Quantitative Trait Loci for Light Sensitivity,Body Weight,Body Size,and Morphological Eye Parameters in the Bumblebee,Bombus terrestris

Bumblebees such as Bombus terrestris are essential pollinators in natural and managed ecosystems. In addition, this species is intensively used in agriculture for its pollination services, for instance in tomato and pepper greenhouses. Here we performed a quantitative trait loci (QTL) analysis on B. terrestris using 136 microsatellite DNA markers to identify genes linked with 20 traits including light sensitivity, body size and mass, and eye and hind leg measures. By composite interval mapping (IM), we found 83 and 34 suggestive QTLs for 19 of the 20 traits at the linkage group wide significance levels of p = 0.05 and 0.01, respectively. Furthermore, we also found five significant QTLs at the genome wide significant level of p = 0.05. Individual QTLs accounted for 7.5-53.3% of the phenotypic variation. For 15 traits, at least one QTL was confirmed with multiple QTL model mapping. Multivariate principal components analysis confirmed 11 univariate suggestive QTLs but revealed three suggestive QTLs not identified by the individual traits. We also identified several candidate genes linked with light sensitivity, in particular the Phosrestin-1-like gene is a primary candidate for its phototransduction function. In conclusion, we believe that the suggestive and significant QTLs, and markers identified here, can be of use in marker-assisted breeding to improve selection towards light sensitive bumblebees, and thus also the pollination service of bumblebees.     

Mapping of Genomic Regions (Quantitative Trait Loci) Controlling Production and Quality in Industrial Cultures of the Edible Basidiomycete Pleurotus ostreatus

Industrial production of the edible basidiomycete Pleurotus ostreatus (oyster mushroom) is based on a solid fermentation process in which a limited number of selected strains are used. Optimization of industrial mushroom production depends on improving the culture process and breeding new strains with higher yields and productivities. Traditionally, fungal breeding has been carried out by an empirical trial and error process. In this study, we used a different approach by mapping quantitative trait loci (QTLs) controlling culture production and quality within the framework of the genetic linkage map of P. ostreatus. Ten production traits and four quality traits were studied and mapped. The production QTLs identified explain nearly one-half of the production variation. More interestingly, a single QTL mapping to the highly polymorphic chromosome VII appears to be involved in control of all the productivity traits studied. Quality QTLs appear to be scattered across the genome and to have less effect on the variation of the corresponding traits. Moreover, some of the new hybrid strains constructed in the course of our experiments had production or quality values higher than those of the parents or other commercial strains. This approach opens the possibility of marker-assisted selection and breeding of new industrial strains of this fungus.     

Quantitative Trait Loci Controlling Vegetative Growth Rate in the Edible Basidiomycete Pleurotus ostreatus

Mycelium growth rate is a quantitative characteristic that exhibits continuous variation. This trait has applied interest, as growth rate is correlated with production yield and increased advantage against competitors. In this work, we studied growth rate variation in the edible basidiomycete Pleurotus ostreatus growing as monokaryotic or dikaryotic mycelium on Eger medium or on wheat straw. Our analysis resulted in identification of several genomic regions (quantitative trait loci [QTLs]) involved in the control of growth rate that can be mapped on the genetic linkage map of this fungus. In some cases monokaryotic and dikaryotic QTLs clustered at the same map position, indicating that there are principal genomic areas responsible for growth rate control. The availability of this linkage map of growth rate QTLs can help in the design of rational strain breeding programs based on genomic information.     

