{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T05:48:36Z","timestamp":1791611316566,"version":"4.3.4"},"reference-count":76,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acsess.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Plant Genome"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Blueberries (\n                    <jats:italic>Vaccinium<\/jats:italic>\n                    spp.) are widely recognized as a superfruit due to their high content health\u2010promoting bioactive compounds, including flavonoids and phenolic acids. While these metabolites provide significant health benefits, their postharvest stability and implications for shelf life are not well characterized, a fact that is essential to delivery nutritious foods to consumers. To understand it, this study aimed to investigate the variability and changes of 32 flavonoid and phenolic acid metabolites evaluated in a diverse blueberry panel of 165 genotypes advanced selections after 1 and 7 weeks of cold storage at 1\u00b0C. Longitudinal mixed models combined with genomic information were used to estimate genetic parameters, assess genotype\u2010by\u2010time interactions, and estimate the potential of genomic selection for improving phytochemical retention. Our contribution is fourfold. First, we detected significant variation in metabolomic profiles across genotypes in both the quantity and composition of health\u2010related compounds. Second, moderate to high heritability estimates indicated strong genetic control of these traits during postharvest storage, indicating potential for breeding. Next, the use of longitudinal mixed model analyses showed significant genotype\u2010by\u2010time interactions for most metabolites, highlighting the complex relationship between genetic background and storage conditions. Finally, we showed promising prediction ability using genomic prediction models for most of the traits, indicating that molecular breeding is an important alternative to guide decisions related to improve health components. Together, these results demonstrate the strong genetic control of postharvest metabolite behavior and support the use of genomic selection to breed blueberry cultivars that maintain high nutritional value throughout the supply chain, maximizing both health benefits and market value.\n                  <\/jats:p>","DOI":"10.1002\/tpg2.70317","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:52:52Z","timestamp":1791607972000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Genetic control underlying dynamics of blueberry bioactive compounds during postharvest can drive breeding for functional fruits"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3160-412X","authenticated-orcid":false,"given":"Estefania","family":"Tavares Flores","sequence":"first","affiliation":[{"name":"Blueberry Breeding &amp; Genomics Lab, IFAS Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9994-4874","authenticated-orcid":false,"given":"Gonzalo","family":"Casorzo","sequence":"additional","affiliation":[{"name":"Blueberry Breeding &amp; Genomics Lab, IFAS Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Adunola","sequence":"additional","affiliation":[{"name":"Blueberry Breeding &amp; Genomics Lab, IFAS Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renee","family":"Strauch","sequence":"additional","affiliation":[{"name":"Plants for Human Health Institute North Carolina State University  Kannapolis North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary","family":"Grace","sequence":"additional","affiliation":[{"name":"Plants for Human Health Institute North Carolina State University  Kannapolis North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary Ann","family":"Lila","sequence":"additional","affiliation":[{"name":"Plants for Human Health Institute North Carolina State University  Kannapolis North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camila F.","family":"Azevedo","sequence":"additional","affiliation":[{"name":"Blueberry Breeding &amp; Genomics Lab, IFAS Gainesville Florida USA"},{"name":"Statistics Department Universidade Federal de Vicosa  Vi\u00e7osa Minas Gerais Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9655-4838","authenticated-orcid":false,"given":"Luis Felipe","family":"Ferr\u00e3o","sequence":"additional","affiliation":[{"name":"Blueberry Breeding &amp; Genomics Lab, IFAS Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8973-9351","authenticated-orcid":false,"given":"Patricio R.","family":"Munoz","sequence":"additional","affiliation":[{"name":"Blueberry Breeding &amp; Genomics Lab, IFAS Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/10942912.2016.1220393"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","DOI":"10.1002\/tpg2.20488","article-title":"Genomic selection optimization in blueberry: Data\u2010driven methods for marker and training population design","volume":"17","author":"Adunola P.","year":"2024","journal-title":"The Plant Genome"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3835\/plantgenome2016.01.0009"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/15538362.2022.2038341"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00122\u2010023\u201004512\u2010w"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules28083610"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19031217"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.arplant.54.031902.134938"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.832153"},{"key":"e_1_2_10_11_1","unstructured":"Butler D. 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