Bias correction for estimated QTL effects using the penalized maximum likelihood method.

A simulation study was performed to see whether selection affected quantitative trait loci (QTL) mapping. Populations under random selection, under selection among full-sib families, and under selection within a full-sib family were simulated each with heritability of 0.3, 0.5, and 0.7. They were analyzed with the marker spacing of 10 cM and 20 cM. The accuracy for QTL detection decreased for the populations under selection within full-sib family. Estimates of QTL effects and positions differed (P < .05) from their input values. The problems could be ignored when mapping a QTL for the populations under selection among full-sib families. A large heritability helped reduction of such problems. When the animals were selected within a full-sib family, the QTL was detected for the populations with heritability of 0.5 or larger using the marker spacing of 10 cM, and with heritability of 0.7 using the marker spacing of 20 cM. This study implied that when selection was introduced, the accuracy for QTL detection decreased and the estimates of QTL effects were biased. A caution was warranted on the decision of data (including selected animals to be genotyped) for QTL mapping.

Strategies for mapping quantitative trait (QT) loci (QTL) need special studies in species where inbred lines cannot easily be obtained. We propose the detection of QTL with schemes including several full-sib families and study here the efficiency of such schemes — diallel, factorial, cyclic or single-pair mating designs — for a varying number of parents, on the basis of the power of tests for QTL detection. We considered a single, fully informative marker completely linked to the QTL. The tests proposed for QTL detection are based on linear additive models. The results show that for a given number of parents, in given conditions, the mating design has no effect on power. It increases as the total number of individuals genotyped increases. The number of parents affects the power of QTL detection insofar as the effect of substituting QT alleles varies from one parent to another and as some parents can be homozygous at the QTL: with only two parents QTL detection is often less powerful than with more parents. The size of the genetic effects that can be detected with a given power decreases as the number of parents increases from two to six and is quite stable for more than six parents. If QTL heterozygote frequency in the base population is high enough (greater than 0.2) mating designs with six parents should give a good sample of variance attributable to QTL and allow the detection of QTL with reasonable power.

The brace-root is a crucial part of the whole root system in maize and plays an important role in the maintenance of lodging resistance. In this study, a BC2F1 population with three replicates, derived from the cross between Yi17 (well-developed brace-root) and Yi16 (poorly developed brace-root) was used for quantitative trait locus (QTL) detection. The total lengths of the genetic linkage map for the three replicates were 659.6, 662.9 and 936.8 cM, respectively, and the average distances between adjacent markers were 3.43, 3.68 and 4.61 cM, respectively. In total, 21 QTLs were detected in the BC2F1 population. The detected QTLs were mainly located at bin 3.05 (four QTLs) and 8.04–8.05 (three QTLs). Bin 3.05 was first detected to be rich in QTLs for brace-root traits in all three replicates, and QTLs detected in bin 8.04–8.05 were consistent with our previous results in the F2:3 population. The QTLs qW3a and qVA3 were coincident QTLs; of these, qW3a was a major effect QTL. These results may provide important information for maize breeders to pyramid favorable chromosome fragments or QTL in breeding programmes targeted at well-developed brace-root materials.

Mapping studies using populations with introgressed marker-defined genomic regions are continuously increasing knowledge about quantitative trait loci (QTL) that correlate with variation in important crop traits. This knowledge is useful for plant breeding, although combining desired traits in one genotype might be complicated by the mode of inheritance and co-localization of QTL with antagonistic effects, and by physiological trade-offs, and feed-back or feed-forward mechanisms. Therefore, integrating advances at the genetic level with insight into influences of environment and crop management on crop performance remains difficult. Whereas mapping studies can pinpoint correlations between QTL and phenotypic traits for specific conditions, ignoring or overlooking the importance of environment or crop management can jeopardize the relevance of such assessments. Here, we focus on fruit load (a measure determining competition among fruits on one plant) and its strong modulation of QTL effects on fruit size and composition. Following an integral approach, we show which fruit traits are affected by fruit load, to which underlying processes these traits can be linked, and which processes at lower and higher integration levels are affected by fruit load (and subsequently influence fruit traits). This opinion paper (i) argues that a mechanistic framework to interpret interactions between fruit load and QTL effects is needed, (ii) pleads for consideration of the context of agronomic management when detecting QTL, (iii) makes a case for incorporating interacting factors in the experimental set-up of QTL mapping studies, and (iv) provides recommendations to improve efficiency in QTL detection and use, with particular focus on model-based marker-assisted breeding.

