Association of Whole-Person Eigen-Polygenic Risk Scores with Alzheimer’s Disease

Abstract

AbstractLate-Onset Alzheimer’s Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the “whole person” genetic risk landscape of polygenic risk scores for 2,218 traits in 2,044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Principal component analyses of thousands of PRSs found generally poor global correlation among traits. However, component loadings confirmed covariance of clinically and biologically related traits and diagnoses, with the top PCs representing autoimmune traits, cardiovascular traits, and general pain medication prescriptions, depending on the PRS variant inclusion threshold. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) derived from these clusters were significantly associated with LOAD-related phenotypes and improved predictive model performance over the state-of-the-art LOAD PRS alone. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of brain-wide beta-amyloid burden by 1.7% (likelihood ratio test p=9.02×10−7). While many associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci, some modules (e.g. retinal defects, acidosis, colon health, ischaemic heart disease) showed associations at an unadjusted type I error rate. Our approach reveals new relationships between genetic risk for vascular, inflammatory, and other age-related traits and offers improvements over the existing single-trait PRS approach to capturing heritable risk for cognitive decline and beta-amyloid accumulation. Our results are catalogued for the scientific community, to aid in the generation of new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.