Simulating variance heterogeneity in qantitative genome wide association studies

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages762-763
Number of pages2
ISBN (Electronic)9781450347228
DOIs
Publication statusPublished - 20 Aug 2017
Event8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017 - Boston, United States
Duration: 20 Aug 201723 Aug 2017

Other

Other8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017
CountryUnited States
CityBoston
Period20/8/1723/8/17

Fingerprint

Genome-Wide Association Study
Genes
Genotype
Gene-Environment Interaction
Haploidy
Diploidy
Genome

Keywords

  • Genome wide association studies
  • GWAS simulation
  • Variance heterogeneity

ASJC Scopus subject areas

  • Software
  • Biomedical Engineering
  • Health Informatics
  • Computer Science Applications

Cite this

Kawam, A. A., Alshawaqfeh, M., Cai, J., Serpedin, E., & Datta, A. (2017). Simulating variance heterogeneity in qantitative genome wide association studies. In ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 762-763). Association for Computing Machinery, Inc. https://doi.org/10.1145/3107411.3110407

Simulating variance heterogeneity in qantitative genome wide association studies. / Kawam, Ahmad Al; Alshawaqfeh, Mustafa; Cai, James; Serpedin, Erchin; Datta, Aniruddha.

ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc, 2017. p. 762-763.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kawam, AA, Alshawaqfeh, M, Cai, J, Serpedin, E & Datta, A 2017, Simulating variance heterogeneity in qantitative genome wide association studies. in ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc, pp. 762-763, 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017, Boston, United States, 20/8/17. https://doi.org/10.1145/3107411.3110407
Kawam AA, Alshawaqfeh M, Cai J, Serpedin E, Datta A. Simulating variance heterogeneity in qantitative genome wide association studies. In ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc. 2017. p. 762-763 https://doi.org/10.1145/3107411.3110407
Kawam, Ahmad Al ; Alshawaqfeh, Mustafa ; Cai, James ; Serpedin, Erchin ; Datta, Aniruddha. / Simulating variance heterogeneity in qantitative genome wide association studies. ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc, 2017. pp. 762-763
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