TY - JOUR
T1 - The challenge of detecting genotype-by-methylation interaction
T2 - GAW20 01 Mathematical Sciences 0104 Statistics
AU - De Andrade, Mariza
AU - Warwick Daw, E.
AU - Kraja, Aldi T.
AU - Fisher, Virginia
AU - Wang, Lan
AU - Hu, Ke
AU - Li, Jing
AU - Romanescu, Razvan
AU - Veenstra, Jenna
AU - Sun, Rui
AU - Weng, Haoyi
AU - Zhou, Wenda
N1 - Publisher Copyright:
© 2018 The Author(s).
PY - 2018/9/17
Y1 - 2018/9/17
N2 - 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. Results: The 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. Conclusions: In 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.
AB - 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. Results: The 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. Conclusions: In 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.
KW - Adaptive W-test
KW - Candidate gene association
KW - Genome wide association
KW - Interaction
KW - LASSO
KW - Mediation analysis
KW - Methylation
KW - Multi-level Gaussian model
KW - Region based association
KW - Regression and random forest trees
UR - http://www.scopus.com/inward/record.url?scp=85053407840&partnerID=8YFLogxK
U2 - 10.1186/s12863-018-0650-7
DO - 10.1186/s12863-018-0650-7
M3 - Article
C2 - 30255819
AN - SCOPUS:85053407840
SN - 1471-2156
VL - 19
JO - BMC genetics
JF - BMC genetics
M1 - 81
ER -