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PubMed2006

QTL analysis for rice grain length and fine mapping of an identified QTL with stable and major effects.

Grain length in rice plays an important role in determining rice appearance, milling, cooking and eating quality. In this study, the genetic basis of grain length was dissected into six main-effect quantitative trait loci (QTLs) and twelve pairs of epistatic QTLs. The stability of these QTLs was evaluated in four environments using an F7 recombinant inbred line (RIL) population derived from the cross between a Japonica variety, Asominori, and an Indica variety, IR24. Moreover, chromosome segment substitution lines (CSSLs) harboring each of the six main-effect QTLs were used to evaluate gene action of QTLs across eight environments. A major QTL denoted as qGL-3a, was found to express stably not only in the isogenic background of Asominori but also in the recombinant background of Asominori and IR24 under multiple environments. The IR24 allele at qGL-3a has a positive effect on grain length. Based on the test of advanced backcross progenies, qGL-3a was dissected as a single Mendelian factor, i.e., long rice grain was controlled by a recessive gene gl-3. High-resolution genetic and physical maps were further constructed for fine mapping gl-3 by using 11 simple sequence repeat (SSR) markers designed using sequence information from seven BAC/PAC clones and a BC4F2 population consisting of 2,068 individuals. Consequently, the gl-3 gene was narrowed down to a candidate genomic region of 87.5 kb long defined by SSR markers RMw357 and RMw353 on chromosome 3, which provides a basis for map-based cloning of this gene and for marker-aided QTL pyramiding in rice quality breeding.

Alleles↗
PubMed2004

Multivariate QTL linkage analysis suggests a QTL for platelet count on chromosome 19q.

Platelet count is a highly heritable trait with genetic factors responsible for around 80% of the phenotypic variance. We measured platelet count longitudinally in 327 monozygotic and 418 dizygotic twin pairs at 12, 14 and 16 years of age. We also performed a genome-wide linkage scan of these twins and their families in an attempt to localize QTLs that influenced variation in platelet concentrations. Suggestive linkage was observed on chromosome 19q13.13-19q13.31 at 12 (LOD = 2.12, P = 0.0009), 14 (LOD = 2.23, P = 0.0007) and 16 (LOD = 1.01, P = 0.016) years of age and multivariate analysis of counts at all three ages increased the LOD to 2.59 (P = 0.0003). A possible candidate in this region is the gene for glycoprotein VI, a receptor involved in platelet aggregation. Smaller linkage peaks were also seen at 2p, 5p, 5q, 10p and 15q. There was little evidence for linkage to the chromosomal regions containing the genes for thrombopoietin (3q27) and the thrombopoietin receptor (1q34), suggesting that polymorphisms in these genes do not contribute substantially to variation in platelet count between healthy individuals.

Adolescent↗
PubMed1998

Moving from QTL experimental results to the utilization of QTL in breeding programmes.

Results from quantitative trait loci studies cannot be readily implemented into breeding schemes through marker assisted selection because of uncertainty about whether the quantitative trait loci identified are real and whether the identified quantitative trait loci are segregating in the breeding population. The present paper outlines and discusses strategies to reduce uncertainty in the results from quantitative trait loci studies. One strategy to confirm results from quantitative trait loci studies is to combine P-values from many quantitative trait loci experiments, while another is to establish a confirmation study. The power of a confirmation study must be high to ensure that the postulated quantitative trait loci can be verified. In the calculation of the experimental power, there are many issues that have to be addressed: size of the quantitative trait loci to be detected, significance level required, experimental design and expected heterozygosity for the design. To ensure marker assisted selection can be quickly implemented once quantitative trait loci are confirmed, DNA samples should be retained from daughters, and the sires and dams of elite sires.

Animals↗
PubMed2006

Gene by environment QTL mapping through multiple trait analyses in blood pressure salt-sensitivity: identification of a novel QTL in rat chromosome 5.

