respiratory tractJacoby, P.; Watson, K.; Bowman, J.; Taylor, A.; Riley, T.; Smith, D.; Lehmann, Deborah (2007)Go to ScienceDirect® Home Skip Main Navigation Links Brought to you by: The University of Western Australia Library Login: + Register Athens/Institution Login Not Registered? - User Name: Password: ...
Uppu, Suneetha; Krishna, Aneesh; Gopalan, Raj (2015)The advancements in sequencing high-throughput human genome and computational abilities have tremendously improved the understanding of the genetic architecture behind the complex diseases. The development of high-throughput ...
Opposite gene by environment interactions in Karelia for CD14 and CC16 single nucleotide polymorphisms and allergyZhang, Guicheng; Khoo, S.; Laatikainen, T.; Pekkarinen, P.; Vartiainen, E.; Von Hertzen, L.; Hayden, C.; Goldblatt, J.; Mäkelä, M.; Haahtela, T.; Le Souëf, P. (2009)Background: Finnish Karelians have a higher prevalence of allergic disease than Russian Karelians. As both populations are generally from the same ethnic group, the Karelian population offers a unique opportunity to analyse ...