Quantitative trait locus (QTL) isogenic recombinant analysis: a method for high-resolution mapping of QTL within a single population

In the quest for fine mapping quantitative trait loci (QTL) at a subcentimorgan scale, several methods that involve the construction of inbred lines and the generation of large progenies of such inbred lines have been developed (Complex Trait Consortium 2003). Here we present an alternative method that significantly speeds up QTL fine mapping by using one segregating population. As a first step, a rough mapping analysis is performed on a small part of the population. Once the QTL have been mapped to a chromosomal interval by standard procedures, a large population of 1000 plants or more is analyzed with markers flanking the defined QTL to select QTL isogenic recombinants (QIRs). QIRs bear a recombination event in the QTL interval of interest, while other QTL have the same homozygous genotype. Only these QIRs are subsequently phenotyped to fine map the QTL. By focusing at an early stage on the informative individuals in the population only, the efforts in population genotyping and phenotyping are significantly reduced as compared to prior methods. The principles of this approach are demonstrated by fine mapping an erucic acid QTL of rapeseed at a subcentimorgan scale.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

A comparison of interval mapping procedures including the QTL-cluster mapping

Using the deterministic sampling, patterns of the log-likelihood surfaces expected in some interval mapping procedures for estimating the position of, and the effect for, QTL(s) were investigated for the situations where a single QTL or closely linked QTLs are contained in a chromosome segment bracketed with two markers. The mapping procedures compared were the conventional, likelihood-based interval mapping (IM), the regression interval mapping (RIM), and the QTL-cluster mapping (CM) in which the conditional probabilities of transmission of the whole segment marked by the flanking markers were taken into consideration. The half-sib design was used here, and several cases of the true genetic model were considered, differing in the number of QTLs contained in the marker interval, the linkage phase for the sire, and the magnitude of the QTL(s) effect. For the true genetic models where a single QTL or closely linked QTLs being in coupling phase are contained in the interval, with (R)IM, clear global maxima of the log-likelihood were observed within the range of the marker interval. It was shown that the estimates of the QTL(s) effect at the marked segment level are expected to be unbiased. On the other hand, in a setting where the linkage phase for the linked QTLs located in the interval was different from coupling and repulsion, there was found a ridge along the interval for the log-likelihood surface with (R)IM, indicating the dependency between the estimates of the position of, and the effect for, the putative QTL. For this case, it was found that the position of the putative QTL could be estimated as that of one of the flanking markers, and the estimate of the QTL effect be biased. In contrast, it was revealed that CM is expected to provide the unbiased estimate of the QTL(s)-effect at the segment level for any case of the true genetic models considered here. If the aim is for marker-assisted selection rather than mapping closely linked QTLs individually, then the CM approach is considered to be useful.     

Study of strategies for selecting quantitative trait locus mapping procedures by computer simulation

Along with the development and integration of molecular genetics and quantitative genetics, many quantitative trait locus (QTL) mapping studies have been conducted using different mapping populations in various crop species. Existing QTLs can be used for marker-assisted breeding and map-based cloning, whereas the false-positive QTLs are no use. The purpose of this study is to evaluate the suitability of different mapping procedures for data from different genetic models. In this study, four types of recombinant inbred lines (RILs) with different genetic models, viz. additive QTLs (Model I), additive and epistatic QTLs (Model II), additive QTLs and QTL × environment interaction (Model III), additive, epistatic QTLs and QTL × environment interaction (Model IV), were simulated by computer. Six types of QTL mapping procedures, viz. CIM, MIMF, MIMR, ICIM, MQM and NWIM, on four kinds of QTL mapping software, viz. WinQTL Cartographer Version 2.5, IciMapping Version 2.0, MapQTL Version 5.0 and QTLnetwork Version 2.0, were used for screening QTLs of the simulated RILs. The results showed that different mapping procedures have different suitability for different genetic models. CIM and MQM can only screen Model I data. MIMR, MIMF and ICIM can only screen Model I and Model II data. NWIM can screen all four models’ data. It can be concluded that different genetic models’ data have different most suitable mapping procedures. In practical experiments where the genetic model of the data is unknown, a multiple model mapping strategy should be used, that is a full model scanning with complex model procedure followed by verification with other procedures corresponding to the scanning results.     

