QTLs identified for Biofortification Traits in Wheat: A Review

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.

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Advances in Genomic Interventions for Wheat Biofortification: A Review

Agronomy ◽

10.3390/agronomy10010062 ◽

2020 ◽

Vol 10 (1) ◽

pp. 62 ◽

Cited By ~ 6

Author(s):

Dinesh Kumar Saini ◽

Pooja Devi ◽

Prashant Kaushik

Keyword(s):

Qtl Mapping ◽

Crop Improvement ◽

Essential Elements ◽

Wheat Breeding ◽

Wheat Varieties ◽

New Horizons ◽

Hidden Hunger ◽

Modern Wheat ◽

Breeding Efficiency ◽

Molecular Bases

Wheat is an essential constituent of cereal-based diets, and one of the most significant sources of calories. However, modern wheat varieties are low in proteins and minerals. Biofortification is a method for increasing the availability of essential elements in the edible portions of crops through agronomic or genetic and genomic interventions. Wheat biofortification, as a research topic, has become increasingly prevalent. Recent accomplishments in genomic biofortification could potentially be helpful for the development of biofortified wheat grains, as a sustainable solution to the issue of “hidden hunger”. Genomic interventions mainly include quantitative trait loci (QTL) mapping, marker-assisted selection (MAS), and genomic selection (GS). Developments in the identification of QTL and in the understanding of the physiological and molecular bases of the QTLs controlling the biofortification traits in wheat have revealed new horizons for the improvement of modern wheat varieties. Markers linked with the QTLs of desirable traits can be identified through QTL mapping, which can be employed for MAS. Besides MAS, a powerful tool, GS, also has great potential for crop improvement. We have compiled information from QTL mapping studies on wheat, carried out for the identification of the QTLs associated with biofortification traits, and have discussed the present status of MAS and different prospects of GS for wheat biofortification. Accelerated mapping studies, as well as MAS and GS schemes, are expected to improve wheat breeding efficiency further.

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Identifying Meta-QTLs for Stay-Green in Sorghum

10.1101/2021.08.06.455413 ◽

2021 ◽

Author(s):

Ahmed Aquib ◽

Shadma Nafis

Keyword(s):

Quantitative Trait Loci ◽

Drought Tolerance ◽

Qtl Mapping ◽

Qtl Analysis ◽

Quantitative Trait ◽

Strong Association ◽

Consensus Map ◽

Climatic Response ◽

Stay Green ◽

Genomic Regions

To develop resilient crops it is necessary to understand the underlying genetics of climatic response. A strong association between stay-green and post-flowering drought tolerance in Sorghum has been established. Being a complex quantitative trait, Quantitative Trait Loci (QTL) mapping experiments of stay-green in Sorghum have been frequently performed. The objective of the current study was to find consensus genomic regions that control stay-green by integrating the QTLs mapped in previous studies. Meta-QTL analysis was performed to summarize 115 QTLs projected on the consensus map. The analysis generated 32 Meta-QTL regions within which candidate gene (CG) identification was undertaken. 7 candidate genes were identified using the markers tightly linked to the Meta-QTLs. The results from this study will facilitate future attempts aiming to improve and understand drought tolerance in Sorghum.

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Quantitative Trait Loci Associated with Rotylenchulus reniformis Host Suitability in Soybean

Phytopathology ◽

10.1094/phyto-02-20-0035-r ◽

2020 ◽

Vol 110 (9) ◽

pp. 1511-1521

Author(s):

Juliet Wilkes ◽

Christopher Saski ◽

Mariola Klepadlo ◽

Benjamin Fallen ◽

Paula Agudelo

Keyword(s):

