Constructing a new integrated genetic linkage map and mapping quantitative trait loci for vegetative mycelium growth rate in Lentinula edodes

The most saturated linkage map for Lentinula edodes to date was constructed based on a monokaryotic population of 146 single spore isolates (SSIs) using sequence-related amplified polymorphism (SRAP), target region amplification polymorphism (TRAP), insertion–deletion (InDel) markers, and the mating-type loci. Five hundred and twenty-four markers were located on 13 linkage groups (LGs). The map spanned a total length of 1006.1 cM, with an average marker spacing of 2.0 cM. Quantitative trait loci (QTLs) mapping was utilized to uncover the loci regulating and controlling the vegetative mycelium growth rate on various synthetic media, and complex medium for commercial cultivation of L. edodes. Two and 13 putative QTLs, identified respectively in the monokaryotic population and two testcross dikaryotic populations, were mapped on seven different LGs. Several vegetative mycelium growth rate-related QTLs uncovered here were clustered on LG4 (Qmgr1, Qdgr1, Qdgr2 and Qdgr9) and LG6 (Qdgr3, Qdgr4 and Qdgr5), implying the presence of main genomic areas responsible for growth rate regulation and control. The QTL hotspot region on LG4 was found to be in close proximity to the region containing the mating-type A (MAT-A) locus. Moreover, Qdgr2 on LG4 was detected on different media, contributing 8.07 %–23.71 % of the phenotypic variation. The present study provides essential information for QTL mapping and marker-assisted selection (MAS) in L. edodes.     

Quantitative trait loci (QTLs) for pollen thermotolerance detected in maize

Pollen thermotolerance is an important component of the adaptability of crops to high temperature stress. The tolerance level of the different genotypes in a population of 45 maize recombinant inbred lines was determined as the degree of injury caused by high temperature to pollen germinability (IPGG) and pollen tube growth (IPTG) in an in vitro assay. Both traits revealed quantitative variability and high heritability. The traits were genetically dissected by the analysis of molecular markers using 184 mapped restriction fragment length polymorphisms (RFLPs). Significant genetic correlation between the markers and the trait allowed us to identify a minimum number of five quatitative trait loci (QTLs) for IPGG and six QTLs for IPTG. Their chromosomal localization indicated that the two characters are controlled by different sets of genes. In addition, IPGG and IPTG were shown to be basically independent of the pollen germination ability and pollen tube growth rate under non-stress conditions. These results are discussed in relation to their possible utilization in a breeding strategy for the improvement of thermotolerance in maize.     

Accuracy of mapping quantitative trait loci in autogamous species

Summary The development of linkage maps with large numbers of molecular markers has stimulated the search for methods to map genes involved in quantitative traits (QTLs). A promising method, proposed by Lander and Botstein (1989), employs pairs of neighbouring markers to obtain maximum linkage information about the presence of a QTL within the enclosed chromosomal segment. In this paper the accuracy of this method was investigated by computer simulation. The results show that there is a reasonable probability of detecting QTLs that explain at least 5% of the total variance. For this purpose a minimum population of 200 backcross or F₂ individuals is necessary. Both the number of individuals and the relative size of the genotypic effect of the QTL are important factors determining the mapping precision. On the average, a QTL with 5% or 10% explained variance is mapped on an interval of 40 or 20 centiMorgans, respectively. Of course, QTLs with a larger genotypic effect will be located more precisely. It must be noted, however, that the interval length is rather variable.     

Quantitative trait loci for growth and body size in the nine‐spined stickleback Pungitius pungitius L.

Body size is an ecologically important trait shown to be genetically variable both within and among different animal populations as revealed by quantitative genetic studies. However, few studies have looked into underlying genetic architecture of body size variability in the wild using genetic mapping methods. With the aid of quantitative trait loci (QTL) analyses based on 226 microsatellite markers, we mapped body size and growth rate traits in the nine‐spined stickleback (Pungitius pungitius) using an F₂‐intercross (n = 283 offspring) between size‐divergent populations. In total, 17 QTL locations were detected. The proportion of phenotypic variation explained by individual body size‐related QTL ranged from 3% to 12% and those related to growth parameters and increments from 3% to 10%. Several of the detected QTL affected either early or late growth. These results provide a solid starting point for more in depth investigations of structure and function of genomic regions involved in determination of body size in this popular model of ecological and evolutionary research.     

