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At least 127 records · Page 7Linked to original sources
PubMed2004

IGF2 antisense transcript expression in porcine postnatal muscle is affected by a quantitative trait nucleotide in intron 3.

A paternally expressed quantitative trait locus (QTL) for muscle mass was mapped to the insulin-like growth factor II (IGF2) locus at the distal end of pig chromosome 2p. It was recently demonstrated that a G to A transition at position intron 3-nt 3072 is the quantitative trait nucleotide (QTN) underlying this QTL. In this study we report for the first time the existence of imprinted porcine IGF2 antisense transcripts (IGF2-AS) and demonstrate that their expression in postnatal muscle is also affected by the QTN. A coregulated expression of IGF2 and IGF2-AS RNAs in muscle and liver with decreasing transcription from fetal to adult age was demonstrated. Further, the significant difference found in expression of IGF2-AS in postnatal muscle between individuals with different QTL genotypes and the lack of significant differences in fetal muscle and liver reflect completely what has been found for IGF2 sense transcripts.

Animals↗
PubMed1999

Quantitative trait transmission disequilibrium test: allowance for missing parents.

The association of a quantitative trait with transmission disequilibrium of parental marker alleles can be a powerful method to discover the genetic basis of the trait. However, current methods require marker genotypes on all parents. We present a new method that allows the assessment of the association of a quantitative trait with transmission disequilibrium of marker alleles when parental marker genotypes are missing, but marker genotypes for sibships are available. Because our approach is based on regression methods, additional covariates, including multiple marker loci, can be included in our proposed method of analysis. Application of this methodology to the COGA data to assess the association of quantitative traits (ERPs) with marker loci on chromosome 7 failed to identify statistically significant transmission disequilibrium.

Alcoholism↗
PubMed1999

Multiple interval mapping for quantitative trait loci.

A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from approximately 1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed approximately 10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0. 3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).

Chromosome Mapping↗
PubMed1991

Identification of markers associated with quantitative trait loci in chickens by DNA fingerprinting.

Three approaches for identifying VNTR alleles associated with quantitative traits in chickens are described. One approach is based on the comparison of well-defined selected and non-selected control strains. The second approach is based on analyzing chickens within a breeding population ranked according to specific traits and the third approach involves segregation analysis. In this latter approach a large number of offspring of a single male segregating for a quantitative trait are produced and tested for trait association of the male DNA fingerprinting bands. In all cases pooled DNA samples of birds, rather than individual samples, are analyzed and band intensity is assumed to reflect the relative frequency of an allele. Examples from the literature and from our laboratory indicate that these methods permit the identification of DNA fingerprinting bands associated with quantitative traits. After developing locus-specific probes for these bands it should ultimately be possible to detect and map quantitative trait loci.

Animals↗
PubMed2000

Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus.

One strategy for localization of a quantitative-trait locus (QTL) is to test whether the distribution of a quantitative trait depends on the number of copies of a specific genetic-marker allele that an individual possesses. This approach tests for association between alleles at the marker and the QTL, and it assumes that association is a consequence of the marker being physically close to the QTL. However, problems can occur when data are not from a homogeneous population, since associations can arise irrespective of a genetic marker being in physical proximity to the QTL-that is, no information is gained regarding localization. Methods to address this problem have recently been proposed. These proposed methods use family data for indirect stratification of a population, thereby removing the effect of associations that are due to unknown population substructure. They are, however, restricted in terms of the number of children per family that can be used in the analysis. Here we introduce tests that can be used on family data with parent and child genotypes, with child genotypes only, or with a combination of these types of families, without size restrictions. Furthermore, equations that allow one to determine the sample size needed to achieve desired power are derived. By means of simulation, we demonstrate that the existing tests have an elevated false-positive rate when the size restrictions are not followed and that a good deal of information is lost as a result of adherence to the size restrictions. Finally, we introduce permutation procedures that are recommended for small samples but that can also be used for extensions of the tests to multiallelic markers and to the simultaneous use of more than one marker.

Alleles↗
PubMed2002

Association analysis for quantitative traits by data mining: QHPM.

