The genetics of falling susceptibility and identification of causal risk factors.

Falls represent a huge health and economic burden. Whilst many factors are associated with fall risk (e.g. obesity and physical inactivity) there is limited evidence for the causal role of these risk factors. Here, we used hospital and general practitioner records in UK Biobank, deriving a balance specific fall phenotype in 20,789 cases and 180,658 controls, performed a Genome Wide Association Study (GWAS) and used Mendelian Randomisation (MR) to test causal pathways. GWAS indicated a small but significant SNP-based heritability (4.4%), identifying one variant (rs429358) in APOE at genome-wide significance (P < 5e-8). MR provided evidence for a causal role of higher BMI on higher fall risk even in the absence of adverse metabolic consequences. Depression and neuroticism predicted higher risk of falling, whilst higher hand grip strength and physical activity were protective. Our findings suggest promoting lower BMI, higher physical activity as well as psychological health is likely to reduce falls.

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

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 summary statistics to principal components. We then choose a subset of the principal components based on data-driven cutoffs, and estimate their strength as instruments with an adjusted F-statistic. Finally, we perform MR with these transformed exposures. This pipeline is demonstrated in a simulation study of highly correlated exposures and an applied example using summary data from a genome-wide association study of 97 highly correlated lipid metabolites. As a positive control, we tested the causal associations of the transformed exposures on coronary heart disease (CHD). Compared to the conventional inverse-variance weighted MVMR method and a weak instrument robust MVMR method (MR GRAPPLE), sparse component analysis achieved a superior balance of sparsity and biologically insightful grouping of the lipid traits.