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.     

Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations of Arabidopsis thaliana

Multiple environmental cues regulate the transition to flowering. In natural environments, plants perceive seasonal progression by changes in day length and growth temperature, and plant density is monitored by changes in the light quality reflected from neighbouring vegetation. To understand the seasonal and plant-density dependence associated with natural allelic variation in flowering time, we conducted a quantitative trait loci (QTL) mapping study in Ler x Cvi, Bay x Sha and Ler x No-0 recombinant inbred line (RIL) populations of Arabidopsis thaliana. Days and total leaf number to bolting were examined under low and high plant density (200 or 1600 plants m(-2)) in autumn-winter and spring seasons. We found between 4 and 10 QTLs associated with seasonal and density variations in each RIL population. For Ler x Cvi and Bay x Sha RIL populations, a major proportion of QTLs showed seasonal and density interaction (up to 63%) and four QTLs were common to all environments (21%). Only three QTLs showed seasonal or density dependency. By aligning the linkage maps onto a common physical map, we detected at least one QTL at chromosome 2 and two QTLs at chromosome 5 that overlap between the three RIL populations, suggesting that these QTLs play a crucial role in the adaptive control of flowering time.     

Bayesian shrinkage analysis of quantitative trait Loci for dynamic traits

Many quantitative traits are measured repeatedly during the life of an organism. Such traits are called dynamic traits. The pattern of the changes of a dynamic trait is called the growth trajectory. Studying the growth trajectory may enhance our understanding of the genetic architecture of the growth trajectory. Recently, we developed an interval-mapping procedure to map QTL for dynamic traits under the maximum-likelihood framework. We fit the growth trajectory by Legendre polynomials. The method intended to map one QTL at a time and the entire QTL analysis involved scanning the entire genome by fitting multiple single-QTL models. In this study, we propose a Bayesian shrinkage analysis for estimating and mapping multiple QTL in a single model. The method is a combination between the shrinkage mapping for individual quantitative traits and the Legendre polynomial analysis for dynamic traits. The multiple-QTL model is implemented in two ways: (1) a fixed-interval approach where a QTL is placed in each marker interval and (2) a moving-interval approach where the position of a QTL can be searched in a range that covers many marker intervals. Simulation study shows that the Bayesian shrinkage method generates much better signals for QTL than the interval-mapping approach. We propose several alternative methods to present the results of the Bayesian shrinkage analysis. In particular, we found that the Wald test-statistic profile can serve as a mechanism to test the significance of a putative QTL.     

Modeling quantitative trait Loci and interpretation of models

A quantitative genetic model relates the genotypic value of an individual to the alleles at the loci that contribute to the variation in a population in terms of additive, dominance, and epistatic effects. This partition of genetic effects is related to the partition of genetic variance. A number of models have been proposed to describe this relationship: some are based on the orthogonal partition of genetic variance in an equilibrium population. We compare a few representative models and discuss their utility and potential problems for analyzing quantitative trait loci (QTL) in a segregating population. An orthogonal model implies that estimates of the genetic effects are consistent in a full or reduced model in an equilibrium population and are directly related to the partition of the genetic variance in the population. Linkage disequilibrium does not affect the estimation of genetic effects in a full model, but would in a reduced model. Certainly linkage disequilibrium would complicate the detection of QTL and epistasis. Using different models does not influence the detection of QTL and epistasis. However, it does influence the estimation and interpretation of genetic effects.     

