Mapping of quantitative trait loci for oil content in cottonseed kernel

Oil content in cottonseed is a major quality trait which when improved through breeding could enhance the competitiveness of cottonseed oil among other vegetable oils. Cottonseed oil content is a quantitative trait controlled by genes in the tetraploid embryo and tetraploid maternal plant genomes, and the knowledge of quantitative trait loci (QTLs) and the genetic effects related to oil content in both genomes could facilitate the improvement in its quality and quantity. However, till date, QTL mapping and genetic analysis related to this trait in cotton have only been conducted in the tetraploid embryo genome. In the current experiment, an IF₂ population of cottonseed kernels from the random crossing of 188 intraspecific recombinant inbred lines which were derived from the hybrid of two parents, HS46 and MARCABUCAG8US-1-88, were used to simultaneously locate QTLs for oil content in the embryo and maternal plant genomes. The four QTLs found to be associated with oil content in cottonseed were: qOC-18-1 on chromosome 18; qOC-LG-11 on linkage group 11; qOC-18-2 on chromosome 18; and qOC-22 on chromosome 22. At a high selection threshold of 0.05, there was strong evidence linking the QTLs above the oil content in cottonseed. Embryo additive and dominant effects from the tetraploid embryo genome, as well as maternal additive effects from the tetraploid maternal plant genome were found to be significant contributors to genetic variation in cottonseed oil content.     

Inheritance of kernel row number, a multicategorical threshold trait of maize ears

Information about the inheritance of threshold traits is scarce, especially in plants. We examined the genetic control of kernel row number in maize (Zea mays). Knowledge of this inheritance is especially important because it is a primary component of grain yield. This trait has a discontinuous distribution. Characters like these are conceptualized as threshold traits. Crosses were made between the inbred line Geneze 3 (G3) with many kernel rows and the inbreds Argentino IV (A4) and Dente de Cravo (DC), with fewer kernel rows. The F(1) and F(2) generations and the backcrosses BC(11) and BC(21) were obtained for the combinations G3 x A4 and G3 x DC. These populations were evaluated under field conditions, and the kernel row number was determined by direct counting of approximately 14, 140 and 75 ears for the F(1), F(2) and backcrosses, respectively. Genetic control was determined through estimates of generation means and variance analysis and was also performed by Wright's method for threshold traits. It was found that genetic control is predominantly due to additive alleles. The component a, was greater than zero, additive variance was positive and the variance of dominance did not differ from zero. In the F(2) generation, the range of the kernel row number was 10 to 28 in G3 x A4, while in G3 x DC it was 12 to 26. Inheritance of the number of kernel rows, estimated by the two methods, gave similar results. This correspondence is due to adjusting of the data to the normal distribution.     

Mapping quantitative trait loci in tetraploid populations

Knowledge of quantitative trait locus (QTL) mapping in polyploids is almost void, albeit many exquisite strategies of QTL mapping have been proposed and extensive investigations have been carried out in diploid animals and plants. In this paper we develop a simple algorithm which uses an iteratively reweighted least square method to map QTLs in tetraploid populations. The method uses information from all markers in a linkage group to infer the probability distribution of QTL genotype under the assumption of random chromosome segregation. Unlike QTL mapping in diploid species, here we estimate and test the compound 'gametic effect', which consists of the composite 'genic effect' of alleles and higher-order gene interactions. The validity and efficiency of the proposed method are investigated through simulation studies. Results show that the method can successfully locate QTLs and separates different sources (e.g. additive and dominance) of variance components contributed by the QTLs.     

Mapping quantitative trait loci in oligogenic models

We discuss strategies for mapping quantitative trait loci with emphasis on certain issues of study design that have recently received attention: e.g. genotyping only selected pedigrees and the comparative value of large pedigrees versus sib pairs. We use a standard variance components model and a parametrization of the genetic effects in which the 'segregation' parameters are locally orthogonal to the 'linkage' parameters. This permits simple explicit expressions for the expectation of the score statistic, which we use to compare the power of different strategies. We also discuss robustness of the score statistic.     

