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At least 1,297 records · Page 72Linked to original sources
PubMed2003

Estimates of genetic parameters and genetic change for reproduction, weight, and wool characteristics of Targhee sheep.

Genetic parameters from both single-trait and bivariate analyses for prolificacy, weight, and wool traits were estimated using REML with animal models for Targhee sheep from data collected from 1950 to 1998 at the U.S. Sheep Experiment Station, Dubois, ID. Breeding values from both single-trait and seven-trait analyses calculated with the parameters estimated from the single-trait and bivariate analyses were compared across years of birth with respect to genetic trends. The numbers of observations were 38,625 for litter size at birth and litter size at weaning, 33,994 for birth weight, 32,715 for weaning weight, 36,807 for fleece weight and fleece grade, and 3,341 for staple length. Direct heritability estimates from single-trait analyses were 0.10 for litter size at birth, 0.07 for litter size at weaning, 0.25 for birth weight, 0.22 for weaning weight, 0.54 for fleece weight, 0.41 for fleece grade, and 0.65 for staple length. Estimate of direct genetic correlation between litter size at birth and weaning was 0.77 and between birth and weaning weights was 0.52. The estimate of genetic correlation between fleece weight and staple length was positive (0.54), but was negative between fleece weight and fleece grade (-0.47) and between staple length and fleece grade (-0.69). Estimates of genetic correlations were near zero between birth weight and litter size traits and small and positive between weaning weight and litter size traits. Fleece weight was slightly and negatively correlated with both litter size traits. Fleece grade was slightly and positively correlated with both litter size traits. Estimates of correlations between staple length and litter size at birth (-0.14) and litter size at weaning (0.05) were small. Estimates of correlations between weight traits and fleece weight were positive and low to moderate. Estimates of correlations between weight traits and fleece grade were negative and small, whereas estimates between weight traits and staple length were positive and small. Estimated breeding values averaged by year of birth from both the single- and seven-trait analyses for the prolificacy and weight traits increased over time, whereas those for fleece weight decreased slightly and those for the other wool traits were unchanged. Estimated changes in breeding values over time did not differ substantially for the single-trait and seven-trait analyses, except for traits highly correlated with another trait that was responding to selection.

Animals↗
PubMed2005

Estimates of genetic correlations between days to calving and reproductive and weight traits in Nelore cattle.

Data comprising 53,181 calving records were analyzed to estimate the genetic correlation between days to calving (DC), and days to first calving (DFC), and the following traits: scrotal circumference (SC), age at first calving (AFC), and weight adjusted for 550 d of age (W550) in a Nelore herd. (Co)variance components were estimated using the REML method fitting bivariate animal models. The fixed effects considered for DC were contemporary group, month of last calving, and age at breeding season (linear and quadratic effects). Contemporary groups were composed by herd, year, season, and management group at birth; herd and management group at weaning; herd, season, and management group at mating; and sex of calf and mating type (multiple sires, single sire, or AI). In DFC analysis, the same fixed effects were considered excluding the month of last calving. For DC, a repeatability animal model was applied. Noncalvers were not considered in analyses because an attempt to include them, attributing a penalty, did not improve the identification of genetic differences between animals. Heritability estimates ranged from 0.04 to 0.06 for DC, from 0.06 to 0.13 for DFC, from 0.42 to 0.44 for SC, from 0.06 to 0.08 for AFC, and was 0.30 for W550. The genetic correlation estimated between DC and SC was low and negative (-0.10), between DC and AFC was high and positive (0.76), and between DC and W550 was almost null (0.07). Similar results were found for genetic correlation estimates between DFC and SC (-0.14), AFC (0.94), and W550 (-0.02). The genetic correlation estimates indicate that the use of DC in the selection of beef cattle may promote favorable correlated responses to age at first mating and, consequently, higher gains in sexual precocity can be expected.

Age Factors↗
PubMed1991

Variance of neutral genetic variances within and between populations for a quantitative character.

