ArticleThe plant genome2019
Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits.
Article in The plant genome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.The plant genome · 2024Article
- Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle.JDS communications · 2023Article
- Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.Frontiers in plant science · 2023Article
- Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models.The plant genome · 2022Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.The New phytologist · 2022Article
- A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data.The plant genome · 2022Article
- Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of advanced research · 2022Review
- Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.Frontiers in plant science · 2022Article
- High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production.Frontiers in plant science · 2021Article
- Estimation of dynamic SNP-heritability with Bayesian Gaussian process models.Bioinformatics (Oxford, England) · 2020Article
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- Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data.Plant methods · 2020Article
- Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.Frontiers in plant science · 2020Review
- Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines.G3 (Bethesda, Md.) · 2019Article
- Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies.Plant methods · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the genetic basis of dynamic plant phenotypes has largely been limited because of a lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to nondestructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g., genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits and provide a robust framework for modeling trait trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice ( L.) from 33,674 single nucleotide polymorphisms. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent as well as time-specific transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.
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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.