Literature DB >> 15689339

Model analysis of flowering phenology in recombinant inbred lines of barley.

Xinyou Yin1, Paul C Struik, Jianjun Tang, Changhan Qi, Taoju Liu.   

Abstract

A generic model for flowering phenology as a function of daily temperature and photoperiod was applied to predict differences of flowering times among 96 individuals (including the two parents) of a recombinant inbred line population in barley (Hordeum vulgare L.). Because of the large number of individuals to study, there is a need for simple ways to derive model parameters for each genotype. Therefore the number of genotype-specific parameters was reduced to four, namely f(o) (the minimum number of days to flowering at the optimum temperature and photoperiod), (1) and (2) (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and delta (the photoperiod sensitivity). Values of these parameters were estimated using a newly described methodological framework based on data from a photoperiod-controlled experiment where plants were mutually transferred between long-day and short-day environments at regular intervals. This modelling approach was tested in eight independent field environments of different sowing dates in two growing seasons. The four-parameter model predicted 37-67% of observed phenotypic variation in an environment, 76% of variation in across-environment mean days to flowering among the genotypes, and 96% of variation in across-genotype mean among the eight environments. When all the observations of the 96 genotypes across the eight environments were pooled, the model explained 81% of the total variation. Sensitivity analysis showed that all four model parameters were important for predicting differences in flowering time among the genotypes; but their relative importance differed and the ranking was in the order of f(o), delta, theta1, and theta2. This study highlighted the potential of using ecophysiological models to assist the genetic analysis of quantitative crop traits whose phenotype is often environment-dependent.

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Year:  2005        PMID: 15689339     DOI: 10.1093/jxb/eri089

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  11 in total

1.  Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approach.

Authors:  Karine Chenu; Scott C Chapman; François Tardieu; Greg McLean; Claude Welcker; Graeme L Hammer
Journal:  Genetics       Date:  2009-09-28       Impact factor: 4.562

2.  Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

Authors:  Junfei Gu; Xinyou Yin; Chengwei Zhang; Huaqi Wang; Paul C Struik
Journal:  Ann Bot       Date:  2014-07-01       Impact factor: 4.357

3.  Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis.

Authors:  Dijun Chen; Kerstin Neumann; Swetlana Friedel; Benjamin Kilian; Ming Chen; Thomas Altmann; Christian Klukas
Journal:  Plant Cell       Date:  2014-12-11       Impact factor: 11.277

4.  Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.

Authors:  C Eduardo Vallejos; James W Jones; Mehul S Bhakta; Salvador A Gezan; Melanie J Correll
Journal:  BMC Plant Biol       Date:  2022-06-06       Impact factor: 5.260

5.  Analysis of reciprocal-transfer experiments to estimate the length of phases having different responses to temperature.

Authors:  Xinyou Yin
Journal:  Ann Bot       Date:  2008-01-17       Impact factor: 4.357

6.  Genome-Based Prediction of Time to Curd Induction in Cauliflower.

Authors:  Arne Rosen; Yaser Hasan; William Briggs; Ralf Uptmoor
Journal:  Front Plant Sci       Date:  2018-02-05       Impact factor: 5.753

7.  Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields.

Authors:  Niteen N Kadam; S V Krishna Jagadish; Paul C Struik; C Gerard van der Linden; Xinyou Yin
Journal:  J Exp Bot       Date:  2019-04-29       Impact factor: 6.992

8.  Field trial evidence of non-transgenic and transgenic Bt. rice genotypes in north of Iran.

Authors:  Salman Dastan; Behzad Ghareyazie; Shahpour Abdollahi
Journal:  J Genet Eng Biotechnol       Date:  2020-05-01

9.  Genetic and Management Effects on Barley Yield and Phenology in the Mediterranean Basin.

Authors:  Davide Cammarano; Domenico Ronga; Enrico Francia; Taner Akar; Adnan Al-Yassin; Abdelkader Benbelkacem; Stefania Grando; Ignacio Romagosa; Antonio Michele Stanca; Nicola Pecchioni
Journal:  Front Plant Sci       Date:  2021-04-15       Impact factor: 5.753

10.  Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.

Authors:  Bangyou Zheng; Ben Biddulph; Dora Li; Haydn Kuchel; Scott Chapman
Journal:  J Exp Bot       Date:  2013-07-19       Impact factor: 6.992

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