SEARCH · PubMed Health

Results for “QTL mapping”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39Linked to original sources

PubMed2006

High-resolution physical mapping and construction of a porcine contig spanning the intramuscular fat content QTL.

We previously mapped a locus for porcine intramuscular fat content (IMF) by linkage analysis to a 17.1-cM chromosome interval on Sus scrofa chromosome 7 (SSC7) flanked by microsatellite markers SW1083 and SW581. In this study, we identified 34 microsatellite markers and 14 STSs from the 17.1-cM IMF quantitative trait loci (QTL) region corresponding to HSA14q and aligned those loci using the INRA-University of Minnesota porcine radiation hybrid (IMpRH) panel. We then constructed a 5.2-Mb porcine bacterial artificial chromosome (BAC) contig of this region that was aligned using the RH panel. Finally, the IMF QTL was fine-mapped to 12.6 cM between SJ169 and MM70 at the 0.1% chromosome-wise significance level by genotyping the previously studied F2 resource family with 17 additional microsatellites. We also demonstrated that the SJ169-MM70 interval spans approximately 3.0 Mb and contains at least 12 genes: GALC, GPR65, KCNK10, SPATA7, PTPN21, FLJ11806, EML5, TTC8, CHES1, CAP2P1, CHORDC2P and C14orf143.

Adipose Tissue↗

PubMed2003

From plant genomics to breeding practice.

New alleles are constantly accumulated during intentional crop selection. The molecular understanding of these alleles has stimulated new genomic approaches to mapping quantitative trait loci (QTL) and haplotype multiplicity of the genes concerned. A limited number of quantitative trait nucleotides responsible for QTL variation have been described, but an acceleration in their rate of discovery is expected with the adoption of linkage disequilibrium and candidate gene strategies for QTL fine mapping and cloning. Additional layers of regulatory variation have been studied that could also contribute to the molecular basis of quantitative genetics of crop traits. Despite this progress, the role of marker-assisted selection in plant breeding will ultimately depend on the genetic model underlying quantitative variation.

Breeding↗

PubMed2005

The full EM algorithm for the MLEs of QTL effects and positions and their estimated variances in multiple-interval mapping.

The advent of complete genetic linkage maps of DNA markers has made systematic studies of mapping quantitative trait loci (QTL) in experimental organisms feasible. The method of multiple-interval mapping provides an appropriate way for mapping QTL using genetic markers. However, efficient algorithms for the computation involved remain to be developed. In this article, a full EM algorithm for the simultaneous computation of the MLEs of QTL effects and positions is developed. EM-based formulas are derived for computing the observed Fisher information matrix. The full EM algorithm is compared with an ECM algorithm developed by Kao and Zeng (1997, Biometrics 53, 653-665). The validity of the inverted observed Fisher information matrix as an estimate of the variance matrix of the MLEs is demonstrated by a simulation study.

Algorithms↗

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↗

PubMed1994

Empirical threshold values for quantitative trait mapping.

The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.

Chromosome Mapping↗

PubMed2006

Haplotype variation in structure and expression of a gene cluster associated with a quantitative trait locus for improved yield in rice.

By constructing nearly isogenic lines (NILs) that differ only at a single quantitative trait locus (QTL), we fine-mapped the yield-improving QTL qGY2-1 to a 102.9-kb region on rice chromosome 2. Comparison analysis of the genomic sequences in the mapped QTL region between the donor (Dongxiang wild rice, Oryza rufipogon Griff.) and recurrent (Guichao2, Oryza sativa ssp. indica) parents used for the development of NILs identified the haplotypes of a leucine-rich repeat receptor kinase gene cluster, which showed extensive allelic variation. The sequences between genes in the cluster had a very high rate of divergence. More importantly, the genes themselves also differed between two haplotypes: Only 92% identity was observed for one allele, and another allele was found to have completely lost its allelic counterpart in Guichao2. The other six shared genes all showed >98% identity, and four of these exhibited obvious regulatory variation. The same haplotype segments also differed in length (43.9-kb in Guichao2 vs. 52.6-kb in Dongxiang wild rice). Such extensive sequence variation was also observed between orthologous regions of indica (cv. 93-11) and japonica (cv. Nipponbare) subspecies of Oryza sativa. Different rates of sequence divergence within the cluster have resulted in haplotype variability in 13 rice accessions. We also detected allelic expression variation in this gene cluster, in which some genes gave unequal expression of alleles in hybrids. These allelic variations in structure and expression suggest that the leucine-rich repeat receptor kinase gene cluster identified in our study should be a particularly good candidate for the source of the yield QTL.

