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In Wenchang chickens, reducing excessive abdominal fat represents a critical breeding objective. However, as a typical carcass trait, abdominal fat thickness has traditionally been measurable only post-slaughter, resulting in inefficient and costly selection processes that hinder genetic progress for these traits. To overcome this limitation, we developed an integrated approach combining non-invasive ultrasound phenotyping and multi-model genomic selection to evaluate growth and fat-related traits in Wenchang chickens.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We genotyped 3,737 chickens using the \u201cJingxin No.1\u201d 55K SNP array and performed longitudinal measurement of abdominal fat thickness (AFT) via ultrasound imaging. A comprehensive evaluation of genomic prediction models revealed that WGBLUP (informed by wssGWAS), and GBLUP models based on LD-pruned whole-genome sequencing (WGS) data significantly outperformed standard GBLUP, with accuracy gains of 5.25% and 6.58%\u201315.30%, respectively. Among the machine learning algorithms tested, kernel ridge regression (KRR) and support vector regression (SVR) achieved the highest predictive improvement (3.00%\u20134.15%) while maintaining superior computational efficiency, whereas ensemble methods provide no consistent advantage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our work established ultrasound imaging as a scalable, non-invasive phenotyping platform for poultry breeding. Results demonstrated that integrating wssGWAS-derived biological priors with WGS data substantially improves genomic prediction accuracy for complex traits. This integration, enhanced by computationally efficient machine learning algorithms, provides a powerful and practical strategy to accelerate genetic gain.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40104-026-01384-0","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T23:01:41Z","timestamp":1777071701000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing genomic prediction for key production traits in chickens through ultrasound phenotyping and multi-model comparative analysis"],"prefix":"10.1186","volume":"17","author":[{"given":"Ranran","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanyi","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Lian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danni","family":"Gou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhandeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemei","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"issue":"4","key":"1384_CR1","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1093\/genetics\/157.4.1819","volume":"157","author":"THE Meuwissen","year":"2001","unstructured":"Meuwissen THE, Hayes BJ, Goddard ME. 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