scholarly journals Genetic Mapping of Quantitative Trait Loci for End-Use Quality and Grain Minerals in Hard Red Winter Wheat

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2519
Author(s):  
Shuhao Yu ◽  
Silvano Assanga ◽  
Joseph Awika ◽  
Amir Ibrahim ◽  
Jackie Rudd ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Rheological Properties ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Rheological Parameters ◽  
Quality Traits ◽  
Quality Improvements ◽  
Trait Loci ◽  
Variation Explained ◽  
End Use

To meet the demands of different wheat-based food products, traits related to end-use quality become indispensable components in wheat improvement. Thus, markers associated with these traits are valuable for the timely evaluation of protein content, kernel physical characteristics, and rheological properties. Hereunder, we report the mapping results of quantitative trait loci (QTLs) linked to end-use quality traits. We used a dense genetic map with 5199 SNPs from a 90K array based on a recombinant inbred line (RIL) population derived from ‘CO960293-2′/’TAM 111′. The population was evaluated for flour protein concentration, kernel characteristics, dough rheological properties, and grain mineral concentrations. An inclusive composite interval mapping model for individual and across-environment QTL analyses revealed 22 consistent QTLs identified in two or more environments. Chromosomes 1A, 1B, and 1D had clustered QTLs associated with rheological parameters. Glu-D1 loci from CO960293-2 and either low-molecular-weight glutenin subunits or gliadin loci on 1A, 1B, and 1D influenced dough mixing properties substantially, with up to 34.2% of the total phenotypic variation explained (PVE). A total of five QTLs associated with grain Cd, Co, and Mo concentrations were identified on 3B, 5A, and 7B, explaining up to 11.6% of PVE. The results provide important genetic resources towards understanding the genetic bases of end-use quality traits. Information about the novel and consistent QTLs provided solid foundations for further characterization and marker designing to assist selections for end-use quality improvements.

scholarly journals Quantitative trait loci associated with soft wheat quality in a cross of good by moderate quality parents

2017 ◽  
Author(s):  
Gioconda Garcia-Santamaria ◽  
Duc Hua ◽  
Clay Sneller
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Soft Wheat ◽  
Quality Traits ◽  
Gluten Strength ◽  
Retention Capacity ◽  
Significant Difference ◽  
Trait Loci ◽  
End Use ◽  
Trait Associations

Information on the genetic control of the quality traits of soft wheat (Triticum aestivum L. em. Thell) is essential for breeding. Gluten strength is a measure of quality and has particular relevance to soft wheat as identity-preserved programs for strong-gluten soft red winter wheat in the eastern US that is essential to effective biscuit industry. Identifying areas of the soft wheat genome harboring genes for functional end-use quality may assist in selective breeding and in understanding the genetic components of this trait. Our objective was to identify Quantitative Trait Loci associated with end-use quality. We developed 150 F4-derived lines from a cross of Pioneer 26R46 × SS550 and tested them in four environments. We measured flour yield (FY), softness equivalent (SE), test weight (TW), flour protein content (FP), alkaline water retention capacity (AWRC), and solvent retention capacity (SRC) of water (WA), lactic acid (LA), sucrose (SU), sodium carbonate (SO) SRCs. Analyses of variance for the ten quality parameters detected a significant difference between parental means for nine traits except for FP. Recombinant inbred lines presented transgressive segregation and high heritability (0.67 to 0.90) for all traits. Strong positive correlations between AWRC with WA, SO, SU and strong negative correlations of FY with AWRC and the SRC traits were observed. We report 28 marker-trait associations. Many QTL were coincident and in accordance with the trait correlations. There were 10 marker-trait associations from four regions for these traits and only one was not coincident with another. We detected QTL distributed on 8 chromosomes. Loci associated with FP mapped on chromosomes 2B, 5A and 5D explained 16 %, 10 % and 12.9 % of the variation for this trait, respectively. QTLs on chromosome 2B co-segregated for SE. SE was negatively correlated (-0.26) with FP. A positive significant correlation between FP and LA (0.36) was detected, yet; the QTL for these two traits were not coincident in this study. The QTL with the greatest effects were located on chromosome 1A, 1B, and 6B with each affecting at least five of ten quality traits. In particular, QTL with the largest effect on LA and consequently gluten strength were on chromosomes 1A with LOD 9 that explained 42.6 % of LA variation and QTLs on chromosome 1B with LOD 9 that explained 33 % of the variation in LA. Loci on chromosomes 1A and 1B were also important contributors of additive effects for this trait with an increase of 6.5 % and 5.6 %, respectively. The largest QTL on 1A co-segregated for AWRC (25 %), SO (26 %) and SE (25 %), and FY (15 %) may explicate why Pioneer 26R46 has such superior quality. All alleles that increased a trait came from the parent with the highest trait value. This suggests that in any population that marker-assisted selection for these quality traits could be conducted by simply selecting for the alleles from the parent with the best phenotype.