Mapping quantitative trait loci for bovine ovulation rate

An elite, three-generation family from the USDA Meat Animal Research Center twinning population was examined for evidence of ovulation rate quantitative trait loci (QTL). This work was both a continuation of previously reported results suggesting evidence for ovulation rate QTL on bovine Chromosome (Chr) 7 and an extension of a genome-wide search for QTL. Additional markers were typed on Chr 7 to facilitate interval mapping and testing of the hypothesis of one versus two QTL on that chromosome. In addition, 14 other informative markers were added to a selective genotyping genome screening of this family, and markers exhibiting nominal significance were used to identify chromosomal regions that were then subjected to more exhaustive analysis. For Chr 7, a total of 12 markers were typed over a region spanning the proximal two-thirds of the chromosome. Results from interval mapping analyses indicated evidence suggestive of the presence of QTL (nominal P < 0.00077) within this region. Subsequent analysis with a model postulating two QTL provided evidence (P < 0.05) for two rather than one QTL on this chromosome. Preliminary analysis with additional markers indicated nominal significance (P < 0.05) for regions of Chrs 5, 10, and 19. Each of these regions was then typed with additional markers for the entire three-generation pedigree. Significant evidence (P < 0.000026) of ovulation rate QTL was found for Chrs 5 and 19, while support on Chr 10 failed to exceed a suggestive linkage threshold (P > 0.00077). Received: 14 May 1999 / Accepted: 14 October 1999     

Mapping quantitative trait loci (QTLs) for resistance to Gibberella zeae infection in maize

The basic prerequisite for an efficient breeding program to improve levels of resistance to pathogens in plants is the identification of genes controlling the resistance character. If the response to pathogens is under the control of a multilocus system, the utilization of molecular markers becomes essential. Stalk and ear rot caused by Gibberella zeae is a widespread disease of corn: resistance to G. zeae is quantitatively inherited. Our experimental approach to understanding the genetic basis of resistance to Gibberella is to estimate the genetic linkage between available molecular markers and the character, measured as the amount of diseased tissue 40 days after inoculation of a suspension of Fusarium graminearum, the conidial form of G. zeae, into the first stalk internode. Sensitive and resistant parental inbreds were crossed to obtain F₁ and F₂ populations: the analysis of the segregation of 95 RFLP (restriction fragment length polymorphism) clones and 10 RAPD (random amplified polymorphic DNA) markers was performed on a population of 150 F₂ individuals. Analysis of resistance was performed on the F₃ families obtained by selfing the F₂ plants. Quantitative trait loci (QTL) detection was based either on analysis of regression coefficients between family mean value and allele values in the F₂ population, or by means of interval mapping, using MAPMAKER-QTL. A linkage map of maize was obtained, in which four to five genomic regions are shown to carry factors involved in the resistance to G. zeae.     

Mapping quantitative trait loci for cytokines in the pig

Increased disease resistance through improved general immune capacity would be beneficial for the welfare and productivity of farm animals. Cytokines are essential diagnostic parameters in veterinary practice. To identify quantitative trait loci (QTL) for cytokine levels in serum in the pig, Interferon‐gamma (IFN‐γ) and Interleukin 10 (IL‐10) levels and the ratio of IFN‐γ to IL‐10 were measured in a composite pig population, before and after challenge with modified live CSF (classical swine fever) vaccine. Through interval mapping using the variance component approach and the permutation test, 11 QTL (five for IFN‐γ, two for IL‐10 and four for the ratio of IFN‐γ to IL‐10) with significance levels of P < 0.10 were identified, of which five were significant at the P < 0.05 level. The most significant QTL (P < 0.01) was found on chromosome 16, with effect on the ratio of IFN‐γ to IL‐10. Within these QTL regions, a number of known genes were revealed and their potential relationships to the studied traits were discussed. Some of these genes may serve as candidate genes for these traits in swine.     