Look before you leap: a new approach to mapping QTL

In this paper, we present an innovative and powerful approach for mapping quantitative trait loci (QTL) in experimental populations. This deviates from the traditional approach of (composite) interval mapping which uses a QTL profile to simultaneously determine the number and location of QTL. Instead, we look before we leap by employing separate detection and localization stages. In the detection stage, we use an iterative variable selection process coupled with permutation to identify the number and synteny of QTL. In the localization stage, we position the detected QTL through a series of one-dimensional interval mapping scans. Results from a detailed simulation study and real analysis of wheat data are presented. We achieve impressive increases in the power of QTL detection compared to composite interval mapping. We also accurately estimate the size and position of QTL. An R library, DLMap, implements the methods described here and is freely available from CRAN ().     

Numerical comparison between powers of maximum likelihood and analysis of variance methods for QTL detection in progeny test designs: the case of monogenic inheritance

Simulations are used to compare four statistics for the detection of a quantitative trait locus (QTL) in daughter and grand-daughter designs as defined by Soller and Genizi (1978) and Weller et al. (1990): (1) the Fisher test of a linear model including a marker effect within sire or grand-sire effect; (2) the likelihood ratio test of a segregation analysis without the information given by the marker; (3) the likelihood ratio test of a segregation analysis considering the information from the marker; and (4) the lod score which is the likelihood ratio test of absence of linkage between the marker and the QTL. In all cases the two segregation analyses are more powerful for QTL detection than are either the linear method or the lod score. The differences in power are generally limited but may be significant (in a ratio of 1 to 3 or 4) when the QTL has a small effect (0.2 standard deviations) and is not closely linked to the marker (recombination rate of 20% or more).     

Quantitative trait loci for the circadian clock in Neurospora crassa

Neurospora crassa has been a model organism for the study of circadian clocks for the past four decades. Among natural accessions of Neurospora crassa, there is significant variation in clock phenotypes. In an attempt to investigate natural allelic variants contributing to quantitative variation, we used a quantitative trait loci mapping approach to analyze three independent mapping populations whose progenitors were collected from geographically isolated locations. Two circadian clock phenotypes, free-running period and entrained phase, were evaluated in the 188 F(1) progeny of each mapping population. To identify the clock QTL, we applied two QTL mapping analyses: composite interval mapping (CIM) and Bayesian multiple QTL analysis (BMQ). When controlling false positive rates < or =0.05, BMQ appears to be the more sensitive of the two approaches. BMQ confirmed most of the QTL from CIM (18 QTL) and identified 23 additional QTL. While 13 QTL colocalize with previously identified clock genes, we identified 30 QTL that were not linked with any previously characterized clock genes. These are candidate regions where clock genes may be located and are expected to lead to new insights in clock regulation.     

Mapping quantitative trait loci in the case of a spike in the phenotype distribution

A common departure from the usual normality assumption in QTL mapping concerns a spike in the phenotype distribution. For example, in measurements of tumor mass, some individuals may exhibit no tumors; in measurements of time to death after a bacterial infection, some individuals may recover from the infection and fail to die. If an appreciable portion of individuals share a common phenotype value (generally either the minimum or the maximum observed phenotype), the standard approach to QTL mapping can behave poorly. We describe several alternative approaches for QTL mapping in the case of such a spike in the phenotype distribution, including the use of a two-part parametric model and a nonparametric approach based on the Kruskal-Wallis test. The performance of the proposed procedures is assessed via computer simulation. The procedures are further illustrated with data from an intercross experiment to identify QTL contributing to variation in survival of mice following infection with Listeria monocytogenes.     

Genetic architecture of complex traits in plants

Genetic architecture refers to the numbers and genome locations of genes that affect a trait, the magnitude of their effects, and the relative contributions of additive, dominant, and epistatic gene effects. Quantitative trait locus (QTL) mapping techniques are commonly used to investigate genetic architectures, but the scope of inferences drawn from QTL studies are often restricted by the limitations of the experimental designs. Recent advances in experimental and statistical procedures, including the simultaneous analysis of QTL that segregate in diverse germplasm, should improve genetic architecture studies. High-resolution QTL mapping methods are being developed that may define the specific DNA sequence variants underlying QTL. Studies of genetic architecture, combined with improved knowledge of the structure of plant populations, will impact our understanding of plant evolution and the design of crop improvement strategies.     

