Mendelian Factors Underlying Quantitative Traits in Tomato: Comparison across Species, Generations, and Environments

As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 in only a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii).(ABSTRACT TRUNCATED AT 400 WORDS)     

QTL mapping for teat number in an Iberian-by-Meishan pig intercross

The aim of this study was to investigate chromosomal regions affecting the number of teats in pigs and possible epistatic interactions between the identified quantitative trait loci (QTL). An experimental F2 cross between Iberian and Chinese Meishan lines was used for this purpose. A genomic scan was conducted with 117 markers covering the 18 porcine autosomes. Linkage analyses were performed by interval mapping using an animal model to estimate QTL and additive polygenic effects. Complementary analyses with models fitting two QTL were also carried out. The results showed three genomewide significant QTL mapping on chromosomes 5, 10 and 12, whose joint action control up to 30% of the phenotypic variance of the trait. Meishan alleles had a positive additive effect on teat number, and a positive-additive x additive-epistatic interaction was detected between QTL on chromosomes 10 and 12.     

High Resolution of Quantitative Traits into Multiple Loci via Interval Mapping

A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F(1) data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F(2) generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F(2) progeny, using additional data from the parents and their F(1) progeny.     

远交群体动态性状基因定位的似然分析Ⅰ.理论方法

受动物遗传育种中用来估计动态性状育种值的随机回归测定日模型思想的启发 ,将关于时间 (测定日期 )的Legendre多项式镶嵌在遗传模型的每个遗传效应中 ,以刻画QTL对动态性状变化过程的作用 ,从而建立起动态性状基因定位的数学模型。利用远交设计群体 ,阐述了动态性状基因定位的似然分析原理 ,推导了定位参数似然估计的EM法两步求解过程。结合动态性状遗传分析的特点和普通数量性状基因定位研究进展 ,还提出了有关动态性状基因定位进一步研究的设想     

A Random Model Approach to Interval Mapping of Quantitative Trait Loci

Mapping quantitative trait loci in outbred populations is important because many populations of organisms are noninbred. Unfortunately, information about the genetic architecture of the trait may not be available in outbred populations. Thus, the allelic effects of genes can not be estimated with ease. In addition, under linkage equilibrium, marker genotypes provide no information about the genotype of a QTL (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). To circumvent this problem, an interval mapping procedure based on a random model approach is described. Under a random model, instead of estimating the effects, segregating variances of QTLs are estimated by a maximum likelihood method. Estimation of the variance component of a QTL depends on the proportion of genes identical-by-descent (IBD) shared by relatives at the locus, which is predicted by the IBD of two markers flanking the QTL. The marker IBD shared by two relatives are inferred from the observed marker genotypes. The procedure offers an advantage over the regression interval mapping in terms of high power and small estimation errors and provides flexibility for large sibships, irregular pedigree relationships and incorporation of common environmental and fixed effects.     

Multivariate mixed inheritance models for QTL detection on porcine chromosome 6

A series of multivariate mixed-inheritance models is fitted to the data from an outbred-line pig cross commercially used in Norway. Each model accommodates information on polygenic (co)variances between F2 individuals and their F1 parents across the five traits through incorporation of a random animal effect. Considered traits relate to meat quality and are chosen following up the results from a previous evaluation, in which a putative quantitative trait locus (QTL) was identified on chromosome six that affects the amount of intramuscular fat (IMF), meat percentage, meat tenderness and smell intensity (Grindflek et al., 2001). An additional trait included in the model, based on results of other studies, is the backfat thickness. The analysed material comprises data scored for 305 F2 individuals, whereas marker information is available for F1 and F2 generations. Based on the results of the multivariate analysis with the mixed-inheritance model, it was possible to conclude that the evidence for QTLs for meat percentage, meat tenderness and smell intensity in the study of Grindflek et al. (2001) do not represent separate QTLs, but is caused by the fact that the applied pre-adjustment of trait values for polygenic effects failed properly to remove the polygenic variation. The QTL effect on IMF on chromosome six was confirmed.     