Previously, we have presented a data mining-based algorithmic approach to genetic association analysis, Haplotype Pattern Mining. We have now extended the approach with the possibility of analysing quantitative traits and utilising covariates. This is accomplished by using a linear model for measuring association. We present results with the extended version, QHPM, with simulated quantitative trait data. One data set was simulated with the population simulator package Populus, and another was obtained from GAW12. In the former, there were 2-3 underlying susceptibility genes for a trait, each with several ancestral disease mutations, and 1 or 2 environmental components. We show that QHPM is capable of finding the susceptibility loci, even when there is strong allelic heterogeneity and environmental effects in the disease models. The power of finding quantitative trait loci is dependent on the ascertainment scheme of the data: collecting the study subjects from both ends of the quantitative trait distribution is more effective than using unselected individuals or individuals ascertained based on disease status, but QHPM has good power to localize the genes even with unselected individuals. Comparison with quantitative trait TDT (QTDT) showed that QHPM has better localization accuracy when the gene effect is weak.

Chromosome Mapping↗
PubMed2000

Power of selective genotyping in genetic association analyses of quantitative traits.

A power calculation is described in which the power of selective genotyping in genetic association studies of quantitative traits is evaluated. The method of selective genotyping implies the selection of the extremely high and low scoring individuals from the continuous distribution of a quantitative trait. The selected individuals are genotyped and association is tested. In the power calculation the following parameters are varied: total sample size of the phenotyped group (1000, 1500, 2000); selection of extremely high and low scoring individuals (2.5%, 5%, 10%); allele frequency of the risk increasing allele (0.10 to 0.90); mode of inheritance and proportion of variance explained by the quantitative trait locus (QTL) (omega 2 = 0.01, 0.05, 0.10). We conclude that the method of selective genotyping is a powerful method to detect association for a quantitative trait.

Chromosome Mapping↗
PubMed2006

Complex genetic interactions in a quantitative trait locus.

Whether in natural populations or between two unrelated members of a species, most phenotypic variation is quantitative. To analyze such quantitative traits, one must first map the underlying quantitative trait loci. Next, and far more difficult, one must identify the quantitative trait genes (QTGs), characterize QTG interactions, and identify the phenotypically relevant polymorphisms to determine how QTGs contribute to phenotype. In this work, we analyzed three Saccharomyces cerevisiae high-temperature growth (Htg) QTGs (MKT1, END3, and RHO2). We observed a high level of genetic interactions among QTGs and strain background. Interestingly, while the MKT1 and END3 coding polymorphisms contribute to phenotype, it is the RHO2 3'UTR polymorphisms that are phenotypically relevant. Reciprocal hemizygosity analysis of the Htg QTGs in hybrids between S288c and ten unrelated S. cerevisiae strains reveals that the contributions of the Htg QTGs are not conserved in nine other hybrids, which has implications for QTG identification by marker-trait association. Our findings demonstrate the variety and complexity of QTG contributions to phenotype, the impact of genetic background, and the value of quantitative genetic studies in S. cerevisiae.

3' Untranslated Regions↗
PubMed2002

Linkage disequilibrium mapping of novel lung tumor susceptibility quantitative trait loci in mice.

Linkage disequilibrium (LD) has been used to map chromosomal regions regulating quantitative traits, also called quantitative trait loci (QTLs). With the increasing number of available mouse polymorphic genetic markers, LD can be estimated for the purpose of fine-mapping a given QTL or in the identification of novel QTLs. A whole-genome LD analysis was conducted for mapping mouse lung tumor susceptibility QTLs in 25 strains of mice with known susceptibility to lung cancer using 5638 genetic markers. A total of 63 markers were found to be significantly associated with lung tumor susceptibility, many of which were novel QTLs. This study demonstrates the feasibility of using LD to map QTLs on a whole genome level. Further characterization of the newly identified lung tumor susceptibility QTLs may lead to the identification of genes whose human homologue may predispose some individuals to lung cancer.

Animals↗
PubMed2002

[A linkage analysis of quantitative trait loci for familial schizophrenia on chromosome 1].

