Indirect effect inference and application to GAW20 data

BMC Genetics

Shili Lin¹  Chan Wang²  Tianyuan Lu²  Liming Li²  Yue-Qing Hu² 

[1] Department of Statistics, The Ohio State University;State Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University;

关键词: Epigenetics;    Differentially methylated regions;    DNA methylation;   

DOI  :  10.1186/s12863-018-0638-3

来源: DOAJ

【 摘 要 】

Abstract Background Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed. Results We propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied. Conclusions The proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.

【 授权许可】

Unknown