ArticleScientific reports2017
Controlling the Overfitting of Heritability in Genomic Selection through Cross Validation.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Metabolic pathways associated with cardiometabolic risk effects on cognition in middle-aged adults: the CARDIA study.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection.BMC plant biology · 2026Article
- Deep learning application for genomic data analysis.BMB reports · 2026Review
- Genomic prediction models for morpho-phenological traits in durum wheat based on Vrn, Ppd, and Rht alleles.The plant genome · 2025Article
- Application and development prospect of genomic selection breeding in coniferous trees.Planta · 2025Review
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Medication use and risk of amyotrophic lateral sclerosis: using machine learning for an exposome-wide screen of a large clinical database.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2024Article
- A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties.Frontiers in plant science · 2024Article
- New Diagnostic Modality Combining Mass Spectrometry and Machine Learning for the Discrimination of Malignant Intraductal Papillary Mucinous Neoplasms.Annals of surgical oncology · 2023Article
- Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.Frontiers in medicine · 2023Article
- Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction inPlants (Basel, Switzerland) · 2022Article
- Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients.Scientific reports · 2022Article
- Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.Scientific reports · 2022Article
- Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs.The plant genome · 2021Article
- Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing.Genetics, selection, evolution : GSE · 2021Article
- NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.Nucleic acids research · 2020Article
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Abstract
In genomic selection (GS), all the markers across the entire genome are used to conduct marker-assisted selection such that each quantitative trait locus of complex trait is in linkage disequilibrium with at least one marker. Although GS improves estimated breeding values and genetic gain, in most GS models genetic variance is estimated from training samples with many trait-irrelevant markers, which leads to severe overfitting in the calculation of trait heritability. In this study, we demonstrated overfitting heritability due to the inclusion of trait-irrelevant markers using a series of simulations, and such overfitting can be effectively controlled by cross validation experiment. In the proposed method, the genetic variance is simply the variance of the genetic values predicted through cross validation, the residual variance is the variance of the differences between the observed phenotypic values and the predicted genetic values, and these two resultant variance components are used for calculating the unbiased heritability. We also demonstrated that the heritability calculated through cross validation is equivalent to trait predictability, which objectively reflects the applicability of the GS models. The proposed method can be implemented with the Mixed Procedure in SAS or with our R package "GSMX" which is publically available at https://cran.r-project.org/web/packages/GSMX/index.html .
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