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PubMed2001

Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits.

An approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F(2)/F(3) mapping population of tetraploid wheat (Triticum durum x T. dicoccoides) is provided.

Chromosome Mapping↗

PubMed1998

A rank-based nonparametric method for mapping quantitative trait loci in outbred half-sib pedigrees: application to milk production in a granddaughter design.

We describe the development of a multipoint nonparametric quantitative trait loci mapping method based on the Wilcoxon rank-sum test applicable to outbred half-sib pedigrees. The method has been evaluated on a simulated dataset and its efficiency compared with interval mapping by using regression. It was shown that the rank-based approach is slightly inferior to regression when the residual variance is homoscedastic normal; however, in three out of four other scenarios envisaged, i.e., residual variance heteroscedastic normal, homoscedastic skewed, and homoscedastic positively kurtosed, the latter outperforms the former one. Both methods were applied to a real data set analyzing the effect of bovine chromosome 6 on milk yield and composition by using a 125-cM map comprising 15 microsatellites and a granddaughter design counting 1158 Holstein-Friesian sires.

Animals↗

PubMed2004

[Maximum likelihood analysis for mapping dynamic trait QTL in outbred population. I . Methodology].

The quantitative traits whose phenotypic values change with time in life or other quantitative factors, were defined as dynamic traits. Based on the idea about random regression test-day model for estimating breeding values in animal evaluation, a mathematic model was constructed for mapping dynamic trait loci by using Legendre polynomials to model dynamic changes of each genetic effect. The Maximum likelihood analysis implemented via EM algorithm was used to estimate the parameters, including QTL position, fixed genetic regression effects for dynamic trait QTL mapping in outbred population. Compared with the existing method for dynamic trait mapping QTL, the new method presented here not only allowed to sample dynamic trait in disequillibrium way, but also can achieve to map dynamic traits QTL in any resource population by just one step. The further study on dynamic trait mapping QTL was theoretically discussed by incorporating genetic analysis of dynamic trait into general mapping QTL.

Likelihood Functions↗

PubMed1999

Quantitative trait linkage mapping in anthropology.

Recent years have seen rapid progress in several areas of both biomedical and anthropological genetics. While genetic analyses have come to play a significant role in biological anthropology, there has been little use of modern methods for linkage mapping of quantitative trait loci (QTLs). It is now feasible to design research studies to investigate the quantitative genetics of complex phenotypes that are of primary importance to traditional questions in biological anthropology. Complex traits such as functionally significant morphological features, physiological characteristics or aspects of behavior can be examined to estimate the influence of genetic variation on within-species phenotypic variation. In addition, new methods for mapping quantitative trait loci provide opportunities to identify the regions within chromosomes that contain the functional genes of interest. This review summarizes molecular genetic and statistical genetic approaches to QTL mapping, and presents examples of how this approach can expand the scope of anthropological genetics to include mapping and identifying individual genes that influence complex phenotypic traits relevant to fundamental questions in biological anthropology.

Animals↗

PubMed2006

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) x PI 90763 (resistant) and Magellan (susceptible) x 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.

Chromosome Mapping↗

PubMedDate not supplied

Quantitative trait loci (QTLs) mapping for growth traits in the mouse: a review.

The attainment of a specific mature body size is one of the most fundamental differences among species of mammals. Moreover, body size seems to be the central factor underlying differences in traits such as growth rate, energy metabolism and body composition. An important proportion of this variability is of genetic origin. The goal of the genetic analysis of animal growth is to understand its "genetic architecture", that is the number and position of loci affecting the trait, the magnitude of their effects, allele frequencies and types of gene action. In this review, the different strategies developed to identify and characterize genes involved in the regulation of growth in the mouse are described, with emphasis on the methods developed to map loci contributing to the regulation of quantitative traits (QTLs).

Animals↗

PubMedDate not supplied

Quantitative trait loci mapping of floral and leaf morphology traits in Arabidopsis thaliana: evidence for modular genetic architecture.

Summary Morphological variation within organisms is integrated and often modular in nature. That is to say, the size and shape of traits tend to vary in a coordinated and structured manner across sets of organs or parts of an organism. The genetic basis of this morphological integration is largely unknown. Here, we report on quantitative trait loci (QTL) analysis of leaf and floral organ size in Arabidopsis thaliana. We evaluate patterns of genetic correlations among traits and perform whole-genome scans using QTL mapping methods. We detected significant genetic variation for the size and shape of each floral and leaf trait in our study. Moreover, we found large positive genetic correlations among sets of either flower or leaf traits, but low and generally nonsignificant genetic correlations between flower and leaf traits. These results support the hypothesis of independent floral and vegetative modules. We consider co-localization of QTL for different traits as support for a pleiotropic basis of morphological integration and modularity. A total of eight QTL affecting flower and three QTL affecting leaf traits were identified. Most QTL affected either floral or leaf traits, providing a general explanation for high correlations within and low correlations between modules. Only two genomic locations affected both flower and leaf growth. These results are discussed in the context of the evolution of modules, pleiotropy, and the putative homologous relationship between leaves and flowers.