Mixture generalized linear models for multiple interval mapping of quantitative trait Loci in experimental crosses

Summary .  Quantitative trait loci mapping in experimental organisms is of great scientific and economic importance. There has been a rapid advancement in statistical methods for quantitative trait loci mapping. Various methods for normally distributed traits have been well established. Some of them have also been adapted for other types of traits such as binary, count, and categorical traits. In this article, we consider a unified mixture generalized linear model (GLIM) for multiple interval mapping in experimental crosses. The multiple interval mapping approach was proposed by Kao, Zeng, and Teasdale (1999, Genetics 152, 1203–1216) for normally distributed traits. However, its application to nonnormally distributed traits has been hindered largely by the lack of an efficient computation algorithm and an appropriate mapping procedure. In this article, an effective expectation–maximization algorithm for the computation of the mixture GLIM and an epistasis-effect-adjusted multiple interval mapping procedure is developed. A real data set, Radiata Pine data, is analyzed and the data structure is used in simulation studies to demonstrate the desirable features of the developed method.     

A quantitative trait loci analysis of zinc hyperaccumulation in Arabidopsis halleri

The mechanisms of metal hyperaccumulation are still not understood, so we conducted a quantitative trait locus (QTL) analysis of zinc (Zn) hyperaccumulation in Arabidopsis halleri, in a cross between this and its sister species, A. petraea, in order to determine the number and approximate location of the genomic regions significantly contributing to this adaptation. An F2 cross between the two species was made, and the leaf Zn concentration of 92 individuals was measured at both low (10 microm) and high (100 microm) Zn concentrations. Twenty-five markers were established that were distributed on all of the eight chromosomes. Mapping of the markers established that they were essentially collinear with previous studies. QTLs exceeding a logarithm to the base 10 of the odds (LOD) value of 3 were found on chromosomes 4 (low Zn), 6 (high Zn) and 7 (both high and low Zn). Evidence for a QTL on chromosome 3 (low Zn) was also found. This analysis validates a previously used method of QTL analysis, based on microarray analysis of segregating families. Genes that have altered during the evolution of this character should also be QTL: this analysis calls into question a number of candidate genes from consideration as such primary genes because they do not appear to be associated with QTLs.     

Epistasis and genotype-environment interaction for quantitative trait loci affecting flowering time in Arabidopsis thaliana

A major goal of evolutionary biology is to understand the genetic architecture of the complex quantitative traits that may lead to adaptations in natural populations. Of particular relevance is the evaluation of the frequency and magnitude of epistasis (gene–gene and gene–environment interaction) as it plays a controversial role in models of adaptation within and among populations. Here, we explore the genetic basis of flowering time in Arabidopsis thaliana using a series of quantitative trait loci (QTL) mapping experiments with two recombinant inbred line (RIL) mapping populations [Columbia (Col) x Landsberg erecta (Ler), Ler x Cape Verde Islands (Cvi)]. We focus on the response of RILs to a series of environmental conditions including drought stress, leaf damage, and apical damage. These data were explicitly evaluated for the presence of epistasis using Bayesian based multiple-QTL genome scans. Overall, we mapped fourteen QTL affecting flowering time. We detected two significant QTL–QTL interactions and several QTL–environment interactions for flowering time in the Ler x Cvi population. QTL–environment interactions were due to environmentally induced changes in the magnitude of QTL effects and their interactions across environments – we did not detect antagonistic pleiotropy. We found no evidence for QTL interactions in the Ler x Col population. We evaluate these results in the context of several other studies of flowering time in Arabidopsis thaliana and adaptive evolution in natural populations.     