Alleles↗

PubMed2006

Assessing the importance of genotype x environment interaction for root traits in rice using a mapping population II: conventional QTL analysis.

Modifying plant root systems is considered a means of crop improvement targeted to low-resource environments, particularly low nutrient and drought-prone agriculture. The identification of quantitative trait loci (QTLs) for root traits has stimulated marker-assisted breeding to this end, but different QTLs have been detected in different populations of the same species, and importantly, in the same population when grown in different experimental environments. The presence of QTL x environment interaction is implicated, and this must be characterised if the utility of the target QTLs is to be realised. Previous attempts to do this suffer from a lack of control over replicate environments and inadequate statistical rigour. The Bala x Azucena mapping population was grown in two replicate experiments of four treatment environments, a control, a low light, a low soil nitrogen and a low soil water treatment. After a 4 weeks growth, maximum root length, maximum root thickness, root mass below 50 cm, total plant dry mass, % root mass and shoot length were measured. A summary of the overall results is presented in an accompanying paper. Here, QTL analysis by composite interval mapping is presented. A total of 145 QTLs were detected, mapping to 37 discrete loci on all chromosomes. Superficial evidence of QTL x E (great difference in LOD score) was tested by single-marker analysis which confirmed QTL x E for five loci representing only five individual trait-loci interactions. Some loci appeared to be stable across environments. Some QTLs were clearly more or less active under low light, low nitrogen or drought. A few notable loci on chromosomes 1, 2, 3, 5, 7 and 9 are briefly discussed. Also discussed are some remaining statistical shortcomings that will be addressed in another companion paper.

Adaptation, Physiological↗

PubMed2005

Mapping of a major QTL for pre-harvest sprouting tolerance on chromosome 3A in bread wheat.

Quantitative trait loci (QTL) analysis was conducted for pre-harvest sprouting tolerance (PHST) in bread wheat for a solitary chromosome 3A, which was shown to be important for this trait in earlier studies. An inter-varietal mapping population in the form of recombinant inbred lines (RILs) developed from a cross between SPR8198 (a PHS tolerant genotype) and HD2329 (a PHS susceptible cultivar) was used for this purpose. The parents and the RIL population were grown in six different environments and the data on PHS were collected in each case. A framework linkage map of chromosome 3A with 13 markers was prepared and used for QTL analysis. A major QTL (QPhs.ccsu-3A.1) was detected on 3AL at a genetic distance of approximately 183 cM from centromere, the length of the map being 279.1 cM. The QTL explained 24.68% to 35.21% variation in individual environments and 78.03% of the variation across the environments (pooled data). The results of the present study are significant on two counts. Firstly, the detected QTL is a major QTL, explaining up to 78.03% of the variation and, secondly, the QTL showed up in all the six environments and also with the pooled data, which is rather rare in QTL analysis. The positive additive effects in the present study suggest that a superior allele of the QTL is available in the superior parent (SPR8198), which can be used for marker-aided selection for the transfer of this QTL allele to obtain PHS-tolerant progeny. It has also been shown that the red-coloured grain of PHS tolerant parent is not associated with the QTL for PHST identified during the present study, suggesting that PHS tolerant white-grained cultivars can be developed.

Chromosome Mapping↗

PubMed2004

Investigation of obesity candidate genes on porcine fat deposition quantitative trait loci regions.

OBJECTIVES: To investigate possible obesity candidate genes in regions of porcine quantitative trait loci (QTL) for fat deposition and obesity-related phenotypes. RESEARCH METHODS AND PROCEDURES: Chromosome mapping and QTL analyses of obesity candidate genes were performed using DNA panels from a reference pig family. Statistical association analyses of these genes were performed for fat deposition phenotypes in several other commercial pig populations. RESULTS: Eight candidate genes were mapped to QTL regions of pig chromosomes in this study. These candidate genes also served as anchor loci to determine homologous human chromosomal locations of pig fat deposition QTL. Preliminary analyses of relationships among polymorphisms of individual candidate genes and a variety of phenotypic measurements in a large number of pigs were performed. On the basis of available data, gene-gene interactions were also studied. DISCUSSION: Comparative analysis of obesity-related genes in the pig is not only important for development of marker-assisted selection on growth and fat deposition traits in the pig but also provides for an understanding of their genetic roles in the development of human obesity.

