Polygenic architecture of rare coding variation across 400,000 exomes

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have discovered thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes[1][1]–[3][2]. However, rare-variant genetic architecture is not well characterized, and the relationship between common- and rare-variant architecture is unclear[4][3]. Here, we quantify the heritability explained by gene-wise burden of rare coding variants and compare the genetic architecture of common and rare variation across 22 common traits and diseases in 400,000 UK Biobank exomes[5][4]. Rare coding variants (AF = 1e-6 - 1e-3) explain 1.3% (SE = 0.03%) of phenotypic variance on average – much less than common variants – and most burden heritability is explained by ultra-rare loss-of-function variants (AF = 1e-6 - 1e-5). Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in a limited number of significant genes (median: 6 genes explaining 19% of h[2][5]), while common-variant heritability is much more polygenic. Burden heritability is also more strongly concentrated in constrained genes (median enrichment: 4.5x vs. 2.1x for common variants), indicating that negative selection affects common- and rare-variant architecture differently. Finally, we find that burden heritability for schizophrenia and bipolar disorder[6][6],[7][7] is approximately 2%. Our results show that there are a tractable number of large-effect genes to discover by studying rare variants, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification. ### Competing Interest Statement KJK is a consultant for Vor Biopharma and AlloDx. BMN is a member of the scientific advisory board at Deep Genomics and Neumora, consultant of the scientific advisory board for Camp4 Therapeutics and consultant for Merck. The remaining authors have no competing interests. ### Funding Statement The authors are grateful for support from National Institute Mental Health (F30MH129009 to Daniel J. Weiner), National Library of Medicine (T15LM007092 to Daniel Weiner), National Institute of General Medical Science (T32GM007753 to Ajay Nadig), Simons Foundation Autism Research Initiative (704413 to Elise Robinson and Luke O'Connor), and the Broad Institute. ### 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: Source data (Genebass) was available before initiation of this study: https://app.genebass.org/downloads 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data used in this manuscript is publicly available and documented in Supplementary Tables. All results are available in the Supplementary Tables. Neale Lab UKB GWAS summary statistics: http://www.nealelab.is/uk- biobank/ [1]: #ref-1 [2]: #ref-3 [3]: #ref-4 [4]: #ref-5 [5]: #ref-2 [6]: #ref-6 [7]: #ref-7