Linkage analysis of quantitative trait loci in multiple line crosses

Simple line crosses, for example, backcross and F₂, are commonly used in mapping quantitative trait loci (QTL). However, these simple crosses are rarely used alone in commercial plant breeding; rather, crosses involving multiple inbred lines or several simple crosses but connected by shared inbred lines may be common in plant breeding. Mapping QTL using crosses of multiple lines is more relevant to plant breeding. Unfortunately, current statistical methods and computer programs of QTL mapping are all designed for simple line crosses or multiple line crosses but under a regular mating system. It is not straightforward to extend the existing methods to handle multiple line crosses under irregular and complicated mating designs. The major hurdle comes from irregular inbreeding, multiple generations, and multiple alleles. In this study, we develop a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm for mapping QTL using complicated multiple line crosses. With the MCMC algorithm, we are able to draw a complete path of the gene flow from founder alleles to their descendents via a recursive process. This has greatly simplified the problem caused by irregular mating and inbreeding in the mapping population. Adopting the reversible jump MCMC algorithm, we are able to simultaneously search for multiple QTL along the genome. We can even infer the posterior distribution of the number of QTL, one of the most important parameters in QTL study. Application of the new MCMC based QTL mapping procedure is demonstrated using two different mating designs. Design I involves two inbred lines and their derived F₁, F₂, and BC populations. Design II is a half-diallel cross involving three inbred lines. The two designs appear different, but can be handled with the same robust computer program.     

A unified model for functional and statistical epistasis and its application in quantitative trait Loci analysis

Interaction between genes, or epistasis, is found to be common and it is a key concept for understanding adaptation and evolution of natural populations, response to selection in breeding programs, and determination of complex disease. Currently, two independent classes of models are used to study epistasis. Statistical models focus on maintaining desired statistical properties for detection and estimation of genetic effects and for the decomposition of genetic variance using average effects of allele substitutions in populations as parameters. Functional models focus on the evolutionary consequences of the attributes of the genotype-phenotype map using natural effects of allele substitutions as parameters. Here we provide a new, general and unified model framework: the natural and orthogonal interactions (NOIA) model. NOIA implements tools for transforming genetic effects measured in one population to the ones of other populations (e.g., between two experimental designs for QTL) and parameters of statistical and functional epistasis into each other (thus enabling us to obtain functional estimates of QTL), as demonstrated numerically. We develop graphical interpretations of functional and statistical models as regressions of the genotypic values on the gene content, which illustrates the difference between the models--the constraint on the slope of the functional regression--and when the models are equivalent. Furthermore, we use our theoretical foundations to conceptually clarify functional and statistical epistasis, discuss the advantages of NOIA over previous theory, and stress the importance of linking functional and statistical models.     

Histochemical analysis reveals organ-specific quantitative trait loci for enzyme activities in Arabidopsis

To identify genetic loci involved in the regulation of organ-specific enzyme activities, a specific histochemical staining protocol was used in combination with quantitative trait locus (QTL) analysis. Using phosphoglucomutase (PGM) as an example, it is shown that enzyme activity can specifically, and with high resolution, be visualized in non-sectioned seedlings of Arabidopsis. The intensities of staining were converted to quantitative data and used as trait for QTL analysis using Landsberg erecta x Cape Verde Islands recombinant inbred lines. Independently, PGM activities were quantified in whole-seedling extracts, and these data were also used for QTL analysis. On the basis of extract data, six significant (P < 0.05) loci affecting PGM activity were found. From the histochemical data, one or more specific QTLs were found for each organ analyzed (cotyledons, shoot apex, hypocotyl, root, root neck, root tip, and root hairs). Loci detected for PGM activity in extracts colocated with loci for histochemical staining. QTLs were found coinciding with positions of (putative) PGM genes but also at other positions, the latter ones supposedly pointing toward regulatory genes. Some of this type of loci were also organ specific. It is concluded that QTL analysis based on histochemical data is feasible and may reveal organ-specific loci involved in the regulation of metabolic pathways.     

Simultaneous fine mapping of multiple closely linked quantitative trait Loci using combined linkage disequilibrium and linkage with a general pedigree