OBJECTIVE: To explore the molecular genetic relationship between chromosome 1 and quantitative trait loci for familial schizophrenia. METHODS: A series of assessment scales included positive and negative syndrome scale (PANSS), global assessment of functional scale (GAFS), premorbid schizoid and schizotypal traits scale (PSST), premorbid social adjustment scale (PSA) were applied to quantify the phenotypes of schizophrenia. Non-parametric linkage analysis of quantitative traits was conducted in 32 multiplex pedigrees with schizophrenia by using 29 microsatellite makers on chromosome 1. RESULTS: Haseman-Elston quantitative trait analysis detected a maximum Traditional H-E Lods of 1.73 and a maximum EH H-E Lods of 1.65 of negative symptoms (PANSS-N ) at 147.64 cM, which was overlapped to the positive region of 1q21-23 in qualitative linkage analysis. CONCLUSION: The results suggest there might be an independent quantitative trait locus of negative symptoms on 1q21-23 for familial schizophrenia.

Chromosomes, Human, Pair 1↗
PubMed2001

Finding the molecular basis of quantitative traits: successes and pitfalls.

Understanding the molecular basis of quantitative genetic variation is a principal goal for biomedicine. Although the complex genetic architecture of quantitative traits has so far largely frustrated attempts to identify genes in humans by standard linkage methodologies, quantitative trait loci (QTL) have been mapped in plants, insects and rodents. However, identifying the molecular bases of QTL remains a challenge. Here, we discuss why this is and how new experimental strategies and analytical techniques, combined with the fruits of the genome projects, are beginning to identify candidate genes for QTL studies in several model organisms.

Animals↗
PubMed2000

Detecting the undetected: estimating the total number of loci underlying a quantitative trait.

Recent studies have begun to reveal the genes underlying quantitative trait differences between closely related populations. Not all quantitative trait loci (QTL) are, however, equally likely to be detected. QTL studies involve a limited number of crosses, individuals, and genetic markers and, as a result, often have little power to detect genetic factors of small to moderate effects. In this article, we develop an estimator for the total number of fixed genetic differences between two parental lines. Like the Castle-Wright estimator, which is based on the observed segregation variance in classical crossbreeding experiments, our QTL-based estimator requires that a distribution be specified for the expected effect sizes of the underlying loci. We use this expected distribution and the observed mean and minimum effect size of the detected QTL in a likelihood model to estimate the total number of loci underlying the trait difference. We then test the QTL-based estimator and the Castle-Wright estimator in Monte Carlo simulations. When the assumptions of the simulations match those of the model, both estimators perform well on average. The 95% confidence limits of the Castle-Wright estimator, however, often excluded the true number of underlying loci, while the confidence limits for the QTL-based estimator typically included the true value approximately 95% of the time. Furthermore, we found that the QTL-based estimator was less sensitive to dominance and to allelic effects of opposite sign than the Castle-Wright estimator. We therefore suggest that the QTL-based estimator be used to assess how many loci may have been missed in QTL studies.

Algorithms↗
PubMed1994

Mapping quantitative trait loci with DNA microsatellites in a commercial dairy cattle population.

Individual loci affecting economically important traits can be located using genetic linkage between quantitative trait loci and genetic markers. In the 'granddaughter' experimental design, heterozygous grandsires and their sons are genotyped for the genetic marker, while the quantitative trait records of the granddaughters are used for statistical analysis. Ten DNA microsatellite markers were used to look for associations with quantitative trait loci affecting milk production traits in seven Israeli Holstein grandsire families. At least 60% more grandsires were heterozygous, and 40% fewer individuals were discarded because of unknown paternal allele origin, as compared with diallelic markers. The effects of paternal alleles for locus D21S4 on kg milk and protein were significant (P < 0.025). The allele substitution effects for sire 783 were 283 kg milk and 5.7 kg protein. For both traits, progeny of sire 783 that inherited allele '18' had higher evaluations than progeny that inherited allele '21'. These results were verified by genotyping 151 of his daughters. Thus, the rate of genetic gain for protein production can be increased by selecting progeny of sire 783 carrying allele '18' at this locus.

Animals↗
PubMed2005

Using progenitor strain information to identify quantitative trait nucleotides in outbred mice.

We have developed a fast and economical strategy for dissecting the genetic architecture of quantitative trait loci at a molecular level. The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis. We apply the method to a 4.8-Mb region on mouse chromosome 1 that contains a QTL influencing anxiety segregating in a heterogeneous stock and show that, under the assumption that a single QTN is present and lies in a region conserved between the human and mouse genomes, it is possible to reduce the number of variants likely to be the quantitative trait nucleotide from many thousands to <20.

