The impact of rare pathogenic CNVs is exacerbated by assortative mating

Abstract Copy-number variants (CNVs) are linked to a spectrum of outcomes and carriers of the same variant exhibit variable disease severity. We explored the impact of an individual’s polygenic score (PGS) on explaining these differences, focusing on 119 established CNV-trait associations involving 43 clinically-relevant phenotypes. We called CNVs among white British UK Biobank participants, then divided samples into a training set (n = 264,372) to derive independent PGS weights, and a CNV-carrier-enriched test set (n = 96,716). Assessing the individual, joint, and synergistic contribution of CNVs and PGSs, we identified a significant additive effect for 45 (38%) CNV-trait pairs, as well as two scale-dependent interactions between PGSs for gamma-glutamyltransferase levels and grip strength, and a 22q11.23 duplication encompassing multiple key genes in glutathione metabolism. A (spurious) negative correlation between an individual’s CNV carrier status and their PGS would be expected under selective participation-induced collider bias. Instead, we observed a widespread positive correlation, which could only be partially accounted for by linkage disequilibrium. Given a non-null inheritance rate for all 17 testable CNVs, we explored whether assortative mating could explain this positive CNV-PGS association. We found strong agreement between this correlation and the one predicted by assortment (r = 0.45, p = 2.0 × 10-7). Similar suggestive trends of positive correlation were observed between PGSs and genome-wide burden of CNVs or rare loss-of-function variants. Our results demonstrate that PGSs contribute to the variable expressivity of CNVs and rare variants, and improve the identification of individuals at higher risk of clinically relevant comorbidities. We also highlight pervasive assortative mating as a likely mechanism contributing to the compounding of genetic effects across mutational classes. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was supported by funding from the Department of Computational Biology and the Center for Integrative Genomics from the University of Lausanne, as well as grants from the Swiss National Science Foundation (P500-3_235131, 31003A_182632 and IZSTZ0_216615) and Horizon2020 Twinning projects (ePerMed 692145). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: NorthWest Multi-centre Research Ethics Committee gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes New power and sensitivity analyses added. Data Availability Data used in this study is publicly available in tabular format. The code to reproduce analyses is accessible on Github.