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Thursday, July 6 • 5:55pm - 6:00pm
Simulate phenotype(s) with epistatic interactions

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Keywords: simulation, multiple phenotyes, epistatic interactions
Webpages: https://CRAN.R-project.org/package=SimPhe
For complex traits, genome-wide association studies (GWAS) are the standard tool to detect variants contributing to the variance of the phenotype of interest. However, limited to single-locus effects they can only explain a small fraction of the heritability of complex traits. Epistasis, generally defined as the interaction between different genes, has been hypothesized as one of the factors contributing to missing heritability. This has been a hot topic in quantitative genetics for a long time and there is a controversy about the role of epistasis because the majority of researchers only concentrate on additive effects as most genetic variation is (approximately) additive. Even for epistasis analysis, many tools cannot take the dominance effects into consideration properly. Recently, the detection of dominance or the interactions it is involved in have been reported. Meanwhile, simulation tools have been developed for evaluating type I error rates for new statistical association tests or power comparisons between the new tests and other existing tests. However, few of them focus on the dominance effect and its interactions with other genetic items. Here, we present an R package, SimPhe, to simulate single or multiple quantitative phenotypes based on genotypes with additive, dominance and epistatic effects using the Cockerham epistasis model. With optional parameters in different functions, users can easily specify the number of quantitative trait loci (QTLs), genetic effect size, the number of quantitative traits, and proportions of variance explained by the QTLs.
References Cockerham, C. Clark, and Bruce Spencer Weir. 1977. “Quadratic Analyses of Reciprocal Crosses.” Biometrics 33 (1). JSTOR: 187–203. doi:10.2307/2529312.

Gibran, Hemani, Shakhbazov Konstantin, Harm-Jan Westra, Tonu Esko, Anjali K. Henders, Allan F. McRae, Jian Yang, et al. 2014. “Detection and Replication of Epistasis Influencing Transcription in Humans.” Nature 508 (April). Nature Publishing Group: 249–53. doi:10.1038/nature13005.

Kao, Chen-Hung, and Zhao-Bang Zeng. 2002. “Modeling Epistasis of Quantitative Trait Loci Using Cockerham’s Model.” Genetics 160 (3). Genetics Society of America: 1243–61. doi:10.1534/genetics.104.035857.




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Thursday July 6, 2017 5:55pm - 6:00pm
4.01 Wild Gallery

Attendees (37)