Multiple trait analysis of genetic mapping for quantitative trait loci

Advanced backcross QTL analysis of a hard winter wheat x synthetic wheat population

Advanced backcross quantitative trait locus (AB-QTL) analysis was used to identify QTLs for yield and yield components in a backcross population developed from a cross between hard red winter wheat (Triticum aestivum L.) variety Karl 92 and the synthetic wheat line TA 4152-4. Phenotypic data were collected for agronomic traits including heading date, plant height, kernels per spike, kernel weight, tiller number, biomass, harvest index, test weight, grain yield, protein content, and kernel hardness on 190 BC2F(2:4) lines grown in three replications in two Kansas environments. Severity of wheat soil-borne mosaic virus (WSBMV) reaction was evaluated at one location. The population was genotyped using 151 microsatellite markers. Of the ten putative QTLs identified, seven were located on homologous group 2 and group 3 chromosomes. The favorable allele was contributed by cultivated parent Karl 92 at seven QTLs including a major one for WSBMV resistance, and by the synthetic parent at three QTLs: for grain hardness, kernels per spike, and tiller number.

Mapping density response in maize: a direct approach for testing genotype and treatment interactions

Maize yield improvement has been strongly linked to improvements in stress tolerance, particularly to increased interplant competition. As a result, modern hybrids are able to produce kernels at high plant population densities. Identification of the genetic factors responsible for density response in maize requires direct testing of interactions between genetic effects and density and evaluation of that response in multiple traits. In this article we take a broad view of the problem and use a general approach based upon mixed models to analyze data from eight segmental inbred lines in a B73 background and their crosses to the unrelated parent Mo17 (hybrids). We directly test for the interaction between treatment effects and genetic effects instead of the commonly used overlaying of results on a common map. Additionally, we demonstrate one way to handle heteroscedasticity of variances common in stress responses. We find that some SILs are consistently different from the recurrent parent regardless of the density, while others differ from the recurrent parent in one density level but not in the other. Thus, we find positive evidence for both main effects and interaction between genetic loci and density in cases where the approach of overlapping results fails to find significant results. Furthermore, our study clearly identifies segments that respond differently to density depending upon the inbreeding level (inbred/hybrid).

Quantitative trait loci for cell wall components in recombinant inbred lines of maize (Zea mays L.) II: leaf sheath tissue

While maize silage is a significant feed component in animal production operations, little information is available on the genetic bases of fiber and lignin concentrations in maize, which are negatively correlated with digestibility. Fiber is composed largely of cellulose, hemicellulose and lignin, which are the primary components of plant cell walls. Variability for these traits in maize germplasm has been reported, but the sources of the variation and the relationships between these traits in different tissues are not well understood. In this study, 191 recombinant inbred lines of B73 (low-intermediate levels of cell wall components, CWCs) x De811 (high levels of CWCs) were analyzed for quantitative trait loci (QTL) associated with CWCs in the leaf sheath. Samples were harvested from plots at two locations in 1998 and one in 1999 and assayed for neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL). QTL were detected on all ten chromosomes, most in tissue specific clusters in concordance with the high genotypic correlations for CWCs within the same tissue. Adjustment of NDF for its subfraction, ADF, revealed that most of the genetic variation in NDF was probably due to variation in ADF. The low to moderate genotypic correlations for the same CWC across leaf sheath and stalk tissues indicate that some genes for CWCs may only be expressed in certain tissues. Many of the QTL herein were detected in other populations, and some are linked to candidate genes for cell wall carbohydrate biosynthesis.

Combined expression trait correlations and expression quantitative trait locus mapping

Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F₂ sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein–coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.

In order to annotate gene function and identify potential members of regulatory networks, the authors explore correlation of expression profiles across a genetic dimension, namely genotypes segregating in a panel of 60 F₂ mice derived from a cross used to explore diabetes in obese mice. They first identified 6,016 seed transcripts for which they observe that the gene expression is linked to a particular region of the genome. Then they searched for transcripts whose expression is highly correlated with the seed transcripts and tested for enrichment of common biological functions among the lists of correlated transcripts. They found and explored the properties of 1,341 sets of transcripts that share a particular “gene ontology” term. Thirty-eight seeds in the G protein–coupled receptor protein signaling pathway were correlated with 174 transcripts, all of which are also annotated as G protein–coupled receptor protein signaling pathway and 131 of which share a regulatory locus on Chromosome 2. The authors note many of these findings would have been missed by simple expression quantitative trait loci analysis without the correlation step. The approach was used to identify a common set of genes involved in lipid metabolism.

Pleiotropic quantitative trait loci contribute to population divergence in traits associated with life-history variation in Mimulus guttatus

Evolutionary biologists seek to understand the genetic basis for multivariate phenotypic divergence. We constructed an F2 mapping population (N = 539) between two distinct populations of Mimulus guttatus. We measured 20 floral, vegetative, and life-history characters on parents and F1 and F2 hybrids in a common garden experiment. We employed multitrait composite interval mapping to determine the number, effect, and degree of pleiotropy in quantitative trait loci (QTL) affecting divergence in floral, vegetative, and life-history characters. We detected 16 QTL affecting floral traits; 7 affecting vegetative traits; and 5 affecting selected floral, vegetative, and life-history traits. Floral and vegetative traits are clearly polygenic. We detected a few major QTL, with all remaining QTL of small effect. Most detected QTL are pleiotropic, implying that the evolutionary shift between these annual and perennial populations is constrained. We also compared the genetic architecture controlling floral trait divergence both within (our intraspecific study) and between species, on the basis of a previously published analysis of M. guttatus and M. nasutus. Eleven of our 16 floral QTL map to approximately the same location in the interspecific map based on shared, collinear markers, implying that there may be a shared genetic basis for floral divergence within and among species of Mimulus.

Regression-based quantitative trait loci mapping: robust, efficient and effective

Regression has always been an important tool for quantitative geneticists. The use of maximum likelihood (ML) has been advocated for the detection of quantitative trait loci (QTL) through linkage with molecular markers, and this approach can be very effective. However, linear regression models have also been proposed which perform similarly to ML, while retaining the many beneficial features of regression and, hence, can be more tractable and versatile than ML in some circumstances. Here, the use of linear regression to detect QTL in structured outbred populations is reviewed and its perceived shortfalls are revisited. It is argued that the approach is valuable now and will remain so in the future.

Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. II. Multivariate and principal component analyses1

The merits of complementary multivariate techniques to identify QTL associated with multiple traits were evaluated. Records from 806 F2 pigs pertaining to a Berkshire x Duroc three-generation population were available. Six multitrait groups on SSC 2, 6, 13, and 18 with information on 30 markers were studied. Multivariate techniques studied included multivariate models and principal components analysis of each multitrait group. All models included, in addition to systematic effects, additive, dominance, and imprinting coefficients corresponding to a one-QTL model and a random family effect. Multivariate analysis identified QTL associated with genomewise significant variation in four of the multitrait groups. The majority of the multivariate analysis provided greater precision of parameter estimates and higher statistical significance in some cases than univariate approaches, because of the greater parameterization of the multivariate models and moderate information content of the data. Principal component analysis results were consistent with univariate and multivariate analyses and recovered the levels of statistical significance observed in univariate analyses on the original data. In addition, principal component analysis was able to provide a location associated with LM area not detected by other analyses. The relative advantage of multivariate over the univariate approaches varied with the level of genetic covariance between traits because of the modeled QTL effect and information contained in the data; however, multivariate approaches have the unique capability to identify pleiotropic effects or multiple linked QTL.