Arabidopsis↗

PubMed1995

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.

Algorithms↗

PubMed2003

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

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

Cacao↗

PubMed1995

A random model approach to interval mapping of quantitative trait loci.

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

Alleles↗

PubMed1994

Controlling the type I and type II errors in mapping quantitative trait loci.

Although the interval mapping method is widely used for mapping quantitative trait loci (QTLs), it is not very well suited for mapping multiple QTLs. Here, we present the results of a computer simulation to study the application of exact and approximate models for multiple QTLs. In particular, we focus on an automatic two-stage procedure in which in the first stage "important" markers are selected in multiple regression on markers. In the second stage a QTL is moved along the chromosomes by using the preselected markers as cofactors, except for the markers flanking the interval under study. A refined procedure for cases with large numbers of marker cofactors is described. Our approach will be called MQM mapping, where MQM is an acronym for "multiple-QTL models" as well as for "marker-QTL-marker." Our simulation work demonstrates the great advantage of MQM mapping compared to interval mapping in reducing the chance of a type I error (i.e., a QTL is indicated at a location where actually no QTL is present) and in reducing the chance of a type II error (i.e., a QTL is not detected).

Chromosome Mapping↗

PubMed1998

Iteratively reweighted least squares mapping of quantitative trait loci.

Mapping quantitative trait loci (QTL) is a typical problem of regression with uncertain independent variables because the genotype of a putative QTL is not observed. Rather, the genotype is inferred from marker information. The method of maximum likelihood (ML) methods is considered to be the optimal solution for this problem because the distribution of the unobserved QTL genotype is fully taken into account. The simple linear regression method (REG) is a first-order approximation to ML and usually performs very well. In this study, an iteratively reweighted least squares method (IRWLS) is proposed. The new method is a second-order approximation to ML because both the expectation and the variance of the unobserved QTL genotype are taken into consideration. The IRWLS is developed in the context of a single large outbred family. The properties of IRWLS are demonstrated and compared with REG and ML via replicated Monte Carlo simulations. The conclusions are: (1) when marker information content is high, the three methods perform equally well, but ML and IRWLS outperform REG when marker information content is low and the variance explained by the QTL is high; (2) when the residual distribution is not normal, ML can fail or have low power to detect small QTLs, but REG and IRWLS are robust to non-normality; and (3) when the residual distribution is normal, the performance of IRWLS is almost identical to ML, but the computational speed of IRWLS is many times faster than that of ML.

Chromosome Mapping↗

PubMed2005

Quantitative trait loci mapping for meat quality and muscle fiber traits in a Japanese wild boar x Large White intercross.

Three generations of a swine family produced by crossing a Japanese wild boar and three Large White female pigs were used to map QTL for various production traits. Here we report the results of QTL analyses for skeletal muscle fiber composition and meat quality traits based on phenotypic data of 353 F(2) animals and genotypic data of 225 markers covering almost the entire pig genome for all of the F(2) animals as well as their F(1) parents and F(0) grandparents. The results of a genome scan using least squares regression interval mapping provided evidence that QTL (<1% genome-wise error rate) affected the proportion of the number of type IIA muscle fibers on SSC2, the number of type IIB on SSC14, the relative area (RA) of type I on SSCX, the RA of type IIA on SSC6, the RA of type IIB on SSC6 and SSC14, the Minolta a* values of loin on SSC4 and SSC6, the Minolta b* value of loin on SSC15, and the hematin content of the LM on SSC6. Quantitative trait loci (<5% genome-wise error rate) were found for the number of type I on SSC1, SSC14, and SSCX, for the number of type IIA on SSC14, for the number of type IIB on SSC2, for the RA of type IIA on SSC2, for the Minolta b* value of loin on SSC3, for the pH of loin on SSC15, and for the i.m. fat content on SSC15. Twenty-four QTL were detected for 11 traits at the 5% genome-wise level. Some traits were associated with each other, so the 24 QTL were located on 11 genomic regions. In five QTL located on SSC2, SSC6, and SSC14, each wild boar allele had the effect of increasing types I and IIA muscle fibers and decreasing type IIB muscle fibers. These effects are expected to improve meat quality.

Animals↗

PubMed1996

Extensions to quantitative trait locus mapping in experimental organisms.