Mapping quantitative trait loci associated with root penetration ability of wheat in contrasting environments

The aim of this research was to investigate the genetic basis for variation in root penetration ability and associated traits in the mapping population derived from the Australian bread wheat cultivars Halberd and Cranbrook in soil columns containing wax layers grown in controlled conditions and to compare this with performance in the field. Root and shoot traits of the doubled haploid line (DHL) from a cross of Halberd and Cranbrook were evaluated in soil columns containing wax layers. Contrasting DHLs that varied in the ability to penetrate a wax layer in soil columns were then evaluated for maximum root depth in the field on contrasting soils at Merredin, Western Australia. Genetic control was complex, and numerous quantitative trait loci (QTL) (53 in total) were located across most chromosomes that had a small genetic effect (LOD scores of 3.2–9.1). Of these QTL, 29 were associated with root traits, 37 % of which were contributed positively by the Halberd with key traits being located on chromosomes 2D, 4A, 6B, and 7B. Variation in root traits of DHL in soil columns was linked with field performance. Despite the complexity of the traits and a large number of small QTL, the results can be potentially used to explore allelic diversity in root traits for hardpan penetration.     

Mapping quantitative trait loci for bean traits and ovule number in Theobroma cacao L.

Quantitative trait loci (QTL) mapping for bean traits and the number of ovules per ovary was carried out in cocoa (Theobroma cacao L.) using three test-cross progenies derived from crosses between a lower Amazon Forastero male parent (Catongo) and three female parents: one upper Amazon Forastero (IMC78) and two Trinitario (DR1 and S52). RFLP (restriction fragment length polymorphism), microsatellite, and AFLP (amplified fragment lengthpolymorphism) markers were used for mapping. Between one and six QTL for bean traits (length, weight, and shape index) and one and four QTL for the number of ovules per ovary were detected using composite interval mapping (CIM). Individual QTL explained between 5 and 24% of the phenotypic variation. QTL clusters were identified on several chromosomes, but particularly on chromosome 4. QTL related to bean traits were detected in the same region in both Trinitario parents and in a close region in the upper Amazon Forastero parent. In reference to a previous diversity study where alleles specific to Criollo and Forastero genotypes were identified, it was possible to speculate on the putative origin (Criollo or Forastero) of favorable QTL alleles segregating in both Trinitario studied.     

np2QTL: networking phenotypic plasticity quantitative trait loci across heterogeneous environments

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment‐induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part‐whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta?Volterra ordinary differential equations (LVODE) into an R‐based computing platform, np²QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np²QTL constructs a bidirectional, signed and weighted network of QTL?QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype?environment interactions over any range of environmental change. The utility of np²QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np²QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.     

Mapping quantitative trait loci with censored observations

The existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and is fully observed. These assumptions may not be satisfied when the phenotype pertains to the survival time or failure time, which has a skewed distribution and is usually subject to censoring due to random loss of follow-up or limited duration of the experiment. In this article, we propose an interval-mapping approach for censored failure time phenotypes. We formulate the effects of QTL on the failure time through parametric proportional hazards models and develop efficient likelihood-based inference procedures. In addition, we show how to assess genome-wide statistical significance. The performance of the proposed methods is evaluated through extensive simulation studies. An application to a mouse cross is provided.     

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.     

Mapping quantitative trait loci with epistatic effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.     

Mapping quantitative trait loci in noninbred mosquito crosses

The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.     

Mapping quantitative trait loci for traits defined as ratios

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.     

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.     