Quantitative trait loci for electrical seizure threshold mapped in C57BLKS/J and C57BL/10SnJ mice

We mapped the quantitative trait loci (QTL) that contribute to the robust difference in maximal electroshock seizure threshold (MEST) between C57BLKS/J (BKS) and C57BL10S/J (B10S) mice. BKS, B10S, BKS × B10S F1 and BKS × B10S F2 intercross mice were tested for MEST at 8-9 weeks of age. Results of F2 testing showed that, in this cross, MEST is a continuously distributed trait determined by polygenic inheritance. Mice from the extremes of the trait distribution were genotyped using microarray technology. MEST correlated significantly with body weight and sex; however, because of the high correlation between these factors, the QTL mapping was conditioned on sex alone. A sequential series of statistical analyses was used to map QTLs including single-point, multipoint and multilocus methods. Two QTLs reached genome-wide levels of significance based upon an empirically determined permutation threshold: chromosome 6 (LOD = 6.0 at ～69 cM) and chromosome 8 (LOD = 5.7 at ～27 cM). Two additional QTLs were retained in a multilocus regression model: chromosome 3 (LOD = 2.1 at ～68 cM) and chromosome 5 (LOD = 2.7 at ～73 cM). Together the four QTLs explain one third of the total phenotypic variance in the mapping population. Lack of overlap between the major MEST QTLs mapped here in BKS and B10S mice and those mapped previously in C57BL/6J and DBA/2J mice (strains that are closely related to BKS and B10S) suggest that BKS and B10S represent a new polygenic mouse model for investigating susceptibility to seizures.     

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

Fixed effects models have dominated the statistical analysis of genetic crosses between inbred strains. In spite of their popularity, the traditional models ignore polygenic background and must be tailored to each specific cross. We reexamine the role of random effect models in gene mapping with inbred strains. The biggest difficulty in implementing random effect models is the lack of a coherent way of calculating trait covariances between relatives. The standard model for outbred populations is based on premises of genetic equilibrium that simply do not apply to crosses between inbred strains since every animal in a strain is genetically identical and completely homozygous. We fill this theoretical gap by introducing novel combinatorial entities called strain coefficients. With an appropriate theory, it is possible to reformulate QTL mapping and QTL association analysis as an application of mixed models involving both fixed and random effects. After developing this theory, our first example compares the mixed effects model to a standard fixed effects model using simulated advanced intercross line (AIL) data. Our second example deals with hormone data. Here multivariate traits and parameter identifiability questions arise. Our final example involves random mating among eight strains and vividly demonstrates the versatility of our models.     

QTL mapping in outbred tetraploid (and diploid) diallel populations

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.     

Genetic mapping of allometric scaling laws

Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling relationships between rate and size (power laws). A statistical model for mapping specific quantitative trait loci (QTLs) that are responsible for allometric scaling laws has been developed. We present an improved model for allometric mapping of QTLs based on a more general allometry equation. This improved model includes two steps: (1) use model II regression analysis to estimate the parameters underlying universal allometric scaling laws, and (2) substitute the estimated allometric parameters in the mixture-based mapping model to obtain the estimation of QTL position and effects. This model has been validated by a real example for a mouse F2 progeny, in which two QTLs were detected on different chromosomes that determine the allometric relationship between growth rate and body weight.     

Power of quantitative trait locus mapping for polygenic binary traits using generalized and regression interval mapping in multi-family half-sib designs

A generalized interval mapping (GIM) method to map quantitative trait loci (QTL) for binary polygenic traits in a multi-family half-sib design is developed based on threshold theory and implemented using a Newton-Raphson algorithm. Statistical power and bias of QTL mapping for binary traits by GIM is compared with linear regression interval mapping (RIM) using simulation. Data on 20 paternal half-sib families were simulated with two genetic markers that bracketed an additive QTL. Data simulated and analysed were: (1) data on the underlying normally distributed liability (NDL) scale, (2) binary data created by truncating NDL data based on three thresholds yielding data sets with three different incidences, and (3) NDL data with polygenic and QTL effects reduced by a proportion equal to the ratio of the heritabilities on the binary versus NDL scale (reduced-NDL). Binary data were simulated with and without systematic environmental (herd) effects in an unbalanced design. GIM and RIM gave similar power to detect the QTL and similar estimates of QTL location, effects and variances. Presence of fixed effects caused differences in bias between RIM and GIM, where GIM showed smaller bias which was affected less by incidence. The original NDL data had higher power and lower bias in QTL parameter estimates than binary and reduced-NDL data. RIM for reduced-NDL and binary data gave similar power and estimates of QTL parameters, indicating that the impact of the binary nature of data on QTL analysis is equivalent to its impact on heritability.     

