{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:13:32Z","timestamp":1762377212728,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 LM013463","U01 AG068057","R01 AG071470","R01 AG058854","P30 AG010133","IIS 1837964"],"award-info":[{"award-number":["R01 LM013463","U01 AG068057","R01 AG071470","R01 AG058854","P30 AG010133","IIS 1837964"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["R01 LM013463","U01 AG068057","R01 AG071470","R01 AG058854","P30 AG010133","IIS 1837964"],"award-info":[{"award-number":["R01 LM013463","U01 AG068057","R01 AG071470","R01 AG058854","P30 AG010133","IIS 1837964"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer\u2019s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP\u2013SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.<\/jats:p>","DOI":"10.3390\/genes13091520","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T21:03:51Z","timestamp":1661375031000},"page":"1520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns"],"prefix":"10.3390","volume":"13","author":[{"given":"Ruiming","family":"Wu","sequence":"first","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxuan","family":"Bao","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mansu","family":"Kim","sequence":"additional","affiliation":[{"name":"The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew J.","family":"Saykin","sequence":"additional","affiliation":[{"name":"Indiana University, Indianapolis, IN 46202, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason H.","family":"Moore","sequence":"additional","affiliation":[{"name":"Cedars-Sinai, West Hollywood, CA 90048, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5443-0503","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"on behalf of ADNI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1212\/WNL.0000000000002923","article-title":"A\/T\/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers","volume":"87","author":"Jack","year":"2016","journal-title":"Neurology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1126\/science.1566067","article-title":"Alzheimer\u2019s disease: The amyloid cascade hypothesis","volume":"256","author":"Hardy","year":"1992","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s41588-018-0311-9","article-title":"Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer\u2019s disease risk","volume":"51","author":"Jansen","year":"2019","journal-title":"Nat. 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