Quantitative trait loci for red blood cell traits in swine

Haematological traits are essential diagnostic parameters in veterinary practice but knowledge on the genetic architecture controlling variability of erythroid traits is sparse, especially in swine. To identify QTL for erythroid traits in the pig, haematocrit (HCT), haemoglobin (HB), erythrocyte counts (RBC) and mean corpuscular haemoglobin content (MCHC) were measured in 139 F₂ pigs from a Meishan/Pietrain family, before and after challenge with the protozoan pathogen Sarcocystis miescheriana . The pigs passed through three stages representing acute disease, reconvalescence and chronic disease. Forty-three single QTL controlling erythroid traits were identified on 16 chromosomes. Twelve of the QTL were significant at the genome-wide level while 31 were significant at a chromosome-wide level. Because erythroid traits varied with health and disease status, QTL influencing the erythroid phenotypes showed specific health/disease patterns. Regions on SSC5, 7, 8, 12 and 13 contained QTL for baseline erythroid traits, while the other QTL regions affected distinct stages of the disease model. Single QTL explained 9–17% of the phenotypic variance in the F₂ animals. Related traits were partly under common genetic influence. Our analysis confirms that erythroid trait variation differs between Meishan and Pietrain breeds and that this variation is associated with multiple chromosomal regions.     

Quantitative trait loci for glucosinolate accumulation in Brassica rapa leaves

Glucosinolates and their breakdown products have been recognized for their effects on plant defense, human health, flavor and taste of cruciferous vegetables. Despite this importance, little is known about the regulation of the biosynthesis and degradation in Brassica rapa. Here, the identification of quantitative trait loci (QTL) for glucosinolate accumulation in B. rapa leaves in two novel segregating double haploid (DH) populations is reported: DH38, derived from a cross between yellow sarson R500 and pak choi variety HK Naibaicai; and DH30, from a cross between yellow sarson R500 and Kairyou Hakata, a Japanese vegetable turnip variety. An integrated map of 1068 cM with 10 linkage groups, assigned to the international agreed nomenclature, is developed based on the two individual DH maps with the common parent using amplified fragment length polymorphism (AFLP) and single sequence repeat (SSR) markers. Eight different glucosinolate compounds were detected in parents and F(1)s of the DH populations and found to segregate quantitatively in the DH populations. QTL analysis identified 16 loci controlling aliphatic glucosinolate accumulation, three loci controlling total indolic glucosinolate concentration and three loci regulating aromatic glucosinolate concentrations. Both comparative genomic analyses based on Arabidopsis-Brassica rapa synteny and mapping of candidate orthologous genes in B. rapa allowed the selection of genes involved in the glucosinolate biosynthesis pathway that may account for the identified QTL.     

Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model

The incorporation of developmental control mechanisms of growth has proven to be a powerful tool in mapping quantitative trait loci (QTL) underlying growth trajectories. A theoretical framework for implementing a QTL mapping strategy with growth laws has been established. This framework can be generalized to an arbitrary number of time points, where growth is measured, and becomes computationally more tractable, when the assumption of variance stationarity is made. In practice, however, this assumption is likely to be violated for age-specific growth traits due to a scale effect. In this article, we present a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984, Journal of the American Statistical Association 79, 321-328). The TBS-based model for mapping growth QTL cannot only maintain the original biological properties of a growth model, but also can increase the accuracy and precision of parameter estimation and the power to detect a QTL responsible for growth differentiation. Using the TBS-based model, we successfully map a QTL governing growth trajectories to a linkage group in an example of forest trees. The statistical and biological properties of the estimates of this growth QTL position and effect are investigated using Monte Carlo simulation studies. The implications of our model for understanding the genetic architecture of growth are discussed.     