Quantitative Trait Loci ◽

Qtl Analysis ◽

Quantitative Trait ◽

The United States ◽

Host Suitability ◽

Reniform Nematode ◽

Rotylenchulus Reniformis ◽

Chromosome 18 ◽

Nematode Reproduction ◽

Trait Loci

Reniform nematode (Rotylenchulus reniformis) is a yield-limiting pathogen of soybean (Glycine max) in the southeastern region of the United States. A population of 250 recombinant inbred lines (RIL) (F2:8) developed from a cross between reniform nematode resistant soybean cultivar Forrest and susceptible cultivar Williams 82 was utilized to identify regions associated with host suitability. A genetic linkage map was constructed using single-nucleotide polymorphism markers generated by genotyping-by-sequencing. The phenotype was measured in the RIL population and resistance was characterized using normalized and transformed nematode reproduction indices in an optimal univariate cluster analysis. Quantitative trait loci (QTL) analysis using normalized phenotype scores identified two QTLs on each arm of chromosome 18 (rrn-1 and rrn-2). The same QTL analysis performed with log10(x) transformed phenotype data also identified two QTLs: one on chromosome 18 overlapping the same region in the other analysis (rrn-1), and one on chromosome 11 (rrn-3). While rrn-1 and rrn-3 have been reported associated with reduced reproduction of reniform nematode, this is the first report of the rrn-2 region associated with host suitability to reniform nematode. The resistant parent allele at rrn-2 showed an inverse relationship with the resistance phenotype, correlating with an increase in nematode reproduction or host suitability. Several candidate genes within these regions corresponded with host plant defense systems. Interestingly, a characteristic pathogen resistance gene with a leucine-rich repeat was discovered within rrn-2. These genetic markers can be used by soybean breeders in marker-assisted selection to develop lines with resistance to reniform nematode.

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On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽

10.1093/genetics/156.2.855 ◽

2000 ◽

Vol 156 (2) ◽

pp. 855-865 ◽

Cited By ~ 2

Author(s):

Chen-Hung Kao

Keyword(s):

Quantitative Trait Loci ◽

Maximum Likelihood ◽

Qtl Mapping ◽

Quantitative Trait ◽

Mean Squared Error ◽

Interval Mapping ◽

Size Difference ◽

Ratio Test ◽

Trait Loci ◽

Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

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The Genetic Basis of the Interspecific Differences in Wing Size in Nasonia (Hymenoptera; Pteromalidae): Major Quantitative Trait Loci and Epistasis

Genetics ◽

10.1093/genetics/161.2.673 ◽

2002 ◽

Vol 161 (2) ◽

pp. 673-684

Author(s):

J Gadau ◽

R E Page ◽

J H Werren

Keyword(s):

Quantitative Trait Loci ◽

Qtl Analysis ◽

Quantitative Trait ◽

Wing Length ◽

Size Difference ◽

Phenotypic Variance ◽

Wing Size ◽

Epistatic Interactions ◽

Trait Loci ◽

Wing Width

Abstract There is a 2.5-fold difference in male wing size between two haplodiploid insect species, Nasonia vitripennis and N. giraulti. The haploidy of males facilitated a full genomic screen for quantitative trait loci (QTL) affecting wing size and the detection of epistatic interactions. A QTL analysis of the interspecific wing-size difference revealed QTL with major effects and epistatic interactions among loci affecting the trait. We analyzed 178 hybrid males and initially found two major QTL for wing length, one for wing width, three for a normalized wing-size variable, and five for wing seta density. One QTL for wing width explains 38.1% of the phenotypic variance, and the same QTL explains 22% of the phenotypic variance in normalized wing size. This corresponds to a region previously introgressed from N. giraulti into N. vitripennis that accounts for 44% of the normalized wing-size difference between the species. Significant epistatic interactions were also found that affect wing size and density of setae on the wing. Screening for pairwise epistatic interactions between loci on different linkage groups revealed four additional loci for wing length and four loci for normalized wing size that were not detected in the original QTL analysis. We propose that the evolution of smaller wings in N. vitripennis males is primarily the result of major mutations at few genomic regions and involves epistatic interactions among some loci.

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Quantitative trait loci mapping of Meloidogyne incognita and M. hapla resistance in a recombinant inbred line population of soybean

Nematology ◽

10.1163/15685411-00003157 ◽

2018 ◽

Vol 20 (6) ◽

pp. 525-537

Author(s):

Chunjie Li ◽

Jialin Wang ◽

Jia You ◽

Xinpeng Wang ◽

Baohui Liu ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Inbred Line ◽

Recombinant Inbred Line ◽

Meloidogyne Incognita ◽

Qtl Analysis ◽

Recombinant Inbred Line Population ◽

Quantitative Trait ◽

Recombinant Inbred ◽

Nematode Reproduction ◽

Root Response

Summary A recombinant inbred line population of soybean (Glycine max) was utilised to identify the quantitative trait loci (QTLs) determining the response to infection by two root-knot nematode species, Meloidogyne incognita and M. hapla, in glasshouse assays. QTL analysis detected seven major and four minor QTLs on seven soybean chromosomes ((Chrs) 1, 7, 8, 10, 14, 18, 20) explaining 6-41% phenotypic variance (PVE) for M. incognita root response and nematode reproduction. Three of the major QTLs, on Chrs 7, 10 and 18, were confirmed in previous reports and two major QTLs on Chrs 14 and 20 were detected for the first time. The QTL analysis with M. hapla provides the first report of a major QTL region mapped on Chr 7, explaining 70-82% PVE in M. hapla root response and nematode reproduction. These novel identified QTLs with flanking markers will be helpful in marker-assisted breeding for nematode resistance in soybean.

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