Within a small region (e.g., <10 cM), there can be multiple quantitative trait loci (QTL) underlying phenotypes of a trait. Simultaneous fine mapping of closely linked QTL needs an efficient tool to remove confounded shade effects among QTL within such a small region. We propose a variance component method using combined linkage disequilibrium (LD) and linkage information and a reversible jump Markov chain Monte Carlo (MCMC) sampling for model selection. QTL identity-by-descent (IBD) coefficients between individuals are estimated by a hybrid MCMC combining the random walk and the meiosis Gibbs sampler. These coefficients are used in a mixed linear model and an empirical Bayesian procedure combines residual maximum likelihood (REML) to estimate QTL effects and a reversible jump MCMC that samples the number of QTL and the posterior QTL intensities across the tested region. Note that two MCMC processes are used, i.e., an (internal) MCMC for IBD estimation and an (external) MCMC for model selection. In a simulation study, the use of the multiple-QTL model clearly removes the shade effects between three closely linked QTL located at 1.125, 3.875, and 7.875 cM across the region of 10 cM, using 40 markers at 0.25-cM intervals. It is shown that the use of combined LD and linkage information gives much more useful information compared to using linkage information alone for both single- and multiple-QTL analyses. When using a lower marker density (11 markers at 1-cM intervals), the signal of the second QTL can disappear. Extreme values of past effective size (resulting in extreme levels of LD) decrease the mapping accuracy.     

Mapping quantitative trait loci associated with selenate tolerance in Arabidopsis thaliana

Selenium is essential for many organisms, but is toxic at higher levels. To investigate the genetic basis of selenate tolerance in Arabidopsis thaliana, quantitative trait loci (QTL) associated with selenate tolerance in accessions Landsberg erecta and Columbia were mapped using recombinant inbred lines (RILs). The selenate tolerance index (TI(D10) = root growth + 30 microm selenate/root growth control x 100%) was fourfold higher for parental line Col-4 (59%) than for parent Ler-0 (15%). Among the 96 F8 RILs, TI(D10) ranged from 11 to 75% (mean 37%). Using composite interval mapping, three QTL were found on chromosomes 1, 3 and 5, which together explained 24% of variation in TI(D10) and 32% of the phenotypic variation for the difference in root length +/- Se (RL(D10)). Highly significant epistatic interactions between the QTL and markers on chromosome 2 explained additional variation for both traits. Potential candidate genes for Se tolerance in each of the QTL regions are discussed. These results offer insight into the genetic basis of selenate tolerance, and may be useful for identification of selenate-tolerance genes.     

Sex-specific regulation of mitochondrial DNA levels: genome-wide linkage analysis to identify quantitative trait Loci

Altered mitochondrial DNA (mtDNA) levels have been associated with common diseases in humans. We investigated the genetic mechanism that controls mtDNA levels using genome-wide linkage analyses in families from the Genetic Analysis of Idiopathic Thrombophilia Project (GAIT). We measure mtDNA levels by quantitative real-time PCR in 386 subjects from 21 extended Spanish families. A variance component linkage method using 485 microsatellites was conducted to evaluate linkage and to detect quantitative trait loci (QTLs) involved in the control of mtDNA levels. The heritalibility of mtDNA levels was 0.33 (p = 1.82e-05). We identified a QTL on Chromosome 2 (LOD = 2.21) using all of the subjects, independently on their sex. When females and males were analysed separately, three QTLs were identified. Females showed the same QTL on Chromosome 2 (LOD = 3.09), indicating that the QTL identified in the analysis using all of the subjects was a strong female QTL, and another one on Chromosome 3 (LOD = 2.67), whereas in males a QTL was identified on Chromosome 1 (LOD = 2.81). These QTLs were fine-mapped to find associations with mtDNA levels. The most significant SNP association was for the rs10888838 on Chromosome 1 in males. This SNP mapped to the gene MRPL37, involved in mitochondrial protein translation. The rs2140855 on Chromosome 2 showed association in the analysis using all of the subjects. It was near the gene CMPK2, which encodes a mitochondrial enzyme of the salvage pathway of deoxyribonucleotide synthesis. Our results provide evidence of a sex-specific genetic mechanism for the control of mtDNA levels and provide a framework to identify new genes that influence mtDNA levels.     

