{
  "abstract": "Breeding programs increasingly need prediction systems that support reliable candidate selection across heterogeneous environments, which makes cross-environment genomic prediction an important component of multienvironment crop improvement. However, model evaluation still relies heavily on Pearson’s correlation coefficient ( r ), a global summary that may not fully capture which top-ranked candidates would be advanced. Using maize and switchgrass datasets as focused case studies, we compared global predictive ability with phenotype-referenced candidate diagnostics across within-environment and directed cross-environment evaluations of rrBLUP/ridge, Light Gradient Boosting Machine (LightGBM), a multilayer perceptron, and mixed-effect deep neural network (MeNet). Rather than proposing a new selection metric, we used a conditional comparison framework that distinguishes nominal metric reversal from strict decision-level discordance among models with similar Pearson’s r . The r -best and candidate-metric-best models disagreed in 25%–55% of evaluation contexts, but large differences in candidate metrics among near- r models (|Δ r | ≤ 0.02) were uncommon. Nevertheless, models with nearly identical r did not always prioritize the same candidates: LightGBM and rrBLUP/ridge shared, on average, 72.9% of the top 5% candidates in maize and 68.8% in switchgrass. These results indicate that Pearson’s r remains informative for broad model comparison but does not fully describe decision-level behavior. Phenotype-referenced candidate diagnostics are therefore best used as targeted complements when models have similar predictive ability, selection intensity is high, or candidate identity is consequential across environments.",
  "abstract_shown": true,
  "affiliations": [
    "Frontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xueyang Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jun Yan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b7kz7yq9rpwnd.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1951182",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668517/?tool=EBI",
  "id": "b7kz7yq9rpwnd",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13668517",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Genetic and phenotypic traits in livestock"
    ]
  ],
  "openalex": "W7215113558",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668517/pdf/?tool=EBI",
  "pmcid": "PMC13668517",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/b7kz7yq9rpwnd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:18Z",
      "native_id": "PMC:PMC13668517",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13668517"
    },
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1951182",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1951182/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic and phenotypic traits in livestock",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Selection-aware metrics provide complementary decision-level information in plant genomic prediction",
  "title_text": "Selection-aware metrics provide complementary decision-level information in plant genomic prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/b7kz7yq9rpwnd",
  "venue": "Frontiers in plant science",
  "versions": []
}