BACKGROUND: The genetic mechanisms underlying interindividual blood pressure variation reflect the complex interplay of both genetic and environmental variables. The current standard statistical methods for detecting genes involved in the regulation mechanisms of complex traits are based on univariate analysis. Few studies have focused on the search for and understanding of quantitative trait loci responsible for gene x environmental interactions or multiple trait analysis. Composite interval mapping has been extended to multiple traits and may be an interesting approach to such a problem. METHODS: We used multiple-trait analysis for quantitative trait locus mapping of loci having different effects on systolic blood pressure with NaCl exposure. Animals studied were 188 rats, the progenies of an F2 rat intercross between the hypertensive and normotensive strain, genotyped in 179 polymorphic markers across the rat genome. To accommodate the correlational structure from measurements taken in the same animals, we applied univariate and multivariate strategies for analyzing the data. RESULTS: We detected a new quantitative train locus on a region close to marker R589 in chromosome 5 of the rat genome, not previously identified through serial analysis of individual traits. In addition, we were able to justify analytically the parametric restrictions in terms of regression coefficients responsible for the gain in precision with the adopted analytical approach. CONCLUSION: Future work should focus on fine mapping and the identification of the causative variant responsible for this quantitative trait locus signal. The multivariable strategy might be valuable in the study of genetic determinants of interindividual variation of antihypertensive drug effectiveness.

Animals↗
PubMed2005

Combined line-cross and half-sib QTL analysis of crosses between outbred lines.

Data from an F 2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele frequencies (FD) between parental breeds were simulated. Use of all models increased power to detect QTL. The line-cross model was the most powerful model to detect QTL for FD>0.6. The combined and half-sib models had similar power for FD<0.4. The proportion of detected QTL declared as LC QTL decreased with FD. The opposite was observed for HS QTL. The proportion of CB QTL decreased as FD deviated from 0.5. Accuracy of map position tended to be greatest for CB QTL. Models were applied to a cross of Berkshire and Yorkshire pig breeds and revealed 160 (40) QTL at the 5% chromosome (genome)-wise level for the 39 growth, carcass composition and quality traits, of which 72, 54, and 34 were declared as LC, HS and CB QTL. Fourteen CB QTL were detected only by the combined model. Thus, the combined model can increase power to detect QTL and mapping accuracy and enable characterization of QTL that segregate within breeds.

Animals↗
PubMed2003

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia.

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.

Asia↗
PubMed2006

QTL for microstructural and biophysical muscle properties and body composition in pigs.

BACKGROUND: The proportion of muscle fibre types and their size affect muscularity as well as functional properties of the musculature and meat quality. We aimed to identify QTL for microstructural muscle properties including muscle fibre size, their numbers and fibre type proportions as well as biophysical parameters of meat quality and traits related to body composition, i.e. pH, conductivity, area of M. longissimus dorsi and lean meat content. A QTL scan was conducted in a porcine experimental population that is based on Duroc and Berlin Miniature Pig. RESULTS: Least square regression interval mapping revealed five significant and 42 suggestive QTL for traits related to muscle fibre composition under the line-cross model as well as eight significant and 40 suggestive QTL under the half-sib model. For traits related to body composition and biophysical parameters of meat quality five and twelve significant plus nine and 22 suggestive QTL were found under the line-cross and half-sib model, respectively. Regions with either significant QTL for muscle fibre traits or significant QTL for meat quality and muscularity or both were detected on SSC1, 2, 3, 4, 5, 13, 14, 15, and 16. QTL for microstructural properties explained a larger proportion of variance than did QTL for meat quality and body composition. CONCLUSION: Microstructural properties of pig muscle and meat quality are governed by genetic variation at many loci distributed throughout the genome. QTL analysis under both, the line-cross and half-sib model, allows detecting QTL in case of fixation or segregation of the QTL alleles among the founder populations and thus provide comprehensive insight into the genetic variation of the traits under investigation. Genomic regions affecting complex traits of muscularity and meat quality as well as microstructural properties might point to QTL that in first instance affect muscle fibre traits and by this in second instance meat quality. Disentangling complex traits in their constituent phenotypes might facilitate the identification of QTL and the elucidation of the pleiotropic nature of QTL effects.

Animals↗
PubMedDate not supplied

A simulation study on the accuracy of position and effect estimates of linked QTL and their asymptotic standard deviations using multiple interval mapping in an F(2) scheme.

