ArticleBMC genomics2022
Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Optimizing multi-trait selection for profitability in Illinois wheat using a factor analytic mixed model and an economic selection index.The plant genome · 2026Article
- Integrating molecular networks and physiological adaptation for heat-resilient crops.Journal of integrative plant biology · 2026Review
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- Comparative analysis of genomic prediction approaches for multiple time-resolved traits in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Physiological determinants of heterosis in hybrid rice (Frontiers in plant science · 2026Article
- Multi-trait multi-environment genomic prediction strategies forFrontiers in plant science · 2026Article
- Performance of multi-trait and single-trait genomic selection for grain Fe and Zn concentrations in sorghum under different breeding constraints.Scientific reports · 2025Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.G3 (Bethesda, Md.) · 2025Article
- Article
- PCA-Driven Multivariate Trait Integration in Alfalfa Breeding: A Selection Model for High-Yield and Stable Progenies.Plants (Basel, Switzerland) · 2025Article
- Multi-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought-Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture.Plants (Basel, Switzerland) · 2025Review
- Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.The plant genome · 2025Article
- Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Dissecting the genetic basis of fruiting efficiency for genetic enhancement of harvest index, grain number, and yield in wheat.BMC plant biology · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Deciphering the genetic basis of novel traits that discriminate useful and non-useful biomass to enhance harvest index in wheat.The plant genome · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2.
resultsIn this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model.
conclusionsThe study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.