ArticleGenetics2006
Mapping quantitative trait loci for longitudinal traits in line crosses.
Article in Genetics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Modeling production curves in a strawberry breeding program to optimize early season productivity.Frontiers in plant science · 2026Article
- Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data.G3 (Bethesda, Md.) · 2025Article
- SPANature communications · 2025Article
- Natural variation in the prolyl 4-hydroxylase gene PtoP4H9 contributes to perennial stem growth in Populus.The Plant cell · 2023Article
- Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.Frontiers in plant science · 2023Article
- Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies.Genomics & informatics · 2022Article
- 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
- The Genetic Architecture of Juvenile Growth Traits in the ConiferFrontiers in plant science · 2022Article
- Genetic Association Analysis for Relative Growths of Body Compositions and Metabolic Traits to Body Weights in Broilers.Animals : an open access journal from MDPI · 2021Article
- High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production.Frontiers in plant science · 2021Article
- Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence.PLoS genetics · 2021Article
- Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops.Frontiers in plant science · 2020Review
- Genome-wide association study identified novel candidate loci affecting wood formation in Norway spruce.The Plant journal : for cell and molecular biology · 2019Article
- Article
- Functional Mapping of Quantitative Trait Loci (QTLs) Associated With Plant Performance in a Wheat MAGIC Mapping Population.Frontiers in plant science · 2018Article
- Modelling QTL effect on BTA06 using random regression test day models.Journal of applied genetics · 2013Article
- Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling.Heredity · 2012Article
- Generalized linear mixed model for segregation distortion analysis.BMC genetics · 2011Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.
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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.