Genome-Wide Association Study Reveals Novel Genomic Regions Associated with 10 Grain Minerals in Synthetic Hexaploid Wheat

Bhatta, Madhav; Baenziger, P. S.; Waters, Brian M.; Poudel, Rachana; Belamkar, Vikas; Poland, Jesse; Morgounov, Alexey

doi:10.3390/ijms19103237

Cited by 81 publications

(91 citation statements)

References 49 publications

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“…We analyzed 16 agronomic-, 16 grain mineral-, and 13 wheat rust-related phenotypes in the current study. Agronomic traits including grain yield (GY), harvest index (HI), biomass weight (BMWT), grain volume weight (GVWT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), rachis break (RB), sterile spikelet (SP), spike length (SL), seeds per spike (SPS), spikelet number (SN), fertile spikelet (FS), spike weight (SW), grain weight per spike (GPS), and spike harvest index (SHI) were measured using previously described standard procedures (Bhatta et al, 2018a;Morgounov et al, 2018;Hussain et al, 2017). Grain minerals including arsenic (As), calcium (Ca), cadmium (Cd), cobalt (Co), copper (Cu), iron (Fe), potassium (K), lithium (Li), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), phosphorous (P), sulfur (S), titanium (Ti), and zinc (Zn) were measured via inductively-coupled plasma mass spectrometry (ICP-MS, Agilent 7500cx, Agilent Technologies, Santa Clara, CA, USA) at the University of Nebraska Redox Biology Center, Proteomics and Metabolomics Core (Guttieri et al, 2015;Bhatta et al, 2018a).…”

Section: Phenotypic and Genotypic Datamentioning

confidence: 99%

“…Previous studies revealed the negative genetic associations of yellow and stem rust traits with grain yield traits. Wheat rust diseases are foliar fungal diseases whose infection on the flag leaf close to the grain filling period causes a decline in the photosynthetic ability of the plant, drastically decreasing the grain filling process and reducing the biomass yield, thousand kernel weight, and harvest index (He et al, 2019;Bhatta et al, 2018a;Herrera-Foessel et al, 2006). Thus, the reduction of these important traits results in a reduction in the final grain yield (SRDH → GYL and YRD → GYL).…”

Section: Biological Meaning Of the Inferred Relationshipsmentioning

confidence: 99%

“…Several studies have reported negative associations between grain minerals and architecturerelated traits. A larger number of seeds per spike and kernel size in wheat is associated with lower grain mineral accumulation in the grain, which is mainly attributed to the grain mineral dilution effect (Bhatta et al, 2018a;Guttieri et al, 2015). Similarly, the nitrogen concentration in the grains depends on their position within the spike Calderini and Ortiz-Monasterio (2003); Herzog and Stamp (1983), suggesting that spike architecture traits have important impacts on grain mineral traits (MIN → ARC).…”

Section: Biological Meaning Of the Inferred Relationshipsmentioning

confidence: 99%

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Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

Momen

Bhatta

Hussain

et al. 2020

Preprint

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Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multi-dimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum Aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven explanatory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

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Section: Phenotypic and Genotypic Datamentioning

confidence: 99%

Section: Biological Meaning Of the Inferred Relationshipsmentioning

confidence: 99%

Section: Biological Meaning Of the Inferred Relationshipsmentioning

confidence: 99%

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Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

Momen

Bhatta

Hussain

et al. 2020

Preprint

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“…The synthetics were phenotyped in Turkey under moisture stress in 2016 and 2017 and variable disease infections and genotyped by sequencing in USA. A number of genes were identified for grain yield and its components under drought [32] , common bunt resistance [33] and minerals content [34] . Several synthetic wheat lines were tested in Azerbaijan and also demonstrated superior yield performance and tolerance to salinity [35] .…”

Section: Impactmentioning

confidence: 99%

International Winter Wheat Improvement Program: history, activities, impact and future

Morgounov¹,

Özdemir²,

Keser³

et al. 2019

Front. Agr. Sci. Eng.