Identification and validation of quantitative trait loci controlling seed isoflavone content across multiple environments and backgrounds in soybean

Soybean is important throughout the world not only due to the high seed protein and oil but also owing to the seed isoflavone. To improve the isoflavone concentration in seeds, detecting and mining the stable and reliable quantitative trait loci (QTLs) and related genes in multiple environments and genetic backgrounds become more and more important. In view of this, a F_6:7 recombinant inbred line (RIL) population of 345 lines derived from a cross between Zheng 92116 and Liaodou14 (ZL) was genotyped using 1739 polymorphic SNP and 127 SSR markers in this study and was phenotyped for individual and total seed isoflavone in four environments over 2 years. In total, 48 additive QTLs, which explained 3.00–29.83% of seed isoflavone variation, were identified. Of them, eight QTLs (qDA1_1, qGA1_1, qTIA1_1, qDA1_2, qGA1_2, qTIA1_2, qDA1_3, qTIA1_3) with phenotypic variation explained (PVE) ranging from 14.09 to 28.59% for daidzin, genistin, and total isoflavone were located on the same region of linkage group (LG) A1. These QTLs were further verified in another RIL population derived from Zheng 92116 × Qihuang 30 (ZQ). Meanwhile, the other four overlapping QTLs on linkage group B1, which were associated with glycitin content (qGLB1_1, qGLB1_2, qGLB1_3, qGLB1_4) and explained 16.52 to 29.83% of phenotypic variation, were also verified using the ZQ population. Moreover, the individuals with different genotypes at the common flanking SNP markers for these QTLs on LGs A1 and B1 in the two mapping populations showed significant different isoflavone content, which further validate the QTL mapping results. And also, some candidate genes might participate in the isoflavone biosynthesis processes were found in these stable QTL regions. Thus, the novel and stable QTLs identified and verified in this study could be applied in marker-assisted selection breeding or map-based candidate genes cloning in soybean seed isoflavone genetic improvement in future.     

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F_2:3 mapping populations, Hamilton (susceptible) × PI 90763 (resistant) and Magellan (susceptible) × PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.     

The Korea brassica genome project: a glimpse of the brassica genome based on comparative genome analysis with Arabidopsis

A complete genome sequence provides unlimited information in the sequenced organism as well as in related taxa. According to the guidance of the Multinational Brassica Genome Project (MBGP), the Korea Brassica Genome Project (KBGP) is sequencing chromosome 1 (cytogenetically oriented chromosome #1) of Brassica rapa. We have selected 48 seed BACs on chromosome 1 using EST genetic markers and FISH analyses. Among them, 30 BAC clones have been sequenced and 18 are on the way. Comparative genome analyses of the EST sequences and sequenced BAC clones from Brassica chromosome 1 revealed their homeologous partner regions on the Arabidopsis genome and a syntenic comparative map between Brassica chromosome 1 and Arabidopsis chromosomes. In silico chromosome walking and clone validation have been successfully applied to extending sequence contigs based on the comparative map and BAC end sequences. In addition, we have defined the (peri)centromeric heterochromatin blocks with centromeric tandem repeats, rDNA and centromeric retrotransposons. In-depth sequence analyses of five homeologous BAC clones and an Arabidopsis chromosomal region reveal overall co-linearity, with 82% sequence similarity. The data indicate that the Brassica genome has undergone triplication and subsequent gene losses after the divergence of Arabidopsis and Brassica. Based on in-depth comparative genome analyses, we propose a comparative genomics approach for conquering the Brassica genome. In 2005 we intend to construct an integrated physical map, including sequence information from 500 BAC clones and integration of fingerprinting data and end sequence data of more than 100 000 BAC clones. The sequences have been submitted to GenBank with accession numbers: 10 204 BAC ends of the KBrH library (CW978640-CW988843); KBrH138P04, AC155338; KBrH117N09, AC155337; KBrH097M21, AC155348; KBrH093K03, AC155347; KBrH081N08, AC155346; KBrH080L24, AC155345; KBrH077A05, AC155343; KBrH020D15, AC155340; KBrH015H17, AC155339; KBrH001H24, AC155335; KBrH080A08, AC155344; KBrH004D11, AC155341; KBrH117M18, AC146875; KBrH052O08, AC155342.     