Looking into flowering time in almond (Prunus dulcis (Mill) D. A. Webb): the candidate gene approach

Blooming time is one of the most important agronomic traits in almond. Biochemical and molecular events underlying flowering regulation must be understood before methods to stimulate late flowering can be developed. Attempts to elucidate the genetic control of this process have led to the identification of a major gene (Lb) and quantitative trait loci (QTLs) linked to observed phenotypic differences, but although this gene and these QTLs have been placed on the Prunus reference genetic map, their sequences and specific functions remain unknown. The aim of our investigation was to associate these loci with known genes using a candidate gene approach. Two almond cDNAs and eight Prunus expressed sequence tags were selected as candidate genes (CGs) since their sequences were highly identical to those of flowering regulatory genes characterized in other species. The CGs were amplified from both parental lines of the mapping population using specific primers. Sequence comparison revealed DNA polymorphisms between the parental lines, mainly of the single nucleotide type. Polymorphisms were used to develop co-dominant cleaved amplified polymorphic sequence markers or length polymorphisms based on insertion/deletion events for mapping the candidate genes on the Prunus reference map. Ten candidate genes were assigned to six linkage groups in the Prunus genome. The positions of two of these were compatible with the regions where two QTLs for blooming time were detected. One additional candidate was localized close to the position of the Evergrowing gene, which determines a non-deciduous behaviour in peach.     

Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.     

Improving the quality of African robustas: QTLs for yield- and quality-related traits in Coffea canephora

Coffea canephora breeding requires combining sustainable productivity with improved technological and cup quality characteristics. Beverage quality is a complex and subjective trait, and breeding for this trait is time consuming and depends on knowledge of the genetics of its components. A highly variable C. canephora progeny resulting from an intraspecific cross was assessed for 63 traits over 5?years. To identify quantitative trait loci (QTLs) controlling agronomic, technological, and quality-related traits, a genetic map comprising 236 molecular markers was constructed, and composite interval mapping was performed. Beverage quality was evaluated in relation to biochemical and cup tasting traits. QTLs were identified for almost half of the traits evaluated, with effects ranging from 6% to 80% of phenotypic variation. Most of them present a consistent detection over years. The strongest QTLs explained a high percentage of the variation for yield in 2006 (34% to 57%), bean size (25% to 35%), content of chlorogenic acids (22% to 35%), sucrose and trigonelline content (29% to 81%), and acidity and bitterness of coffee beverages (30% to 55%). Regions of the C. canephora genome influencing beverage quality were identified. Five QTL zones were co-localized with candidate genes related to the biosynthesis of the analyzed traits: two genes coding for caffeine biosynthesis, one gene implicated in the biosynthesis of chlorogenic acids, and two genes implicated in sugar metabolism. This is one of the first studies on the identification of QTLs combining agronomic and quality traits in coffee. The high variability of quality traits within C. canephora and the presence of consistent QTLs offer breeders a promising tool to improve coffee cup quality.     

QTL‐seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations

The majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). Mapping and isolation of QTLs is important for efficient crop breeding by marker‐assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. However, since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been time‐consuming and labor‐intensive. Here we report the rapid identification of plant QTLs by whole‐genome resequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL‐seq as applied to plant species. We applied QTL‐seq to rice recombinant inbred lines and F₂ populations and successfully identified QTLs for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL‐seq is able to detect QTLs over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.     

Transmission of important chromosomal regions under selection revealed in rice pedigree breeding programs

Genetic analysis across a whole plant genome based on pedigree information offers considerable potential for enhancing genetic gain from plant breeding programs through quantitative trait loci (QTL) mapping and marker-assisted selection. Here, we report its application for graphically genotyping varieties used in Chinese japonica rice (Oryza sativa L.) pedigree breeding programs. We identified 34 important chromosomal regions from the founder parent that are under selection in the breeding programs, and by comparing donor genomic regions that are under selection with QTL locations of agronomic traits, we found that QTL clustered in important genomic regions, in accordance with association analyses of natural populations and other previous studies. The convergence of genomic regions under selection with QTL locations suggests that donor genomic regions harboring key genes/QTL for important agronomic traits have been selected by plant breeders since the 1950s from the founder rice plants. The results provide better understanding of the effects of selection in breeding programs on the traits of rice cultivars. They also provide potentially valuable information for enhancing rice breeding programs through screening candidate parents for targeted molecular markers, improving crop yield potential and identifying suitable genetic material for use in future breeding programs.     

