Prioritizing quantitative trait loci for root system architecture in tetraploid wheat

Maccaferri, Marco; El-Feki, Walid M.; Nazemi, Ghasemali; Salvi, Silvio; Canè, Maria Angela; Colalongo, Maria Chiara; Stefanelli, Sandra; Tuberosa, Roberto

doi:10.1093/jxb/erw039

Cited by 171 publications

(222 citation statements)

References 90 publications

(138 reference statements)

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“…The analysis of a network of dry field experiments has shown that a given allele at a QTL can have a markedly positive effect in one category of experiments, a markedly negative effect in another category, and nearly no effect in half of experimental fields (Bonneau et al, 2013). Hence, we have first performed single-environment GWAS that allows identification of QTLs strongly associated with specific experiments and multi-environment GWAS that allows identification of QTLs with both main effect and QTL 3 E effects (Boer et al, 2007;Maccaferri et al, 2008;Malosetti et al, 2008a;Maccaferri et al, 2016). We have then analyzed the effects of QTL alleles conditional on scenarios and measured environmental conditions.…”

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confidence: 99%

Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios

et al. 2016

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Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years 3 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.With climate changes, crops will be subjected to more frequent episodes of drought and high temperature that may threaten food security (IPCC, 2014). Reducing the impacts of these effects is an urgent priority that (not exclusively) involves the genetic progress of plant performance under heat and drought stresses (Tester and Langridge, 2010;Lobell et al., 2011). Because hundreds of new genotypes of most cereals are commercialized every year, a generic approach is needed to avoid an endless series of experiments assessing the performances of the newly released genotypes. A systematic exploration of the natural genetic diversity used in breeding can provide information usable for large groups of genotypes. This entails the identification, among the thousands of accessions existing in gene banks, of allelic variants exhibiting specific adaptation traits by addressing three questions: (1) Is there a genetic variability for yield and related traits in dry and hot environments? (2) Can this genetic variability be dissected into the effect of genomic regions (quantitative trait loci, QTLs), and (3) have these genomic

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confidence: 99%

Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios

et al. 2016

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“…In this scenario, enhancing the genetic capacity of the plant to acquire soil resources (water and nutrients) is a primary target and can be accomplished by including the crop root system in the list of traits of interest for plant breeders (Lobet et al, 2013). From a methodological standpoint, phenotyping roots of crops is highly cost effective for evaluating hundreds of genotypes as required in QTL discovery studies (Maccaferri et al, 2016). The role of root architecture in plant performance in the field is indicated by the coincidence of root QTL and other agronomic importance traits.…”

Section: Discussionmentioning

confidence: 99%

“…In some cases, these studies have suggested possible roles for these QTL in determining plant yield, due to the overlap of QTL for root features with those for traits related to productivity (Tuberosa et al, 2002a, b;Steele et al, 2007). A number of QTLs for root traits in rice (Horii et al 2005;Steele et al, 2007), barley (Arifuzzaman et al, 2014), maize (Tuberosa et al, 2002b;Burton et al, 2015), durum wheat (Maccaferri et al, 2016) have been mapped in many studies. The more and fresh amount of data for specific QTL, enhances our physiological and evolutionary understanding and reveals links between root morphology and root functions that will be essential in designing root surface aria for target environments.…”

Section: Introductionmentioning

confidence: 99%

Detection of consensus genomic regions associated with root architecture of bread wheat on groups 2 and 3 chromosomes using QTL meta–analysis

Darzi-Ramandi¹,

Shariati²,

Tavakol³

et al. 2017

Aust J Crop Sci

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Root architecture is an important bread wheat phenomenon that highly influences its production and adaptation to environmental stresses, in particular drought stress. Several QTL studies have been conducted to ascertain chromosomal regions associated with root morphology resulting in identification of various loci depending on evaluated population types and experimental conditions. In order to identify the most consistent and reliable QTLs involved in various root morphological traits in bread wheat, a meta-QTL (MQTL) analysis was performed using 106 QTLs derived from 12 different populations under both normal and drought stress conditions. Among them, 125 QTLs related to root traits were successfully projected onto the reference map and further metaanalysis was focused on chromosomes of homeologous groups 2 and 3 with most assigned QTLs. Consequently, a total of seven MQTLs were identified on chromosomes 2A, 2B, 3A and 3B originated from 2 to 17 initial QTLs with a confidence interval (CI) of 5.3-6.6 to 39.5-55.0 cM. Three MQTLs located on 2A, 3A and 3B derived from 7 to 17 QTLs related to different root morphological traits pointed out the most important chromosomal regions. A reduction in the average 95% confidence interval from 20.8 cM to 6.4 cM was observed when comparing the individual QTL to the MQTL. Further analysis on investigation of candidate genes located in these genomic regions resulted in identification of some genes mainly associated with lignin catabolic process, potassium transporters and leucine-rich repeats receptor-like kinases (LRR-RLKs). These results provid fundamental information on most important genomic regions and candidate genes related to root morphology in bread wheat.

