Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields
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Abdi, 2003, Partial least squares (PLS) regression., The SAGE encyclopedia of social sciences research methods, 792
Ali, 2013, Stress indices and selectable traits in SALTOL QTL introgressed rice genotypes for reproductive stage tolerance to sodicity and salinity stresses, Field Crops Research, 154, 65, 10.1016/j.fcr.2013.06.011
Al-Tamimi, 2016, Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping, Nature Communications, 7, 13342, 10.1038/ncomms13342
Amelong, 2015, Predicting maize kernel number using QTL information, Field Crops Research, 172, 119, 10.1016/j.fcr.2014.11.014
Barnabás, 2008, The effect of drought and heat stress on reproductive processes in cereals, Plant, Cell & Environment, 31, 11, 10.1111/j.1365-3040.2007.01727.x
Bertin, 2010, Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits, Journal of Experimental Botany, 61, 955, 10.1093/jxb/erp377
Bheemanahallia, 2017, Is early morning flowering an effective trait to minimize heat stress damage during flowering in rice?, Field Crops Research, 203, 1
Brun, 2006, Working with dynamic crop models: evaluation, analysis, parameterization, and applications
Bustos-Korts, 2016, Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics., Crop systems biology: narrowing the gaps between crop modelling and genetics, 55, 10.1007/978-3-319-20562-5_3
Chenu, 2008, Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize, Plant, Cell & Environment, 31, 378, 10.1111/j.1365-3040.2007.01772.x
Cooper, 2016, Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial, Crop Science, 56, 2141, 10.2135/cropsci2015.08.0512
Dingkuhn, 2017, Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 1. Phenology, Journal of Experimental Botany, 68, 4369, 10.1093/jxb/erx249
Dingkuhn, 2017, Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 2. Thermal stress and spikelet sterility, Journal of Experimental Botany, 68, 4389, 10.1093/jxb/erx250
Génard, 2016, Process-based simulation models are essential tools for virtual profiling and design of ideotypes: example of fruit and root., Crop systems biology: narrowing the gaps between crop modelling and genetics, 83, 10.1007/978-3-319-20562-5_4
Gu, 2014, Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress, Annals of Botany, 114, 499, 10.1093/aob/mcu127
Hammer, 2006, Models for navigating biological complexity in breeding improved crop plants, Trends in Plant Science, 11, 587, 10.1016/j.tplants.2006.10.006
Hammer, 2016, Molecular breeding for complex adaptive traits: how integrating crop ecophysiology and modelling can enhance efficiency., Crop systems biology: narrowing the gaps between crop modelling and genetics, 147, 10.1007/978-3-319-20562-5_7
Hammer, 2010, Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops, Journal of Experimental Botany, 61, 2185, 10.1093/jxb/erq095
Heslot, 2014, Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions, Theoretical and Applied Genetics, 127, 463, 10.1007/s00122-013-2231-5
Isidro, 2015, Training set optimization under population structure in genomic selection, Theoretical and Applied Genetics, 128, 145, 10.1007/s00122-014-2418-4
Jagadish, 2007, High temperature stress and spikelet fertility in rice (Oryza sativa L.), Journal of Experimental Botany, 58, 1627, 10.1093/jxb/erm003
Julia, 2013, Predicting temperature induced sterility of rice spikelets requires simulation of crop-generated microclimate, European Journal of Agronomy, 49, 50, 10.1016/j.eja.2013.03.006
Kadam, 2018, Genome-wide association reveals novel genomic loci controlling rice grain yield and its component traits under water-deficit stress during the reproductive stage, Journal of Experimental Botany, 69, 4017, 10.1093/jxb/ery186
Kadam, 2017, Genetic control of plasticity in root morphology and anatomy of rice in response to water deficit, Plant Physiology, 174, 2302, 10.1104/pp.17.00500
Kazan, 2016, The link between flowering time and stress tolerance, Journal of Experimental Botany, 67, 47, 10.1093/jxb/erv441
Kikuchi, 2017, Genome-wide association mapping for phenotypic plasticity in rice, Plant, Cell & Environment, 40, 1565, 10.1111/pce.12955
Kromdijk, 2014, Crop management impacts the efficiency of quantitative trait loci (QTL) detection and use: case study of fruit load×QTL interactions, Journal of Experimental Botany, 65, 11, 10.1093/jxb/ert365
Laperche, 2006, A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen deficiency, Theoretical and Applied Genetics, 113, 1131, 10.1007/s00122-006-0373-4
Lawas, 2018, Combined drought and heat stress impact during flowering and grain filling in contrasting rice cultivars grown under field conditions, Field Crops Research, 229, 66, 10.1016/j.fcr.2018.09.009
Li, 2014, The 3000 rice genomes project: new opportunities and challenges for future rice research, GigaScience, 3, 8, 10.1186/2047-217X-3-8