Adipose Tissue↗

PubMed2004

Identification and fine mapping of quantitative trait loci for growth traits on bovine chromosomes 2, 6, 14, 19, 21, and 23 within one commercial line of Bos taurus.

We report the identification and fine mapping of QTL for birth weight (BWT), preweaning ADG (PWADG), and postweaning ADG on feed (ADGF) in a commercial line of Bos taurus using an identical-by-descent haplotype sharing method. One hundred seventy-six calves of 12 bulls (9 to 30 male calves from each sire) of the Beefbooster, Inc., M1 line were typed using 71 genetic markers from bovine chromosomes (BTA) 2, 6, 14, 19, 21, and 23 (8 to 16 markers from each chromosome). Sixteen haplotypes were found to have significant (P <0.05) associations with BWT at the comparison-wise threshold. The 16 haplotypes span 13 chromosomal regions, two on BTA 2 (9.1 to 22.5 cM and 95.0 to 100.3 cM), three on BTA 6 (8.2 to 11.8 cM, 35.5 to 49.7 cM, and 83.0 to 86.2 cM), three on BTA 14 (26.0 to 26.7 cM, 36.2 to 46.2 cM, and 52.0 to 67.7 cM), one on BTA 19 (52.0 to 52.7 cM), two on BTA 21 (9.9 to 20.4 cM and 28.2 to 46.1 cM), and two on BTA 23 (23.9 to 36.0 cM and 45.1 to 50.9 cM). Thirteen haplotypes spanning seven chromosomal regions significantly affected (P <0.05) PWADG at the comparison-wise threshold. The seven chromosomal regions include two regions on BTA 6 (11.8 to 44.2 cM and 83.0 to 86.2 cM), one on BTA 14 (26.7 to 50.8 cM), one on BTA 19 (4.8 to 15.9 cM), one on BTA 21 (9.9 to 20.4 cM), and two on BTA 23 (17.3 to 36.0 cM and 45.1 to 50.9 cM). For ADGF, 11 haplotypes were identified to have significant associations (P <0.05) at the comparison-wise threshold. The 11 haplotypes represented eight chromosomal regions, one on BTA 2 (9.1 to 22.5 cM), two on BTA 6 (49.7 to 50.1 cM and 59.6 to 63.6 cM), two on BTA 14 (17.0 to 24.0 cM and 36.2 to 46.2 cM), two on BTA 19 (52.0 to 52.7 cM and 65.1 to 65.7 cM), and one on BTA 21 (46.1 to 53.1 cM). The QTL regions identified and fine mapped in this study will provide a reference for future positional candidate gene research and marker-assisted selection of various growth traits.

Alleles↗

PubMed2005

Quantitative trait loci that control dengue-2 virus dissemination in the mosquito Aedes aegypti.

The mosquito Aedes aegypti is the most important vector of yellow fever and dengue fever flaviviruses. Ae. aegypti eradication campaigns have not been sustainable and there are no effective vaccines for dengue viruses. Alternative control strategies may depend upon identification of mosquito genes that condition flavivirus susceptibility and may ultimately provide clues for interrupting transmission. Quantitative trait loci affecting the ability of Ae. aegypti to develop a dengue-2 infection in the midgut have been mapped previously. Herein we report on QTL that determine whether mosquitoes with a dengue-2-infected gut can then disseminate the virus to other tissues. A strain selected for high rates of dengue-2 dissemination was crossed to a strain selected for low dissemination rates. QTL were mapped in the F(2) and again in an F(5) advanced intercross line. QTL were detected at 31 cM on chromosome I, at 32 cM on chromosome II, and between 44 and 52 cM on chromosome III. Alleles at these QTL were additive or dominant in determining rates of dengue-2 dissemination and accounted for approximately 45% of the phenotypic variance. The locations of dengue-2 midgut infection and dissemination QTL correspond to those found in earlier studies.

Aedes↗

PubMed2005

Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross.