Identification of Quantitative Trait Loci (QTLs) for Fruit Quality Traits in Apple

A segregating mapping population of “Co-op 17” × “Co-op 16” was used to identify quantitative trait loci (QTLs) associated with various fruit quality traits in apple. Phenotypic data were collected over 2 years for fruit circumference (in centimeter), diameter at midpoint (in centimeter), length (in centimeter), weight (in gram), total soluble solids (in degree Brix), and total titratable acids (in percent) for the segregating population. The phenotypic data along with a previously constructed genetic map, based on simple sequence repeat markers derived from expressed sequence tag and bacterial artificial chromosome end sequence databases, were used in marker–trait association analysis. Interval mapping identified two QTLs linked to fruit size components on linkage groups 03 and 05 with limit of detection scores of 3.27–4.06 and 3.29–4.02 along with phenotypic variation accounting for 15.4–46.4 and 18.3–21.9 %, respectively.     

Quantitative Trait Loci for Thermal Time to Flowering and Photoperiod Responsiveness Discovered in Summer Annual-Type Brassica napus L

Time of flowering is a key adaptive trait in plants and is conditioned by the interaction of genes and environmental cues including length of photoperiod, ambient temperature and vernalisation. Here we investigated the photoperiod responsiveness of summer annual-types of Brassica napus (rapeseed, canola). A population of 131 doubled haploid lines derived from a cross between European and Australian parents was evaluated for days to flowering, thermal time to flowering (measured in degree-days) and the number of leaf nodes at flowering in a compact and efficient glasshouse-based experiment with replicated short and long day treatments. All three traits were under strong genetic control with heritability estimates ranging from 0.85–0.93. There was a very strong photoperiod effect with flowering in the population accelerated by 765 degree-days in the long day versus short day treatments. However, there was a strong genetic correlation of line effects (0.91) between the long and short day treatments and relatively low genotype x treatment interaction indicating that photoperiod had a similar effect across the population. Bivariate analysis of thermal time to flowering in short and long days revealed three main effect quantitative trait loci (QTLs) that accounted for 57.7% of the variation in the population and no significant interaction QTLs. These results provided insight into the contrasting adaptations of Australian and European varieties. Both parents responded to photoperiod and their alleles shifted the population to earlier flowering under long days. In addition, segregation of QTLs in the population caused wide transgressive segregation in thermal time to flowering. Potential candidate flowering time homologues located near QTLs were identified with the aid of the Brassica rapa reference genome sequence. We discuss how these results will help to guide the breeding of summer annual types of B. napus adapted to new and changing environments.     

Combination of Eight Alleles at Four Quantitative Trait Loci Determines Grain Length in Rice

Grain length is an important quantitative trait in rice (Oryza sativa L.) that influences both grain yield and exterior quality. Although many quantitative trait loci (QTLs) for grain length have been identified, it is still unclear how different alleles from different QTLs regulate grain length coordinately. To explore the mechanisms of QTL combination in the determination of grain length, five mapping populations, including two F₂ populations, an F₃ population, an F₇ recombinant inbred line (RIL) population, and an F₈ RIL population, were developed from the cross between the U.S. tropical japonica variety ‘Lemont’ and the Chinese indica variety ‘Yangdao 4’ and grown under different environmental conditions. Four QTLs (qGL-3-1, qGL-3-2, qGL-4, and qGL-7) for grain length were detected using both composite interval mapping and multiple interval mapping methods in the mapping populations. In each locus, there was an allele from one parent that increased grain length and another allele from another parent that decreased it. The eight alleles in the four QTLs were analyzed to determine whether these alleles act additively across loci, and lead to a linear relationship between the predicted breeding value of QTLs and phenotype. Linear regression analysis suggested that the combination of eight alleles determined grain length. Plants carrying more grain length-increasing alleles had longer grain length than those carrying more grain length-decreasing alleles. This trend was consistent in all five mapping populations and demonstrated the regulation of grain length by the four QTLs. Thus, these QTLs are ideal resources for modifying grain length in rice.     

