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At least 163 records · Page 9Linked to original sources
PubMed2004

Quantitative trait Loci for health traits in Finnish Ayrshire cattle.

A whole-genome scan was conducted to search for quantitative trait loci (QTL) affecting health traits in Finnish Ayrshire dairy cattle. The mapping population consisted of 12 bulls and their 491 sons in a granddaughter design. A total of 150 markers were typed covering all 29 autosomes. The traits under study were somatic cell score, mastitis, and a group of other veterinary treatments. Effects of the QTL and positions were estimated with the regression method. When carrying out interval mapping on each chromosome, cofactors were used to adjust for QTL identified at other chromosomes. Empirical P-values were obtained by permutation. Altogether 17 QTL were detected with genomewise significant P-values in the across family analysis. Quantitative trait loci affecting SCS were identified on chromosomes 1, 3, 11, 18, 21, 24, 27, 29, and QTL for mastitis on chromosomes 14, 18. Quantitative trait loci for other veterinary treatments were found on chromosomes 1, 2, 5, 8, 15, 22, and 23. The allele substitution effects were from 0.5 to 1.7 genetic standard deviations. The positions of these health QTL did not overlap with milk QTL detected in previous studies of the same population.

Animals↗
PubMed2006

Genome-wide identification of quantitative trait loci in a cross between Hampshire and Landrace I: carcass traits.

We report the identification of quantitative trait loci (QTL) affecting carcass composition, carcass length, fat deposition and lean meat content using a genome scan across 462 animals from a combined intercross and backcross between Hampshire and Landrace pigs. Data were analysed using multiple linear regression fitting additive and dominance effects. This model was compared with a model including a parent-of-origin effect to spot evidence of imprinting. Several precisely defined muscle phenotypes were measured in order to dissect body composition in more detail. Three significant QTL were detected in the study at the 1% genome-wide level, and twelve significant QTL were detected at the 5% genome-wide level. These QTL comprise loci affecting fat deposition and lean meat content on SSC1, 4, 9, 10, 13 and 16, a locus on SSC2 affecting the ratio between weight of meat and bone in back and weight of meat and bone in ham and two loci affecting carcass length on SSC12 and 17. The well-defined phenotypes in this study enabled us to detect QTL for sizes of individual muscles and to obtain information of relevance for the description of the complexity underlying other carcass traits.

Animal Husbandry↗
PubMed1999

Maximum-Likelihood-Binomial method for genetic model-free linkage analysis of quantitative traits in sibships.

Sib-pair linkage studies are widely used to investigate the genetic factors implicated in complex quantitative traits. To analyze these data, we propose a Maximum-Likelihood-Binomial (MLB) approach, which considers the sibship as a whole and relies on the idea of binomial distributions of parental alleles among offsprings. The method is based on the introduction of a latent binary variable capturing the linkage information between the observed quantitative trait and the marker, and the final likelihood can be expressed assuming a parametric distribution for the studied trait but also without any assumption on this distribution. The test for linkage is a simple likelihood ratio test involving a single parameter. The performances of the MLB method are assessed by a simulation study in different kinds of family samples. In the case of families with various sibship sizes, both MLB approaches (assuming or not a parametric distribution for the quantitative trait) provide very consistent results in terms of type I errors and yield power levels generally higher than those of the classical Haseman-Elston method. In the case of extremely discordant sib pairs, we analytically show that, for a common asymptotic type I error, the distribution-free MLB statistic is expected to be more powerful than the test proposed by Risch and Zhang [(1995) Science 268:1584-1589]. In samples including both extremely concordant and discordant sib-pairs, simulation studies show that the MLB approach is at least as powerful as the EDAC method [Gu et al. (1996) Genet Epidemiol 13:513-533]. This MLB method, which can be easily extended to perform multipoint analysis and to account for genetic heterogeneity, appears to be quite an interesting alternative for mapping quantitative trait loci in humans.

Alleles↗
PubMed2001

Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci.

