A statistical model for the genetic origin of allometric scaling laws in biology

Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.     

Functional mapping of quantitative trait loci underlying the character process: a theoretical framework

Unlike a character measured at a finite set of landmark points, function-valued traits are those that change as a function of some independent and continuous variable. These traits, also called infinite-dimensional characters, can be described as the character process and include a number of biologically, economically, or biomedically important features, such as growth trajectories, allometric scalings, and norms of reaction. Here we present a new statistical infrastructure for mapping quantitative trait loci (QTL) underlying the character process. This strategy, termed functional mapping, integrates mathematical relationships of different traits or variables within the genetic mapping framework. Logistic mapping proposed in this article can be viewed as an example of functional mapping. Logistic mapping is based on a universal biological law that for each and every living organism growth over time follows an exponential growth curve (e.g., logistic or S-shaped). A maximum-likelihood approach based on a logistic-mixture model, implemented with the EM algorithm, is developed to provide the estimates of QTL positions, QTL effects, and other model parameters responsible for growth trajectories. Logistic mapping displays a tremendous potential to increase the power of QTL detection, the precision of parameter estimation, and the resolution of QTL localization due to the small number of parameters to be estimated, the pleiotropic effect of a QTL on growth, and/or residual correlations of growth at different ages. More importantly, logistic mapping allows for testing numerous biologically important hypotheses concerning the genetic basis of quantitative variation, thus gaining an insight into the critical role of development in shaping plant and animal evolution and domestication. The power of logistic mapping is demonstrated by an example of a forest tree, in which one QTL affecting stem growth processes is detected on a linkage group using our method, whereas it cannot be detected using current methods. The advantages of functional mapping are also discussed.     

Different models of genetic variation and their effect on genomic evaluation

Background

The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.

Methods

Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.

Results

This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.

Conclusions

Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.     

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

Key message

A QTL model for the genetic control of tillering in sorghum is proposed, presenting new opportunities for sorghum breeders to select germplasm with tillering characteristics appropriate for their target environments.

Abstract

Tillering in sorghum can be associated with either the carbon supply–demand (S/D) balance of the plant or an intrinsic propensity to tiller (PTT). Knowledge of the genetic control of tillering could assist breeders in selecting germplasm with tillering characteristics appropriate for their target environments. The aims of this study were to identify QTL for tillering and component traits associated with the S/D balance or PTT, to develop a framework model for the genetic control of tillering in sorghum. Four mapping populations were grown in a number of experiments in south east Queensland, Australia. The QTL analysis suggested that the contribution of traits associated with either the S/D balance or PTT to the genotypic differences in tillering differed among populations. Thirty-four tillering QTL were identified across the populations, of which 15 were novel to this study. Additionally, half of the tillering QTL co-located with QTL for component traits. A comparison of tillering QTL and candidate gene locations identified numerous coincident QTL and gene locations across populations, including the identification of common non-synonymous SNPs in the parental genotypes of two mapping populations in a sorghum homologue of MAX1, a gene involved in the control of tiller bud outgrowth through the production of strigolactones. Combined with a framework for crop physiological processes that underpin genotypic differences in tillering, the co-location of QTL for tillering and component traits and candidate genes allowed the development of a framework QTL model for the genetic control of tillering in sorghum.     

Canonical rules for plant organ biomass partitioning and annual allocation

Here we review a general allometric model for the allometric relationships among standing leaf, stem, and root biomass (M(L), M(S), and M(R), respectively) and the exponents for the relationships among annual leaf, stem, and root biomass production or "growth rates" (G(L), G(S), and G(R), respectively). This model predicts that M(L) ∝ M(S)(3/4) ∝ M(R)(3/4) such that M(S) ∝ M(R) and that G(L) ∝ G(S) ∝ G(R). A large synoptic data set for standing plant organ biomass and organ biomass production spanning ten orders of magnitude in total plant body mass supports these predictions. Although the numerical values for the allometric "constants" governing these scaling relationships differ between angiosperms and conifers, across all species, standing leaf, stem, and root biomass, respectively, comprise 8%, 67%, and 25% of total plant biomass, whereas annual leaf, stem, and root biomass growth represent 30%, 57%, and 13% of total plant growth. Importantly, our analyses of large data sets confirm the existence of scaling exponents predicted by theory. These scaling "rules" emerge from simple biophysical mechanisms that hold across a remarkably broad spectrum of ecologically and phyletically divergent herbaceous and tree-sized monocot, dicot, and conifer species. As such, they are likely to extend into evolutionary history when tracheophytes with the stereotypical "leaf," "stem," and "root" body plan first appeared.     