Animals↗
PubMed1997

A multivariate model for the analysis of sibship covariance structure using marker information and multiple quantitative traits.

A model was developed to detect effects of quantitative trait loci (QTLs) in sibships from simulated nuclear family data using the full covariance structure of the data and analyzing all five quantitative traits simultaneously in a multivariate model. Evidence of the presence of loci was detected on chromosomes 4, 8, 9, and 10. The method provided stable results and is worth further exploration for its performance and optimal sample size requirements under realistic conditions.

Alleles↗
PubMed1990

Selecting pedigrees for linkage analysis of a quantitative trait: the expected number of informative meioses.

With evidence of segregation at a major locus for a quantitative trait having been found, a logical next step is to select a subset of the pedigrees to include in a linkage study to map the major locus. Ideally this subset should include much of the linkage information in the sample but include only a fraction of the pedigrees. We previously described a strategy for selecting pedigrees for linkage analysis of a quantitative trait on the basis of a pedigree likelihood-ratio statistic. For quantitative traits controlled by a major locus with a rare dominant allele, the likelihood-ratio strategy extracted nearly all the information for linkage while typically requiring marker data on only about one-third of the pedigrees. Here, we describe a new strategy to select pedigrees for linkage analysis on the basis of the expected number of potentially informative meioses in each pedigree. We demonstrate that this informative-meioses strategy provides an efficient and more general means to select pedigrees for a linkage study of a quantitative trait.

Female↗
PubMed2000

Estimation of variance components of quantitative traits in inbred populations.

Use of variance-component estimation for mapping of quantitative-trait loci in humans is a subject of great current interest. When only trait values, not genotypic information, are considered, variance-component estimation can also be used to estimate heritability of a quantitative trait. Inbred pedigrees present special challenges for variance-component estimation. First, there are more variance components to be estimated in the inbred case, even for a relatively simple model including additive, dominance, and environmental effects. Second, more identity coefficients need to be calculated from an inbred pedigree in order to perform the estimation, and these are computationally more difficult to obtain in the inbred than in the outbred case. As a result, inbreeding effects have generally been ignored in practice. We describe here the calculation of identity coefficients and estimation of variance components of quantitative traits in large inbred pedigrees, using the example of HDL in the Hutterites. We use a multivariate normal model for the genetic effects, extending the central-limit theorem of Lange to allow for both inbreeding and dominance under the assumptions of our variance-component model. We use simulated examples to give an indication of under what conditions one has the power to detect the additional variance components and to examine their impact on variance-component estimation. We discuss the implications for mapping and heritability estimation by use of variance components in inbred populations.

Algorithms↗
PubMed2002

Unified sampling approach for multipoint linkage disequilibrium mapping of qualitative and quantitative traits.

Rapid development in biotechnology has enhanced the opportunity to deal with multipoint gene mapping for complex diseases, and association studies using quantitative traits have recently generated much attention. Unlike the conventional hypothesis-testing approach for fine mapping, we propose a unified multipoint method to localize a gene controlling a quantitative trait. We first calculate the sample size needed to detect linkage and linkage disequilibrium (LD) for a quantitative trait, categorized by decile, under three different modes of inheritance. Our results show that sampling trios of offspring and their parents from either extremely low (EL) or extremely high (EH) probands provides greater statistical power than sampling in the intermediate range. We next propose a unified sampling approach for multipoint LD mapping, where the goal is to estimate the map position (tau) of a trait locus and to calculate a confidence interval along with its sampling uncertainty. Our method builds upon a model for an expected preferential transmission statistic at an arbitrary locus conditional on the sampling scheme, such as sampling from EL and EH probands. This approach is valid regardless of the underlying genetic model. The one major assumption for this model is that no more than one quantitative trait locus (QTL) is linked to the region being mapped. Finally we illustrate the proposed method using family data on total serum IgE levels collected in multiplex asthmatic families from Barbados. An unobserved QTL appears to be located at tau; = 41.93 cM with 95% confidence interval of (40.84, 43.02) through the 20-cM region framed by markers D12S1052 and D12S1064 on chromosome 12. The test statistic shows strong evidence of linkage and LD (chi-square statistic = 18.39 with 2 df, P-value = 0.0001).

Asthma↗