Adult Plant Development in Triticale (× <i>Triticosecale</i> Wittmack) Is Controlled by Dynamic Genetic Patterns of Regulation

Genomic prediction has emerged as a powerful genomic tool to assist breeding of complex traits. In this study, we employed a population of 647 triticale doubled haploid lines derived from four families to assess the potential of this approach for triticale breeding. All lines were phenotyped for grain yield, thousand‐kernel weight, biomass yield, plant height, frost tolerance and Fusarium head blight resistance. The obtained prediction accuracies were moderate to high and consisted to varying degrees of within‐ and among‐family variance, in line with the different degrees of phenotypic differences between family means. The prediction accuracy within individual families also varied with the genetic complexity of the traits and was generally highest based on effect estimation with lines from the respective family, whereas the prediction accuracy decreased with decreasing relatedness among the families. Taken together, our results illustrate the potential of genomic prediction to increase selection gain in triticale breeding, but the composition of the training set is of utmost importance, and consequently, the implementation of this approach in applied breeding programmes is not straightforward.

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“…,b, , , Würschum et al. ,c). In brief, the study is based on a mapping population with 647 doubled haploid (DH) (Würschum et al.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of within‐ and among‐family genomic prediction in triticale

Würschum

Maurer

Weissmann³

et al. 2017

Plant Breeding

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“…These and the vast majority of other QTL studies in plants assessed the expression of traits at a certain stage, frequently at final harvest or at the time point when a certain developmental stage such as flowering and kernel architecture has been reached (Buckler et al ., ; Hung et al ., ; Cook et al ., ). More recently, some studies have been reported on dynamically acting genetic factors in plants assessed via monitoring trait expression at multiple time points (Yan et al ., ; Bian et al ., ; Busemeyer et al ., ; Moore et al ., ; Liu et al ., ; Würschum et al ., ,b; Bac‐Molenaar et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non‐invasive phenotyping

et al. 2017

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Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase-specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non-invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome-wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker-trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non-parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage-specific growth affecting genes to elucidate important processes operating at different developmental phases.

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“…Due to the noncontact nature of the sensors, and their placement on vehicles capable of traversing research plots at a rapid pace, HTPP systems are capable of collecting vast amounts of data in an efficient manner ( Andrade-Sanchez et al 2014 ; White et al 2012 ; Busemeyer et al 2013 ). The ability to collect data rapidly also permits comprehensive assessment of crop development, and, with it, the ability to map QTL expression as a function of time, which is critical given that most traits of agronomic and economic importance are dynamic in nature ( Wu and Lin 2006 ; Würschum et al 2014 ). Despite awareness of this reality, the majority of QTL studies rely on phenotypic data collected at a single time point, offering only a final view of accumulated QTL effects.…”

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confidence: 99%

Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton

Pauli

Andrade-Sánchez

Carmo‐Silva

et al. 2016

G3 Genes|Genomes|Genetics

102

105

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The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010–2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.

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Adult Plant Development in Triticale (× Triticosecale Wittmack) Is Controlled by Dynamic Genetic Patterns of Regulation

Cited by 18 publications

References 45 publications

Accuracy of within‐ and among‐family genomic prediction in triticale

Accuracy of within‐ and among‐family genomic prediction in triticale

Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non‐invasive phenotyping

Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton

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