Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping.

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.

BackgroundVariance component (VC) models are commonly used for Quantitative Trait Loci (QTL) mapping in outbred populations. Here, the QTL effect is given as a random effect and a critical part of the model is the relationship between the phenotypic values and the random effect. In the traditional VC model, each individual has a unique QTL effect and the relationship between these random effects is given as a covariance structure (known as the identity-by-descent (IBD) matrix).ResultsWe present an alternative notation of the variance component model, where the elements of the random effect are independent base generation allele effects and sampling term effects. The relationship between the phenotypic vales and the random effect is given by an incidence matrix, which results in a novel, but statistically equivalent, version of the traditional VC model. A general algorithm to estimate this incidence matrix is presented. Since the model is given in terms of base generation allele effects and sampling term effects, these effects can be estimated separately using best linear unbiased prediction (BLUP). From simulated data, we showed that biallelic QTL effects could be accurately clustered using the BLUP obtained from our model notation when markers are fully informative, and that the accuracy increased with the size of the QTL effect. We also developed a measure indicating whether a base generation marker homozygote is a QTL heterozygote or not, by comparing the variances of the sampling term BLUP and the base generation allele BLUP. A ratio greater than one gives strong support for a QTL heterozygote.ConclusionWe developed a simple presentation of the VC QTL model for identification of base generation allele effects in QTL linkage analysis. The base generation allele effects and sampling term effects were separated in our model notation. This clarifies the assumptions of the model and should also enhance the development of genome scan methods.

In a previous study on maize (Zea mays, L.) several quantitative trait loci (QTL) showing high dominance-additive ratio for agronomic traits were identified in a population of recombinant inbred lines derived from B73 × H99. For four of these mapped QTL, namely 3.05, 4.10, 7.03 and 10.03 according to their chromosome and bin position, families of near-isogenic lines (NILs) were developed, i.e., couples of homozygous lines nearly identical except for the QTL region that is homozygote either for the allele provided by B73 or by H99. For two of these QTL (3.05 and 4.10) the NILs families were produced in two different genetic backgrounds. The present research was conducted in order to: (i) characterize these QTL by estimating additive and dominance effects; (ii) investigate if these effects can be affected by genetic background, inbreeding level and environmental growing conditions (low vs. high plant density). The six NILs’ families were tested across three years and in three Experiments at different inbreeding levels as NILs per se and their reciprocal crosses (Experiment 1), NILs crossed to related inbreds B73 and H99 (Experiment 2) and NILs crossed to four unrelated inbreds (Experiment 3). Experiment 2 was conducted at two plant densities (4.5 and 9.0 plants m-2). Results of Experiments 1 and 2 confirmed previous findings as to QTL effects, with dominance-additive ratio superior to 1 for several traits, especially for grain yield per plant and its component traits; as a tendency, dominance effects were more pronounced in Experiment 1. The QTL effects were also confirmed in Experiment 3. The interactions involving QTL effects, families and plant density were generally negligible, suggesting a certain stability of the QTL. Results emphasize the importance of dominance effects for these QTL, suggesting that they might deserve further studies, using NILs’ families and their crosses as base materials.

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.

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.

Validation of a quantitative trait locus (QTL) for outcrossing perennial plants is rarely reported due to complexity of plausible genetic models and reliance on field designs already available. Here, a particular marker-QTL haplotype exerted a large, positive effect on height for Pinus taeda and its origin could be traced to a founder, GP(3), in a three-generation QTL pedigree. To validate this QTL effect, we used an extended GP(3)-based pedigree. In the validation cross, each of the 46 offspring was clonally propagated from developing seeds using somatic embryogenesis technology. Subsequent analyses were conducted separately for seedlings and for other somatic emblings. For seedlings, the original QTL effect could not be fully validated. For somatic emblings, a strong negative QTL effect was detected in the validation cross; some evidence from another cross-supported the original positive QTL effect. From this part of the analysis, three distinct marker-QTL haplotypes at a single locus could be inferred. Validating QTL haplotypes in readily available field tests was feasible despite the genetic model complexity inherent to outcrossing long-lived perennials.