In silico mapping of quantitative trait loci in maize

Quantitative trait loci (QTL) are most often detected through designed mapping experiments. An alternative approach is in silico mapping, whereby genes are detected using existing phenotypic and genomic databases. We explored the usefulness of in silico mapping via a mixed-model approach in maize (Zea mays L.). Specifically, our objective was to determine if the procedure gave results that were repeatable across populations. Multilocation data were obtained from the 1995–2002 hybrid testing program of Limagrain Genetics in Europe. Nine heterotic patterns comprised 22,774 single crosses. These single crosses were made from 1,266 inbreds that had data for 96 simple sequence repeat (SSR) markers. By a mixed-model approach, we estimated the general combining ability effects associated with marker alleles in each heterotic pattern. The numbers of marker loci with significant effects—37 for plant height, 24 for smut [Ustilago maydis (DC.) Cda.] resistance, and 44 for grain moisture—were consistent with previous results from designed mapping experiments. Each trait had many loci with small effects and few loci with large effects. For smut resistance, a marker in bin 8.05 on chromosome 8 had a significant effect in seven (out of a maximum of 18) instances. For this major QTL, the maximum effect of an allele substitution ranged from 5.4% to 41.9%, with an average of 22.0%. We conclude that in silico mapping via a mixed-model approach can detect associations that are repeatable across different populations. We speculate that in silico mapping will be more useful for gene discovery than for selection in plant breeding programs.     

Identifying loci under selection across contrasting environments in Avena barbata using quantitative trait locus mapping

We constructed recombinant inbred lines of a cross between naturally occurring ecotypes of Avena barbata (Pott ex Link), Poaceae, associated with contrasting moisture environments. These lines were assessed for fitness in common garden reciprocal transplant experiments in two contrasting field sites in each of two years, as well as a novel, benign greenhouse environment. An AFLP (amplified fragment length polymorphism) linkage map of 129 markers spanned 644 cM in 19 linkage groups, which is smaller, with more linkage groups, than expected. Therefore parts of the A. barbata genome remain unmapped, possibly because they lack variation between the ecotypes. Nevertheless, we identified QTL (quantitative trait loci) under selection in both native environments and in the greenhouse. Across years at the same site, the same loci remain under selection, for the same alleles. Across sites, an overlapping set of loci are under selection with either (i) the same alleles favoured at both sites or (ii) loci under selection at one site and neutral at the other. QTL under selection in the greenhouse were generally unlinked to those under selection in the field because selection acted on a different trait. We found little evidence that selection favours alternate alleles in alternate environments, which would be necessary if genotype by environment interaction were to maintain genetic variation in A. barbata. Additive effect QTL were best able to explain the genetic variation among recombinant inbred lines for the greenhouse environment where heritability was highest, and past selection had not eliminated variation.     

Mapping of quantitative trait loci affecting behaviour in swine

Behavioural indices in vertebrates are under genetic control at least to some extent. In spite of significant behavioural problems in farm animals, information on the genetic background of behaviour is sparse. The aim of this study was to map QTL for behavioural indices in swine under healthy conditions and after infection with Sarcocystis miescheriana , as behaviour can be significantly influenced by disease . This well-described parasite model subsequently leads to acute (day 14 p.i.), subclinical (day 28 p.i.) and chronic disease (day 42 p.i.), allowing the study and comparison of the behaviour of pigs under four different states of health or disease. The study was based on a well-described Pietrain/Meishan F₂ family that has recently allowed the detection of QTL for disease resistance. We have mapped six genome-wide significant and 24 chromosome-wide significant QTL for six behavioural indices in swine. Six of these QTL (i.e. 20% of total QTL) showed effects on behavioural traits of the healthy pigs (day 0). Some of them (QTL on SSC11 and 18) lost influence on behavioural activities during disease, while the effects of others (QTL on SSC5, SSC8) partly remained during the whole experiment, although with different effects on the distinct behavioural indices. The disease model has been of high relevance to detect effects of gene loci on behavioural indices. Considering the importance of segregating alleles and environmental conditions that allow the identification of the phenotype, we conclude that there are indeed QTL with interesting effects on behavioural indices in swine.