Quantitative trait loci for baseline white blood cell count, platelet count, and mean platelet volume

A substantial genetic contribution to baseline peripheral blood counts has been established. We performed quantitative trait locus/loci (QTL) analyses to identify chromosome (Chr) regions harboring genes influencing the baseline white blood cell (WBC) count, platelet (Plt) count, and mean platelet volume (MPV) in F(2) intercrosses between NZW/LacJ, SM/J, and C57BLKS/J inbred mice. We identified six significant WBC QTL: Wbcq1 (peak LOD score at 38 cM, Chr 1), Wbcq2 (42 cM, Chr 3), Wbcq3 (0 cM, Chr 15), Wbcq4 (58 cM, Chr 1), Wbcq5 (82 cM, Chr 1), and Wbcq6 (8 cM, Chr 14). Three significant Plt QTL were identified: Pltq1 (24 cM, Chr 2), Pltq2 (36 cM, Chr 7), and Pltq3 (10 cM, Chr 12). Two significant MPV QTL were identified, Mpvq1 (62 cM, Chr 15) and Mpvq2 (44 cM, Chr 8). In total, the WBC QTL accounted for up to 31% of the total variance in baseline WBC count, while the Plt and MPV QTL accounted for up to 30% and 49% of the total variance, respectively. These analyses underscore the genetic complexity underlying these traits in normal populations and provide the basis for future studies to identify novel genes involved in the regulation of mammalian hematopoiesis.

Phenotypic impacts of repetitive DNA in flowering plants

The discovery that nuclear DNA content varies widely among species, and even within species, was unexpected because it was thought that the number of genes required for an organism should be common across taxa. We now know that the bulk of nuclear DNA content variation is caused by repetitive DNA sequences characterized according to the nature of repeat (tandem vs dispersed) or chromosomal location/mechanism of replication (pericentromeric, telomeric or subtelomeric, microsatellites, minisatellites, satellites, transposable elements, retroelements). Variation in repetitive DNA, manifested as variation in nuclear DNA content, has been shown to have broad ecological and life-history consequences. For example, large genome size appears to limit fitness in extreme environmental conditions. Within species, variation in DNA content has been coupled to growth and development, such as maturation time in crop species. In Silene latifolia, DNA content is negatively correlated with flower size, a character that, in turn, has well documented ecological significance. These intraspecific studies suggest a connection between repetitive DNA and quantitative genetic determination of continuous characters. Novel insights into mechanisms by which repetitive DNA influences phenotype will lead to models of evolutionary change that extend well beyond the conventional view of evolution by allelic substitution.

Fine mapping of a major locus on chromosome 10 for exploratory and fear-like behavior in mice

Advanced intercross lines (AIL) and interval-specific congenic strains (ISCS) were used to fine map previously coarsely defined quantitative trait loci (QTL) on Chromosomes 1, 10, and 19, influencing behaviors in the open Field (OF) and light-dark (LD) paradigms in mice. F12(A x B) AIL mice (N = 1130) were phenotyped, genotyped, and mapped. The ISCS were studied only in the telomeric Chromosome 10 region of interest, containing the exploratory and excitability QTL1 (Exq1). The Chromosome 10 Exq1 and Chromosome 19 Exq4 loci mapped robustly in the AIL. The most significant QTL findings (2.0 LOD score intervals; peak; LOD score) came from the TD15 and LD transitions traits, yielding estimated intervals of 2.2 cM for Exq1 (71.3-73.5 cM; peak 72.3 cM; LOD 11.9) and 9.0 cM for Exq4 (29.0-38.2 cM; peak 34 cM; LOD 4.2). The replicated QTLs on Chromosome 1 failed to map in this AIL population. The ISCS data confirmed Exq1 loci in general. However, the ISCS data were complex and less definitive for localizing the Exq1 loci. These exploratory and fear-like behaviors result from inheriting "many small things," namely, QTL explaining 2%-7% of the phenotypic variance. These results highlight the challenges of positionally cloning loci of small effect for complex traits. In particular, fine-mapping success may depend on the genetic architecture underlying complex traits.

Ontogenetics of QTL: the genetic architecture of trichome density over time in Arabidopsis thaliana

Although much is known about the molecular genetic basis of trichome development in Arabidopsis thaliana, less is known about the underlying genetic basis of continuous variation in a trait known to be of adaptive importance: trichome density. The density of leaf trichomes is known to be a major determinant of herbivore damage in natural populations of A. thaliana and herbivores are a significant selective force on genetic variation for trichome density. A number of developmental changes occur during ontogeny in A. thaliana, including changes in trichome density. I used multiple interval mapping (MIM) analysis to identify QTL responsible for trichome density on both juvenile leaves and adult leaves in replicate, independent trials and asked whether those QTL changed with ontogeny. In both juvenile and adult leaves, I detected a single major QTL on chromosome 2 that explained much of the genetic variance. Although additional QTL were detected, there were no consistent differences in the genetic architecture of trichome density measured on juvenile and adult leaves. The finding of a single QTL of major effect for a trait of known adaptive importance suggests that genes of major effect may play an important role in adaptation.

Quantitative trait locus analysis of longitudinal quantitative trait data in complex pedigrees

There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.

An integrative genomics approach to infer causal associations between gene expression and disease

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

Genetic mapping of maize stripe disease resistance from the Mascarene source

Maize stripe virus (MStV) is a potentially threatening virus disease of maize in the tropics. We mapped quantitative trait loci (QTLs) controlling resistance to MStV in a maize population of 157 F(2:3) families derived from the cross between two maize lines, Rev81 (tropical resistant) and B73 (temperate susceptible). Resistance was evaluated under artificial inoculations in replicated screenhouse trials across different seasons in Réunion Island, France. Composite interval mapping was employed for QTL detection with a linkage map of 143 microsatellite markers. Heritability estimates across seasons were 0.96 and 0.90 for incidence and severity, respectively, demonstrating a high genotypic variability and a good control of the environment. Three regions on chromosomes 2L, 3 and 5, with major effects, and another region on chromosome 2S, with minor effects, provided resistance to MStV in Rev81. In individual seasons, the chr2L QTL explained 60-65% of the phenotypic variation for disease incidence and 21-42% for severity. The chr3 QTL, mainly associated with incidence and located near centromere, explained 42-57% of the phenotypic variation, whereas the chr5 QTL, mainly associated with severity, explained 26-53%. Overall, these QTLs explained 68-73% of the phenotypic variance for incidence and 50-59% for severity. The major QTLs on chr2 and 3 showed additive gene action and were found to be stable over time and across seasons. They also were found to be included in genomic regions with important clusters of resistance genes to diseases and pests. The major QTL on chr5 appeared to be partially dominant in favour of resistance. It was stable over time but showed highly significant QTL x season interactions. Possible implications of these QTLs in different mechanisms of resistance against the virus or the insect vector are discussed. The prospects for transferring these QTLs in susceptible maize cultivars and combining them with other resistances to virus diseases by conventional or marker-assisted breeding are promising.

