{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:47:03Z","timestamp":1780426023518,"version":"3.54.1"},"reference-count":71,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Short structural variants (SSVs), including insertions\/deletions (indels), are common in the human genome and impact disease risk. The role of SSVs in late\u2010onset Alzheimer's disease (LOAD) has been understudied. In this study, we developed a bioinformatics pipeline of SSVs within LOAD\u2013genome\u2010wide association study (GWAS) regions to prioritize regulatory SSVs based on the strength of their predicted effect on transcription factor (TF) binding sites.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The pipeline utilized publicly available functional genomics data sources including candidate cis\u2010regulatory elements (cCREs) from ENCODE and single\u2010nucleus (sn)RNA\u2010seq data from LOAD patient samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We catalogued 1581 SSVs in candidate cCREs in LOAD GWAS regions that disrupted 737 TF sites. That included SSVs that disrupted the binding of <jats:italic>RUNX3<\/jats:italic>, <jats:italic>SPI1<\/jats:italic>, and <jats:italic>SMAD3<\/jats:italic>, within the <jats:italic>APOE<\/jats:italic>\u2010<jats:italic>TOMM40<\/jats:italic>, <jats:italic>SPI1<\/jats:italic>, and <jats:italic>MS4A6A<\/jats:italic> LOAD regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The pipeline developed here prioritized non\u2010coding SSVs in cCREs and characterized their putative effects on TF binding. The approach integrates multiomics datasets for validation experiments using disease models.<\/jats:p><\/jats:sec>","DOI":"10.1002\/alz.13168","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T14:59:55Z","timestamp":1685458795000},"page":"4094-4109","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Bioinformatics pipeline to guide post\u2010GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants"],"prefix":"10.1002","volume":"19","author":[{"given":"Michael W.","family":"Lutz","sequence":"first","affiliation":[{"name":"Division of Translational Brain Sciences Department of Neurology Duke University Medical Center  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ornit","family":"Chiba\u2010Falek","sequence":"additional","affiliation":[{"name":"Division of Translational Brain Sciences Department of 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