Quantitative trait loci affecting fatness in the chicken

An F2 chicken population of 442 individuals from 30 families, obtained by crossing a broiler line with a layer line, was used for detecting and mapping Quantitative Trait Loci (QTL) affecting abdominal fat weight, skin fat weight and fat distribution. Within-family regression analyses using 102 microsatellite markers in 27 linkage groups were carried out with genome-wide significance thresholds. The QTL for abdominal fat weight were found on chromosomes 3, 7, 15 and 28; abdominal fat weight adjusted for carcass weight on chromosomes 1, 5, 7 and 28; skin and subcutaneous fat on chromosomes 3, 7 and 13; skin fat weight adjusted for carcass weight on chromosomes 3 and 28; and skin fat weight adjusted for abdominal fat weight on chromosomes 5, 7 and 15. Interactions of the QTL with sex or family were unimportant and, for each trait, there was no evidence for imprinting or of multiple QTL on any chromosome. Significant dominance effects were obtained for all but one of the significant locations for QTL affecting the weight of abdominal fat, none for skin fat and one of the three QTL affecting fat distribution. The magnitude of each QTL ranged from 3.0 to 5.2% of the residual phenotypic variation or 0.2-0.8 phenotypic standard deviations. The largest additive QTL (on chromosome 7) accounted for more than 20% of the mean weight of abdominal fat. Significant positive and negative QTL were identified from both lines.     

Application of a canonical transformation to detection of quantitative trait loci with the aid of genetic markers in a multi-trait experiment

Effects of individual quantitative trait loci (QTLs) can be isolated with the aid of linked genetic markers. Most studies have analyzed each marker or pair of linked markers separately for each trait included in the analysis. Thus, the number of contrasts tested can be quite large. The experimentwise type-I error can be readily derived from the nominal type-I error if all contrasts are statistically independent, but different traits are generally correlated. A new set of uncorrelated traits can be derived by application of a canonical transformation. The total number of effective traits will generally be less than the original set. An example is presented for DNA microsatellite D21S4, which is used as a marker for milk production traits of Israeli dairy cattle. This locus had significant effects on milk and protein production but not on fat. It had a significant effect on only one of the canonical variables that was highly correlated with both milk and protein, and this variable explained 82% of the total variance. Thus, it can be concluded that a single QTL is affecting both traits. The effects on the original traits could be derived by a reverse transformation of the effects on the canonical variable.     

Quantitative trait loci analysis for the developmental behavior of tiller number in rice (Oryza sativa L.)

 A doubled-haploid rice population of 123 lines from Azucena/IR64 was used for analyzing the developmental behavior of tiller number by conditional and unconditional QTL mapping methods. It was indicated that the number of QTLs significantly affecting tiller number was different at different measuring stages. Many QTLs controlling tiller growth identified at the early stages were undetectable at the final stage. Only one QTL could be detected across the whole growth period. By conditional QTL mapping, more QTLs for tiller number could be detected than that by unconditional mapping. The temporal patterns of gene expression for tiller number could be different at different stages. Even an individual gene or genes at the same genomic region might have opposite genetic effects at various growth stages. Received: 7 July 1997 / Accepted: 10 February 1998     

Quantitative trait loci (QTL) for lean body mass and body length in MRL/MPJ and SJL/J F(2) mice

Studies on the genetic mechanisms involved in the regulation of lean body mass (LBM) in mammals are minimal, although LBM is associated with a competent immune system and an overall good (healthy) body functional status. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F₂ intercross to identify the quantitative trait loci (QTL) involved in the regulation of LBM. We hypothesized that additional QTL can be identified using a different mouse cross (MRL/SJL cross). Ten QTL were identified for LBM on chromosomes (chrs) 2, 6, 7, 9,13 and 14. Of those ten, QTL on chrs 6, 7 and 14 were exclusive to LBM, while QTL on chrs 4 and 11 were exclusively body length. LBM QTL on chrs 2 and 9 overlap with those of size. Altogether, the ten LBM QTL explained 41.2% of phenotypic variance in F₂ mice. Five significantly interacting loci that may be involved in the regulation of LBM were identified and accounted for 24.4% of phenotypic variance explained by the QTL. Five epistatic interactions, contributing 22.9% of phenotypic variance, were identified for body length. Interacting loci on chr 2 may influence LBM by regulating body length. Therefore, epistatic interactions as well as single QTL effects play an important role in the regulation of LBM. Electronic Publication     

Prediction of the power of detection of marker-quantitative trait locus linkages using analysis of variance

Analysis of variance can be used to detect the linkage of segregating quantitative trait loci (QTLs) to molecular markers in outbred populations. Using independent full-sib families and assuming linkage equilibrium, equations to predict the power of detection of a QTL are described. These equations are based on an hierarchical analysis of variance assuming either a completely random model or a mixed model, in which the QTL effect is fixed. A simple prediction of power from the mean squares is used that assumes a random model so that in the mixed-model situation this is an approximation. Simulation is used to illustrate the failure of the random model to predict mean squares and, hence, the power. The mixed model is shown to provide accurate prediction of the mean squares and, using the approximation, of power.     