scholarly journals Quantitative trait loci associated with soft wheat quality in a cross of good by moderate quality parents

2017 ◽  
Author(s):  
Gioconda Garcia-Santamaria ◽  
Duc Hua ◽  
Clay Sneller
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Soft Wheat ◽  
Quality Traits ◽  
Gluten Strength ◽  
Retention Capacity ◽  
Significant Difference ◽  
Trait Loci ◽  
End Use ◽  
Trait Associations

Information on the genetic control of the quality traits of soft wheat (Triticum aestivum L. em. Thell) is essential for breeding. Gluten strength is a measure of quality and has particular relevance to soft wheat as identity-preserved programs for strong-gluten soft red winter wheat in the eastern US that is essential to effective biscuit industry. Identifying areas of the soft wheat genome harboring genes for functional end-use quality may assist in selective breeding and in understanding the genetic components of this trait. Our objective was to identify Quantitative Trait Loci associated with end-use quality. We developed 150 F4-derived lines from a cross of Pioneer 26R46 × SS550 and tested them in four environments. We measured flour yield (FY), softness equivalent (SE), test weight (TW), flour protein content (FP), alkaline water retention capacity (AWRC), and solvent retention capacity (SRC) of water (WA), lactic acid (LA), sucrose (SU), sodium carbonate (SO) SRCs. Analyses of variance for the ten quality parameters detected a significant difference between parental means for nine traits except for FP. Recombinant inbred lines presented transgressive segregation and high heritability (0.67 to 0.90) for all traits. Strong positive correlations between AWRC with WA, SO, SU and strong negative correlations of FY with AWRC and the SRC traits were observed. We report 28 marker-trait associations. Many QTL were coincident and in accordance with the trait correlations. There were 10 marker-trait associations from four regions for these traits and only one was not coincident with another. We detected QTL distributed on 8 chromosomes. Loci associated with FP mapped on chromosomes 2B, 5A and 5D explained 16 %, 10 % and 12.9 % of the variation for this trait, respectively. QTLs on chromosome 2B co-segregated for SE. SE was negatively correlated (-0.26) with FP. A positive significant correlation between FP and LA (0.36) was detected, yet; the QTL for these two traits were not coincident in this study. The QTL with the greatest effects were located on chromosome 1A, 1B, and 6B with each affecting at least five of ten quality traits. In particular, QTL with the largest effect on LA and consequently gluten strength were on chromosomes 1A with LOD 9 that explained 42.6 % of LA variation and QTLs on chromosome 1B with LOD 9 that explained 33 % of the variation in LA. Loci on chromosomes 1A and 1B were also important contributors of additive effects for this trait with an increase of 6.5 % and 5.6 %, respectively. The largest QTL on 1A co-segregated for AWRC (25 %), SO (26 %) and SE (25 %), and FY (15 %) may explicate why Pioneer 26R46 has such superior quality. All alleles that increased a trait came from the parent with the highest trait value. This suggests that in any population that marker-assisted selection for these quality traits could be conducted by simply selecting for the alleles from the parent with the best phenotype.