Genetic consequences of selection during the evolution of cultivated sunflower

We mapped quantitative trait loci (QTL) controlling differences in seed oil content and composition between cultivated and wild sunflower and used the results, along with those of a previous study of domestication-related QTL, to guide a genome-wide analysis of genetic variation for evidence of past selection. The effects of the seed oil QTL were almost exclusively in the expected direction with respect to the parental phenotypes. A major, oil-related QTL cluster mapped near a cluster of domestication-related QTL on linkage group six (LG06), the majority of which have previously been shown to have effects that are inconsistent with the parental phenotypes. To test the hypothesis that this region was the target of a past selective sweep, perhaps resulting in the fixation of the antagonistic domestication-related QTL, we analyzed simple sequence repeat (SSR) diversity from 102 markers dispersed throughout the sunflower genome. Our results indicate that LG06 was most likely the target of multiple selective sweeps during the postdomestication era. Strong directional selection in concert with genetic hitchhiking therefore offers a possible explanation for the occurrence of numerous domestication-related QTL with apparently maladaptive phenotypic effects.

Quantitative trait loci (QTL) detection in multicross inbred designs: recovering QTL identical-by-descent status information from marker data

Mapping quantitative trait loci in plants is usually conducted using a population derived from a cross between two inbred lines. The power of such QTL detection and the parameter estimates depend largely on the choice of the two parental lines. Thus, the QTL detected in such populations represent only a small part of the genetic architecture of the trait. In addition, the effects of only two alleles are characterized, which is of limited interest to the breeder, while common pedigree breeding material remains unexploited for QTL mapping. In this study, we extend QTL mapping methodology to a generalized framework, based on a two-step IBD variance component approach, applicable to any type of breeding population obtained from inbred parents. We then investigate with simulated data mimicking conventional breeding programs the influence of different estimates of the IBD values on the power of QTL detection. The proposed method would provide an alternative to the development of specifically designed recombinant populations, by utilizing the genetic variation actually managed by plant breeders. The use of these detected QTL in assisting breeding would thus be facilitated.

Methods for predicting superior genotypes under multiple environments based on QTL effects

Methods were developed for predicting two kinds of superior genotypes (superior line and superior hybrid) based on quantative trait locus (QTL) effects including epistatic and QTL x environment interaction effects. Formulae were derived for predicting the total genetic effect of any individual with known QTLs genotype derived from the mapping population in a specific environment. Two algorithms, enumeration algorithm and stepwise tuning algorithm, were used to select the best multi-locus combination of all the putative QTLs. Grain weight per plant (GW) in rice was analyzed as a working example to demonstrate the proposed methods. Results showed that the predicted superior lines and superior hybrids had great superiorities over the F(1) hybrid, indicating large breeding potential remained for further improvement on GW. Results also showed that epistatic effects and their interaction with environments largely contributed to the superiorities of the predicted superior lines and superior hybrids. User-friendly software, QTLNetwork, version 1.0, was developed based on the methods in the present paper.

Light-response quantitative trait loci identified with composite interval and eXtreme array mapping in Arabidopsis thaliana

Genetic analysis of natural variation in ecotypes of Arabidopsis thaliana can facilitate the discovery of new genes or of allelic variants of previously identified genes controlling physiological processes in plants. We mapped quantitative trait loci (QTL) for light response in recombinant inbred lines (RILs) derived from the Columbia and Kashmir accessions via two methods: composite interval mapping and eXtreme array mapping (XAM). After measuring seedling hypocotyl lengths in blue, red, far-red, and white light, and in darkness, eight QTL were identified by composite interval mapping and five localized near photoreceptor loci. Two QTL in blue light were associated with CRY1 and CRY2, two in red light were near PHYB and PHYC, and one in far-red light localized near PHYA. The RED2 and RED5 QTL were verified in segregating lines. XAM was tested for the identification of QTL in red light with pools of RILs selected for extreme phenotypes. Thousands of single feature polymorphisms detected by differential DNA hybridized to high-density oligo-nucleotide arrays were used to estimate allele frequency differences between the pools. The RED2 QTL was identified clearly; differences exceeded a threshold of significance determined by simulations. The sensitivities of XAM to population type and size and genetic models were also determined by simulation analysis.

Plasticity genes and plasticity costs: a new approach using an Arabidopsis recombinant inbred population

Earlier flowering is triggered by vernalization in some but not all Arabidopsis ecotypes, often reflecting allelic variation at the FRIGIDA (FRI) locus. Using a recombinant inbred (RI) population polymorphic at FRI, we examined fitness consequences of variation for plasticity. Flowering and fitness were scored for 68 RI genotypes following full and partial vernalization treatments. Within-environment and mixed-model anovas estimated variance components for a genotype effect and a G x E term, respectively. Selection analyses examined whether delayed bolting increases fitness; a plasticity costs analysis asked whether increased plasticity lowers fitness. We also explored whether trait QTL had environment-specific effects, colocated in the immediate vicinity of FRI, or overlapped with fitness QTL. Selection may favor fri alleles and constitutive early flowering, especially in conditions that only partially vernalize plants. Plasticity costs, detected only after partial vernalization and only marginally significant, were nonetheless consistent with FRI-FLC function. We discuss how information about QTL with environment-specific effects, fitness QTL, and knowledge about plasticity genes can improve interpretation of selection or plasticity cost analyses.

Analysis of genetic marker-phenotype relationships by jack-knifed partial least squares regression (PLSR)

The utility of a relatively new multivariate method, bi-linear modelling by cross-validated partial least squares regression (PLSR), was investigated in the analysis of QTL. The distinguishing feature of PLSR is to reveal reliable covariance structures in data of different types with regard to the same set objects. Two matrices X (here: genetic markers) and Y (here: phenotypes) are interactively decomposed into latent variables (PLS components, or PCs) in a way which facilitates statistically reliable and graphically interpretable model building. Natural collinearities between input variables are utilized actively to stabilise the modelling, instead of being treated as a statistical problem. The importance of cross-validation/jack-knifing as an intuitively appealing way to avoid overfitting, is emphasized. Two datasets from chromosomal mapping studies of different complexity were chosen for illustration (QTL for tomato yield and for oat heading date). Results from PLSR analysis were compared to published results and to results using the package PLABQTL in these data sets. In all cases PLSR gave at least similar explained validation variances as the reported studies. An attractive feature is that PLSR allows the analysis of several traits/replicates in one analysis, and the direct visual identification of individuals with desirable marker genotypes. It is suggested that PLSR may be useful in structural and functional genomics and in marker assisted selection, particularly in cases with limited number of objects.

