Sparse dimensionality reduction approaches in Mendelian randomisation with highly correlated exposures.

ABSTRACT: Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of MVMR estimates thus depends heavily on the correlation of exposures. Dimensionality reduction techniques such as principal component analysis (PCA) provide transformations of all the included variables that are effectively uncorrelated. We propose the use of sparse PCA (sPCA) algorithms that create principal components of subsets of the exposures with the aim of providing more interpretable and reliable MR estimates. The approach consists of three steps. We first apply a sparse dimension reduction method and transform the variant-exposure summ

SUBMITTER: Karageorgiou V 

PROVIDER: S-EPMC10229118 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature