ArticleHeredity2015
Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model.
Article in Heredity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs.BMC genomics · 2023Pooled it
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Multi-trait multi-environment genomic prediction strategies forFrontiers in plant science · 2026Article
- Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.Scientific reports · 2024Article
- Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.Scientific reports · 2023Article
- Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.BMC genomics · 2022Article
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle.Animals : an open access journal from MDPI · 2022Article
- Multi-trait genome prediction of new environments with partial least squares.Frontiers in genetics · 2022Article
- Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat.Frontiers in plant science · 2022Article
- Accounting for Correlation Between Traits in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.Frontiers in genetics · 2022Article
- Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine.G3 (Bethesda, Md.) · 2021Article
- Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat.Frontiers in plant science · 2021Article
- Genome-based prediction of multiple wheat quality traits in multiple years.The plant genome · 2020Article
- Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.Genes · 2020Article
- KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters.Genome biology · 2020Article
- Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (G3 (Bethesda, Md.) · 2020Article
- Genomic selection for lentil breeding: Empirical evidence.The plant genome · 2020Article
- Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.Frontiers in plant science · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.