Efficiency of direct selection on quantitative trait loci for a two-trait breeding objective

Selection on known loci affecting quantitative traits (DSQ) was compared to phenotypic selection index for a single and a two-trait selection objective. Two situations were simulated; a single known quantitative locus, and ten identified loci accounting for all the additive genetic variance. Selection efficiency of DSQ relative to traitbased selection was higher for two-trait selection, than was selection on a single trait with the same heritability. The advantage of DSQ was greater when the traits were negatively correlated. Relative selection efficiency (RSE) for a single locus responsible for 0.1 of the genetic variance was 1.11 with heritabilities of 0.45 and 0.2 and zero genetic and phenotypic correlations between the traits. RSE of DSQ for ten known loci was 1.5 to 1.8 in the first 3 generations of selection, but declined in each subsequent generation. With DSQ most loci reached fixation after 7 generations. Response to trait-based selection continued through generation 15 and approached the response obtained with DSQ after 10 generations. The cumulative genetic response after 10 generations of DSQ was only 93% to 97% of the economically optimum genotype because the less favorable allele reached fixation for some loci, generally those with effects in opposite directions on the two traits.     

Power of different sampling strategies to detect quantitative trait loci variance effects

Summary Many studies have shown that segregating quantitative trait loci (QTL) can be detected via linkage to genetic markers. Power to detect a QTL effect on the trait mean as a function of the number of individuals genotyped for the marker is increased by selectively genotyping individuals with extreme values for the quantitative trait. Computer simulations were employed to study the effect of various sampling strategies on the statistical power to detect QTL variance effects. If only individuals with extreme phenotypes for the quantitative trait are selected for genotyping, then power to detect a variance effect is less than by random sampling. If 0.2 of the total number of individuals genotyped are selected from the center of the distribution, then power to detect a variance effect is equal to that obtained with random selection. Power to detect a variance effect was maximum when 0.2 to 0.5 of the individuals selected for genotyping were selected from the tails of the distribution and the remainder from the center.     

Sequential sampling in determining linkage between marker loci and quantitative trait loci

Summary As compared to classical, fixed sample size techniques, simulation studies showed that a proposed sequential sampling procedure can provide a substantial decrease (up to 50%, in some cases) in the mean sample size required for the detection of linkage between marker loci and quantitative trait loci. Sequential sampling with truncation set at the required sample size for the non-sequential test, produced a modest further decrease in mean sample size, accompanied by a modest increase in error probabilities. Sequential sampling with observations taken in groups produced a noticeable increase in mean sample size, with a considerable decrease in error probabilities, as compared to straightforward sequential sampling. It is concluded that sequential sampling has a particularly useful application to experiments aimed at investigating the genetics of differences between lines or strains that differ in some single outstanding trait.     

Quantitative trait loci analysis of phytate and phosphate concentrations in seeds and leaves of Brassica rapa

Phytate, being the major storage form of phosphorus in plants, is considered to be an anti-nutritional substance for human, because of its ability to complex essential micronutrients. In the present study, we describe the genetic analysis of phytate and phosphate concentrations in Brassica rapa using five segregating populations, involving eight parental accessions representing different cultivar groups. A total of 25 quantitative trait loci (QTL) affecting phytate and phosphate concentrations in seeds and leaves were detected, most of them located in linkage groups R01, R03, R06 and R07. Two QTL affecting seed phytate (SPHY), two QTL affecting seed phosphate (SPHO), one QTL affecting leaf phosphate and one major QTL affecting leaf phytate (LPHY) were detected in at least two populations. Co-localization of QTL suggested single or linked loci to be involved in the accumulation of phytate or phosphate in seeds or leaves. Some co-localizing QTL for SPHY and SPHO had parental alleles with effects in the same direction suggesting that they control the total phosphorus concentration. For other QTL, the allelic effect was opposite for phosphate and phytate, suggesting that these QTL are specific for the phytate pathway.     