Inheritance of Flowering Time in Apricot (<Emphasis Type="Italic">Prunus armeniaca</Emphasis> L.) and Analysis of Linked Quantitative Trait Loci (QTLs) using Simple Sequence Repeat (SSR) Markers

Time of flowering was studied during 3 years in a BC1 apricot progeny of 73 seedlings derived from a cross between the F1 selection “Z506-07” (“Orange Red” × “Currot”) and the Spanish cultivar “Currot”. Results indicated a quantitative inheritance of flowering time in apricot with an influence of juvenility and environmental conditions (chill accumulation) on the evaluation and expression of this trait. Genetic maps consisting of 11 linkage groups for both parents representing the eight chromosomes of apricot were developed using 46 apricot and peach simple sequence repeat (SSR-microsatellites) markers and were used for the identification of quantitative trait loci (QTL). QTL analysis for flowering time allowed the identification of one significant QTL on the linkage group 5 (G5) of “Z506-07”, and explaining most of the phenotypic variation. Two microsatellite loci (UDAp-423r and AMPA-105) were found to be tightly linked to this important agronomic trait. Finally, we discuss the stability of the QTL described during the 3 years of the study and the development of efficient marker-assisted selection strategies applied to apricot and other Prunus breeding programs.     

QTL mapping for brown rot (Monilinia fructigena) resistance in an intraspecific peach (Prunus persica L. Batsch) F1 progeny

Brown rot (BR) caused by Monilinia spp. leads to significant post-harvest losses in stone fruit production, especially peach. Previous genetic analyses in peach progenies suggested that BR resistance segregates as a quantitative trait. In order to uncover genomic regions associated with this trait and identify molecular markers for assisted selection (MAS) in peach, an F1 progeny from the cross “Contender” (C, resistant)?×?“Elegant Lady” (EL, susceptible) was chosen for quantitative trait loci (QTL) analysis. Over two phenotyping seasons, skin (SK) and flesh (FL) artificial infections were performed on fruits using a Monilinia fructigena isolate. For each treatment, infection frequency (if) and average rot diameter (rd) were scored. Significant seasonal and intertrait correlations were found. Maturity date (MD) was significantly correlated with disease impact. Sixty-three simple sequence repeats (SSRs) plus 26 single-nucleotide polymorphism (SNP) markers were used to genotype the C?×?EL population and to construct a linkage map. C?×?EL map included the eight Prunus linkage groups (LG), spanning 572.92 cM, with an average interval distance of 6.9 cM, covering 78.73 % of the peach genome (V1.0). Multiple QTL mapping analysis including MD trait as covariate uncovered three genomic regions associated with BR resistance in the two phenotyping seasons: one containing QTLs for SK resistance traits near M1a (LG C?×?EL-2, R ²?=?13.1–31.5 %) and EPPISF032 (LG C?×?EL-4, R ²?=?11–14 %) and the others containing QTLs for FL resistance, near markers SNP_IGA_320761 and SNP_IGA_321601 (LG3, R ²?=?3.0–11.0 %). These results suggest that in the C?×?EL F1 progeny, skin resistance to fungal penetration and flesh resistance to rot spread are distinguishable mechanisms constituting BR resistance trait, associated with different genomic regions. Discovered QTLs and their associated markers could assist selection of new cultivars with enhanced resistance to Monilinia spp. in fruit.     

Generalized Linear Model for Mapping Discrete Trait Loci Implemented with LASSO Algorithm

Generalized estimating equation (GEE) algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS) method for continuous traits to discrete traits. In contrast to mixture model-based expectation–maximization (EM) algorithm, the GEE algorithm can well detect quantitative trait locus (QTL), especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO) is derived for generalized linear model (GLM) with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.     