Genomic regions conferring resistance to multiple fungal pathogens in synthetic hexaploid wheat

Fungal diseases are among the most devastating biotic stresses and often cause significant losses in wheat production worldwide. A set of 173 synthetic hexaploid wheat (SHW) characterized for resistance against fungal pathogens that cause leaf, stem and yellow rusts, yellow leaf spot, Septoria nodorum and crown rot were used in genome-wide association study (GWAS). Diversity Arrays Technology (DArT) and DArTSeq markers were employed for marker–trait association in which 74 markers associated with 35 quantitative trait loci (QTL) were found to be significantly linked with disease resistances using a unified mixed model (P = 10^?3 to 10^?5); Of these 15 QTL originated from D genome. Six markers on 1BL, 3BS, 4BL, 6B, and 6D conferred resistance to two diseases representing 10 of the 35 QTL. A further set of 147 SHW genotyped with DArT only markers validated 11 QTL detected in the previous 173 SHW. We also confirmed the presence of the gene Lr46/Yr29/Sr58/Pm39/Ltn2 on 1BL in the SHW germplasm. In addition, gene–gene interactions between significantly associated loci and all loci across the genome revealed five significant interactions at FDR <0.05. Two significant leaf rust and one stem rust interactions were thought to be synergistic, while another two QTL for yellow leaf spot involved antagonistic relations. To the best of our knowledge, this is the first GWAS for six fungal diseases using SHW. Identification of markers associated with disease resistance to one or more diseases represents an important resource for pyramiding favorable alleles and introducing multiple disease resistance from SHW accessions into current elite wheat cultivars.     

Bayesian QTL analyses using pedigreed families of an outcrossing species,with application to fruit firmness in apple

Key message

Proof of concept of Bayesian integrated QTL analyses across pedigree-related families from breeding programs of an outbreeding species. Results include QTL confidence intervals, individuals’ genotype probabilities and genomic breeding values.

Abstract

Bayesian QTL linkage mapping approaches offer the flexibility to study multiple full sib families with known pedigrees simultaneously. Such a joint analysis increases the probability of detecting these quantitative trait loci (QTL) and provide insight of the magnitude of QTL across different genetic backgrounds. Here, we present an improved Bayesian multi-QTL pedigree-based approach on an outcrossing species using progenies with different (complex) genetic relationships. Different modeling assumptions were studied in the QTL analyses, i.e., the a priori expected number of QTL varied and polygenic effects were considered. The inferences include number of QTL, additive QTL effect sizes and supporting credible intervals, posterior probabilities of QTL genotypes for all individuals in the dataset, and QTL-based as well as genome-wide breeding values. All these features have been implemented in the FlexQTL^? software. We analyzed fruit firmness in a large apple dataset that comprised 1,347 individuals forming 27 full sib families and their known ancestral pedigrees, with genotypes for 87 SSR markers on 17 chromosomes. We report strong or positive evidence for 14 QTL for fruit firmness on eight chromosomes, validating our approach as several of these QTL were reported previously, though dispersed over a series of studies based on single mapping populations. Interpretation of linked QTL was possible via individuals’ QTL genotypes. The correlation between the genomic breeding values and phenotypes was on average 90 %, but varied with the number of detected QTL in a family. The detailed posterior knowledge on QTL of potential parents is critical for the efficiency of marker-assisted breeding.     

QTL affecting stress response to crowding in a rainbow trout broodstock population

Background

Genomic analyses have the potential to impact selective breeding programs by identifying markers that serve as proxies for traits which are expensive or difficult to measure. Also, identifying genes affecting traits of interest enhances our understanding of their underlying biochemical pathways. To this end we conducted genome scans of seven rainbow trout families from a single broodstock population to identify quantitative trait loci (QTL) having an effect on stress response to crowding as measured by plasma cortisol concentration. Our goal was to estimate the number of major genes having large effects on this trait in our broodstock population through the identification of QTL.

Results

A genome scan including 380 microsatellite markers representing 29 chromosomes resulted in the de novo construction of genetic maps which were in good agreement with the NCCCWA genetic map. Unique sets of QTL were detected for two traits which were defined after observing a low correlation between repeated measurements of plasma cortisol concentration in response to stress. A highly significant QTL was detected in three independent analyses on Omy16, many additional suggestive and significant QTL were also identified. With linkage-based methods of QTL analysis such as half-sib regression interval mapping and a variance component method, we determined that the significant and suggestive QTL explain about 40-43% and 13-27% of the phenotypic trait variation, respectively.

Conclusions

The cortisol response to crowding stress is a complex trait controlled in a sub-sample of our broodstock population by multiple QTL on at least 8 chromosomes. These QTL are largely different from others previously identified for a similar trait, documenting that population specific genetic variants independently affect cortisol response in ways that may result in different impacts on growth. Also, mapping QTL for multiple traits associated with stress response detected trait specific QTL which indicate the significance of the first plasma cortisol measurement in defining the trait. Fine mapping these QTL can lead towards the identification of genes affecting stress response and may influence approaches to selection for this economically important stress response trait.

