{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T11:06:09Z","timestamp":1789729569126,"version":"4.0.1"},"reference-count":29,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Plant Journal"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                  <jats:p>As genomic datasets expand in both sample size and marker density, genome\u2010wide association studies (GWAS) and genomic selection (GS) require workflows that remain statistically rigorous, computationally efficient, and reproducible across the full analysis path, from genotype matrix to decision\u2010relevant outputs. Here we present JanusX, an integrated high\u2010performance framework that provides a streamlined, user\u2010oriented workflow for GWAS and GS by unifying data handling, model execution, and visualization. Across simulated and real datasets, JanusX maintained high concordance with established baselines while substantially reducing runtime and memory usage. In GWAS, JanusX achieved up to a 19\u2010fold speedup over GEMMA in linear mixed model (LMM) inference, and additionally implemented LMM inference based on a sparse genomic relationship matrix with GRAMMAR\u2010Gamma calibration, alleviating computational and memory bottlenecks in large\u2010scale cohorts. JanusX also provides a FarmCPU implementation within its GWAS module, achieving a median 11.4\u2010fold runtime improvement and reducing peak memory usage by 84.9% relative to rMVP. In GS, JanusX integrates an optimized best linear unbiased prediction (BLUP) backend that adaptively selects sample\u2010 and single\u2010nucleotide polymorphism (SNP)\u2010space solvers and incorporates a Preconditioned Conjugate Gradient solver. This implementation efficiently completes five\u2010fold cross\u2010validation of 500k individuals \u00d7\u2009500k SNPs in 35.1\u2009min with only 14.3 gibibytes (GiB) of peak memory. Beyond BLUP, JanusX integrates Bayesian and machine\u2010learning predictors under a single interface with compact automatic tuning to ensure robust cross\u2010model performance. JanusX therefore enables efficient locus discovery and genomic prediction under consistent analytical assumptions, even in large\u2010scale cohorts.<\/jats:p>","DOI":"10.1111\/tpj.71105","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T04:27:11Z","timestamp":1789532831000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>JanusX<\/scp>\n                    : an integrated and high\u2010performance platform for scalable genome\u2010wide association studies and genomic selection"],"prefix":"10.1111","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4173-732X","authenticated-orcid":false,"given":"Jingxian","family":"Fu","sequence":"first","affiliation":[{"name":"National Laboratory of Crop Genetic Improvement College of Bio\u2010X, Hainan Research Institute, Huazhong Agricultural University  Wuhan 430070 China"},{"name":"Hubei Hongshan Laboratory  Wuhan 430070 China"},{"name":"Yazhouwan National Laboratory  Sanya 572024 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7133-6425","authenticated-orcid":false,"given":"Anqiang","family":"Jia","sequence":"additional","affiliation":[{"name":"Yazhouwan National Laboratory  Sanya 572024 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Yazhouwan National Laboratory  Sanya 572024 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7717-893X","authenticated-orcid":false,"given":"Hai\u2010Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Crop Genetic Improvement College of Bio\u2010X, Hainan Research Institute, Huazhong Agricultural University  Wuhan 430070 China"},{"name":"Hubei Hongshan Laboratory  Wuhan 430070 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0156744"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/14737159.2017.1282822"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3835\/plantgenome2011.08.0024"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-018-0082-2"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-arplant-050213-035715"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-019-0530-8"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.548"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05473-8"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","unstructured":"Lin X. Dey R. Li X.&Li Z.(2024)Scalable analysis of large multi\u2010ancestry biobanks by leveraging sparse ancestry\u2010adjusted sample\u2010relatedness.Research Square[Preprint].","DOI":"10.21203\/rs.3.rs-5343361\/v1"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1681"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-025-03807-z"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-020-1930-x"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1005767"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3190"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-024-02044-7"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molp.2025.03.007"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/157.4.1819"},{"key":"e_1_2_10_19_1","first-page":"2825","article-title":"Scikit\u2010learn: machine learning in python","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.114.164442"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btag236"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2410"},{"key":"e_1_2_10_23_1","first-page":"144","volume-title":"International conference on artificial intelligence and statistics","author":"Terenin A.","year":"2020"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.3168\/jds.2007-0980"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2017.06.005"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2010.11.011"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gpb.2020.10.007"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkad074"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng1702"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2310"}],"container-title":["The Plant Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tpj.71105","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/tpj.71105","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tpj.71105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T10:46:01Z","timestamp":1789728361000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/tpj.71105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":29,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1111\/tpj.71105"],"URL":"https:\/\/doi.org\/10.1111\/tpj.71105","archive":["Portico"],"relation":{},"ISSN":["0960-7412","1365-313X"],"issn-type":[{"value":"0960-7412","type":"print"},{"value":"1365-313X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"2026-04-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-08-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e71105"}}