RT Journal Article
SR Electronic
T1 MedDiC: high dimensional mediation analysis via difference in coefficients
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.09.08.507169
DO 10.1101/2022.09.08.507169
A1 Qi Zhang
A1 Zhikai Yang
A1 Jinliang Yang
YR 2022
UL http://biorxiv.org/content/early/2022/09/12/2022.09.08.507169.abstract
AB High dimensional mediation analysis has been receiving increasing popularity, largely motivated by the scientific problems in genomics and biomedical imaging. Previous literature has primarily focused on mediator selection for high dimensional mediators. In this paper, we aim at the estimation and inference of overall indirect effect for high dimensional exposures and high dimensional mediators. We propose MedDiC, a novel debiased estimator of the high dimensional overall indirect effect based on difference-in-coefficients approach. We evaluate the proposed method using intensive simulations and find that MedDiC provides valid inference and offers higher power and shorter computing time than the competitors for both low dimensional and high dimensional exposures. We also apply MedDiC to a mouse f2 dataset for diabetes study and a dataset composed of diverse maize inbred lines for flowering time, and show that MedDiC yields more biologically meaningful gene lists, and the results are reproduciable across analyses using different measures of identical biological signal or related phenotype as the outcome.Upon the acceptance of the paper, the code will be available on GitHub (https://github.com/QiZhangStat/MedDiC).Competing Interest StatementThe authors have declared no competing interest.