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International Winter Wheat Improvement Program (IWWIP) was established in 1986 between the Government of Turkey and CIMMYT with three main objectives: (1) develop winter/facultative germplasm for Central and West Asia, (2) facilitate global winter wheat germplasm exchange, and (3) training wheat scientists. ICARDA joined the program in 1991 making it a threeway partnership that continues to work effectively. The germplasm developed by IWWIP as well as the winter wheat cultivars and lines received from global cooperators are assembled into international nurseries. These nurseries are offered annually to public and private entities (IWWIP website) and distributed to more than 100 cooperators in all continents. IWWIP impact has primarily been in new winter wheat cultivars combining broad adaptation, high yield potential, drought tolerance and disease resistance. A total of 93 IWWIP cultivars have been released in 11 countries occupying annually an estimated 2.5-3.0 Mha. IWWIP cooperation with researchers in Turkey, Central and West Asia and several US universities has resulted in a number of publications reviewed in this paper. Important IWWIP impacts include national inventories of wheat landraces in Turkey, Tajikistan and Uzbekistan, their collection, characterization, evaluation and utilization.

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“…Nevertheless, the developments in sequencing methodologies, decreased sequencing price, together with the advancements in the computational resources have permitted the spread of these resources [39,40]. Besides, the comparative genomics among plant species is demonstrating to be an efficient method for the identification of novel genes regarding the biofortification of modern wheat [41]. Therefore, in this review, we have compiled the QTLs Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:…”

mentioning

confidence: 99%

QTLs identified for Biofortification Traits in Wheat: A Review

Devi¹,

Kaushik²,

Saini³

2019

Preprint

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Wheat is the essential constituent of cereal-based diets and one of the most significant sources of calories. However, there is an inherently low bioavailability of proteins, mineral, and vitamins in modern wheat grains. Biofortification has earned recognition as an outstanding approach, at the same time as a cure for world hunger. The developments in the identifications of quantitative trait loci (QTL) analysis and understanding of the physiological and molecular basis of QTLs controlling the biofortification traits in wheat has revealed new horizons for the improvement of modern wheat varieties. Within this review, we have compiled the information from the studies carried out in wheat using QTL mapping methodologies that is among the best methods for biofortification traits. We hope this review will serve as an essential reference for the QTLs identified for the several important biofortification traits in wheat.that could contribute to underexploited wild relatives [25,26]. The different steps involved in the development of biofortification wheat with the aid of marker-assisted breeding are presented in Figure1. Figure 1.Representation of different strategies for biofortification of the wheat, especially explaining different steps of marker-assisted breeding.Conventional breeding methods are easy to operate with qualitative traits. These traits depend on a single gene whereas traits like yield are quantitative and therefore are impacted by several genes [27,28,29]. There are several techniques for mapping quantitative trait loci (QTL) in an experimental cross [30]. The molecular basis of QTLs is challenging to dissect, even for model plants like Arabidopsis and rice, because of the problems in precisely narrowing intervals to single genes [31]. Experimental design, type of plant population analysed and the level of polymorphisms between parental genomes also affect the predictions of QTLs. Statistical methods to determine quantitative trait loci (QTL) require numerous molecular markers with high-resolution genetic maps [32,33]. This approach is also related to genomics methods which are geared toward the dissection of complex phenotypes [34].Genomic resources of wheat have provided important support for functional genomics and conservation biology (by conserving the important landraces) [35,36]. The wheat genome is complex to interpret simply because of broadly dispersed repetitive sequences, heterozygosity, and polyploidy [37,38]. Nevertheless, the developments in sequencing methodologies, decreased sequencing price, together with the advancements in the computational resources have permitted the spread of these resources [39,40]. Besides, the comparative genomics among plant species is demonstrating to be an efficient method for the identification of novel genes regarding the biofortification of modern wheat [41]. Therefore, in this review, we have compiled the QTLs Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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Genome-Wide Association Study Reveals Novel Genomic Regions Associated with 10 Grain Minerals in Synthetic Hexaploid Wheat

Cited by 81 publications

References 49 publications

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

International Winter Wheat Improvement Program: history, activities, impact and future

QTLs identified for Biofortification Traits in Wheat: A Review

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