Linkage disequilibrium and association studies in higher plants: present status and future prospects

During the last two decades, DNA-based molecular markers have been extensively utilized for a variety of studies in both plant and animal systems. One of the major uses of these markers is the construction of genome-wide molecular maps and the genetic analysis of simple and complex traits. However, these studies are generally based on linkage analysis in mapping populations, thus placing serious limitations in using molecular markers for genetic analysis in a variety of plant systems. Therefore, alternative approaches have been suggested, and one of these approaches makes use of linkage disequilibrium (LD)-based association analysis. Although this approach of association analysis has already been used for studies on genetics of complex traits (including different diseases) in humans, its use in plants has just started. In the present review, we first define and distinguish between LD and association mapping, and then briefly describe various measures of LD and the two methods of its depiction. We then give a list of different factors that affect LD without discussing them, and also discuss the current issues of LD research in plants. Later, we also describe the various uses of LD in plant genomics research and summarize the present status of LD research in different plant genomes. In the end, we discuss briefly the future prospects of LD research in plants, and give a list of softwares that are useful in LD research, which is available as electronic supplementary material (ESM).

Mapping genome-genome epistasis: a high-dimensional model

MOTIVATION

The proper development of any organ or tissue requires the coordinated expression of its underlying genes that can be located on different genomes present in an organism. For instance, each step in the development of seed for a higher plant is the consequence of gene interactions from the maternal, embryo and endosperm genomes.

RESULTS

We present a multivariate statistical model for mapping quantitative trait loci (QTL) by incorporating two important aspects of seed development in plants-QTL interactions derived from different genomes, the maternal, embryo and endosperm, and genetic correlations among phenotypic traits expressed in different genome-specific tissues. This model, which has a high dimensionality, is constructed within the maximum-likelihood context based on a finite mixture model. The implementation of the expectation-maximization algorithm allows for the efficient estimation of QTL positions, their action and interaction effects and pleiotropic effects. The application of this high-dimensional model to a real rice dataset has validated its usefulness.

CONCLUSIONS

Our model was derived for self-pollinated plants, but it can be extended to cross-pollinated plants and to animals. With the burgeoning of genetic and genomic data, this high-dimensional model will have many implications for agricultural and evolutionary genetic research.

AVAILABILITY

A package of software will be provided from the corresponding author upon request.

Statistical tests for QTL and QTL-by-environment effects in segregating populations derived from line crosses

Quantitative trait locus (QTL) studies in plants frequently employ phenotypic data on a population of lines (doubled haploid lines, recombinant inbred lines, etc.) tested in multiple environments. An important feature of such data is the genetic correlation among observations on the same genotype in different environments. Detection of QTL-by-environment interaction requires tests which take this correlation into account. In this article, a comparison was made of the properties of several such tests by means of simulation. The results indicate that a split-plot analysis of variance (ANOVA), being an approximate method, tends to be too liberal under departures from the Huynh-Feldt condition. A standard two-way ANOVA, which ignores genetic correlation, yields inappropriate tests and should be avoided. In contrast, mixed model approaches as well as univariate and multivariate repeated-measures ANOVA yield valid results. This supports the use of a flexible mixed model framework in more complex settings, which are difficult to tackle by repeated-measures ANOVA. .

A new intervarietal linkage map and its application for quantitative trait locus analysis of "gigas" features in bread wheat

A doubled-haploid (DH) population from an intervarietal cross between the Japanese cultivar 'Fukuho-komugi' and the Israeli wheat line 'Oligoculm' was produced by means of wheat × maize crosses. One hundred seven DH lines were genotyped to construct a simple sequence repeat (SSR) based linkage map with RFLP, RAPD, and inter-simple sequence repeat markers. Out of 570 loci genotyped, 330 were chosen based on their positions on the linkage map to create a "framework" map for quantitative trait locus (QTL) analysis. Among the 28 linkage groups identified, 25 were assigned to the 21 chromosomes of wheat. The total map length was 3948 cM, including the three unassigned linkage groups (88 cM), and the mean interval between loci was 12.0 cM. Loci with segregation distortion were clustered on chromosomes 1A, 4B, 4D, 5A, 6A, 6B, and 6D. After vernalization, the DH lines were evaluated for spike number per plant (SN) and spike length (SL) in a greenhouse under 24-h daylength to assess the "gigas" features (extremely large spikes and leaves) of 'Oligoculm'. The DH lines were also autumn-sown in the field in two seasons (1990–1991 and 1997–1998) for SN and SL evaluation. QTL analysis was performed by composite interval mapping (CIM) with the framework map to detect QTLs for SN and SL. A major QTL on 1AS, which was stable in both greenhouse and field conditions, was found to control SN. This QTL was close to the glume pubescence locus (Hg) and explained up to 62.9% of the total phenotypic variation. The 'Oligoculm' allele restricted spike number. The SSR locus Xpsp2999 was the closest locus to this QTL and is considered to be a possible marker for restricted tillering derived from 'Oligoculm'. Eight QTLs were detected for SL. The largest QTL detected on 2DS was common to the greenhouse and field environments. It explained up to 33.3% of the total phenotypic variation. The second largest QTL on 1AS was common to the greenhouse and the 1997–1998 season. The position of this QTL was close to that for the SN detected on 1AS. The association between SN and SL is discussed.Key words: linkage map, microsatellite, QTL, spike length, spike number.

QTL mapping and the genetic basis of adaptation: recent developments

Quantitative trait loci (QTL) mapping has been used in a number of evolutionary studies to study the genetic basis of adaptation by mapping individual QTL that explain the differences between differentiated populations and also estimating their effects and interaction in the mapping population. This analysis can provide clues about the evolutionary history of populations and causes of the population differentiation. QTL mapping analysis methods and associated computer programs provide us tools for such an inference on the genetic basis and architecture of quantitative trait variation in a mapping population. Current methods have the capability to separate and localize multiple QTL and estimate their effects and interaction on a quantitative trait. More recent methods have been targeted to provide a comprehensive inference on the overall genetic architecture of multiple traits in a number of environments. This development is important for evolutionary studies on the genetic basis of multiple trait variation, genotype by environment interaction, host-parasite interaction, and also microarray gene expression QTL analysis.

Whole genome scan to detect chromosomal regions affecting multiple traits in dairy cattle

Chromosomal regions affecting multiple traits (multiple trait quantitative trait regions or MQR) in dairy cattle were detected using a method based on results from single trait analyses to detect quantitative trait loci (QTL). The covariance between contrasts for different traits in single trait regression analysis was computed. A chromosomal region was considered an MQR when the observed covariance between contrasts deviated from the expected covariance under the null hypothesis of no pleiotropy or close linkage. The expected covariance and the confidence interval for the expected covariance were determined by permutation of the data. Four categories of traits were analyzed: production (5 traits), udder conformation (6 traits), udder health (2 traits), and fertility (2 traits). The analysis of a granddaughter design involving 833 sons of 20 grandsires resulted in 59 MQR (alpha = 0.01, chromosomewise). Fifteen MQR were found on Bos taurus autosome (BTA) 14. Four or more MQR were found on BTA 6, 13, 19, 22, 23, and 25. Eight MQR involving udder conformation and udder health and 4 MQR involving production traits and udder health were found. Five MQR were identified for combinations of fertility and udder conformation traits, and another 5 MQR were identified for combinations of fertility and production traits. For 22 MQR, the difference between the correlation attributable to the MQR and the overall genetic correlation was >0.60. Although the false discovery rate was relatively high (0.52), it was considered important to present these results to assess potential consequences of using these MQR for marker-assisted selection.

Genetic investigation of quantitative traits related to autism: use of multivariate polygenic models with ascertainment adjustment

Autism is a severe developmental disorder of unknown etiology but with evidence for genetic influences. Here, we provide evidence for a genetic basis of several quantitative traits that are related to autism. These traits, from the Broader Phenotype Autism Symptom Scale (BPASS), were measured in nuclear families, each ascertained through two probands affected by autism spectrum disorder. The BPASS traits capture the continuum of severity of impairments and may be more informative for genetic studies than are the discrete diagnoses of autism that have been used by others. Using a sample of 201 nuclear families consisting of a total of 694 individuals, we implemented multivariate polygenic models with ascertainment adjustment to estimate heritabilities and genetic and environmental correlations between these traits. Our ascertainment adjustment uses conditioning on the phenotypes of probands, requires no modeling of the ascertainment process, and is applicable to multiplex ascertainment and multivariate traits. This appears to be the first such implementation for multivariate quantitative traits. The marked difference between heritability estimates of the trait for language onset with and without an ascertainment adjustment (0.08 and 0.22, respectively) shows that conclusions are sensitive to whether or not an ascertainment adjustment is used. Among the five BPASS traits that were analyzed, the traits for social motivation and range of interest/flexibility show the highest heritability (0.19 and 0.16, respectively) and also have the highest genetic correlation (0.92). This finding suggests a shared genetic basis of these two traits and that they may be most promising for future gene mapping and for extending pedigrees by phenotyping additional relatives.

Detection and mapping of QTL for earliness components in a bread wheat recombinant inbred lines population

Earliness, an adaptative trait and factor of variation for agronomic characters, is a major trait in plant breeding. Its constituent traits, photoperiod sensitivity (PS), vernalization requirement (VR) and intrinsic earliness (IE), are largely under independent genetic controls. Mapping of major genes and quantitative trait loci (QTL) controlling these components is in progress. Most of the studies focusing on earliness considered it as a whole or through one (or two) of its components. The purpose of this study was to detect and map QTL for the three traits together through an experimental design combining field trials and controlled growth conditions. QTL were mapped in a population of F(7) recombinant inbred lines derived by single-seed descent from a cross between two French varieties, 'Renan' and 'Recital'. A map was previously constructed, based on 194 lines and 254 markers, covering about 77% of the genome. Globally, 13 QTL with a LOD>2.5 were detected, of which four control PS, five control VR and four control IE. Two major photoperiod sensitive QTL, together explaining more than 31% of the phenotypic variation, were mapped on chromosomes 2B and 2D, at the same position as the two major genes Ppd-B1 and Ppd-D1. One major VR QTL explaining (depending on the year) 21.8-39.6% of the phenotypic variation was mapped on 5A. Among the other QTL, two QTL of PS and VR not referenced so far were detected on 5A and 6D, respectively. A VR QTL already detected on 2B in a connected population was confirmed.

Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize

A population of 300 F(3:4) lines derived from the cross between maize inbred lines F2 and F252 was evaluated for testcross value in a large range of environmental conditions (11 different locations in 2 years: 1995 and 1996) in order to study (1) the magnitude of genotype x environment and (2) the stability of quantitative trait loci (QTL) effects. Several agronomic traits were measured: dry grain yield (DGY), kernel weight, average number of kernels per plant, silking date (SD) and grain moisture at harvest. A large genotype x environment interaction was found, particularly for DGY. A hierarchical classification of trials and an additive main effects and multiplicative interaction (AMMI) model were carried out. Both methods led to the conclusion that trials could be partitioned into three groups consistent with (1) the year of experiment and (2) the water availability (irrigated vs non-irrigated) for the trials sown in 1995. QTL detection was carried out for all the traits in the different groups of trials. Between 9 and 15 QTL were detected for each trait. QTL x group and QTL x trial effects were tested and proved significant for a large proportion of QTL. QTL detection was also performed on coordinates on the first two principal components (PC) of the AMMI model. PC QTL were generally detected in areas where QTL x group and QTL x trial interactions were significant. A region located on chromosome 8 near an SD QTL seemed to play a key role in DGY stability. Our results confirm the key role of water availability and flowering earliness on grain yield stability in maize.

Pleiotropy, Epistasis and New QTL: The Genetic Architecture of Honey Bee Foraging Behavior

The regulation of division of labor in social insects, particularly in the honey bee (Apis mellifera L.), has received considerable attention from a number of biological subdisciplines, including quantitative and behavioral genetics, because of the high complexity of the behavioral traits involved. The foraging choices of honey bee workers can be accurately quantified, and previous studies have made the foraging behavior of honey bees one of the best studied naturally occurring behavioral phenotypes. Three quantitative trait loci (QTL) have been identified that influence a set of foraging variables, including the concentration of nectar collected and the amount of pollen and nectar brought back to the hive. This study extends previous genetic investigations and represents the most comprehensive investigation of the genetic architecture of these foraging variables. We examined the effects of markers for the three established QTL and for one further candidate gene (Amfor), in two reciprocal backcross populations. These populations were also used to carry out two new QTL mapping studies, with over 400 Amplified Fragment Length Polymorphism (AFLP) markers in each. We detected a variety of effects of the genetic markers for the established QTL and the candidate gene, which were mostly epistatic in nature. A few new QTL could be detected with a variety of mapping techniques. Our results add complexity to the genetic architecture of the foraging behavior of the honey bee. Specifically, we support the hypotheses that pln1, pln2, pln3, and Amfor are involved in the regulation of foraging behavior in the honey bee and add some new factors that deserve further study in the future.

Phosphorus deficiency-induced root elongation and its QTL in rice (Oryza sativa L.)