Quantitative Trait Locus Mapping and Candidate Gene Analysis for Plant Architecture Traits Using Whole Genome Re-Sequencing in Rice

Plant breeders have focused on improving plant architecture as an effective means to increase crop yield. Here, we identify the main-effect quantitative trait loci (QTLs) for plant shape-related traits in rice (Oryza sativa) and find candidate genes by applying whole genome re-sequencing of two parental cultivars using next-generation sequencing. To identify QTLs influencing plant shape, we analyzed six traits: plant height, tiller number, panicle diameter, panicle length, flag leaf length, and flag leaf width. We performed QTL analysis with 178 F₇ recombinant in-bred lines (RILs) from a cross of japonica rice line ‘SNUSG1’ and indica rice line ‘Milyang23’. Using 131 molecular markers, including 28 insertion/deletion markers, we identified 11 main- and 16 minor-effect QTLs for the six traits with a threshold LOD value > 2.8. Our sequence analysis identified fifty-four candidate genes for the main-effect QTLs. By further comparison of coding sequences and meta-expression profiles between japonica and indica rice varieties, we finally chose 15 strong candidate genes for the 11 main-effect QTLs. Our study shows that the whole-genome sequence data substantially enhanced the efficiency of polymorphic marker development for QTL fine-mapping and the identification of possible candidate genes. This yields useful genetic resources for breeding high-yielding rice cultivars with improved plant architecture.     

QTL analysis for pseudostem pungency in bunching onion (Allium fistulosum)

Low pungency is one of the most important agronomic traits in bunching onion (Allium fistulosum L.). Although the degree of pungency can be evaluated indirectly using a colorimetric test for pyruvic acid, DNA markers linked to low-pungency quantitative trait loci (QTLs) are still desired. In this study, we evaluated pungency in the bunching onion pseudostem through six trials conducted over 3?years using an F _2:3 population. QTL analysis based on the genetic linkage map revealed that the major pungency QTL was located within a 24.2-cM interval on Chr. 2a. The low-pungency parent-derived allele at AFAT04B03, a simple sequence repeat locus linked to the pungency QTL, was rare among commercial bunching onion cultivars. In addition, individuals homozygous for the low-pungency parent-derived allele at AFAT04B03 were significantly less pungent than those that were homozygous or heterozygous. Thus, these findings suggest that AFAT04B03 is an effective selection marker for low pungency in bunching onion breeding.     

Quantitative trait loci for agronomic traits in an elite barley population for Mediterranean conditions

Advances in plant breeding through marker-assisted selection (MAS) are only possible when genes or quantitative trait loci (QTLs) can contribute to the improvement of elite germplasm. A population of recombinant inbred lines (RILs) was developed for one of the best crosses of the Spanish National Barley Breeding Program, between two six-row winter barley cultivars Orria and Plaisant. The objective of this study was to identify favourable QTLs for agronomic traits in this population, which may help to optimise breeding strategies for these and other elite materials for the Mediterranean region. A genetic linkage map was developed for 217 RILs, using 382 single nucleotide polymorphism markers, selected from the barley oligonucleotide pool assay BOPA1 and two genes. A subset of 112 RILs was evaluated for several agronomic traits over a period of 2 years at three locations, Lleida and Zaragoza (Spain) and Fiorenzuola d’Arda (Italy), for a total of five field trials. An important segregation distortion occurred during population development in the region surrounding the VrnH1 locus. A QTL for grain yield and length of growth cycle was also found at this locus, apparently linked to a differential response of the VrnH1 alleles to temperature. A total of 33 QTLs was detected, most of them for important breeding targets such as plant height and thousand-grain weight. QTL × environment interactions were prevalent for most of the QTLs detected, although most interactions were of a quantitative nature. Therefore, QTLs suitable for MAS for most traits were identified.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

Accounting for Genetic Architecture Improves Sequence Based Genomic Prediction for a Drosophila Fitness Trait

The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.