Linkage disequilibrium mapping of quantitative trait loci under truncation selection

As a dense map of single nucleotide polymorphism (SNP) markers are available, population-based linkage disequilibrium (LD) mapping or association study is becoming one of the major tools for identifying quantitative trait loci (QTL) and for fine gene mapping. However, in many cases, LD between the marker and trait locus is not very strong. Approaches that maximize the potential of detecting LD will be essential for the success of LD mapping of QTL. In this paper, we propose two strategies for increasing the probability of detecting LD: (1) phenotypic selection and (2) haplotype LD mapping. To provide the foundations for LD mapping of QTL under selection, we develop analytic tools for assessing the impact of phenotypic selection on allele and haplotype frequencies, and LD under three trait models: single trait locus, two unlinked trait loci, and two linked trait loci with or without epistasis. In addition to a traditional chi(2) test, which compares the difference in allele or haplotype frequencies in the selected sample and population sample, we present multiple regression methods for LD mapping of QTL, and investigate which methods are effective in employing phenotypic selection for QTL mapping. We also develop a statistical framework for investigating and comparing the power of the single marker and multilocus haplotype test for LD mapping of QTL. Finally, the proposed methods are applied to mapping QTL influencing variation in systolic blood pressure in an isolated Chinese population.     

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.     

Quantitative trait Loci analysis using the false discovery rate

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed. Our aim in this work is to lay a solid foundation for the use of the false discovery rate in QTL mapping. We review the false discovery rate criterion, the appropriate interpretation of the FDR, and alternative formulations of the FDR that appeared in the statistical and genetics literature. We discuss important features of the FDR approach, some stemming from new developments in FDR theory and methodology, which deem it especially useful in linkage analysis. We review false discovery rate-controlling procedures--the BH, the resampling procedure, and the adaptive two-stage procedure-and discuss the validity of these procedures in single- and multiple-trait QTL mapping. Finally we argue that the control of the false discovery rate has an important role in suggesting, indicating the significance of, and confirming QTL and present guidelines for its use.     

Quantitative trait loci mapping and genetic dissection for lint percentage in upland cotton (Gossypium hirsutum)

Lint percentage is an important character of cotton yield components and it is also correlated with cotton fibre development. In this study, we used a high lint percentage variety, Baimian1, and a low lint percentage, TM-1 genetic standard for Gossypium hirsutum, as parents to construct a mapping populations in upland cotton (G. hirsutum). A quantitative trait locus/loci (QTL) analysis of lint percentage was performed by using two mapping procedures; composite interval mapping (CIM), inclusive composite interval mapping (ICIM) and the F_2:3 populations in 2 years. Six main-effect QTL (M-QTL) for lint percentage (four significant and two suggestive) were detected in both years by CIM, and were located on chr. 3, chr. 19, chr. 26 and chr. 5/chr. 19. Of the six QTL, marker intervals and favourable gene sources of the significant M-QTL, qLP-3(2010) and qLP-3(2011) were consistent. These QTL were also detected by ICIM, and therefore, should preferentially be used for marker-assisted selection (MAS) of lint percentage. Another M-QTL, qLP-19(2010), was detected by two mapping procedures, and it could also be a candidate for MAS. We detected the interaction between two M-QTL and environment, and 11 epistatic QTL (E-QTL) and their interaction with environment by using ICIM. The study also found two EST-SSRs, NAU1187 and NAU1255, linked to M-QTL for lint percentage that could be candidate markers affecting cotton fibre development.     

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F(8:9) recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.     

Development of a near-isogenic line population of Arabidopsis thaliana and comparison of mapping power with a recombinant inbred line population

In Arabidopsis recombinant inbred line (RIL) populations are widely used for quantitative trait locus (QTL) analyses. However, mapping analyses with this type of population can be limited because of the masking effects of major QTL and epistatic interactions of multiple QTL. An alternative type of immortal experimental population commonly used in plant species are sets of introgression lines. Here we introduce the development of a genomewide coverage near-isogenic line (NIL) population of Arabidopsis thaliana, by introgressing genomic regions from the Cape Verde Islands (Cvi) accession into the Landsberg erecta (Ler) genetic background. We have empirically compared the QTL mapping power of this new population with an already existing RIL population derived from the same parents. For that, we analyzed and mapped QTL affecting six developmental traits with different heritability. Overall, in the NIL population smaller-effect QTL than in the RIL population could be detected although the localization resolution was lower. Furthermore, we estimated the effect of population size and of the number of replicates on the detection power of QTL affecting the developmental traits. In general, population size is more important than the number of replicates to increase the mapping power of RILs, whereas for NILs several replicates are absolutely required. These analyses are expected to facilitate experimental design for QTL mapping using these two common types of segregating populations.     

Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis

In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates.     

A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana

Identifying natural allelic variation that underlies quantitative trait variation remains a fundamental problem in genetics. Most studies have employed either simple synthetic populations with restricted allelic variation or performed association mapping on a sample of naturally occurring haplotypes. Both of these approaches have some limitations, therefore alternative resources for the genetic dissection of complex traits continue to be sought. Here we describe one such alternative, the Multiparent Advanced Generation Inter-Cross (MAGIC). This approach is expected to improve the precision with which QTL can be mapped, improving the outlook for QTL cloning. Here, we present the first panel of MAGIC lines developed: a set of 527 recombinant inbred lines (RILs) descended from a heterogeneous stock of 19 intermated accessions of the plant Arabidopsis thaliana. These lines and the 19 founders were genotyped with 1,260 single nucleotide polymorphisms and phenotyped for development-related traits. Analytical methods were developed to fine-map quantitative trait loci (QTL) in the MAGIC lines by reconstructing the genome of each line as a mosaic of the founders. We show by simulation that QTL explaining 10% of the phenotypic variance will be detected in most situations with an average mapping error of about 300 kb, and that if the number of lines were doubled the mapping error would be under 200 kb. We also show how the power to detect a QTL and the mapping accuracy vary, depending on QTL location. We demonstrate the utility of this new mapping population by mapping several known QTL with high precision and by finding novel QTL for germination data and bolting time. Our results provide strong support for similar ongoing efforts to produce MAGIC lines in other organisms.     

Interval Mapping of Multiple Quantitative Trait Loci

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.     

On the generalized poisson regression mixture model for mapping quantitative trait loci with count data

Statistical methods for mapping quantitative trait loci (QTL) have been extensively studied. While most existing methods assume normal distribution of the phenotype, the normality assumption could be easily violated when phenotypes are measured in counts. One natural choice to deal with count traits is to apply the classical Poisson regression model. However, conditional on covariates, the Poisson assumption of mean-variance equality may not be valid when data are potentially under- or overdispersed. In this article, we propose an interval-mapping approach for phenotypes measured in counts. We model the effects of QTL through a generalized Poisson regression model and develop efficient likelihood-based inference procedures. This approach, implemented with the EM algorithm, allows for a genomewide scan for the existence of QTL throughout the entire genome. The performance of the proposed method is evaluated through extensive simulation studies along with comparisons with existing approaches such as the Poisson regression and the generalized estimating equation approach. An application to a rice tiller number data set is given. Our approach provides a standard procedure for mapping QTL involved in the genetic control of complex traits measured in counts.     

Genetic dissection of quantitative trait locus for ethanol sensitivity in long- and short-sleep mice

Interval-specific congenic strains (ISCS) allow fine mapping of a quantitative trait locus (QTL), narrowing its confidence interval by an order of magnitude or more. In earlier work, we mapped four QTL specifying differential ethanol sensitivity, assessed by loss of righting reflex because of ethanol (LORE), in the inbred long-sleep (ILS) and inbred short-sleep (ISS) strains, accounting for approximately 50% of the genetic variance for this trait. Subsequently, we generated reciprocal congenic strains in which each full QTL interval from ILS was bred onto the ISS background and vice versa. An earlier paper reported construction and results of the ISCS on the ISS background; here, we describe this process and report results on the ILS background. We developed multiple ISCS for each Lore QTL in which the QTL interval was broken into a number of smaller intervals. For each of the four QTL regions (chromosomes 1, 2, 11 and 15), we were successful in reducing the intervals significantly. Multiple, positive strains were overlapped to generate a single, reduced interval. Subsequently, this reduced region was overlaid on previous reductions from the ISS background congenics, resulting in substantial reductions in all QTL regions by approximately 75% from the initial mapping study. Genes with sequence or expression polymorphisms in the reduced intervals are potential candidates; evidence for these is presented. Genetic background effects can be important in detection of single QTL; combining this information with the generation of congenics on both backgrounds, as described here, is a powerful approach for fine mapping QTL.