Comparative analysis of quantitative trait loci controlling glucosinolates,myrosinase and insect resistance in Arabidopsis thaliana

Evolutionary interactions among insect herbivores and plant chemical defenses have generated systems where plant compounds have opposing fitness consequences for host plants, depending on attack by various insect herbivores. This interplay complicates understanding of fitness costs and benefits of plant chemical defenses. We are studying the role of the glucosinolate-myrosinase chemical defense system in protecting Arabidopsis thaliana from specialist and generalist insect herbivory. We used two Arabidopsis recombinant inbred populations in which we had previously mapped QTL controlling variation in the glucosinolate-myrosinase system. In this study we mapped QTL controlling resistance to specialist (Plutella xylostella) and generalist (Trichoplusia ni) herbivores. We identified a number of QTL that are specific to one herbivore or the other, as well as a single QTL that controls resistance to both insects. Comparison of QTL for herbivory, glucosinolates, and myrosinase showed that T. ni herbivory is strongly deterred by higher glucosinolate levels, faster breakdown rates, and specific chemical structures. In contrast, P. xylostella herbivory is uncorrelated with variation in the glucosinolate-myrosinase system. This agrees with evolutionary theory stating that specialist insects may overcome host plant chemical defenses, whereas generalists will be sensitive to these same defenses.     

Mapping quantitative trait loci in multiple populations of Arabidopsis thaliana identifies natural allelic variation for trichome density

The majority of biological traits are genetically complex. Mapping the quantitative trait loci (QTL) that determine these phenotypes is a powerful means for estimating many parameters of the genetic architecture for a trait and potentially identifying the genes responsible for natural variation. Typically, such experiments are conducted in a single mapping population and, therefore, have only the potential to reveal genomic regions that are polymorphic between the progenitors of the population. What remains unclear is how well the QTL identified in any one mapping experiment characterize the genetics that underlie natural variation in traits. Here we provide QTL mapping data for trichome density from four recombinant inbred mapping populations of Arabidopsis thaliana. By aligning the linkage maps for these four populations onto a common physical map, the results from each experiment were directly compared. Seven of the nine QTL identified are population specific while two were mapped in all four populations. Our results show that many lineage-specific alleles that either increase or decrease trichome density persist in natural populations and that most of this genetic variation is additive. More generally, these findings suggest that the use of multiple populations holds great promise for better understanding the genetic architecture of natural variation.     

Power analysis for quantitative trait locus mapping in populations derived by multiple backcrosses

 Populations derived by multiple backcrosses are potentially useful for quantitative trait locus (QTL) mapping studies. Comparisons of relative power to detect QTL using populations derived by multiple back-crosses are needed to make decisions when mapping projects are initiated. The objective of this study was to theoretically compare the power to detect QTL in populations derived by multiple backcrosses relative to mapping in a recombinant inbred population of equal size. Backcrossing results in a reduction in genetic variance with each generation and also results in an increasing frequency of the recurrent parent marker genotype. The relevant outcome for QTL mapping is a reduction in genetic variance to partition between marker genotype classes and increasing unbalance of the number of individuals contributing to the mean of the marker genotypes. Both of these factors lead to a decrease in the power to detect a QTL as the number of backcross generations increases. Experimental error was held constant with the populations compared. From a theoretical standpoint, backcross-derived populations offer few advantages for QTL detection. If, however, a backcrossing approach is the most efficient method to achieve a desired breeding objective and if QTL detection is an objective of equal or less importance, backcross-derived populations are a reasonable approach to QTL detection. Received: 4 August 1996 / Accepted: 4 April 1997     

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.