A significant level of root elongation was induced in rice (Oryza sativa) grown under phosphorus-deficient conditions. The root elongation clearly varied among a total of 62 varieties screened under two different phosphorus levels. Two contrasting varieties, 'Gimbozu', with a low elongating response and 'Kasalath', with a high elongating response, were chosen and crossed to produce a hybrid population for QTL analyses. QTLs for the phosphorus deficiency-induced root elongation were detected by two linkage maps, i.e., one with 82 F3 families constructed by 97 simple sequence repeat (SSR) and sequence-tag site markers and another with 97 F8 lines by 790 amplified fragment length polymorphism and SSR markers. A single QTL for the elongation response was detected on chromosome 6, with a LOD score of 4.5 in both maps and explained about 20% of total phenotypic variance. In addition, this QTL itself, or a region tightly linked with it, partly explained an ability to reduce accumulation of excess iron in the shoots. The identified QTL will be useful to improve rice varieties against a complex nutritional disorder caused by phosphorus deficiency and iron toxicity.

Joint mapping of quantitative trait Loci for multiple binary characters

Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice.

Quantitative trait loci affecting knockdown resistance to high temperature in Drosophila melanogaster

Knockdown resistance to high temperature is an ecologically important trait in small insects. A composite interval mapping was performed on the two major autosomes of Drosophila melanogaster to search for quantitative trait loci (QTL) affecting knockdown resistance to high temperature (KRHT). Two dramatically divergent lines from geographically different thermal environments were artificially selected on KRHT. These lines were crossed to produce two backcross (BC) populations. Each BC was analysed for 200 males with 18 marker loci on chromosomes 2 and 3. Three X-linked markers were used to test for X-linked QTL in an exploratory way. The largest estimate of autosome additive effects was found in the pericentromeric region of chromosome 2, accounting for 19.26% (BC to the low line) and 29.15% (BC to the high line) of the phenotypic variance in BC populations, but it could represent multiple closely linked QTL. Complete dominance was apparent for three QTL on chromosome 3, where heat-shock genes are concentrated. Exploratory analysis of chromosome X indicated a substantial contribution of this chromosome to KRHT. The results show that a large-effect QTL with dominant gene action maps on the right arm of chromosome 3. Further, the results confirm that QTL for heat resistance are not limited to chromosome 3.

A mechanistic model for genetic machinery of ontogenetic growth

Two different genetic mechanisms can be proposed to explain variation in growth trajectories. The allelic sensitivity hypothesis states that growth trajectory is controlled by the time-dependent expression of alleles at the deterministic quantitative trait loci (dQTL) formed during embryogenesis. The gene regulation hypothesis states that the differentiation in growth process is due to the opportunistic quantitative trait loci (oQTL) through their mediation with new developmental signals. These two hypotheses of genetic control have been elucidated in the literature. Here, we propose a new statistical model for discerning these two mechanisms in the context of growth trajectories by integrating growth laws within a QTL-mapping framework. This model is developed within the maximum-likelihood context, implemented with a grid approach for estimating the genomic positions of the deterministic and opportunistic QTL and the simplex algorithm for estimating the growth curve parameters of the genotypes at these QTL and the parameters modeling the residual (co)variance matrix. Our model allows for extensive hypothesis tests for the genetic control of growth processes and developmental events by these two types of QTL. The application of this new model to an F(2) progeny in mice leads to the detection of deterministic and opportunistic QTL on chromosome 1 for mouse body mass growth. The estimates of QTL positions and effects from our model are broadly in agreement with those by traditional interval-mapping approaches. The implications of this model for biological and biomedical research are discussed.

Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture

A previous genetic map containing 117 microsatellite loci and 400 F(2) plants was used for quantitative trait loci (QTL) mapping in tropical maize. QTL were characterized in a population of 400 F(2:3) lines, derived from selfing the F(2) plants, and were evaluated with two replications in five environments. QTL determinations were made from the mean of these five environments. Grain yield (GY), plant height (PH), ear height (EH) and grain moisture (GM) were measured. Variance components for genotypes (G), environments (E) and GxE interaction were highly significant for all traits. Heritability was 0.69 for GY, 0.66 for PH, 0.67 for EH and 0.23 for GM. Using composite interval mapping (CIM), a total of 13 distinct QTLs were identified: four for GY, four for PH and five for EH. No QTL was detected for GM. The QTL explained 32.73 % of the phenotypic variance of GY, 24.76 % of PH and 20.91 % of EH. The 13 QTLs displayed mostly partial dominance or overdominance gene action and mapped to chromosomes 1, 2, 7, 8 and 9. Most QTL alleles conferring high values for the traits came from line L-14-4B. Mapping analysis identified genomic regions associated with two or more traits in a manner that was consistent with correlation among traits, supporting either pleiotropy or tight linkage among QTL. The low number of QTLs found, can be due to the great variation that exists among tropical environments.

A large-sample QTL study in mice: I. Growth

By use of long-term selection lines for high and low growth, a large-sample (n = approximately 1,000 F2) experiment was conducted in mice to further understand the genetic architecture of complex polygenic traits. In combination with previous work, we conclude that QTL analysis has reinforced classic polygenic paradigms put in place prior to molecular analysis. Composite interval mapping revealed large numbers of QTL for growth traits with an exponential distribution of magnitudes of effects and validated theoretical expectations regarding gene action. Of particular significance, large effects were detected on Chromosome (Chr) 2. Regions on Chrs 1, 3, 6, 10, 11, and 17 also harbor loci with significant contributions to phenotypic variation for growth. Despite the large sample size, average confidence intervals of approximately 20 cM exhibit the poor resolution for initial estimates of QTL location. Analysis with genome-wide and chromosomal polygenic models revealed that, under certain assumptions, large fractions of the genome may contribute little to phenotypic variation for growth. Only a few epistatic interactions among detected QTL, little statistical support for gender-specific QTL, and significant age effects on genetic architecture were other primary observations from this study.

Bivariate linkage analysis of cholesterol and triglyceride levels in the Framingham Heart Study

We performed a bivariate analysis on cholesterol and triglyceride levels on data from the Framingham Heart Study using a new score statistic developed for the detection of potential pleiotropic, or cluster, genes. Univariate score statistics were also computed for each trait. At a significance level 0.001, linkage signals were found at markers GATA48B01 on chromosome 1, GATA21C12 on chromosome 8, and ATA55A11 on chromosome 16 using the bivariate analysis. At the same significance level, linkage signals were found at markers 036yb8 on chromosome 3 and GATA3F02 on chromosome 12 using the univariate analysis. A strong linkage signal was also found at marker GATA112F07 by both the bivariate analysis and the univariate analysis, a marker for which evidence for linkage had been reported previously in a related study.

GENETIC ARCHITECTURE OF A SELECTION RESPONSE IN ARABIDOPSIS THALIANA

Quantitative trait locus (QTL) mapping has become an established and effective method for studying the genetic architecture of complex traits. In this report, we use a QTL mapping approach in combination with data from a large selection experiment in Arabidopsis thaliana to explore a response to selection of experimental populations with differentiated genetic backgrounds. Experimental populations with genetic backgrounds derived from ecotypes Landsberg and Niederzenz were exposed to multiple generations of fertility and viability selection. This selection resulted in phenotypic shifts in a number of life-history and fitness-related characters including early development time, flowering time, dry biomass, longevity, and fruit production. Quantitative trait loci were mapped for these traits and their positions were compared to previously characterized allele frequency changes in the experimental populations (Ungerer et al. 2003). Quantitative trait locus positions largely colocalized with genomic regions under strong and consistent selection in populations with differentiated genetic backgrounds, suggesting that alleles for these traits were selected similarly in differentiated genetic backgrounds. However, one QTL region exhibited a more variable response; being positively selected on one genetic background but apparently neutral in another. This study demonstrates how QTL mapping approaches can be combined with map-based population genetic data to study how selection acts on standing genetic variation in populations.