Quantitative trait locus (QTL) mapping studies involving inbred model organisms have become important and often-used investigative tools in contemporary genetics research. As a result, a number of statistical methods and study designs have been developed that have made it relatively easy for researchers to determine the rough locations of a number of QTLs that influence traits of all types, including a great many of relevance to hypertension and blood pressure regulation. However, as a whole, these methods and designs are limited in that they are not flexible enough to accommodate many of the complexities associated with quantitative trait expression. In this review, we describe a number of extensions to QTL mapping designs that should make QTL mapping experiments more insightful and compelling. These extensions include multiple QTL modeling with an emphasis on epistatic locus interaction and additive locus effects, the use of multiple phenotypic end points, the design of pharmacogenetic studies, the use of whole-genome parameters in quantitative trait investigations, and issues in mapping QTLs that influence traits that exhibit developmental trends. We use studies investigating the genetic basis of blood pressure regulation to exemplify relevant issues.

Animals↗

PubMed2005

Interspecies synteny mapping identifies a quantitative trait locus for bone mineral density on human chromosome Xp22.

Bone mineral density (BMD) is a complex trait with a strong genetic component and an important predictor of osteoporotic fracture risk. Here we report the use of a cross-species strategy to identify genes that regulate BMD, proceeding from quantitative trait mapping in mice to association mapping of the syntenic region in the human genome. We identified a quantitative trait locus (QTL) on the mouse X-chromosome for post-maturity change in spine BMD in a cross of SAMP6 and AKR/J mice and conducted association mapping of the syntenic region on human chromosome Xp22. We studied 76 single nucleotide polymorphisms (SNP) from the human region in two sets of DNA pools prepared from individuals with lumbar spine-BMD (LS-BMD) values falling into the top and bottom 13th percentiles of a population-based study of 3100 post-menopausal women. This procedure identified a region of significant association for two adjacent SNP (rs234494 and rs234495) within the Xp22 locus (P<0.001). Individual genotyping for rs234494 in the BMD pools confirmed the presence of an association for alleles (P=0.018) and genotypes (P=0.008). Analysis of rs234494 and rs234495 in 1053 women derived from the same population who were not selected for BMD values showed an association with LS-BMD for rs234495 (P=0.01) and for haplotypes defined by both SNP (P=0.002). Our study illustrates that interspecies synteny can be used to identify and refine QTL for complex traits and represents the first example where a human QTL for BMD regulation has been mapped using this approach.

Absorptiometry, Photon↗

PubMed2006

The X chromosome in quantitative trait locus mapping.

The X chromosome requires special treatment in the mapping of quantitative trait loci (QTL). However, most QTL mapping methods, and most computer programs for QTL mapping, have focused exclusively on autosomal loci. We describe a method for appropriate treatment of the X chromosome for QTL mapping in experimental crosses. We address the important issue of formulating the null hypothesis of no linkage appropriately. If the X chromosome is treated like an autosome, a sex difference in the phenotype can lead to spurious linkage on the X chromosome. Further, the number of degrees of freedom for the linkage test may be different for the X chromosome than for autosomes, and so an X chromosome-specific significance threshold is required. To address this issue, we propose a general procedure to obtain chromosome-specific significance thresholds that controls the genomewide false positive rate at the desired level. We apply our methods to data on gut length in a large intercross of mice carrying the Sox10Dom mutation, a model of Hirschsprung disease. We identified QTL contributing to variation in gut length on chromosomes 5 and 18. We found suggestive evidence of linkage to the X chromosome, which would be viewed as strong evidence of linkage if the X chromosome was treated as an autosome. Our methods have been implemented in the package R/qtl.

Animals↗

PubMed2005

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.

Arabidopsis↗

PubMed2002

A unified framework for mapping quantitative trait loci in bivalent tetraploids using single-dose restriction fragments: a case study from alfalfa.

The development of statistical methodologies for quantitative trait locus (QTL) mapping in polyploids is complicated by complex polysomic inheritance. In this article, we propose a statistical method for mapping QTL in tetraploids undergoing bivalent formation at meiosis by using single-dose restriction fragments. Our method is based on a unified framework, one that uses chromosome bivalent pairing configuration and gametic recombination to discern different mechanisms of gamete formation. Our bivalent polyploid model can not only provide a simultaneous estimation of the linkage and chromosome pairing configuration-a cytological parameter of evolutionary and systematic interest-but also enhances the precision of estimating QTL effects and position by correctly characterizing gene segregation during polyploid meiosis. By using our method and a linkage map constructed in a previous study, we successfully identify several QTL affecting winter hardiness in bivalent tetraploid alfalfa. Moreover, our results reveal significant preferential chromosome pairing at meiosis in an F1 hybrid population, which indicates the importance of reassessing the traditional view of random chromosome segregation in alfalfa.

Chromosome Pairing↗