To investigate the genetic control of salt-induced hypertension, we performed a quantitative trait locus analysis on male mice from a reciprocal backcross between the salt-sensitive C57BL/6J and the normotensive A/J inbred mouse strains after they were provided with water containing 1% salt for 2 weeks. Genome-wide scans performed on these mice and analyzed with a combination of conventional marker-based regressions and a novel simultaneous search for pairs revealed six significant quantitative trait loci associated with salt-induced blood pressure, two of which were interacting loci. These six loci, named Bpq1-6 for blood pressure quantitative trait loci, mapped to D1Mit334, D1Mit14, D4Mit164, D5Mit31, D6Mit15, and D15Mit13. Furthermore, five of these six loci were concordant with hypertension loci in rats, and four were concordant with hypertension loci in humans, suggesting that quantitative trait loci mapping in model organisms can be used to guide the search for human blood pressure genes.

Analysis of Variance↗
PubMed2000

Quantitative trait locus analysis.

Alcoholism is a quantitative disorder that is caused by the combined influences of numerous genes (i.e., quantitative trait loci [QTLs]) and environmental factors. To identify QTLs for alcoholism, researchers compare subject groups (e.g., inbred mouse strains) that differ in both their genetic makeup (i.e., genotype) and alcohol-related trait (e.g., sensitivity to certain alcohol effects). Using statistical tests one can then determine whether a specific gene or DNA region contributes to the trait of interest. This strategy requires that the relevant gene exists in several variants (i.e., is polymorphic). To conduct such QTL analyses, researchers study either a large population of mice that all differ in their genotypes or compare several strains, each of which has a fixed genotype. However, QTL analyses still have several limitations. Nevertheless, such studies already have identified several DNA regions and genes that may affect the response to alcohol and thus may contribute to the risk for alcoholism.

Alcoholism↗
PubMed2006

Cloning of DOG1, a quantitative trait locus controlling seed dormancy in Arabidopsis.

Genetic variation for seed dormancy in nature is a typical quantitative trait controlled by multiple loci on which environmental factors have a strong effect. Finding the genes underlying dormancy quantitative trait loci is a major scientific challenge, which also has relevance for agriculture and ecology. In this study we describe the identification of the DELAY OF GERMINATION 1 (DOG1) gene previously identified as a quantitative trait locus involved in the control of seed dormancy. This gene was isolated by a combination of positional cloning and mutant analysis and is absolutely required for the induction of seed dormancy. DOG1 is a member of a small gene family of unknown molecular function, with five members in Arabidopsis. The functional natural allelic variation present in Arabidopsis is caused by polymorphisms in the cis-regulatory region of the DOG1 gene and results in considerable expression differences between the DOG1 alleles of the accessions analyzed.

Alleles↗
PubMedDate not supplied

Bayes factors for detection of quantitative trait loci.

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.

Algorithms↗
PubMed2001

Bayesian mapping of quantitative trait loci under complicated mating designs.

Quantitative trait loci (QTL) are easily studied in a biallelic system. Such a system requires the cross of two inbred lines presumably fixed for alternative alleles of the QTL. However, development of inbred lines can be time consuming and cost ineffective for species with long generation intervals and severe inbreeding depression. In addition, restriction of the investigation to a biallelic system can sometimes be misleading because many potentially important allelic interactions do not have a chance to express and thus fail to be detected. A complicated mating design involving multiple alleles mimics the actual breeding system. However, it is difficult to develop the statistical model and algorithm using the classical maximum-likelihood method. In this study, we investigate the application of a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm to QTL mapping under arbitrarily complicated mating designs. We develop the method under a mixed-model framework where the genetic values of founder alleles are treated as random and the nongenetic effects are treated as fixed. With the MCMC algorithm, we first draw the gene flows from the founders to the descendants for each QTL and then draw samples of the genetic parameters. Finally, we are able to simultaneously infer the posterior distribution of the number, the additive and dominance variances, and the chromosomal locations of all identified QTL.

Analysis of Variance↗
PubMed2006

Rearranged gene order between pig and human in a quantitative trait loci region on SSC3.