QTL MAPPING REVEALS A STRIKING COINCIDENCE IN THE POSITIONS OF GENOMIC REGIONS ASSOCIATED WITH ADAPTIVE VARIATION IN BODY SIZE IN PARALLEL CLINES OF DROSOPHILA MELANOGASTER ON DIFFERENT CONTINENTS

Latitudinal genetic clines in body size are common in many ectotherm species and are attributed to climatic adaptation. Here, we use Quantitative Trait Loci (QTL) mapping to identify genomic regions associated with adaptive variation in body size in natural populations of Drosophila melanogaster from extreme ends of a cline in South America. Our results show that there is a significant association between the positions of QTL with strong effects on wing area in South America and those previously reported in a QTL mapping study of Australian cline end populations (P < 0.05). In both continents, the right arm of the third chromosome is associated with QTL with the strongest effect on wing area. We also show that QTL peaks for wing area and thorax length are associated with the same genomic regions, indicating that the clinal variation in the body size traits may have a similar genetic basis. The consistency of the results found for the South American and Australian cline end populations indicate that the genetic basis of the two clines may be similar and future efforts to identify the genes producing the response to selection should be focused on the genomic regions highlighted by the present work.

Genotype-Environment Interactions at Quantitative Trait Loci Affecting Inflorescence Development in Arabidopsis thaliana

Phenotypic plasticity and genotype-environment interactions (GEI) play a prominent role in plant morphological diversity and in the potential functional capacities of plant life-history traits. The genetic basis of plasticity and GEI, however, is poorly understood in most organisms. In this report, inflorescence development patterns in Arabidopsis thaliana were examined under different, ecologically relevant photoperiod environments for two recombinant inbred mapping populations (Ler × Col and Cvi × Ler) using a combination of quantitative genetics and quantitative trait locus (QTL) mapping. Plasticity and GEI were regularly observed for the majority of 13 inflorescence traits. These observations can be attributable (at least partly) to variable effects of specific QTL. Pooled across traits, 12/44 (27.3%) and 32/62 (51.6%) of QTL exhibited significant QTL × environment interactions in the Ler × Col and Cvi × Ler lines, respectively. These interactions were attributable to changes in magnitude of effect of QTL more often than to changes in rank order (sign) of effect. Multiple QTL × environment interactions (in Cvi × Ler) clustered in two genomic regions on chromosomes 1 and 5, indicating a disproportionate contribution of these regions to the phenotypic patterns observed. High-resolution mapping will be necessary to distinguish between the alternative explanations of pleiotropy and tight linkage among multiple genes.

Dimension reduction for mapping mRNA abundance as quantitative traits

The advent of sophisticated genomic techniques for gene mapping and microarray analysis has provided opportunities to map mRNA abundance to quantitative trait loci (QTL) throughout the genome. Unfortunately, simple mapping of each individual mRNA trait on the scale of a typical microarray experiment is computationally intensive, subject to high sample variance, and therefore underpowered. However, this problem can be addressed by capitalizing on correlation among the large number of mRNA traits. We present a method to reduce the dimensionality for mapping gene expression data as quantitative traits. We used a blind method, principal components, and a sighted method, hierarchical clustering seeded by disease relevant traits, to define new traits composed of a small collection of promising mRNAs. We validated the principle of our approach by mapping the expression levels of metabolism genes in a population of F(2)-ob/ob mice derived from the BTBR and C57BL/6J strains. We found that lipogenic and gluconeogenic mRNAs, which are known targets of insulin action, were closely associated with the insulin trait. Multiple interval mapping and Bayesian interval mapping of this new trait revealed significant linkages to chromosome regions that were contained in loci associated with type 2 diabetes in this same mouse sample. As a further statistical refinement, we show that principal component analysis also effectively reduced dimensions for mapping phenotypes composed of mRNA abundances.

High-resolution mapping of quantitative trait loci by selective recombinant genotyping

Selective recombinant genotyping (SRG) is a three-stage procedure for high-resolution mapping of a QTL that has previously been mapped to a known confidence interval (target C.I.). In stage 1, a large mapping population is accessed and phenotyped, and a proportion, P, of the high and low tails is selected. In stage 2, the selected individuals are genotyped for a pair of markers flanking the target C.I., and a group of R individuals carrying recombinant chromosomes in the target interval are identified. In stage 3, the recombinant individuals are genotyped for a set of M markers spanning the target C.I. Extensive simulations showed that: (1) Standard error of QTL location (SEQTL) decreased when QTL effect (d) or population size (N) increased, but was constant for given "power factor" (PF = d(2)N); (2) increasing the proportion selected in the tails beyond 0.25 had only a negligible effect on SEQTL; and (3) marker spacing in the target interval had a remarkably powerful effect on SEQTL, yielding a reduction of up to 10-fold in going from highest (24 cM) to lowest (0.29 cM) spacing at given population size and QTL effect. At the densest marker spacing, SEQTL of 1.0-0.06 cM were obtained at PF = 500-16,000. Two new genotyping procedures, the half-section algorithm and the golden section/half-section algorithm, allow the equivalent of complete haplotyping of the target C.I. in the recombinant individuals to be achieved with many fewer data points than would be required by complete individual genotyping.

Adjusted P values for genome-wide scans

Genome-wide scans for quantitative trait loci (QTL) have traditionally been summarized with plots of logarithm of odds (LOD) scores. A valuable modification is to supplement such plots with an additional vertical axis displaying quantiles of adjusted P values and labeling local maxima of the LOD scores with location-specific adjusted P values. This provides a visible gradation of genome-wide significance for the LOD score curve, instead of the stark dichotomy that a single threshold yields. Adjusted P values give genome-wide significance of individual LOD scores and are obtained through a straightforward modification of the familiar algorithm for generating permutation-based thresholds.

