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The challenge of detecting genotype-by-methylation interaction: GAW20 |
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| Authors: | de Andrade Mariza Warwick Daw E Kraja Aldi T Fisher Virginia Wang Lan Hu Ke Li Jing Romanescu Razvan Veenstra Jenna Sun Rui Weng Haoyi Zhou Wenda |
| Institution: | 1.Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA ;2.Division of Statistical Genomics, Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 Euclid Ave, Saint Louis, MO, 63110, USA ;3.Department of Biostatistics, Boston University School of Public Health, Boston, 715 Albany St, Boston, MA, 02118, USA ;4.Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA ;5.Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Toronto, 600 University Ave, Toronto, ON, M5G 1X5, Canada ;6.Department of Biology, Dordt College, 498 4th Ave. NE, Sioux Center, IA, 51250, USA ;7.Department of Mathematics and Statistics, Dordt College, 498 4th Ave. NE, Sioux Center, IA, 51250, USA ;8.Division of Biostatistics, Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T, Hong Kong, SAR, China ;9.CUHK Shenzhen Research Institute, Shenzhen, China ;10.Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY, 10027, USA ; |
| Abstract: | Background GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, including single-nucleotide polymorphism (SNP) markers, methylation (cytosine-phosphate-guanine CpG]) markers, and phenotype information on up to 995 individuals. In addition, a simulated data set based on the real data was provided. ResultsThe 7 contributed papers analyzed these data sets with a number of different statistical methods, including generalized linear mixed models, mediation analysis, machine learning, W-test, and sparsity-inducing regularized regression. These methods generally appeared to perform well. Several papers confirmed a number of causative SNPs in either the large number of simulation sets or the real data on chromosome 11. Findings were also reported for different SNPs, CpG sites, and SNP–CpG site interaction pairs. ConclusionsIn the simulation (200 replications), power appeared generally good for large interaction effects, but smaller effects will require larger studies or consortium collaboration for realizing a sufficient power. |
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