QTL analysis of leaf morphological characters in a Quercus robur full-sib family (Q. robur x Q. robur ssp. slavonica)

The distinction between white oak species (section Quercus sensu stricto) is largely based on leaf morphological characters. There is, however, considerable within-species variation and no single species-diagnostic character, possibly due to phenotypic plasticity and/or underlying genetic variation. The aim of the present study was to identify quantitative trait loci (QTL) underlying the high within-species variation for leaf morphological characters in an F(1) full-sib family derived from a cross between Q. robur and Q. robur ssp. slavonica. In accordance with an earlier QTL mapping study in an intraspecific Q. robur full-sib family, polygenic inheritance was detected for leaf morphological characters that are used to discriminate between the species Quercus robur and Q. petraea. QTLs were distributed over ten linkage groups, showed a moderate effect in terms of phenotypic variance explained (PVE) in the mapping pedigree (3.6-9.6%), but accounted for a considerable amount of the parental differences. Co-localisation of QTLs on the same linkage group in different genetic backgrounds was found for the number and percentage of intercalary veins (NV, PV) on linkage group 3 and for NV on linkage group 5, revealing a high congruence in the relative QTL positions. The generally low correspondence of the other QTLs in the different mapping pedigrees may be an effect of the genetic background and of the environment. In conclusion, leaf morphological characters were found to be under polygenic control, and a comparison to earlier published results led to the identification of two QTLs that were stable across different genetic backgrounds.     

Statistical model for characterizing epistatic control of triploid endosperm triggered by maternal and offspring QTLs

To study the effects of maternal and endosperm quantitative trait locus (QTL) interaction on endosperm development, we derive a two-stage hierarchical statistical model within the maximum-likelihood context, implemented with an expectation-maximization algorithm. A model incorporating both maternal and offspring marker information can improve the accuracy and precision of genetic mapping. Extensive simulations under different sampling strategies, heritability levels and gene action modes were performed to investigate the statistical properties of the model. The QTL location and parameters are better estimated when two QTLs are located at different intervals than when they are located at the same interval. Also, the additive effect of the offspring QTLs is better estimated than the additive effect of the maternal QTLs. The implications of our model for agricultural and evolutionary genetic research are discussed.     

A Sequential Quantitative Trait Locus Fine-Mapping Strategy Using Recombinant-Derived Progeny

A thorough understanding of the quantitative trait loci(QTLs)that underlie agronomically important traits in crops would greatly increase agricultural productivity.Although advances have been made in QTL cloning,the majority of QTLs remain unknown because of their low heritability and minor contributions to phenotypic performance.Here we summarize the key advantages and disadvantages of current QTL fine-mapping methodologies,and then introduce a sequential QTL fine-mapping strategy based on both genotypes and phenotypes of progeny derived from recombinants.With this mapping strategy,experimental errors could be dramatically diminished so as to reveal the authentic genetic effect of target QTLs.The number of progeny required to detect QTLs atvarious R~2 values was calculated,and the backcross generation suitable to start QTL fine-mapping was also estimated.This mapping strategy has proved to be very powerful in narrowing down QTL regions,particularly minor-effect QTLs,as revealed by fine-mapping of various resistance QTLs in maize.Application of this sequential QTL mapping strategy should accelerate cloning of agronomically important QTLs,which is currently a substantial challenge in crops.     

A multi-marker test based on family data in genome-wide association study

Background

Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.

Results

Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait.

Conclusion

Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.     

Genetic dissection of ``OLETF', a rat model for non-insulin-dependent diabetes mellitus

To elucidate the genetic factors underlying non-insulin-dependent diabetes mellitus (NIDDM), we performed genome-wide quantitative trait locus (QTL) analysis, using the Otsuka Long-Evans Tokushima Fatty (OLETF) rat. The OLETF rat is an excellent animal model of NIDDM because the features of the disease closely resemble human NIDDM. Genetic dissection with two kinds of F2 intercross progeny, from matings between the OLETF rat and non-diabetic control rats F344 or BN, allowed us to identify on Chromosome (Chr) 1 a major QTL associated with features of NIDDM that was common to both crosses. We also mapped two additional significant loci, on Chrs 7 and 14, in the (OLETF × F344)F₂ cross alone, and designated these three loci as Diabetes mellitus, OLETF type Dmo 1, Dmo2 and Dmo3 respectively. With regard to suggestive QTLs, we found loci on Chrs 10, 11, and 16 that were common to both crosses, as well as loci on Chrs 5 and 12 in the (OLETF × F344)F₂ cross and on Chrs 4 and 13 in the (OLETF × BN)F₂ cross. Our results showed that NIDDM in the OLETF rat is polygenic and demonstrated that different genetic backgrounds could affect ``fitness' for QTLs and produce different phenotypic effects from the same locus. Received: 9 October 1997 / Accepted: 29 January 1998     

Least squares interval mapping of quantitative trait loci under the infinitesimal genetic model in outbred populations.

