Multivariate variance components analysis uncovers genetic architecture of brain isoform expression and novel psychiatric disease mechanisms

Multivariate variance components linear mixed models are fundamental statistical models in quantitative genetics, widely used to quantify SNP-based heritability ( h 2SNP) and genetic correlation ( r g) across complex traits. However, maximum likelihood estimation of multivariate variance components models remains numerically challenging when the number of traits and variance components are both greater than two. To address this critical gap, here we introduce a novel statistical method for fitting multivariate variance components models. This method improves on existing methods by allowing for arbitrary number of traits and/or variance components. We illustrate the utility of our method by characterizing for the first time the genetic architecture of isoform expression in the human brain, modeling up to 23 isoforms jointly across ∼900 individuals within PsychENCODE. We find a significant proportion of isoforms to be under genetic control (17,721 of 93,293 isoforms) with substantial shared genetic effects among local (or cis -) relative to distal (or trans -) genetic variants (median r g,cis and r g,trans = 0.31 and 0.06). Importantly, we find that 11.6% of brain-expressed genes (2,900 genes) are heritable only at the isoform-level. Integrating these isoform-specific genetic signals with psychiatric GWAS signals uncovers previously hidden psychiatric disease mechanisms. Specifically, we highlight reduced expression of a specific XRN2 isoform as the underlying driver of the strongest GWAS signal for autism spectrum disorder. Overall, our method for fitting multivariate variance components models is flexible, widely applicable, and is implemented in the Julia programming language and available online. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the National Institutes of Health (R01MH121521, R01MH123922, P50HD103557, T32MH073526, F30MH125523, T32GM008042). ### 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: The study used publicly available PsychENCODE data at: [www.doi.org/10.7303/syn12080241][1] and Resource.PsychENCODE.org. 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 PsychENCODE genotype array and RNA-seq data are available at [www.doi.org/10.7303/syn12080241][1] and processed summary-level data are available at Resource.PsychENCODE.org. [1]: http://www.doi.org/10.7303/syn12080241