Due to the universal presence of genotype by environment interactions, understanding the pattern of quantitative trait loci (QTL)-by-environment interactions is a prerequisite for effective marker-assisted selection. In this report, we describe a biplot approach for investigating QTL-by-environment patterns. This approach involves two steps. It starts with a two-way table containing effects of individual QTLs in individual environments for the trait under investigation. This table is decomposed into principal components via singular value decomposition, and the first two principal components are plotted for both QTLs and environments to form a biplot. The resulting ‘QQE biplot’ contains information on QTL main effects (Q) and QTL-by-environment interactions (QE). A QQE biplot displays the QTL-by-environment patterns and allows visualization of: (1) the magnitude of the effect of a QTL, (2) the average effect of a QTL and its stability across environments, (3) the effects of a QTL in individual environments, (4) the similarity/dissimilarity among QTLs in effect and response to the environments, (5) the similarity/dissimilarity among environments in modulating QTL effects, (6) any differentiation of mega-environments, and (7) the combination of QTL alleles for maximum/minimum expression of the trait for each environment or mega-environment. A case study is provided using the QTL-by-environment two-way table for barley yield.

Yield under drought stress is a highly complex trait with large influence to even a minor fluctuation in the environmental conditions. Genomics-assisted breeding holds great promise for improving such complex traits more efficiently in less time, but requires markers associated with the trait of interest. In this context, a recombinant inbred line mapping population (TAG 24 × ICGV 86031) was used to identify markers associated with quantitative trait loci (QTLs) for yield and yield related traits at two important locations of West Africa under well watered and water stress conditions. Among the traits analyzed under WS condition, the harvest index (HI) and the haulm yield (HYLD) were positively correlated with the pod yield (PYLD) and showed intermediate broad sense heritability. QTL analysis using phenotyping and genotyping data resulted in identification of 52 QTLs. These QTLs had low phenotypic variance (<12 %) for all the nine traits namely plant height, primary branching, SPAD chlorophyll meter reading, percentage of sound mature kernels, 100 kernel weight, shelling percentage, HI, HYLD and PYLD. Interestingly, few QTLs identified in this study were also overlapped with previously reported QTLs detected for drought tolerance related traits identified earlier in Indian environmental conditions using the same mapping population. Accumulating these many small-effect QTLs into a single genetic background is nearly impossible through marker-assisted backcrossing and even marker-assisted recurrent selection. Under such circumstances, the deployment of genomic selection is the most appropriate approach for improving such complex traits with more precision and in less time.Electronic supplementary materialThe online version of this article (doi:10.1007/s10681-015-1472-6) contains supplementary material, which is available to authorized users.

Breeding for fruit quality traits is complex due to the polygenic (quantitative) nature of the genetic control of these traits. Therefore, to improve the speed and efficiency of genotype selection, attention in recent years has focused on the identification of quantitative trait loci (QTLs) and molecular markers associated with these QTLs. However, despite the huge potential of molecular markers in breeding programmes, their implementation in practice has been limited by the lack of information on the stability of QTLs across different environments and within different genetic backgrounds. Here, we present the results from a comprehensive analysis of the inheritance of fruit quality traits within a population derived from a cross between the apple cultivars ‘Telamon’ and ‘Braeburn’ over two successive seasons. A total of 74 different QTLs were identified for all the major fruit physiological traits including fruit height, diameter, weight and stiffness, flesh firmness, rate of flesh browning, acidity, the oBrix content and harvest date. Seventeen of these QTLs were ‘major’ QTLs, accounting for over 20% of the observed population variance of the trait. However, only one third (26) of the identified QTLs were stable over both harvest years, and of these year-stable QTLs only one was a major QTL. A direct comparison with published QTL results obtained using other populations (King et al., Theor Appl Genet 102:1227–1235, 2001; Liebhard et al., Plant Mol Biol 52:511–526, 2003) is difficult because the linkage maps do not share a sufficient number of common markers and due to differences in the trait evaluation protocols. Nonetheless, our results suggest that for the six fruit quality traits which were measured in all populations, nine out of a total of 45 QTLs were common or stable across all population × environments combinations. These results are discussed in the framework of the development and application of molecular markers for fruit quality trait improvement.

Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 20828/SY95-71 (2SY)) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B, and QSd.sau-2SY-2D). They can explain up to 23.14, 19.97, 12.00, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson’s correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicted and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.

The genetic architecture of UV floral patterning in sunflower.

The focus of the thesis is on the detection of quantitative trait loci (QTL) in dairy cattle and their utilisation in breeding programmes. Analysis of one bovine chromosome for quantitative trait loci for milk production traits is described and a QTL for protein percent was identified that was significant at the 1% level.When analysing the chromosome it was observed that the degree of precision in estimating QTL location and size (or variance) is poor. Through stochastic simulation, the effect of incorrect parameter estimates for quantitative trait locus effect, and position on genetic response from marker assisted selection is investigated.From this study it was concluded that studies should be undertaken to verify estimates of QTL and location to enable optimal use of marker assisted selection. Strategies to confirm the existence and size of quantitative trait loci identified in a genome scan are outlined. Also through stochastic simulation the effect of reducing flanking-marker bracket size was found to increase the genetic response from marker assisted selection.Simulation is used to estimate improvements in rate of genetic gain from marker assisted selection for two scenarios, the current situation and a futuristic setting. The increase in rate of genetic gain with marker assisted selection is some 5% in the current situation with the potential of 30% with improvements in level of genetic variance identified and the identified loci being in disequilibrium with marker alleles.The general discussion of this thesis addresses the use of significance levels in quantitative trait loci detection, experimental designs to identify further quantitative trait loci in the New Zealand dairy industry, and the current and possible future application of marker assisted selection in dairy breeding programmes.