Approaches like multiple interval mapping using a multiple-QTL model for simultaneously mapping QTL can aid the identification of multiple QTL, improve the precision of estimating QTL positions and effects, and are able to identify patterns and individual elements of QTL epistasis. Because of the statistical problems in analytically deriving the standard errors and the distributional form of the estimates and because the use of resampling techniques is not feasible for several linked QTL, there is the need to perform large-scale simulation studies in order to evaluate the accuracy of multiple interval mapping for linked QTL and to assess confidence intervals based on the standard statistical theory. From our simulation study it can be concluded that in comparison with a monogenetic background a reliable and accurate estimation of QTL positions and QTL effects of multiple QTL in a linkage group requires much more information from the data. The reduction of the marker interval size from 10 cM to 5 cM led to a higher power in QTL detection and to a remarkable improvement of the QTL position as well as the QTL effect estimates. This is different from the findings for (single) interval mapping. The empirical standard deviations of the genetic effect estimates were generally large and they were the largest for the epistatic effects. These of the dominance effects were larger than those of the additive effects. The asymptotic standard deviation of the position estimates was not a good criterion for the accuracy of the position estimates and confidence intervals based on the standard statistical theory had a clearly smaller empirical coverage probability as compared to the nominal probability. Furthermore the asymptotic standard deviation of the additive, dominance and epistatic effects did not reflect the empirical standard deviations of the estimates very well, when the relative QTL variance was smaller/equal to 0.5. The implications of the above findings are discussed.

Animals↗
PubMed2002

Superiority of QTL-assisted selection in dairy cattle breeding schemes.

The superiority of selection schemes employing information about a known quantitative trait locus (QTL) over conventional schemes is examined for dairy cattle breeding schemes. Stochastic simulation of a dairy cattle population with selection practices, structures, and parameters similar to the US Holstein population was implemented. Additive genetic effects were estimated by an animal model. Two schemes were compared: a QTL-assisted selection scheme in which the genotype of a known QTL was accounted for in the animal model as a fixed factor, and a QTL-free selection scheme in which the QTL was simulated but was not fit separately in the animal model. Under the QTL-assisted selection scheme, all animals in the mixed model were assumed to be genotyped for the QTL. The effect of using QTL information on the genetic response, the frequency of the favorable QTL allele, and the accuracy of evaluation were examined. Moreover, the effect was studied in four distinct paths of selection: active sires, proven young bulls, bull dams, and first-lactation cows. Average superiority values of 4.6, 7.6, 11.7, and 1.1% for genetic response were observed over 16 yr of selection for active sires, young bulls, bull dams, and first-lactation cows, respectively. Frequency of the favorable QTL allele changed faster in bull dams than males, and was the slowest in first-lactation cows. Finally, accuracy of evaluation under the QTL-assisted selection scheme was higher than under the QTL-free selection scheme. Young bulls ofthe QTL-assisted selection scheme on average had 0.049 higher accuracy, and first-lactation cows had on average 0.185 higher accuracy than corresponding animals of the QTL-free selection scheme.

Animals↗
PubMed2005

Stacking quantitative trait loci (QTL) for Fusarium head blight resistance from non-adapted sources in an European elite spring wheat background and assessing their effects on deoxynivalenol (DON) content and disease severity.

Fusarium head blight (FHB) is a devastating disease in wheat that reduces grain yield, grain quality and contaminates the harvest with deoxynivalenol (DON). As potent resistance sources Sumai 3 and its descendants from China and Frontana from Brazil had been analysed by quantitative trait loci (QTL) mapping. We introgressed and stacked two donor QTL from CM82036 (Sumai 3/Thornbird) located on chromosomes 3B and 5A and one donor QTL from Frontana on chromosome 3A in elite European spring wheat and estimated the effects of the three individual donor QTL and their four combinations on DON, Fusarium exoantigen content, and FHB rating adjusted to heading date. One class with the susceptible QTL alleles served as control. Each of the eight QTL classes was represented by 12-15 F(3)-derived lines tested in F(5) generation as bulked progeny possessing the respective marker alleles homozygously. Traits were evaluated in a field experiment across four locations with spray inoculation of Fusarium culmorum. All three individual donor-QTL alleles significantly reduced DON content and FHB severity compared to the marker class with no donor QTL. The only exception was the donor-QTL allele 3A that had a low, but non-significant effect on FHB severity. The highest effect had the stacked donor-QTL alleles 3B and 5A for both traits. They jointly reduced DON content by 78% and FHB rating by 55% compared to the susceptible QTL class. Analysis of Fusarium exoantigen content illustrates that lower disease severity is associated with less mycelium content in the grain. In conclusion, QTL from non-adapted sources could be verified in a genetic background of German elite spring wheat. Within the QTL classes significant (P<0.05) genotypic differences were found among the individual genotypes. An additional phenotypic selection would, therefore, be advantageous after performing a marker-based selection.

