Clustering of rare variants for causal variants identification and effect direction classification

Several gene-based tests, e.g., sequence kernel association test, have been developed for association testing of rare single nucleotide variants (SNVs) in genomic regions with disease traits. A common limitation of these aggregate methods is their inability to discriminate potentially causal variants from null variants within the tested regions. We propose a novel clustering method to classify rare variants into null and signal variant groups using summary statistics from the gene-based tests based on a Gaussian mixture model (GMM). We classify the signal variants into potentially risk and protective subgroups of different effect sizes. We evaluate the performance of the proposed method by a simulation study, considering several statistics such as the adjusted rand index (ARI), mean square error (MSE), and accuracy in specifying the number of clusters. We apply the proposed clustering method to identify possibly risk and protective rare variants in six genes that are significantly associated with blood pressure (BP) traits in the most recent large genomewide association study (GWAS) and meta-analysis. This proposed method may facilitate the identification of potentially causal rare variant clusters in genomic regions and ultimately help understand the genetic architecture underlying human complex traits for the discovery of drug target and the design of gene therapy. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement R21HL144877 R01AG059727 ### 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: We used simulated data and data from GWAS catalog https://www.ebi.ac.uk/gwas/ I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 All data produced in the present study are available upon reasonable request to the authors