{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:18:48Z","timestamp":1785403128665,"version":"3.56.0"},"posted":{"date-parts":[[2026,7,10]]},"group-title":"Genomics","reference-count":44,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["R01HL175964"],"award-info":[{"award-number":["R01HL175964"]}],"id":[{"id":"https:\/\/ror.org\/012pb6c26","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01HL169511"],"award-info":[{"award-number":["R01HL169511"]}],"id":[{"id":"https:\/\/ror.org\/012pb6c26","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01CA288967"],"award-info":[{"award-number":["R01CA288967"]}],"id":[{"id":"https:\/\/ror.org\/04w2jh416","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Genome-wide association studies (GWAS) have identified numerous variant-trait associations; yet, assigning effector genes to GWAS loci remains challenging. Similarity-based machine-learning methods, such as PoPS, prioritize effector genes from shared functional profiles among trait-relevant genes. These models assign a prioritization score for each gene and nominate a single effector gene within a GWAS locus. However, the scores provide limited insight into why a gene was prioritized or whether the nomination is biologically plausible. To address this gap, we introduce Kernelized Polygenic Priority Score, K-PoPS, a kernelized reformulation of PoPS that enables gene-centric explanations by decomposing each prediction into contributions from training genes. For each prioritized gene, K-PoPS reports top contributor genes and an anchor score that quantifies support from a user-defined set of trait-relevant genes. Across 38 Pan-UK Biobank traits, the full-feature OLS implementation underlying K-PoPS improved closest-gene enrichment relative to default PoPS for 26 of 37 evaluable traits. Across 25 traits with curated anchor sets, predictions supported by anchor scores were more enriched for closest-gene proxies than unsupported predictions. When applying to blood level apolipoprotein B, K-PoPS nominated\n                  <jats:italic>SCARB1<\/jats:italic>\n                  over\n                  <jats:italic>UBC<\/jats:italic>\n                  gene, and further provided convincing explanations that support this prediction. Using explanation evidence, K-PoPS identified multiple plausible effector genes within a dilated cardiomyopathy locus, contrary to the parsimonious assumption. In summary, K-PoPS provides a post hoc framework for examining and interpreting GWAS effector-gene nominations.\n                <\/jats:p>","DOI":"10.64898\/2026.07.08.737338","type":"posted-content","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:15:17Z","timestamp":1783725317000},"source":"Crossref","is-referenced-by-count":0,"title":["Kernelized approach enables explainable gene prioritizations for complex traits"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4904-8059","authenticated-orcid":false,"given":"Taotao","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Abul Hassan","family":"Samee","sequence":"additional","affiliation":[{"name":"Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026071407451197000_2026.07.08.737338v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2022.12.011"},{"key":"2026071407451197000_2026.07.08.737338v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-019-0538-0"},{"key":"2026071407451197000_2026.07.08.737338v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3506"},{"key":"2026071407451197000_2026.07.08.737338v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3259"},{"key":"2026071407451197000_2026.07.08.737338v1.5","doi-asserted-by":"crossref","unstructured":"Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. 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