Alleles↗
PubMed2005

A comparison of regression interval mapping and multiple interval mapping for linked QTL.

Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.

Chromosome Mapping↗
PubMed2000

[The effect of marker density on QTL mapping in a grand-daughter design].

Using Monte Carlo method, the effects of marker density on the accuracy of QTL mapping, measured with mean squared error (MSE) of QTL position estimates, were investigated for a grand-daughter design under different population structures, heritabilities, sizes of QTL effect, and QTL locations. The optimal marker densities for QTL mapping for applying to marker assisted selection, (MAS) were analyzed from an economic point of view. The results show that, in general, with the increase of marker density MSEs decrease much more rapidly when all other factors are at a high level than at a low level. When the sample size reaches a certain level (e.g. 40 families with 100 sons per family), or when the QTL effect is large (e.g. the QTL explains 50% of the genetic variance), the decrease of MSE will be only slightly affected by other factors. For the best benefits of MAS, the optimal marker distances under different combinations of various factors were 5cM (small sample and large QTL, moderate sample, heritability and QTL, or large sample and small QTL), 10cM (moderate sample and high heritability or large QTL, or large sample and moderate heritability and QTL), and 20cM (large sample and high heritability or large QTL).

Chromosome Mapping↗
PubMed2003

Identification of putative QTL that underlie yield in interspecific soybean backcross populations.

Glycine soja, the wild progenitor of soybean, is a potential source of useful genetic variation in soybean improvement. The objective of our study was to map quantitative trait loci (QTL) from G. soja that could improve the crop. Five populations of BC(2)F(4)-derived lines were developed using the Glycine max cultivar IA2008 as a recurrent parent and the G. soja plant introduction (PI) 468916 as a donor parent. There were between 57 and 112 BC(2)F(4)-derived lines in each population and a total of 468 lines for the five populations. The lines were evaluated with simple sequence repeat markers and in field tests for yield, maturity, plant height, and lodging. The field testing was done over 2 years and at two locations each year. Marker data were analyzed for linkage and combined with field data to identify QTL. Using an experimentwise significance threshold of P=0.05, four yield QTL were identified across environments on linkage groups C2, E, K, and M. For these yield QTL, the IA2008 marker allele was associated with significantly greater yield than the marker allele from G. soja. In addition, one lodging QTL, four maturity QTL, and five QTL for plant height were identified across environments. Of the 14 QTL identified, eight mapped to regions where QTL with similar effects were previously mapped. Many regions carrying the yield QTL were also significant for other traits, such as plant height and lodging. When the significance threshold was reduced and the data were analyzed with simple linear regression, four QTL with a positive allele for yield from G. soja were mapped. One epistatic interaction between two genetic regions was identified for yield using an experimentwise significance threshold of P=0.05. Additional research is needed to establish whether multiple trait associations are the result of pleiotropy or genetic linkage and to retest QTL with a positive effect from G. soja.

Analysis of Variance↗
PubMed2004

What proportion of declared QTL in plants are false?

