Impact of polygenic risk score on normative models of hippocampal volumes

Abstract

AbstractBackgroundNormative Models are widely used across clinical applications from infant growth charts to brain volume charts. For Alzheimer’s disease (AD), reduction in hippocampal volume (HV) is an early and often presymptomatic feature. Thus, precisely measuring deviation of HV from healthy aging facilitates earlier diagnosis. Brain volumes have been reported with heritability of 40‐90%, thus, implying that normative ranges of HV are heavily influenced by genetics. Here we investigate whether genetic information can improve HV nomograms for AD.MethodWe used Gaussian Process Regression (GPR) to generate nomograms of HV from 40,000 participants in UKBB aged 45‐82. These nomograms were contrasted to a previously reported Sliding Window method (SWM; PMID: 31254939). Next, we computed a Polygenic Risk Score (PRS) for HV using a large independent GWAS (N=33,536; PMID: 28098162). Participants were stratified into high and low PRS for HV; nomograms were built for each strata separately. Next, we used subjects from the ADNI dataset labelled cognitively normal (CN) or AD (N=226 and N=122) to examine the difference between using stratified versus non‐stratified nomograms.ResultNomograms generated using GPR matched those generated with the SWM with an average of difference of 17 mm3 (about 0.4%). Compared to the SWM, GPR extended the age range of the nomograms by ten years in either direction (from 56‐73 y/o to 47‐83 y/o: Figure 1). Nomograms stratified by PRS showed an average shift of 28 mm3 upward/downward for high/low PRS scoring samples (Figure 1). In ADNI, using non‐stratified nomograms led to a difference of 13% in median HV percentiles between high‐PRS and low‐PRS CN participants, which was reduced to 3.3% using stratified nomograms (Figure 2); this effect was not observed for the AD group.ConclusionGPR‐based nomograms were similar to the SWM‐nomograms while extending the range into areas critical for the analysis of aging diseases. Including genetics through a PRS for HV shifted HV nomograms in the expected direction. The small shift in nomograms of HV did not affect the sensitivity of AD diagnosis. However, it led to better adjusted estimates within CN subjects. Thus, genetics‐adjusted HV estimates may improve biomarker‐based AD‐risk predictions (PMID: 31526625).