Quantitative trait loci for white blood cell numbers in swine

Differential white blood cell counts are essential diagnostic parameters in veterinary practice but knowledge on the genetic architecture controlling variability of leucocyte numbers and relationships is sparse, especially in swine. Total leucocyte numbers (Leu) and the differential leucocyte counts, i.e. the fractions of lymphocytes (Lym), polymorphonuclear leucocytes [neutrophils (Neu), eosinophils (Eos) and basophils (Bas)] and monocytes (Mon) were measured in 139 F₂ pigs from a Meishan/Pietrain family, before and after challenge with the protozoan pathogen Sarcocystis miescheriana for genome-wide quantitative trait loci (QTL) analysis. After infection, the pigs passed through three stages representing acute disease, reconvalescence and chronic disease. Nine genome-wide significant and 29 putative, single QTL controlling leucocyte traits were identified on 15 chromosomes. Because leucocyte traits varied with health and disease status, QTL influencing the leucocyte phenotypes showed specific health/disease patterns. Regions on SSC1, 8 and 12 contained QTL for baseline leucocyte traits. Other QTL regions reached control on leucocyte traits only at distinct stages of the disease model. Two-thirds of the QTL have not been described before. Single QTL explained up to 19% of the phenotypic variance in the F₂ animals. Related traits were partly under common genetic influence. Our analysis confirms that leucocyte trait variation is associated with multiple chromosomal regions.     

Quantitative trait loci that harbor genes regulating muscle size in (MRL/MPJ x SJL/J) F(2) mice

The genetic mechanisms that determine muscle size have not been elucidated, even though it is a key musculoskeletal parameter that reflects muscle strength. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F₂ intercross 7-week-old mice to identify quantitative trait loci (QTL) involved in the determination of muscle size. Significant QTL were identified for muscle size and body length. Muscle size (adjusted by body length) QTL were identified on chromosomes 7, 9, 11, 14 (two QTL) and 17, which together explained 19.2% of phenotypic variance in F₂ mice, while body length QTL were located on chromosome 2 (two QTL), 9, 11 and 17 which accounted for 28.3% of phenotypic variance in F₂ mice. Three significant epistatic interactions between different QTL positions from muscle size and body length were identified (P <0.01) on chromosomes 2, 9, 14 and 17, which explained 16.1% of the variance in F₂ mice. Electronic Publication     

A new approach for mapping quantitative trait loci using complete genetic marker linkage maps

A new approach based on nonlinear regression for the mapping of quantitative trait loci (QTLs) using complete genetic marker linkage maps is advanced in this paper. We call the approach joint mapping as it makes comprehensive use of the information from every marker locus on a chromosome. With this approach, both the detection of the existence of QTLs and the estimation of their positions, with corresponding confidence intervals, and effects can be realized simultaneously. This approach is widely applicable because only moments are used. It is simple and can save considerable computer time. It is especially useful when there are multiple QTLs and/or interactions between them on a chromosome.     

Molecular genetics of growth and development in Populus (Salicaceae). v. mapping quantitative trait loci affecting leaf variation

We examined the genetic variation of leaf morphology and development in the 2-yr-old replicated plantation of an interspecific hybrid pedigree of Populus trichocarpa T. & G. and P. deltoides Marsh. via both molecular and quantitative genetic methods. Leaf traits chosen were those that show pronounced differences between the original parents, including leaf size, shape, orientation, color, structure, petiole size, and petiole cross section. Leaves were sampled from the current terminal, proleptic, and sylleptic branches. In the F2 generation, leaf traits were all significantly different among genotypes, but with significant effects due to genotype X crown-position interaction. Variation in leaf pigmentation, petiole length. And petiole length proportion appeared to be under the control of few quantitative trait loci (QTLs). More QTLs were associated with single leaf area, leaf shape, lamina angle, abaxial color, and petiole flatness, and in these traits the number of QTLs varied among crown positions. In general, the estimates of QTL numbers from Wright's biometric method were close to those derived from molecular markers. For those traits with few underlying QTLs, a single marker interval could explain from 30 to 60% of the observed phenotypic variance. For multigenic traits, certain markers contributed more substantially to the observed variation than others. Genetic cluster analysis showed developmentally related traits to be more strongly associated with each other than with unrelated traits. This finding was also supported by the QTL mapping. For example, the same chromosomal segment of linkage group L seemed to account for 20% of the phenotypic variation of all dimension-related traits, leaf size, petiole length. and midrib angle. In both traits. the P. deltoides alleles had positive effects and were dominant to the P. trichocarpa alleles. Similar relationships were also found for lamina angle. abaxial greenness, and petiole.