     

Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Background

Quantitative trait loci (QTL) analyses in pig have revealed numerous individual QTL affecting growth, carcass composition, reproduction and meat quality, indicating a complex genetic architecture. In general, statistical QTL models consider only additive and dominance effects and identification of epistatic effects in livestock is not yet widespread. The aim of this study was to identify and characterize epistatic effects between common and novel QTL regions for carcass composition and meat quality traits in pig.

Methods

Five hundred and eighty five F₂ pigs from a Duroc × Pietrain resource population were genotyped using 131 genetic markers (microsatellites and SNP) spread over the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F₂ animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program.

Results

A number of interacting QTL was observed for different traits, leading to the identification of a variety of networks among chromosomal regions throughout the porcine genome. We distinguished 17 epistatic QTL pairs for carcass composition and 39 for meat quality traits. These interacting QTL pairs explained up to 8% of the phenotypic variance.

Conclusions

Our findings demonstrate the significance of epistasis in pigs. We have revealed evidence for epistatic relationships between different chromosomal regions, confirmed known QTL loci and connected regions reported in other studies. Considering interactions between loci allowed us to identify several novel QTL and trait-specific relationships of loci within and across chromosomes.     

Identification and validation of QTL for Sclerotinia midstalk rot resistance in sunflower by selective genotyping

Midstalk rot, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in sunflower (Helianthus annuus L.). Objectives of this study were to: (1) estimate the number, genomic positions and genetic effects of quantitative trait loci (QTL) for resistance to midstalk rot in line TUB-5-3234, derived from an interspecific cross; (2) determine congruency of QTL between this line and other sources of resistance; and (3) make inferences about the efficiency of selective genotyping (SG) in detecting QTL conferring midstalk rot resistance in sunflower. Phenotypic data for three resistance (stem lesion, leaf lesion and speed of fungal growth) and two morphological (leaf length and leaf length with petiole) traits were obtained from 434 F₃ families from cross CM625 (susceptible) × TUB-5-3234 (resistant) under artificial infection in field experiments across two environments. The SG was applied by choosing the 60 most resistant and the 60 most susceptible F₃ families for stem lesion. For genotyping of the respective F₂ plants, 78 simple sequence repeat markers were used. Genotypic variances were highly significant for all traits. Heritabilities and genotypic correlations between resistance traits were moderate to high. Three to four putative QTL were detected for each resistance trait explaining between 40.8% and 72.7% of the genotypic variance ( ). Two QTL for stem lesion showed large genetic effects and corroborated earlier findings from the cross NDBLOS_sel (resistant) × CM625 (susceptible). Our results suggest that SG can be efficiently used for QTL detection and the analysis of congruency for resistance genes across populations.     

Multi-environment QTL analysis of plant and flower morphological traits in tetraploid rose

Key message

Rose morphological traits such as prickles or petal number are influenced by a few key QTL which were detected across different growing environments—necessary for genomics-assisted selection in non-target environments.

Abstract

Rose, one of the world’s most-loved and commercially important ornamental plants, is predominantly tetraploid, possessing four rather than two copies of each chromosome. This condition complicates genetic analysis, and so the majority of previous genetic studies in rose have been performed at the diploid level. However, there may be advantages to performing genetic analyses at the tetraploid level, not least because this is the ploidy level of most breeding germplasm. Here, we apply recently developed methods for quantitative trait loci (QTL) detection in a segregating tetraploid rose population (F₁?=?151) to unravel the genetic control of a number of key morphological traits. These traits were measured both in the Netherlands and Kenya. Since ornamental plant breeding and selection are increasingly being performed at locations other than the production sites, environment-neutral QTL are required to maximise the effectiveness of breeding programmes. We detected a number of robust, multi-environment QTL for such traits as stem and petiole prickles, petal number and stem length that were localised on the recently developed high-density SNP linkage map for rose. Our work explores the complex genetic architecture of these important morphological traits at the tetraploid level, while helping to advance the methods for marker–trait exploration in polyploid species.

     

Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

Background

Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers.

Results

The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping.We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population.

Conclusion

The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0475-6) contains supplementary material, which is available to authorized users.     

Stripe rust and leaf rust resistance QTL mapping,epistatic interactions,and co-localization with stem rust resistance loci in spring wheat evaluated over three continents

Key message

In wheat, advantageous gene-rich or pleiotropic regions for stripe, leaf, and stem rust and epistatic interactions between rust resistance loci should be accounted for in plant breeding strategies.