The false discovery rate (FDR) is the probability that a quantitative trait locus (QTL) is false, given that a QTL has been declared. A misconception in QTL mapping is that the FDR is equal to the comparison-wise significance level, alpha(C). The objective of this simulation study was to determine the FDR in an F(2) mapping population, given different numbers of QTL, population sizes, and trait heritabilities. Markers linked to QTL were detected by multiple regression of phenotype on marker genotype. Phenotypic selection and marker-based recurrent selection were compared. The FDR increased as alpha(C) increased. Notably, the FDR was often 10-30 times higher than the alpha(C) level used. Regardless of the number of QTL, heritability, or size of the genome, the FDR was < or =0.01 when alpha(C) was 0.0001. The FDR increased to 0.82 when alpha(C) was 0.05, heritability was low, and only one QTL controlled the trait. An alpha(C) of 0.05 led to a low FDR when many QTL (30 or 100) controlled the trait, but this lower FDR was accompanied by a diminished power to detect QTL. Larger mapping populations led to both lower a FDR and increased power. Relaxed significance levels of alpha(C)=0.1 or 0.2 led to the largest responses to marker-based recurrent selection, despite the high FDR. To prevent false QTL from confusing the literature and databases, a detected QTL should, in general, be reported as a QTL only if it was identified at a stringent significance level, e.g., alpha(C) congruent with 0.0001.

Computer Simulation↗
PubMed2005

Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust.

The limited population sizes used in many quantitative trait locus (QTL) detection experiments can lead to underestimation of QTL number, overestimation of QTL effects, and failure to quantify QTL interactions. We used the barley/barley stripe rust pathosystem to evaluate the effect of population size on the estimation of QTL parameters. We generated a large (n = 409) population of doubled haploid lines derived from the cross of two inbred lines, BCD47 and Baronesse. This population was evaluated for barley stripe rust severity in the Toluca Valley, Mexico, and in Washington State, USA, under field conditions. BCD47 was the principal donor of resistance QTL alleles, but the susceptible parent also contributed some resistance alleles. The major QTL, located on the long arm of chromosome 4H, close to the Mlo gene, accounted for up to 34% of the phenotypic variance. Subpopulations of different sizes were generated using three methods-resampling, selective genotyping, and selective phenotyping-to evaluate the effect of population size on the estimation of QTL parameters. In all cases, the number of QTL detected increased with population size. QTL with large effects were detected even in small populations, but QTL with small effects were detected only by increasing population size. Selective genotyping and/or selective phenotyping approaches could be effective strategies for reducing the costs associated with conducting QTL analysis in large populations. The method of choice will depend on the relative costs of genotyping versus phenotyping.

Analysis of Variance↗
PubMed2025

Dissecting seed composition QTL from wild soybean: fine-mapping, candidate gene identification, and evaluation of introgression effects on agronomic performance.

Seed composition QTL from wild soybean were confirmed and validated in two genetic backgrounds across multiple environments, candidate genes were identified, and agronomic performance of backcross introgression lines was evaluated. Through selection for soybean yield, breeders have inadvertently reduced seed protein content and increased oil due to phenotypic and genetic correlations between these three traits. Therefore, identifying alleles that increase protein without adversely affecting oil and yield is of interest for breeders and the entire soybean value chain. Previously, a G. max&#x2009;&#xd7;&#x2009;G. soja population was used to map a protein-associated region to&#x2009;~&#x2009;4.6 Mbp on chromosome (Chr) 14. The G. soja allele significantly increased protein 6.5-7.2&#xa0;g&#xa0;kg-1, without significantly decreasing oil. Additionally, two oil quantitative trait loci (QTL) were reported on Chrs 8 and 14. In this study, we aimed to confirm the Chr 14 protein QTL, evaluate QTL effects on seed composition and agronomic performance, and further fine-map to identify candidate genes. We validated and fine-mapped the Chr 14 protein QTL to a 0.6 Mbp region in a different genetic background, where the G. soja allele significantly increased protein by 9.3&#xa0;g&#xa0;kg-1. Further, we confirmed the Chr 14 oil QTL linked to the protein QTL and the Chr 8 oil QTL. Chr 14 protein QTL effects on agronomic traits were evaluated in a backcross population across eight environments. The QTL significantly increased protein content, without significantly impacting oil, maturity, or plant height. While the QTL impacted yield and lodging, its effect and significance varied within environments. The candidate genes identified for these three validated seed composition QTL, along with additional molecular markers developed, offer valuable resources for improving seed composition in soybean breeding programs.

Quantitative Trait Loci↗
PubMed2005

Simultaneous mapping of epistatic QTL in DU6i x DBA/2 mice.

We have mapped epistatic quantitative trait loci (QTL) in an F2 cross between DU6i x DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%-128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype-phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.

Animals↗