A quantitative trait locus (QTL) for ovulation rate on chromosome 3 that peaks at 36 cM has been identified in a Meishan-White composite resource population with an additive effect of 2.2 corpora lutea. As part of an effort to identify the responsible gene(s), typing of additional genes on the INRA-University of Minnesota porcine radiation hybrid (IMpRH) map of SSC3 and comparative analysis of gene order was conducted. We placed 52 known genes and expressed sequence tags, two BAC-end sequences and one microsatellite (SB42) on a framework map that fills gaps on previous RH maps. Data were analysed for two-point and multipoint linkage with the IMpRH mapping tool and were submitted to the IMpRH database (http://imprh.toulouse.inra.fr/). Gene order was confirmed for 42 loci residing in the QTL region (spanning c. 17 Mb of human sequence) by using the high-resolution IMpRH2 panel. Carthagène (http://www.inra.fr/internet/departments/MIA/T/CarthaGene) was used to estimate multipoint marker distance and order using all public markers on SSC3 in the IMpRH database and those typed in this study. For the high-resolution map, only data for markers typed in both panels were used. Comparative analysis of human and porcine maps identified conservation of gene order for SSC3q and multiple blocks of conserved segments for SSC3p, which included six distinct segments of HSA7 and two segments of HSA16. The results of this study allow significant refinement of the SSC3p region that contains an ovulation rate QTL.

Animals↗
PubMed2005

Epistasis, not numbers, regulates functions of clustered Dahl rat quantitative trait loci applicable to human hypertension.

Quantitative trait loci (QTLs) for blood pressure (BP) were found on chromosome 10 of Dahl salt-sensitive rats and are potentially important to human essential hypertension. But their identities and how they influence BP together were not known. Presently, we first fine mapped existing QTLs, C10QTL1, C10QTL2, and C10QTL3, by constructing congenic strains. In the process, a new QTL, C10QTL4, was identified. Because the intervals harboring C10QTL1 and C10QTL4 contain a maximum of 16 and 10 possible genes, respectively, a limited number of specific gene targets has been identified to be QTLs residing in human homologous regions on chromosome 17. Moreover, because none of these candidates encodes a gene known to influence BP, the 2 QTLs will represent novel genes for BP regulations. Second, we used congenic strains with QTL combinations to analyze the interactions between the QTLs. Consequently, a double combination of C10QTL4 and C10QTL1 possessed the same BP as each of the 2 QTLs alone. BP of a triple combination of C10QTL4, C10QTL1, and C10QTL3 was not different from BP of the C10QTL4 and C10QTL1 double combination. These results demonstrate that C10QTL4, C10QTL1, and C10QTL3 are epistatic to one another in their BP effects. In contrast, when adding C10QTL2 into the triple formation of the 3 QTLs above to create a quadruple QTL combination, BP increased proportionately, indicating that C10QTL2 acts independently of C10QTL4, C10QTL1, and C10QTL3. The epistatic and additive interactions uncovered in the animal model will help elucidate similar interactions playing a role in human essential hypertension.

Animals↗
PubMed2001

The impact of genotyping error on family-based analysis of quantitative traits.

Errors in genotyping can substantially influence the power to detect linkage using affected sib-pairs, but it is not clear what effect such errors have on quantitative trait analyses. Here we use Monte Carlo simulation to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs. The analyses are conducted using variance components methods. We contrast the effects of error on quantitative trait analyses with those on the affected sib-pair design. The results indicate that genotyping error influences linkage studies of affected sib pairs more severely than studies of quantitative traits in unselected sibs. In situations of modest effect size, 5% genotyping error eliminates all supporting evidence for linkage to a true susceptibility locus in affected pairs, but may only result in a loss of 15% of linkage information in random pairs. Multipoint analysis does not suffer substantially more than two-point analysis; for moderate error rates (< 5%), multipoint analysis with error is more powerful than two-point with no error. Map density does not appear to be an important factor for linkage analysis. QTL association analyses of common alleles are reasonably robust to genotyping error but power can be affected dramatically with rare alleles.

Alleles↗
PubMed1992

Estimation of effects of single genes on quantitative traits.

Studies involving the effects of single genes on quantitative traits may involve closed populations, selection may be practiced, and the quantitative trait of concern may also be influenced by background genes that are inherited in a polygenic manner. It is shown analytically that analysis of such data by ordinary least squares, the usual method of analysis, can lead to finding an excess of spurious significant effects of single genes, when no effect exists, for both randomly and directionally selected populations and can lead to bias in estimates of single-gene effects when selection has been practiced. The bias depends on heritability of the polygenic effects on the trait, selection intensity, mode of inheritance, magnitude of gene effect, gene frequency, and data structure. It is argued that when genotypes of individuals can be identified for all individuals with observations on the trait, use of mixed-model procedures under an animal model treating single-gene effects as fixed effects can provide unbiased estimates of single-gene effects and exact tests of associated hypotheses for pedigreed populations, even when selection is practiced. Results are illustrated through computer simulation.