BackgroundThe genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identified. The availability of a high-density SNP (single nucleotide polymorphism) chicken array developed by Affymetrix, i.e. the 600K Affymetrix® Axiom® HD genotyping array offers the possibility to narrow down the localization of previously detected QTL and to detect new QTL. This high-density array is also anticipated to take research beyond the classical hypothesis of additivity of QTL effects or of QTL and environmental effects. The aim of our study was to search for QTL that influence laying traits using the 600K SNP chip and to investigate whether the effects of these QTL differed between diets and age at egg collection.ResultsOne hundred and thirty-one QTL were detected for 16 laying traits and were spread across all marked chromosomes, except chromosomes 16 and 25. The percentage of variance explained by a QTL varied from 2 to 10 % for the various traits, depending on diet and age at egg collection. Chromosomes 3, 9, 10 and Z were overrepresented, with more than eight QTL on each one. Among the 131 QTL, 60 had a significantly different effect, depending on diet or age at egg collection. For egg production traits, when the QTL × environment interaction was significant, numerous inversions of sign of the SNP effects were observed, whereas for egg quality traits, the QTL × environment interaction was mostly due to a difference of magnitude of the SNP effects.ConclusionsOur results show that numerous QTL influence egg production and egg quality traits and that the genomic regions, which are involved in shaping the ability of layer chickens to adapt to their environment for egg production, vary depending on the environmental conditions. The next question will be to address what the impact of these genotype × environment interactions is on selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0160-2) contains supplementary material, which is available to authorized users.

Consistent action of two partially effective loci conferring resistance to <i>Globodera pallida</i> Pa2/3 across multiple nematode field populations

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects.

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.

A multiple marker least squares approach is presented for the analysis of a single three-generation pedigree for quantitative trait locus (QTL) characterisation. It is an extension of the approach by Haley et al. (1994) to the situation where grandparents cannot be assumed to be homozygous at QTLs for the trait of interest. The method is applied to the analysis of wood specific gravity in loblolly pine (Pinus taeda L.). Within a similar framework, a series of preliminary analyses are carried out, followed by a more detailed search of the genome for one or more QTLs. The preliminary analyses provide information about whether the contribution from each linkage group appears to be polygenic, localised to a small region (e.g. a single QTL) or oligogenic (i.e. several QTLs). Significance levels are obtained using a permutation test that uses the observed phenotypes and marker genotypes. The conclusion of these analyses is that in this pedigree single QTLs with very large effect on wood specific gravity do not appear to be segregating, although there is evidence for QTLs with small effect. Finally, in order to assess the potential power of this pedigree, we simulated QTLs within the framework of the actual marker data. As expected, QTL effects would need to be large to be reliably detected in this study, and the power to detect QTLs varies at different positions in the genome depending on the level of information in the local markers.

Late flowering sometimes occurs in F1 hybrids between rice varieties (Oryza sativa L.), although the parental varieties show similar days-to-flowering (DTF). The genetic architecture prompting the occurrence of such late flowering is poorly understood. To clarify the genetic architecture of late flowering in F1 hybrids from a cross between rice varieties, 'Koshihikari' and 'IR64', we performed quantitative trait locus (QTL) analysis using an F2 population (selfed progeny of an F1 plant), in which heterozygous genotypes should segregate in a certain proportion in a Mendelian manner. The QTL analysis detected three significant QTLs. At one QTL (putatively Heading date 1), the 'Koshihikari' allele increased DTF, and at the other two QTLs (putatively Heading date 6 and Oryza sativa Pseudo-Response Regulator 37/Heading date 2), the 'IR64' alleles increased DTF. All alleles at these three QTLs showed partial dominance. The combination of the QTLs explained 82.2% of the total phenotypic variance of DTF in the F2 population, with contribution from epistasis between QTLs. There was no difference between DTFs of F1 hybrids and heterozygous genotypes for the three QTLs. Our results demonstrated that the complementary effects accompanied by epistasis of at least three QTLs were responsible for late flowering in F1 hybrids.

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was < 2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F12 recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.

QTL Mapping for Wheat Flour Color with Additive, Epistatic, and QTL×Environmental Interaction Effects