Abstract

Leaf rust (Puccinia triticina Eriks.) and stripe rust (Puccinia striiformis f. tritici Eriks) contribute to major production losses in many regions worldwide. The objectives of this research were to identify and study epistatic interactions of quantitative trait loci (QTL) for stripe and leaf rust resistance in a doubled haploid (DH) population derived from the cross of Canadian wheat cultivars, AC Cadillac and Carberry. The relationship of leaf and stripe rust resistance QTL that co-located with stem rust resistance QTL previously mapped in this population was also investigated. The Carberry/AC Cadillac population was genotyped with DArT^® and simple sequence repeat markers. The parents and population were phenotyped for stripe rust severity and infection response in field rust nurseries in Kenya (Njoro), Canada (Swift Current), and New Zealand (Lincoln); and for leaf rust severity and infection response in field nurseries in Canada (Swift Current) and New Zealand (Lincoln). AC Cadillac was a source of stripe rust resistance QTL on chromosomes 2A, 2B, 3A, 3B, 5B, and 7B; and Carberry was a source of resistance on chromosomes 2B, 4B, and 7A. AC Cadillac contributed QTL for resistance to leaf rust on chromosome 2A and Carberry contributed QTL on chromosomes 2B and 4B. Stripe rust resistance QTL co-localized with previously reported stem rust resistance QTL on 2B, 3B, and 7B, while leaf rust resistance QTL co-localized with 4B stem rust resistance QTL. Several epistatic interactions were identified both for stripe and leaf rust resistance QTL. We have identified useful combinations of genetic loci with main and epistatic effects. Multiple disease resistance regions identified on chromosomes 2A, 2B, 3B, 4B, 5B, and 7B are prime candidates for further investigation and validation of their broad resistance.     

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

Abstract

Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

     

Testing for homogeneity of gametic disequilibrium across strata

Background

Variance 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).

Results

We 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.

Conclusion

We 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.     

Combined detection and introgression of QTL in outbred populations

Background

Detecting a QTL is only the first step in genetic improvement programs. When a QTL with desirable characteristics is found, e.g. in a wild or unimproved population, it may be interesting to introgress the detected QTL into the commercial population. One approach to shorten the time needed for introgression is to combine both QTL identification and introgression, into a single step. This combines the strengths of fine mapping and backcrossing and paves the way for introgression of desirable but unknown QTL into recipient animal and plant lines.

Methods

The method consisting in combining QTL mapping and gene introgression has been extended from inbred to outbred populations in which QTL allele frequencies vary both in recipient and donor lines in different scenarios and for which polygenic effects are included in order to model background genes. The effectiveness of the combined QTL detection and introgression procedure was evaluated by simulation through four backcross generations.

Results

The allele substitution effect is underestimated when the favourable QTL allele is not fixed in the donor line. This underestimation is proportional to the frequency differences of the favourable QTL allele between the lines. In most scenarios, the estimates of the QTL location are unbiased and accurate. The retained donor chromosome segment and linkage drag are similar to expected values from other published studies.

Conclusions

In general, our results show that it is possible to combine QTL detection and introgression even in outbred species. Separating QTL mapping and introgression processes is often thought to be longer and more costly. However, using a combined process saves at least one generation. With respect to the linkage drag and obligatory drag, the results of the combined detection and introgression scheme are very similar to those of traditional introgression schemes.     

Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers

Key message

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay.

Abstract

Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai?×?Shi 4185 (D?×?S), Gaocheng 8901?×?Zhoumai 16 (G?×?Z) and Linmai 2?×?Zhong 892 (L?×?Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5–32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

     

QTL analyses of drought tolerance and growth for a Salix dasyclados × Salix viminalis hybrid in contrasting water regimes

Quantitative trait loci (QTL) for growth traits and water-use efficiency have been identified in two water regimes (normal and drought-treated) and for a treatment index. A tetraploid hybrid F₂ population originating from a cross between a Salix dasyclados clone (SW901290) and a Salix viminalis clone (Jorunn) was used in the study. The growth response of each individual including both above and below ground dry-matter production (i.e. shoot length, shoot diameter, aboveground and root dry weight, internode length, root dry weight/total dry weight, relative growth rate and leaf nitrogen content) was analysed in a replicated block experiment with two water treatments. A composite interval mapping approach was used to estimate number of QTL, the magnitude of the QTL and their position on genetic linkage maps. QTL specific for each treatment and for the treatment index were found, but QTL common across the treatments and the treatment index were also detected. Each QTL explained from 8% to 29% of the phenotypic variation, depending on trait and treatment. Clusters of QTL for different traits were mapped close to each other at several linkage groups, indicating either a common genetic base or tightly linked QTL. Common QTL identified between treatments and treatment index in the complex trait dry weight can be useful tools in the breeding and selection for drought stress tolerance in Salix.