The leptin receptor gene (LEPR) is a candidate for traits related to growth and body composition, and is located on SSC6 in a region where fatness and meat composition quantitative trait loci (QTL) have previously been detected in several F2 experimental designs. The aims of this work were: (i) to fine map these QTL on a larger sample of animals and generations (F3 and backcross) of an Iberian x Landrace intercross and (ii) to examine the effects of LEPR alleles on body composition traits. Eleven single nucleotide polymorphisms (SNPs) were detected by sequencing LEPR coding regions in Iberian and Landrace pig samples. Three missense polymorphisms were genotyped by pyrosequencing in 33 F0, 70 F1, 418 F2, 86 F3 and 128 individuals coming from the backcross of four F2 males with 24 Landrace females. Thirteen microsatellites and one SNP were also genotyped. Traits analysed were: backfat thickness at different locations (BF(T)), intramuscular fat percentage (IMF(P)), eye muscle area (EM(A)), loin depth (LO(D)), weight of shoulder (SH(W)), weight of ribs (RIB(W)) and weight of belly bacon (BB(W)). Different statistical models were applied in order to evaluate the number and effects of QTL on chromosome 6 and the possible causality of the LEPR gene variants with respect to the QTL. The results support the presence of two QTL on SSC6. One, at position 60-100 cM, affects BF(T) and RIB(W). The other and more significant maps in a narrow region (130-132 cM) and affects BF(T), IMF(P), EM(A), LO(D), SH(W), RIB(W) and BB(W). Results also support the association between LEPR alleles and BF(T) traits. The possible functional implications of the analysed polymorphisms are considered.

Alleles↗

PubMed2003

Genetic contributions to body weight in mice: relationship of exploratory behavior to weight.

OBJECTIVE: The A/J and C57BL/6J mouse strains differ markedly in their exploratory behavior and their weight gain on a high-fat diet. We examined the genetic contributions of exploratory behavior to body weight and tested for shared, pleiotropic loci influencing energy homeostasis. RESEARCH METHODS AND PROCEDURES: Segregating (AxB6)F2 intercross (n = 514) and (B6AF1xA/J)N2 backcross (N = 223) populations were studied, phenotyping for weight and exploratory behaviors. Relationships among traits were analyzed by correlations. Weight traits were dissected with a genome-wide scan. RESULTS: Modest correlations were found between exploratory behaviors and weight, explaining 2% to 14% of the variance. Quantitative trait loci (QTL) for body weight at 8 weeks (wgt8), 10 weeks (wgt10), and 2-week weight gain (difference between weeks 8 and 10) on a 6% fat diet were mapped. Two QTL on chromosome 1 (peaks at 66 cM and 100 cM; Bw8q1) affected wgt8 [likelihood of the odds ratio (Lod), 3.0 and 4.4] and wgt10 (Lod, 2.2 and 3.4), respectively. In the backcross, a significant QTL on chromosome 4 (peak at 66 cM; Bw8q2) affected wgt 8 (Lod, 3.3) and wgt10 (Lod, 3.1). For 2-week weight gain, suggestive QTL were mapped on chromosomes 4 and 6. The chromosome 6 QTL region overlaps a human 7q locus for obesity. A search for between-strain sequence polymorphisms in the leptin and NPY genes was unrevealing. DISCUSSION: In mice, loci influencing exploratory activity play a modest role in body-weight regulation. Some forms of obesity may emerge from loci regulating normal body weight.

Animals↗

PubMed2004

An analytical formula to estimate confidence interval of QTL location with a saturated genetic map as a function of experimental design.

Analytical formulae are derived for the confidence interval for location of a quantitative trait locus (QTL) using a saturated genetic map, as a function of the experimental design, the QTL allele substitution effect, and the number of individuals genotyped and phenotyped. The formulae are derived assuming evenly spaced recombination events, rather than the actual unevenly spaced distribution. The formulae are useful for determining desired sample size when designing a wide variety of QTL mapping experiments, and for evaluating a priori the potential of a given mapping population for defining the location of a QTL. The formulae do not take into account the finite number of recombination events in a given sample.

Animals↗

PubMed2004

Analysis of digenic epistatic effects and QE interaction effects QTL controlling grain weight in rice.

Immortalized F(2) population of rice (Oryza sativa L.) was developed by randomly mating F(1) among recombinant inbred (RI) lines derived from (Zhenshan 97B x Minghui 63), which allowed replications within and across environments. QTL (quantitative trait loci) mapping analysis on kilo-grain weight of immortalized F(2) population was performed by using newly developed software for QTL mapping, QTLMapper 2.0. Eleven distinctly digenic epistatic loci included a total of 15 QTL were located on eight chromosomes. QTL main effects of additive, dominance, and additive x additive, additive x dominance, and dominance x dominance interactions were estimated. Interaction effects between QTL main effects and environments (QE) were predicted. Less than 40% of single effects, most of which were additive effects, for identified QTL were significant at 5% level. The directional difference for QTL main effects suggested that these QTL were distributed in parents in the repulsion phase. This should make it feasible to improve kilo-grain weight of both parents by selecting appropriate new recombinants. Only few of the QE interaction effects were significant. Application prospect for QTL mapping achievements in genetic breeding was discussed.

