Simulating Variance Heterogeneity in Quantitative Genome Wide Association Studies Conference Paper uri icon

abstract

  • © 2017 Copyright held by the owner/author(s). Variance heterogeneity in genome wide association studies (vG-WAS) is a recent approach that is gaining interest due to its ability to detect non-additive interactions in the genome. Recent studies have found that in the presence of a non-additive interaction, such as a gene-gene or a gene-environment interaction, variance heterogeneity is introduced in at least one of the interacting loci. As opposed to typical GWAS analysis techniques, vGWAS tests the variance at each targeted location to identify the genotypes that cause a significant differentiation in the variance. The development of vGWAS methods to perform this task is an ongoing process in this relatively new field. In order to contribute to this process, in this work we introduce a mathematical framework and algorithm for simulating quantitative vGWAS data. An accurate simulation process is essential for the development and evaluation of vGWAS methods through establishing a ground truth for comparison. The presented simulation model accounts for both haploid and diploid genotypes under different modes of dominance. We used this simulation process to assess the performance of existing quantitative vGWAS detection algorithms. Finally, we use this assessment to point out the challenges these methods face, in hope of motivating the development of more advanced methods.

name of conference

  • BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

published proceedings

  • Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

author list (cited authors)

  • Al Kawam, A., Alshawaqfeh, M., Cai, J., Serpedin, E., & Datta, A

citation count

  • 0

complete list of authors

  • Al Kawam, Ahmad||Alshawaqfeh, Mustafa||Cai, James||Serpedin, Erchin||Datta, Aniruddha

editor list (cited editors)

  • Haspel, N., Cowen, L. J., Shehu, A., Kahveci, T., & Pozzi, G.

publication date

  • August 2017

publisher

  • ACM  Publisher