Mangin, 2017, Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modelling and genome-wide association, Plant, Cell & Environment, 40, 2276, 10.1111/pce.12961
Martre, 2011, Modelling the size and composition of fruit, grain and seed by process-based simulation models, New Phytologist, 191, 601, 10.1111/j.1469-8137.2011.03747.x
Mevik, 2007, The pls package: principal component and partial least squares regression in R, Journal of Statistical Software, 18, 2, 10.18637/jss.v018.i02
Nakagawa, 2005, Flowering response of rice to photoperiod and temperature: a QTL analysis using a phenological model, Theoretical and Applied Genetics, 110, 778, 10.1007/s00122-004-1905-4
Onogi, 2016, Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates, Theoretical and Applied Genetics, 129, 805, 10.1007/s00122-016-2667-5
O’Toole, 1982, Adaptation of rice to drought prone environment., Drought resistance in crops with emphasis on rice, 195
Peng, 2008, Progress in ideotype breeding to increase rice yield potential, Field Crops Research, 108, 32, 10.1016/j.fcr.2008.04.001
Quilot, 2005, Simulating genotypic variation of fruit quality in an advanced peach×Prunus davidiana cross, Journal of Experimental Botany, 56, 3071, 10.1093/jxb/eri304
Rebolledo, 2015, Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping, Journal of Experimental Botany, 66, 5555, 10.1093/jxb/erv258
Rebolledo, 2016, Combining image analysis, genome wide association studies and different field trials to reveal stable genetic regions related to panicle architecture and the number of spikelets per panicle in rice, Frontiers in Plant Science, 7, 1384, 10.3389/fpls.2016.01384
Remington, 2001, Structure of linkage disequilibrium and phenotypic associations in the maize genome, Proceedings of the National Academy of Sciences, USA, 98, 11479, 10.1073/pnas.201394398
Reymond, 2003, Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit, Plant Physiology, 131, 664, 10.1104/pp.013839
Rincent, 2012, Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.), Genetics, 192, 715, 10.1534/genetics.112.141473
Segura, 2012, An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations, Nature Genetics, 44, 825, 10.1038/ng.2314
Singh, 2009, Responses of SUB1 rice introgression lines to submergence in the field: yield and grain quality, Field Crops Research, 113, 12, 10.1016/j.fcr.2009.04.003
Singh, 1998, Genotypic variation in nitrogen use efficiency in medium- and long-duration rice, Field Crops Research, 58, 35, 10.1016/S0378-4290(98)00084-7
Soltani, 1999, A simple model for chickpea growth and yield, Field Crops Research, 62, 213, 10.1016/S0378-4290(99)00017-9
Technow, 2015, Integrating crop growth models with whole genome prediction through approximate bayesian computation, PLoS One, 10, e0130855, 10.1371/journal.pone.0130855
Uptmoor, 2008, Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in Brassica oleracea, Molecular Breeding, 21, 205, 10.1007/s11032-007-9121-y
Vikram, 2011, qDTY1.1, a major QTL for rice grain yield under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds, BMC Genetics, 12, 89, 10.1186/1471-2156-12-89
Xu, 2016, Simulating genotype–phenotype interaction using extended functional–structural plant models: approaches, applications and potential pitfalls., Crop systems biology: narrowing the gaps between crop modelling and genetics, 33, 10.1007/978-3-319-20562-5_2
Yin, 2013, Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity, Annals of Botany, 112, 465, 10.1093/aob/mct016
Yin, 2000, Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley, Heredity, 85, 539, 10.1046/j.1365-2540.2000.00790.x
Yin, 2002, Use of component analysis in QTL mapping of complex crop traits: a case study on yield in barley, Plant Breeding, 121, 314, 10.1046/j.1439-0523.2002.729117.x
Yin, 2010, Modelling the crop: from system dynamics to systems biology, Journal of Experimental Botany, 61, 2171, 10.1093/jxb/erp375
Yin, 2017, Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS, Journal of Experimental Botany, 68, 2345, 10.1093/jxb/erx085
Yin, 2016, Modelling QTL–trait–crop relationships: past experiences and future prospects., Crop systems biology: narrowing the gaps between crop modelling and genetics, 193, 10.1007/978-3-319-20562-5_9
Yin, 2004, Role of crop physiology in predicting gene-to-phenotype relationships, Trends in Plant Science, 9, 426, 10.1016/j.tplants.2004.07.007
Yin, 2005, Model analysis of flowering phenology in recombinant inbred lines of barley, Journal of Experimental Botany, 56, 959, 10.1093/jxb/eri089
Yin, 2005, Crop systems dynamics: an ecophysiological simulation model for genotype-by-environment interactions, 10.3920/978-90-8686-539-0
Zhang, 2009, Bulked segregant analysis to detect QTL related to heat tolerance in rice (Oryza sativa L.) using SSR markers, Agricultural Sciences in China, 8, 482, 10.1016/S1671-2927(08)60235-7