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

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
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.

scholarly journals Time-Related Mapping of Quantitative Trait Loci Underlying Tiller Number in Rice

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 297-303 ◽  
Author(s):  
Wei-Ren Wu ◽  
Wei-Ming Li ◽  
Ding-Zhong Tang ◽  
Hao-Ran Lu ◽  
A J Worland
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Tiller Number ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Recombinant Inbred Population ◽  
Trait Loci ◽  
Final Observation

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Inbred Line Cross Data

Genetics ◽  
1998 ◽  
Vol 148 (3) ◽  
pp. 1373-1388
Author(s):  
Mikko J Sillanpää ◽  
Elja Arjas
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Backcross Progeny ◽  
Random Variable ◽  
Interval Mapping ◽  
Mapping Method ◽  
Structure Mapping ◽  
Standard Interval ◽  
Trait Loci

Abstract A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL-intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the location wise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/~mjs.

scholarly journals A Rank-Based Nonparametric Method for Mapping Quantitative Trait Loci in Outbred Half-Sib Pedigrees: Application to Milk Production in a Granddaughter Design

Genetics ◽  
1998 ◽  
Vol 149 (3) ◽  
pp. 1547-1555 ◽  
Author(s):  
Wouter Coppieters ◽  
Alexandre Kvasz ◽  
Frédéric Farnir ◽  
Juan-Jose Arranz ◽  
Bernard Grisart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Real Data ◽  
Interval Mapping ◽  
Mapping Method ◽  
Residual Variance ◽  
Data Set ◽  
Wilcoxon Rank Sum Test ◽  
Holstein Friesian ◽  
Trait Loci

Abstract We describe the development of a multipoint nonparametric quantitative trait loci mapping method based on the Wilcoxon rank-sum test applicable to outbred half-sib pedigrees. The method has been evaluated on a simulated dataset and its efficiency compared with interval mapping by using regression. It was shown that the rank-based approach is slightly inferior to regression when the residual variance is homoscedastic normal; however, in three out of four other scenarios envisaged, i.e., residual variance heteroscedastic normal, homoscedastic skewed, and homoscedastic positively kurtosed, the latter outperforms the former one. Both methods were applied to a real data set analyzing the effect of bovine chromosome 6 on milk yield and composition by using a 125-cM map comprising 15 microsatellites and a granddaughter design counting 1158 Holstein-Friesian sires.

scholarly journals Mapping of Quantitative Trait Loci Controlling Adaptive Traits in Coastal Douglas Fir. III. Quantitative Trait Loci-by-Environment Interactions

Genetics ◽  
2003 ◽  
Vol 165 (3) ◽  
pp. 1489-1506
Author(s):  
Kathleen D Jermstad ◽  
Daniel L Bassoni ◽  
Keith S Jech ◽  
Gary A Ritchie ◽  
Nicholas C Wheeler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Moisture Stress ◽  
Interval Mapping ◽  
Douglas Fir ◽  
Adaptive Traits ◽  
Growth Cessation ◽  
Trait Loci ◽  
Growth Initiation

Abstract Quantitative trait loci (QTL) were mapped in the woody perennial Douglas fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) for complex traits controlling the timing of growth initiation and growth cessation. QTL were estimated under controlled environmental conditions to identify QTL interactions with photoperiod, moisture stress, winter chilling, and spring temperatures. A three-generation mapping population of 460 cloned progeny was used for genetic mapping and phenotypic evaluations. An all-marker interval mapping method was used for scanning the genome for the presence of QTL and single-factor ANOVA was used for estimating QTL-by-environment interactions. A modest number of QTL were detected per trait, with individual QTL explaining up to 9.5% of the phenotypic variation. Two QTL-by-treatment interactions were found for growth initiation, whereas several QTL-by-treatment interactions were detected among growth cessation traits. This is the first report of QTL interactions with specific environmental signals in forest trees and will assist in the identification of candidate genes controlling these important adaptive traits in perennial plants.

Genes and Quantitative Trait Loci Controlling Biomass Yield and Forage Quality Traits in Perennial Wildrye

Crop Science ◽  
2014 ◽  
Vol 54 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Steven R. Larson ◽  
Kevin B. Jensen ◽  
Joseph G. Robins ◽  
Blair L. Waldron
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Biomass Yield ◽  
Forage Quality ◽  
Quality Traits ◽  
Trait Loci

Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

Genetics ◽  
2002 ◽  
Vol 161 (2) ◽  
pp. 905-914 ◽  
Author(s):  
Hakkyo Lee ◽  
Jack C M Dekkers ◽  
M Soller ◽  
Massoud Malek ◽  
Rohan L Fernando ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Error Rate ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Error Rates ◽  
Multiple Traits ◽  
Test Statistic ◽  
False Discovery ◽  
Trait Loci

Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

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