Genetic marker and phenotypic data for a quantitative trait were simulated on 20 paternal half-sib families with 100 progeny to investigate properties of within-family-regression interval mapping of a postulated single quantitative trait locus (QTL) in a marker interval under the infinitesimal genetic model, which has been the basis of the application of quantitative genetics to genetic improvement programs, and to investigate use of the infinitesimal model as null hypothesis in testing for presence of a major QTL. Genetic effects on the marked chromosome were generated based on a major gene model, which simulated a central biallelic QTL, or based on 101 biallelic QTL of equal effect, which approximated the infinitesimal model. The marked chromosome contained 0, 3.3%, 13.3%, or 33.3% of genetic variance and heritability was 0.25 or 0.70. Under the polygenic model with 3.3% of genetic variance on the marked chromosome, which corresponds to the infinitesimal model for the bovine, significant QTL effects were found for individual families. Correlations between estimates of QTL effects and true chromosome substitution effects were 0.29 and 0.47 for heritabilities of 0.25 and 0.70 but up to 0.85 with 33.3% of polygenic variance on the marked chromosome. These results illustrate the potential of marker-assisted selection even under the infinitesimal genetic model. Power of tests for presence of QTL was substantially reduced when the polygenic model with 3.3% of genetic variance on the chromosome was used as a null hypothesis. The ability to determine whether genetic variance on a chromosome was contributed by a single QTL of major effect or a large number of QTL with minor effects, corresponding to the infinitesimal model, was limited.     

Mapping of quantitative trait loci controlling broomrape (Orobanche crenata Forsk.) resistance in faba bean (Vicia faba L.).

Orobanche crenata Forsk. is a root parasite that produces devastating effects on many crop legumes and has become a limiting factor for faba bean production in the Mediterranean region. The efficacy of available control methods is minimal and breeding for broomrape resistance remains the most promising method of control. Resistance seems to be scarce and complex in nature, being a quantitative characteristic difficult to manage in breeding programmes. To identify and map the QTLs (quantitative trait loci) controlling the trait, 196 F2 plants derived from the cross between a susceptible and a resistant parent were analysed using isozymes, RAPD, seed protein genes, and microsatellites. F2-derived F3 lines were studied for broomrape resistance under field conditions. Of the 130 marker loci segregating in the F2 population, 121 could be mapped into 16 linkage groups. Simple interval mapping (SIM) and composite interval mapping (CIM) were performed using QTL Cartographer. Composite interval mapping using the maximum number of markers as cofactors was clearly the most efficient way to locate putative QTLs. Three QTLs for broomrape resistance were detected. One of the three QTLs explained more than 35% of the phenotypic variance, whereas the others accounted for 11.2 and 25.5%, respectively. This result suggests that broomrape resistance in faba bean can be considered a polygenic trait with major effects of a few single genes.     

Multiple quantitative trait loci influence intra-specific variation in genital morphology between phylogenetically distinct lines of Drosophila montana

The evolution of animal genitalia has gained renewed interest because of their potential roles during sexual selection and early stages of species formation. Although central to understanding the evolutionary process, knowledge of the genetic basis of natural variation in genital morphology is limited to a very few species. Using an outbred cross between phylogenetically distinct lines of Drosophila montana, we characterized quantitative trait loci (QTLs) affecting the size and shape of the distiphallus, a prominent part of the male intromittent organ. Our microsatellite-based linkage analysis shows that intra-specific variation in the distiphallus involves several QTLs of largely additive effect and that a highly significant QTL co-localizes with the same inversion where we have earlier localized a large QTL for a sexually selected courtship song trait. The latter indicates that inversions can play an important role in shaping the evolution of rapidly evolving traits with a potential influence on speciation.     

Model selection for quantitative trait loci mapping in a full-sib family

Statistical methods for mapping quantitative trait loci (QTLs) in full-sib forest trees, in which the number of alleles and linkage phase can vary from locus to locus, are still not well established. Previous studies assumed that the QTL segregation pattern was fixed throughout the genome in a full-sib family, despite the fact that this pattern can vary among regions of the genome. In this paper, we propose a method for selecting the appropriate model for QTL mapping based on the segregation of different types of markers and QTLs in a full-sib family. The QTL segregation patterns were classified into three types: test cross (1:1 segregation), F₂ cross (1:2:1 segregation) and full cross (1:1:1:1 segregation). Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and the Laplace-empirical criterion (LEC) were used to select the most likely QTL segregation pattern. Simulations were used to evaluate the power of these criteria and the precision of parameter estimates. A Windows-based software was developed to run the selected QTL mapping method. A real example is presented to illustrate QTL mapping in forest trees based on an integrated linkage map with various segregation markers. The implications of this method for accurate QTL mapping in outbred species are discussed.