eprintid: 10205131 rev_number: 15 eprint_status: archive userid: 699 dir: disk0/10/20/51/31 datestamp: 2025-03-07 09:06:59 lastmod: 2025-03-07 09:06:59 status_changed: 2025-03-07 09:06:59 type: thesis metadata_visibility: show sword_depositor: 699 creators_name: Janahi, Mohammed title: Polygenic Scores for Normative Models: Development and Applications ispublished: unpub divisions: UCL divisions: B04 divisions: F42 note: Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. abstract: Normative modeling considers disease as an outlier in a distribution of healthy con- trols. Genetics account for a significant portion of variability in both healthy and diseased subjects. This thesis demonstrates that accounting for genetics in nor- mative models through a novel Multi-Threshold polygenic score (Multi-Threshold- PGS) method improves their accuracy and clinical utility. It also establishes that the Multi-Threshold-PGS is useful outside the context of normative models and in both disease and quantitative traits. I first determine that stratifying normative models (nomograms) of Hippocam- pal Volume (HV) using a HV Polygenic Score (PGS) computed at a single p-value threshold shifts nomograms significantly in both UKBB and ADNI as predicted by the PGS. The magnitude of the shift corresponded to ∼three years of normal aging for a 65-year-old. I found that HV PGS independently correlated to HV across thresholds, with the order in one score being unpredictive of the score in an- other. I next showed that compounding PGS increased the nomogram shift. Across 12,720 models, summing any 2 PGSs increased separation by 22%, and Multi- Threshold-PGS by 60%. Multi-Threshold-PGS provided significant correlations to 9 neurocognitive assessments both cross-sectionally and longitudinally in three out- of-sample datasets. Next, I showed that MAF-based PGSs were well correlated to each other and proved an inferior basis for Multi-Threshold-PGS compared to their p-value coun- terparts. I finally showed that Multi-Threshold-PGS provided benefits in disease traits and outside the context of normative modeling. Multi-Threshold-PGSs of Alzheimer’s Disease (AD) provided significantly better results in four different experimental setups and in out-of-sample data. Overall, Multi-Threshold-PGS provided improvements in a broad range of applications. My results show that PGS and normative modeling can be developed to provide significant utility in both the clinic and clinical trials. Future work will explore the benefits of Multi-Threshold-PGS in other neurocognitive applications and improve this PGS further with pathway-informed construction. date: 2025-02-28 date_type: published oa_status: green full_text_type: other thesis_class: doctoral_open thesis_award: Ph.D language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2363618 lyricists_name: Janahi, Mohammed lyricists_id: MJANA19 actors_name: Janahi, Mohammed actors_id: MJANA19 actors_role: owner full_text_status: public pagerange: 1-242 pages: 242 institution: UCL (University College London) department: Medical Physics & Biomedical Engineering thesis_type: Doctoral citation: Janahi, Mohammed; (2025) Polygenic Scores for Normative Models: Development and Applications. Doctoral thesis (Ph.D), UCL (University College London). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10205131/13/Janahi_10205131_Thesis.pdf