The challenge of detecting genotype-by-methylation interaction: GAW20 01 Mathematical Sciences 0104 Statistics

Mariza De Andrade, E. Warwick Daw, Aldi T. Kraja, Virginia Fisher, Lan Wang, Ke Hu, Jing Li, Razvan Romanescu, Jenna Veenstra, Rui Sun, Haoyi Weng, Wenda Zhou

Research output: Contribution to journalArticle

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. 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.

Original languageEnglish (US)
Article number81
JournalBMC Genetics
Volume19
DOIs
StatePublished - Sep 17 2018

Fingerprint

Methylation
Single Nucleotide Polymorphism
Cytosine
Guanine
Genotype
Phosphates
Chromosomes, Human, Pair 11
Epigenomics
Linear Models
Research Personnel
Diet
Phenotype
Lipids
Pharmaceutical Preparations
Datasets

Keywords

  • Adaptive W-test
  • Candidate gene association
  • Genome wide association
  • Interaction
  • LASSO
  • Mediation analysis
  • Methylation
  • Multi-level Gaussian model
  • Region based association
  • Regression and random forest trees

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

The challenge of detecting genotype-by-methylation interaction : GAW20 01 Mathematical Sciences 0104 Statistics. / 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.

In: BMC Genetics, Vol. 19, 81, 17.09.2018.

Research output: Contribution to journalArticle

De Andrade, M, Warwick Daw, E, Kraja, AT, Fisher, V, Wang, L, Hu, K, Li, J, Romanescu, R, Veenstra, J, Sun, R, Weng, H & Zhou, W 2018, 'The challenge of detecting genotype-by-methylation interaction: GAW20 01 Mathematical Sciences 0104 Statistics', BMC Genetics, vol. 19, 81. https://doi.org/10.1186/s12863-018-0650-7
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. / The challenge of detecting genotype-by-methylation interaction : GAW20 01 Mathematical Sciences 0104 Statistics. In: BMC Genetics. 2018 ; Vol. 19.
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