Animals↗
PubMed1994

Quantitative trait loci (QTLs) for pollen thermotolerance detected in maize.

Pollen thermotolerance is an important component of the adaptability of crops to high temperature stress. The tolerance level of the different genotypes in a population of 45 maize recombinant inbred lines was determined as the degree of injury caused by high temperature to pollen germinability (IPGG) and pollen tube growth (IPTG) in an in vitro assay. Both traits revealed quantitative variability and high heritability. The traits were genetically dissected by the analysis of molecular markers using 184 mapped restriction fragment length polymorphisms (RFLPs). Significant genetic correlation between the markers and the trait allowed us to identify a minimum number of five qualitative trait loci (QTLs) for IPGG and six QTLs for IPTG. Their chromosomal localization indicated that the two characters are controlled by different sets of genes. In addition, IPGG and IPTG were shown to be basically independent of the pollen germination ability and pollen tube growth rate under non-stress conditions. These results are discussed in relation to their possible utilization in a breeding strategy for the improvement of thermotolerance in maize.

Adaptation, Biological↗
PubMed2000

Quantitative trait loci and gene interaction: the quantitative genetics of metapopulations.

Genetic population differentiation is typically viewed as differentiation of population means. However, several theories of evolution and speciation postulate that populations differentiate not only with respect to the population means, but also with respect to the effects of alleles within these populations. I develop herein a measure of population differentiation for the 'local average effects' of alleles, where local average effect is defined as the average effect of an allele in a deme measured as a deviation from the metapopulation mean. The differentiation for local average effects has two components, a component attributable to the population mean and a residual component that is attributable to changes in the local average effects independent of the population mean. The variance in local average effects attributable to the population mean is measured as the variance in the mean local average effect of all alleles. The variance in the residual local average effects is measured as the difference between the variance local average effects of individual alleles and the variance in the mean local average effects of all alleles. Differentiation for population means and differentiation for residual local average effects need not be related. I show that when there is only additive gene action, populations can differentiate for population means, but not for residual local average effects. However, if there is gene interaction then populations can also differentiate for local average effects of alleles. The consequence of this differentiation is that the local average effects of alleles change relative to each other such that an allele that is favoured by selection in one population may be removed by selection in other populations. I discuss the evolutionary consequences of differentiation for local average effects, and the interpretation of QTL data in light of this model.

Alleles↗
PubMed1990

Quantitative traits in relation to population structure: why and how are they used and what do they imply?

I describe the basic ingredients of a population structure analysis and the rationale for using polygenic quantitative traits in such analyses. The complexity of inheritance and the population dynamics of quantitative traits, however, imply that inferences regarding population structure based on such traits must be evaluated with appropriate cautions. Although many studies of quantitative traits in relation to population structure analysis underscore the importance of gene flow between subpopulations, I show that the role of selection in the evolution of a quantitative trait and its relationship to the inferred population structure cannot be overlooked. Finally, I review some recent advances in human quantitative genetic methodologies that can be used profitably in population structure analysis.

Anthropology, Physical↗
PubMed2006

Linkage analysis of quantitative traits for obesity, diabetes, hypertension, and dyslipidemia on the island of Kosrae, Federated States of Micronesia.

Obesity, diabetes, hypertension, and heart disease are highly heritable conditions that in aggregate are the major causes of morbidity and mortality in the developed world and are growing problems in developing countries. To map the causal genes, we conducted a population screen for these conditions on the Pacific Island of Kosrae. Family history and genetic data were used to construct a pedigree for the island. Analysis of the pedigree showed highly significant heritability for the metabolic traits under study. DNA samples from 2,188 participants were genotyped with 405 microsatellite markers with an average intermarker distance of 11 cM. A protocol using loki, a Markov chain Monte Carlo sampling method, was developed to analyze the Kosraen pedigree for height, a model quantitative trait. Robust quantitative trait loci for height were found on 10q21 and 1p31. This protocol was used to map a set of metabolic traits, including plasma leptin to chromosome region 5q35; systolic blood pressure to 20p12; total cholesterol to 19p13, 12q24, and 16qter; hip circumference to 10q25 and 4q23; body mass index to 18p11 and 20q13; apolipoprotein B to 2p24-25; weight to 18q21; and fasting blood sugar to 1q31-1q43. Several of these same chromosomal regions have been identified in previous studies validating the use of loki. These studies add information about the genetics of the metabolic syndrome and establish an analytical approach for linkage analysis of complex pedigrees. These results also lay the foundation for whole genome scans with dense sets of SNPs aimed to identifying causal genes.