Chromosomes, Plant↗

PubMed2006

Comparative analysis of haplotype association mapping algorithms.

BACKGROUND: Finding the genetic causes of quantitative traits is a complex and difficult task. Classical methods for mapping quantitative trail loci (QTL) in miceuse an F2 cross between two strains with substantially different phenotype and an interval mapping method to compute confidence intervals at each position in the genome. This process requires significant resources for breeding and genotyping, and the data generated are usually only applicable to one phenotype of interest. Recently, we reported the application of a haplotype association mapping method which utilizes dense genotyping data across a diverse panel of inbred mouse strains and a marker association algorithm that is independent of any specific phenotype. As the availability of genotyping data grows in size and density, analysis of these haplotype association mapping methods should be of increasing value to the statistical genetics community. RESULTS: We describe a detailed comparative analysis of variations on our marker association method. In particular, we describe the use of inferred haplotypes from adjacent SNPs, parametric and nonparametric statistics, and control of multiple testing error. These results show that nonparametric methods are slightly better in the test cases we study, although the choice of test statistic may often be dependent on the specific phenotype and haplotype structure being studied. The use of multi-SNP windows to infer local haplotype structure is critical to the use of a diverse panel of inbred strains for QTL mapping. Finally, because the marginal effect of any single gene in a complex disease is often relatively small, these methods require the use of sensitive methods for controlling family-wise error. We also report our initial application of this method to phenotypes cataloged in the Mouse Phenome Database. CONCLUSION: The use of inbred strains of mice for QTL mapping has many advantages over traditional methods. However, there are also limitations in comparison to the traditional linkage analysis from F2 and RI lines. Application of these methods requires careful consideration of algorithmic choices based on both theoretical and practical factors. Our findings suggest general guidelines, though a complete evaluation of these methods can only be performed as more genetic data in complex diseases becomes available.

Algorithms↗

PubMed2000

Fine mapping of quantitative trait loci using advanced intercross lines of mice and positional cloning of the corresponding genes.

High-resolution mapping of quantitative trait loci (QTLs) is an essential step towards positional cloning and identification of the corresponding genes. Most QTL detection and mapping studies in mice have been carried out using F2 intercross and backcross populations. As a consequence of the limited number of recombination events in small chromosomal regions, this has generally permitted mapping to only relatively large confidence intervals of 20 to 40 cM. A number of population designs have been proposed to increase recombination level in crosses. This includes advanced intercross lines (AIL) described by Darvasi and Soller [Genomics. 1995; 141: 1199-1207]. In this report demonstration of the utility of the AIL approach for fine mapping of QTL, which previously had been mapped with 95% confidence interval to 20 to 40 cM in a F2 intercross, will be presented. The methodological approaches to go from the fine-mapped QTL to the identification of the actual genes and mutations are discussed.

Animals↗

PubMed2006

Quantitative trait loci for locomotor behavior in Drosophila melanogaster.

Locomotion is an integral component of most animal behaviors and many human diseases and disorders are associated with locomotor deficits, but little is known about the genetic basis of natural variation in locomotor behavior. Locomotion is a complex trait, with variation attributable to the joint segregation of multiple interacting quantitative trait loci (QTL), with effects that are sensitive to the environment. We assessed variation in a component of locomotor behavior (locomotor reactivity) in a population of 98 recombinant inbred lines of Drosophila melanogaster and mapped four QTL affecting locomotor reactivity by linkage to polymorphic roo transposable element insertion sites. We used complementation tests of deficiencies to fine map these QTL to 12 chromosomal regions and complementation tests of mutations to identify 13 positional candidate genes affecting locomotor reactivity, including Dopa decarboxylase (Ddc), which catalyzes the final step in the synthesis of serotonin and dopamine. Linkage disequilibrium mapping in a population of 164 second chromosome substitution lines derived from a single natural population showed that polymorphisms at Ddc were associated with naturally occurring genetic variation in locomotor behavior. These data implicate variation in the synthesis of bioamines as a factor contributing to natural variation in locomotor reactivity.

Animals↗