Identification of quantitative trait loci for phosphorus use efficiency traits in rice using a high density SNP map

Wang, Kai; Cui, Kehui; Liu, Guoling; Xie, Weibo; Yu, Huihui; Pan, Junfeng; Huang, Jikun; Shah, Farooq; Peng, Shaobing

doi:10.1186/s12863-014-0155-y

Cited by 30 publications

(30 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In species where the majority of the genetic variation under selection is controlled by many additive, small effect loci, this should not be a problem. However, in rice and other inbreeding crops, the genetic architecture of many important agronomic traits contains important non-additive features and transgressive variation is common [ 41 , 43 , 57 , 58 , 59 , 60 ]. The selected lines from the RYT are subsequently advanced to the multi-environment trials (MET) where the GEBVs can be used to select parents for the next generation of hybridization.…”

Section: Resultsmentioning

confidence: 99%

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

et al. 2015

View full text Add to dashboard Cite

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

show abstract

Section: Resultsmentioning

confidence: 99%

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Many QTLs have been identified in various crop plants related to low Pstress tolerance which may be useful to improve the P-use efficiency in key crops in the future [17]. QTLs have been identified for key traits of low Pi stress tolerance in many crop plants, including soybean [18], wheat [19], rice [20] and maize [21]. Most of these QTLs are associated with phenotypic modification of root structure to increase the soil volume for enhanced Pi uptake under low Pi stress conditions.…”

Section: Marker-assisted Breeding To Develop Low Pi Stress Tolerant Vmentioning

confidence: 99%

Feeding World Population Amidst Depleting Phosphate Reserves: The Role of Biotechnological Interventions

Ceasar¹

2018

TOBIOTJ

View full text Add to dashboard Cite

Phosphorus (P) is an important macronutrient affecting the growth and yield of all crop plants. Plants absorb P from the soil solution as inorganic phosphate (Pi). More than 70% of the arable land is deficient of Pi which demands the supply of an external source of synthetic P fertilizers to improve the yields. The P fertilizers are manufactured from non-renewable rock phosphate reserves which are expected to be exhausted within the next 100-200 years. This poses a great threat to food security since it is very difficult to meet the food production caused by increasing world population without the supply of an adequate P fertilizer. Several efforts have been made in the past decade to understand the mechanism of Pi uptake and its redistribution in plants. In this minireview, we discuss the details on possible strategies to combat the crisis caused by loss of phosphate rock reserves and to improve the crop yield without much dependency on external P fertilizer. Approaches such as application of functional genomics studies to manipulate the expression levels of key transcription factors and genes involved in low Pi stress tolerance, molecular marker-assisted breeding to develop new varieties with improved yields under Pi-deficient soils and to recapture the Pi released in wastewaters for recycling back to the farm lands, will help improve the crop production without depending much on non-renewable P fertilizers and will also aid for the sustainable food production.

show abstract

“…Molecular marker‐assisted selection (MAS) and marker‐assisted breeding (MAB) are powerful tools for selecting and improving the plants for important agronomic traits. Many quantitative trait loci (QTLs) have been identified in various crop plants related to low P‐stress tolerance, which may be useful to improve the PUE in key crops in future (Baker et al., ) and it was experimentally proved that these QTLs have been associated with important traits for low P‐stress tolerance in some crop plants, including soybean (Liang, Cheng, Mei, Yan, & Liao, ), wheat (Su et al., ), rice (Wang et al., ), maize (Chen, Xu, Cai, & Xu, ) and common bean (Beebe et al., ). So QTLs have been developed for several low P‐stress tolerance‐related traits such as primary root length (PRL), specific root length (SpRL) and root dry weight (RDW) in common bean (Beebe et al., ), shoot P‐use efficiency (SPUE) in maize (Chen et al., ) and PUE for grain yield (PUEg) and Straw P‐use efficiency (StrPUE) in rice (Wang et al., ).…”

Section: Introductionmentioning

confidence: 99%

Utilization of molecular markers for improving the phosphorus efficiency in crop plants

Maharajan¹,

Ceasar

Krishna³

et al. 2017

Plant Breeding

View full text Add to dashboard Cite

Phosphorus (P) is the second most growth limiting macronutrient after nitrogen and plays several important roles in all organisms including plants. In soil, P is available in both organic and inorganic forms. P deficiency reduces the growth and yield of several crop plants. Plants respond to P deficiency by the phenotypic changes especially by the modification of root architecture. Molecular marker-assisted breeding (MAB) has been proposed as an important tool to identify and develop improved varieties of crop plants with efficient P-use efficiency (PUE). Identification of quantitative trait loci (QTLs) for traits related to PUE has been considered as the first step in markerassisted selection (MAS) and improvement of crop yield programmes. In this review, we describe in detail on architectural changes of roots under P deficiency that are reported in various crops and discuss the efforts made to improve PUE using molecular marker tools. Details on QTLs identified for low P-stress tolerance in various crop plants are presented. These QTLs can be used to improve PUE in crop plants through MAS and breeding, which may be beneficial to improve the yields under P-deficient soil. Development of new and improved varieties using MAB will limit the use of nonrenewable fertilizers and improve PUE of key crop plants in low input agriculture.inorganic phosphate, marker-assisted breeding, phosphorus, phosphorus use efficiency, quantitative trait loci, root architecture

show abstract

Identification of quantitative trait loci for phosphorus use efficiency traits in rice using a high density SNP map

Cited by 30 publications

References 51 publications

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

Feeding World Population Amidst Depleting Phosphate Reserves: The Role of Biotechnological Interventions

Utilization of molecular markers for improving the phosphorus efficiency in crop plants

Contact Info

Product

Resources

About