ArticlePLoS genetics2008
Predicting unobserved phenotypes for complex traits from whole-genome SNP data.
Article in PLoS genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
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Who cites it
110 citing papers in PubMed.
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- deepBreaks identifies and prioritizes genotype-phenotype associations using machine learning.Scientific reports · 2025Article
- Multi-task genomic prediction using gated residual variable selection neural networks.BMC bioinformatics · 2025Article
- Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits.Genome biology · 2025Article
- Improved genomic prediction accuracy by genetic relatedness using a crossbred pig population.Translational animal science · 2025Article
- Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.BMC bioinformatics · 2024Article
- Genome-Wide Association Analyses and Population Verification Highlight the Potential Genetic Basis of Horned Morphology during Polled Selection in Tibetan Sheep.Animals : an open access journal from MDPI · 2024Article
- Using DNA to Reunify Families Separated by Disasters.NAM perspectives · 2024Review
- Uncovering genes underlying coat color variation in indigenous cattle breeds through genome-wide positive selection.Animal biotechnology · 2023Article
- Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer.Functional & integrative genomics · 2023Article
- Perspectives and recent progress of genome-wide association studies (GWAS) in fruits.Molecular biology reports · 2022Review
- Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.G3 (Bethesda, Md.) · 2022Article
- Construction of a SNP Fingerprinting Database and Population Genetic Analysis of Cigar Tobacco Germplasm Resources in China.Frontiers in plant science · 2021Article
- Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.Frontiers in plant science · 2021Article
- Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives.Nature communications · 2020Article
- Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton.Frontiers in plant science · 2020Article
- Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations.Frontiers in genetics · 2020Article
- Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores.Frontiers in genetics · 2020Article
- Predicted Genetic Gains from Targeted Recombination in Elite Biparental Maize Populations.The plant genome · 2019Article
50 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs.
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