Microsatellite markers for genes lr34/yr18 and other quantitative trait Loci for leaf rust and stripe rust resistance in bread wheat

ABSTRACT Leaf rust and stripe rust, caused by Puccinia triticina and P. striiformis, respectively, are important diseases of wheat in many countries. In this study we sought to identify molecular markers for adult plant resistance genes that could aid in incorporating such durable resistance into wheat. We used a doubled haploid population from a Japanese cv. Fukuho-komugi x Israeli wheat Oligoculm cross that had segregated for resistance to leaf rust and stripe rust in field trials. Joint and/or single-year analyses by composite interval mapping identified two quantitative trait loci (QTL) that reduced leaf rust severity and up to 11 and 7 QTLs that might have influenced stripe rust severity and infection type, respectively. Four common QTLs reduced stripe rust severity and infection type. Except for a QTL on chromosome 7DS, no common QTL for leaf rust and stripe rust was detected. QTL-7DS derived from 'Fukuho-komugi' had the largest effect on both leaf rust and stripe rust severities, possibly due to linked resistance genes Lr34/Yr18. The microsatellite locus Xgwm295.1, located almost at the peak of the likelihood ratio contours for both leaf and stripe rust severity, was closest to Lr34/Yr18. QTLs located on 1BL for leaf rust severity and 3BS for stripe rust infection type were derived from 'Oligoculm' and considered to be due to genes Lr46 and Yr30, respectively. Most of the remaining QTLs for stripe rust severity or infection type had smaller effects. Our results indicate there is significant diversity for genes that have minor effects on stripe rust resistance, and that successful detection of these QTLs by molecular markers should be helpful both for characterizing wheat genotypes effectively and combining such resistance genes.

Identification of ISSR markers associated with productivity traits in silkworm, Bombyx moni L

Bombyx mori L., commonly recognised around the world as the mulberry silkworm, is characterized by a wide variability in yield and developmental traits, which have been proven through conventional genetic analysis to be of polygenic nature. A large number of morpho-biochemical traits and RFLP and RAPD markers are mapped on different linkage groups, but to this point very little attention has been given to unravelling the genetics of yield traits. To address this issue, polymorphic profiles of 147 markers generated with 12 ISSR primers on the genomic DNA of 20 silkworm stocks of diverse yield status were subjected to multiple regression and discriminant function analyses (DFA). This led to the identification of eight markers generated by six primers, which demonstrated high beta-coefficient indices of -0.451 to -0.940. Furthermore, a significant difference between the yield traits for stocks with and without the specific marker could also be established. The inheritance pattern of one marker, L13800bp, identified at the first step of selection of markers through stepwise regression analyses for five yield parameters is discussed in the context of applying multiple regression analysis for establishing association, if not linkage, between a group of DNA markers and a particular yield trait of polygenic nature and using such markers in molecular marker-assisted breeding programs.

Mapping quantitative trait loci for a common bean (Phaseolus vulgaris L.) ideotype

Breeding a model plant that encompasses individual traits thought to enhance yield potential, known as ideotype breeding, has traditionally focused on phenotypic selection of plants with desirable morphological traits. Broadening this breeding method to the molecular level through the use of molecular markers would avoid the environmental interactions associated with phenotypic selection. A population of 110 F5 recombinant inbred lines (RILs), derived from the cross between WO3391 and 'OAC Speedvale', was used to develop a genetic linkage map consisting of 105 random amplified polymorphic DNA (RAPD), simple sequence repeat (SSR), and sequence-tagged site (STS) markers. The map has a total length of 641 cM distributed across 8 linkage groups (LGs). Five of them were aligned on the core linkage map of bean. Twenty-one quantitative trait loci (QTLs) were identified over three environments for eight agronomic and architectural traits previously defined for a bean (Phaseolus vulgaris L.) ideotype. The QTLs were mapped to seven LGs with several regions containing QTLs for multiple traits. At least one QTL was located for each trait and a maximum of four were associated with lodging. Total explained phenotypic variance ranged from 10.6% for hypocotyl diameter to 45.4% for maturity. Some of the QTLs identified will be useful for early generation selection of tall, upright, high-yielding lines in a breeding program.

Linkage analysis of candidate regions using a composite neurocognitive phenotype correlated with schizophrenia

As schizophrenia is genetically and clinically heterogeneous, systematic investigations are required to determine whether ICD-10 or DSM-IV categorical diagnoses identify a phenotype suitable and sufficient for genetic research, or whether correlated phenotypes incorporating neurocognitive performance and personality traits provide a phenotypic characterisation that accounts better for the underlying variation. We utilised a grade of membership (GoM) model (a mathematical typology developed for studies of complex biological systems) to integrate multiple cognitive and personality measurements into a limited number of composite graded traits (latent pure types) in a sample of 61 nuclear families comprising 80 subjects with ICD-10/DSM-IV schizophrenia or schizophrenia spectrum disorders and 138 nonpsychotic first-degree relatives. GoM probability scores, computed for all subjects, allowed individuals to be partly assigned to more than one pure type. Two distinct and contrasting neurocognitive phenotypes, one familial, associated with paranoid schizophrenia, and one sporadic, associated with nonparanoid schizophrenia, accounted for 74% of the affected subjects. Combining clinical diagnosis with GoM scores to stratify the entire sample into liability classes, and using variance component analysis (SOLAR), in addition to parametric and nonparametric multipoint linkage analysis, we explored candidate regions on chromosomes 6, 10 and 22. The results indicated suggestive linkage for the familial neurocognitive phenotype (multipoint MLS 2.6 under a low-penetrance model and MLS>3.0 under a high-penetrance model) to a 14 cM area on chromosome 6, including the entire HLA region. Results for chromosomes 10 and 22 were negative. The findings suggest that the familial neurocognitive phenotype may be a pleiotropic expression of genes underlying the susceptibility to paranoid schizophrenia. We conclude that use of composite neurocognitive and personality trait measurements as correlated phenotypes supplementing clinical diagnosis can help stratify the liability to schizophrenia across all members of families prior to linkage, allow the search for susceptibility genes to focus selectively on subsets of families at high genetic risk, and augment considerably the power of genetic analysis.

Natural selection for salt tolerance quantitative trait loci (QTLs) in wild sunflower hybrids: implications for the origin of Helianthus paradoxus, a diploid hybrid species

For a new diploid or homoploid hybrid species to become established, it must diverge ecologically from parental genotypes. Otherwise the hybrid neospecies will be overcome by gene flow or competition. We initiated a series of experiments designed to understand how the homoploid hybrid species, Helianthus paradoxus, was able to colonize salt marsh habitats, when both of its parental species (H. annuusxH. petiolaris) are salt sensitive. Here, we report on the results of a quantitative trait locus (QTL) analysis of mineral ion uptake traits and survivorship in 172 BC2 hybrids between H. annuus and H. petiolaris that were planted in H. paradoxus salt marsh habitat in New Mexico. A total of 14 QTLs were detected for mineral ion uptake traits and three for survivorship. Several mineral ion QTLs mapped to the same position as the survivorship QTLs, confirming previous studies, which indicated that salt tolerance in Helianthus is achieved through increased Ca uptake, coupled with greater exclusion of Na and related mineral ions. Of greater general significance was the observation that QTLs with effects in opposing directions were found for survivorship and for all mineral ion uptake traits with more than one detected QTL. This genetic architecture provides an ideal substrate for rapid ecological divergence in hybrid neospecies and offers a simple explanation for the colonization of salt marsh habitats by H. paradoxus. Finally, selection coefficients of +0.126, -0.084 and -0.094 for the three survivorship QTLs, respectively, are sufficiently large to account for establishment of new, homoploid hybrid species.