Body Height↗
PubMed2005

Attack behaviors in mice: from factorial structure to quantitative trait loci mapping.

The emergence or non-emergence of attack behavior results from interaction between the genotype and the conditions under which the mice are tested. Inbred mice of the same strain reared or housed under conditions do not react the same way; reactions also vary according to the place selected for testing and the different opponents. A factor analysis showed that the attack behavior in non-isolated males, tested in neutral area covaried with high testosterone and steroid sulfatase and low brain 5-hydroxytriptamine (5-HT), beta-endorphin and Adrenocorticotropic Hormone (ACTH) concentration, whereas, for isolated males tested in their own housing cage, it covaried with high testosterone activity and low brain 5-HT concentration. A wide genome scan was performed with two independent populations derived from C57BL/6J and NZB/BlNJ, each being reared, housed and tested under highly contrasting conditions, as described above, and confronted with A/J standard males. Common Quantitative Trait Loci emerged for two rearing/testing conditions. For rattling latency we detected Quantitative Trait Loci on Mus musculus chromosome 8 (MMU8) (at 44, LOD score=3.51 and 47 cM, LOD score=6.22, for the first and the second conditions) and on MMU12 (at 39 cM, LOD score=3.69 and at 41 cM, LOD score=2.99, respectively). For the number of attacks, Quantitative Trait Loci were common: on MMU11 at 39 cM LOD score=4.51 and 45 cM, LOD score=3.05, respectively, and on MMU12 (17 cM, LOD score=2.71 and 24 cM, LOD score=3.10). The steroid sulfatase gene (Sts), located on the X-Y pairing region, was linked, but only in non-isolated males, tested in neutral area for rattling latency, first attack latency, and number of attacks (LOD scores=4.9, 4.79 and 3.57, respectively). We found also that the Quantitative Trait Locus encompassing Sts region interacted with other Quantitative Trait Loci. These results indicate that attack behavior measured in different rearing and testing conditions have different biological and genetic correlates. This suggests that further explorations should be done with standardized tests and, in addition, with a wide range of tests, so as to gain an understanding of the true impact of genes or pharmacological treatments on specific categories of aggressive behavior.

Aggression↗
PubMed1994

Mapping and analysis of dairy cattle quantitative trait loci by maximum likelihood methodology using milk protein genes as genetic markers.

Maximum likelihood methodology was used to estimate effects of both a marker gene and a linked quantitative trait locus (QTL) on quantitative traits in a segregating population. Two alleles were assumed for the QTL. In addition to the effects of genotypes at both loci on the mean of the quantitative trait, recombination frequency between the loci, frequency of the QTL alleles and the residual standard deviation were also estimated. Thus six parameters were estimated in addition to the marker genotype means. The statistical model was tested on simulated data, and used to estimate direct and linked effects of the milk protein genes, beta-lactoglobulin, kappa-casein, and beta-casein, on milk, fat, and protein production and fat and protein percent in the Dutch dairy cattle population. beta-Lactoglobulin had significant direct effects on milk yield and fat percent. kappa-Casein had significant direct effects on milk yield, protein percent and fat yield. beta-Casein had significant direct effects on milk yield, fat and protein percent and fat and protein yield. Linked QTL with significant effects on fat percent were found for kappa-casein and beta-casein. Since the beta-casein and kappa-casein genes are closely linked, it is likely that the same QTL was detected for those two markers. Further, a QTL with a significant effect on fat yield was found to be linked to kappa-casein and a QTL with a significant effect on protein yield was linked to beta-lactoglobulin.

Alleles↗