Crown rust, caused by the fungus Puccinia coronata Cda. f. sp. avenae Eriksson, is the most damaging disease of oat (Avena sativa L.). Breeding for resistance to crown rust has been an efficient and economical means to control the disease. Most of the resistance genes in recently released oat cultivars have been introgressed from the hexaploid, wild oat species A. sterilis L. The number of effective resistance genes has been rapidly decreasing. Characterization and utilization of new resistance sources are needed to ensure adequate resistance in future oat cultivars. Crown rust resistance in diploid, wild oat accessions was transferred to the hexaploid, cultivated oat line MAM17-5. The objective of this study was to identify the number, position, and effect of quantitative trait loci (QTL) and digenic QTL epistasis controlling crown rust resistance in MAM17-5. A linkage map of 272 molcular markers was used for this analysis. Reaction type and rust severity data were collected on field-grown adult plants from 152 F5:6 recombinant inbred lines derived from the cross, ‘Ogle’/MAM17-5. Composite interval mapping was conducted to identify genomic regions associated with crown rust resistance. Overall, two QTL, Pcq1 on linkage group 28 and Pcq2 on 29, were consistently identified for crown rust resistance in different years. Pcq1 and Pcq2 explained 48.5 to 70.1% and 9.6 to 14.0%, respectively, of the total phenotypic variation for crown rust resistance. Significant digenic, epistatic interactions were detected for QTL controlling rust severity but not reaction type. Marker-assisted selection targeting the major QTL, Pcq1, should be useful for efficient selection of crown rust resistance.

A majority of traits are determined by multiple quantitative trait loci (QTL) that can have pleiotropic effects. A multi-parent advanced generation inter-cross (MAGIC) population is well suited for genetically analyzing the effects of multiple QTL on traits of interest because it contains a higher number of QTL alleles than a biparental population. We previously produced the JAPAN-MAGIC (JAM) population, derived from eight rice (Oryza sativa L.) cultivars with high yield and biomass in Japan, and developed the method of genome-wide association study (GWAS) using haplotype information on the JAM lines. This method was effective for identifying major genes such as Waxy for eating quality and Sd1 for culm length. Here, we show that haplotype-based GWAS is also effective for the evaluation of multiple QTL with small effects on rice grain shape in the JAM lines. Although both the haplotype- and SNP-based GWAS identified multiple QTL for grain length and width, the sum of the estimated trait values of each allele for the QTL detected by haplotype-based GWAS had higher correlation with observed values than those detected by SNP-based GWAS, indicating high-accuracy QTL detection in the haplotype-based GWAS. Furthermore, the study revealed pleiotropic effects of some QTL regions in regulation of grain shape, suggesting that the haplotype-based GWAS using the JAM lines is an effective means to evaluate the main and side effects of haplotypes at each QTL. Information on the pleiotropic effects of haplotypes on various traits will be useful for designing ideal lines in a breeding program.

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.

Genome wide quantitative trait loci (QTL) mapping was conducted in Arabidopsis thaliana using F2 mapping population (Col-0 × Don-0) and SNPs markers. A total of five linkage groups were obtained with number of SNPs varying from 45 to 59 per linkage group. The composite interval mapping detected a total of 36 QTLs for 15 traits and the number of QTLs ranged from one (root length, root dry biomass, cauline leaf width, number of internodes and internode distance) to seven (for bolting days). The range of phenotypic variance explained (PVE) and logarithm of the odds ratio of these 36 QTLs was found be 0.19-38.17% and 3.0-6.26 respectively. Further, the epistatic interaction detected one main effect QTL and four epistatic QTLs. Five major QTLs viz. Qbd.nbri.4.3, Qfd.nbri.4.2, Qrdm.nbri.5.1, Qncl.nbri.2.2, Qtd.nbri.4.1 with PVE > 15.0% might be useful for fine mapping to identify genes associated with respective traits, and also for development of specialized population through marker assisted selection. The identification of additive and dominant effect QTLs and desirable alleles of each of above mentioned traits would also be important for future research.

&lt;p&gt;Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 20828/SY95-71 (2SY)) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B, and QSd.sau-2SY-2D). They can explain up to 23.14, 19.97, 12.00, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson’s correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicted and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.&lt;/p&gt;