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Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesC\u03c0, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison<jats:italic>p<\/jats:italic>-value of ELPGV over basic methods were varied from 4.853E\u2212118 to 9.640E\u221220 for WTCCC dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>ELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-024-05720-x","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T09:01:42Z","timestamp":1711011702000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Ensemble learning for integrative prediction of genetic values with genomic variants"],"prefix":"10.1186","volume":"25","author":[{"given":"Lin-Lin","family":"Gu","sequence":"first","affiliation":[]},{"given":"Run-Qing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhi-Yong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"5720_CR1","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1534\/genetics.118.301267","volume":"210","author":"L Lello","year":"2018","unstructured":"Lello L, Avery SG, Tellier L, Vazquez AI, de los Campos G, Hsu SDH. 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