Cross-trait and multi-polytranscriptomic score analysis of Parkinson's disease identifies novel associations and improves prediction
TL;DR
Significant transcriptomic overlap between PD and a range of clinically relevant traits is revealed, providing novel insights into disease biology with potential to improve disease prediction.
Abstract
Despite major progress in genomic risk loci identification, biological mechanisms underlying Parkinson's disease (PD) remain incompletely understood and the informativity of polygenic score (PGS)-based prediction remains modest. Polytranscriptomic scores (PTS) - the sum of an individual's observed gene expression weighted by transcriptome-wide association z-scores - combine the stability of genetics with the dynamic biology of gene expression and have the potential to identify associations not captured by genetics alone. We present the first large-scale cross-trait and multi-PTS analysis of PD, calculating ~550 PTS for 100 phenotypes using whole-blood RNA-seq data from three independent clinical cohorts in the Accelerating Medicines Partnership Parkinson's Disease programme (AMP-PD) (N = 2,741; NCASES = 1,644). We identify 26 Bonferroni significant cross-trait PTS associations with PD (p<9x10-5) involving 18 phenotypes and 11 trait categories, including neurodegenerative diseases, respiratory function, sleep and cardiovascular traits. Only one of these associations was observed using corresponding PGS, highlighting the added value of integrating directly measured transcriptomic data. Combining multiple PTS within machine learning multi-PTS models improved prediction of PD case/control status beyond age, sex and PD-PGS in external validation, with a sparse model including an additional seven PTS achieving an AUC of 0.74 [0.70-0.78], representing a 0.09-point improvement. These findings reveal transcriptomic overlap between PD and a range of clinically relevant traits, providing novel insights into disease biology with potential to improve disease prediction.