In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.

Giansant, V., D'Agostino, D., Maj, C., Beretta, S., Merelli, I. (2018). Computing Empirical P-Values for Estimating Gene-Gene Interactions in Genome-Wide Association Studies: A Parallel Computing Approach. In Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018 (pp.406-409) [10.1109/PDP2018.2018.00071].

Computing Empirical P-Values for Estimating Gene-Gene Interactions in Genome-Wide Association Studies: A Parallel Computing Approach

Maj, C;Beretta, S;Merelli, I
2018

Abstract

In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.
paper
Genome-Wide Association Studies; Parallel Implementation
English
Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
2018
Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018
978-1-5386-4975-6
2018
406
409
none
Giansant, V., D'Agostino, D., Maj, C., Beretta, S., Merelli, I. (2018). Computing Empirical P-Values for Estimating Gene-Gene Interactions in Genome-Wide Association Studies: A Parallel Computing Approach. In Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018 (pp.406-409) [10.1109/PDP2018.2018.00071].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/204867
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