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“…In order to overcome the imbalance across the NAM populations at each marker location, a false discovery rate adjustment was made to the Fisher P values to allow a consistent 0.01 % significance value (−log Fisher P value of >3) to declare QTL significance. Co-location of root trait QTL Previously reported QTL for traits related to root system architecture in wheat were collated from three published studies (Hamada et al, 2012;Christopher et al, 2013;Maccaferri et al, 2016). In total, 77 QTL were reported, including 34 QTL for SRA Maccaferri et al, 2016), 39 QTL for SRN (Hamada et al, 2012;Christopher et al, 2013;Maccaferri et al, 2016), and four QTL related to gravitropic responses of wheat root (Hamada et al, 2012).…”

Section: Genome-wide Association Mapping Analysismentioning

confidence: 99%

“…Co-location of root trait QTL Previously reported QTL for traits related to root system architecture in wheat were collated from three published studies (Hamada et al, 2012;Christopher et al, 2013;Maccaferri et al, 2016). In total, 77 QTL were reported, including 34 QTL for SRA Maccaferri et al, 2016), 39 QTL for SRN (Hamada et al, 2012;Christopher et al, 2013;Maccaferri et al, 2016), and four QTL related to gravitropic responses of wheat root (Hamada et al, 2012). QTL identified by Christopher et al (2013) were reassigned from a previous map using an older DArT marker system using the latest wheat DArT consensus map (http://www.diversityarrays.com/).…”

Section: Genome-wide Association Mapping Analysismentioning

confidence: 99%

Breeding wheat for drought adaptation: Development of selection tools for root architectural traits

Richard¹

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A crop's ability to explore the soil profile and extract available water at different depths is largely determined by root system architecture. For instance in wheat (Triticum aestivum L.), it has been suggested that a narrow and deep root system can provide better access to deep soil moisture. Such root systems are particularly beneficial for rain-fed regions where crops rely heavily on stored soil moisture at depth, as encountered in the eastern Australian wheat belt. Thus, by targeting desirable root architectural traits, wheat breeders could increase genetic gain for yield in response to the growing demand for food. Yet, selection for these below-ground traits is challenging because roots are difficult to measure and are under complex genetic control. The aim of this project was to develop new phenotypic and molecular selection tools to facilitate selection for root architectural traits in Australian wheat breeding programs targeting terminal moisture stress adaptation. This project focuses on narrow seminal root angle and high number of seminal roots in wheat seedlings; two proxy traits for desirable mature root system architecture. Firstly, to overcome the lack of efficient root screening methods, a high-throughput and cost-effective method for phenotyping seminal root angle and number in wheat was developed, using clear pots in a controlled environment growth facility. Compared to pre-existing phenotyping methods, the newly developed method successfully provided higher heritability, greater repeatability, and better efficiency in terms of time, space, and labour. Further, the clear-pot method revealed a high degree of phenotypic variation for both seminal root traits. Subsequently, to test the ability to introgressed allelic variation for seminal root angle into elite Australian wheat cultivars via phenotypic selection, backcross tail populations for both narrow and wide root angle were developed, using the clear-pot method. Rapid shifts in both population distribution and allele frequency were observed after just two rounds of selection. Further, comparison of the tail populations revealed some genomic regions under selection, for which marker-assisted selection appeared successful. Hence, genetic diversity can be exploited via phenotypic and molecular selection to target desired root system architecture in wheat breeding programs.

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Prioritizing quantitative trait loci for root system architecture in tetraploid wheat

Abstract: HighlightThe genetic variation of root system architecture in the A and B wheat genomes is described, providing the necessary knowledge ultimately to fine-tune the expression of the root system architecture.

Cited by 171 publications

References 90 publications

Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios

Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios

Detection of consensus genomic regions associated with root architecture of bread wheat on groups 2 and 3 chromosomes using QTL meta–analysis

Breeding wheat for drought adaptation: Development of selection tools for root architectural traits

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