The variances of genetic variances within and between finite populations were systematically studied using a general multiple allele model with mutation in terms of identity by descent measures. We partitioned the genetic variances into components corresponding to genetic variances and covariances within and between loci. We also analyzed the sampling variance. Both transient and equilibrium results were derived exactly and the results can be used in diverse applications. For the genetic variance within populations, sigma 2 omega, the coefficient of variation can be very well approximated as [formula: see text] for a normal distribution of allelic effects, ignoring recurrent mutation in the absence of linkage, where m is the number of loci, N is the effective population size, theta 1(0) is the initial identity by descent measure of two genes within populations and t is the generation number. The first term is due to genic variance, the second due to linkage disequilibrium, and third due to sampling. In the short term, the variation is predominantly due to linkage disequilibrium and sampling; but in the long term it can be largely due to genic variance. At equilibrium with mutation [formula: see text] where u is the mutation rate. The genetic variance between populations is a parameter. Variance arises only among sample estimates due to finite sampling of populations and individuals. The coefficient of variation for sample gentic variance between populations, sigma 2b, can be generally approximated as [formula: see text] when the number of loci is large where S is the number of sampling populations.

Analysis of Variance↗
PubMed1992

Comparing evolvability and variability of quantitative traits.

There are two distinct reasons for making comparisons of genetic variation for quantitative characters. The first is to compare evolvabilities, or ability to respond to selection, and the second is to make inferences about the forces that maintain genetic variability. Measures of variation that are standardized by the trait mean, such as the additive genetic coefficient of variation, are appropriate for both purposes. Variation has usually been compared as narrow sense heritabilities, but this is almost always an inappropriate comparative measure of evolvability and variability. Coefficients of variation were calculated from 842 estimates of trait means, variances and heritabilities in the literature. Traits closely related to fitness have higher additive genetic and nongenetic variability by the coefficient of variation criterion than characters under weak selection. This is the reverse of the accepted conclusion based on comparisons of heritability. The low heritability of fitness components is best explained by their high residual variation. The high additive genetic and residual variability of fitness traits might be explained by the great number of genetic and environmental events they are affected by, or by a lack of stabilizing selection to reduce their phenotypic variance. Over one-third of the quantitative genetics papers reviewed did not report trait means or variances. Researchers should always report these statistics, so that measures of variation appropriate to a variety of situations may be calculated.

Animals↗
PubMed2002

Mapping epigenetic quantitative trait loci (QTL) altering a developmental trajectory.

Genetic variation in a quantitative trait that changes with age is important to both evolutionary biologists and breeders. A traditional analysis of the dynamics of genetic variation is based on the genetic variance-covariance matrix among different ages estimated from a quantitative genetic model. Such an analysis, however, cannot reveal the mechanistic basis of the genetic variation for a growth trait during ontogeny. Age-specific genetic variance at time t conditional on the causal genetic effect at time t - 1 implies the generation of episodes of new genetic variation arising during the interval t - 1 to t. In the present paper, the conditional genetic variance estimated from Zhu's (1995) conditional model was partitioned into its underlying individual quantitative trait loci (QTL) using molecular markers in an F2 progeny of poplars (Populus trichocarpa and Populus deltoides). These QTL, defined as epigenetic QTL, govern the alterations of growth trajectory in a population. Three epigenetic QTL were detected to contribute significantly to variation in growth trajectory during the period from the establishment year to the subsequent year in the field. It is suggested that the activation and expression of epigenetic QTL are influenced by the developmental status of trees and the environment in which they are grown.

Analysis of Variance↗
PubMed2002

Two QTLs located on chromosomes 1 and 5 modulate different aspects of the performance of mice of the B x D Ty RI strain series in the Morris navigation task.

The Morris navigation task is widely used to study spatial abilities in rodents; namely, to analyze the effects of mutations in genetically engineered mice. Although quantitative and Mendelian genetic studies have shown that the variation of these abilities is partly under genetic control, little is known about these genetic factors. In order to analyze the genetic architecture of spatial navigation in mice, a wide genome scan was performed to map the QTLs that control various aspects of the performance, using the RI strain methodology. Latencies to locate the submerged platform across learning sessions and performance to the spatial probe test were analyzed in the 26 strains of the B x D RI series. Both cluster analysis of behavioral measurements and QTL mapping confirmed previous data showing that the escape latencies and the spatial bias rely on two distinct components of the task, controlled by different loci. A QTL on chromosome 1 influenced escape latencies during the four training sessions, whereas another QTL, located on chromosome 5, was shown to control spatial performance at the probe trial and also exhibited epistatic interactions with two other QTLs on chromosomes 2 and 13. The function of these QTLs is examined in the broader context of hippocampal-dependent learning processes and in relation to QTLs already found in similar positions in other behavioral traits.

Animals↗
PubMed2005

Identification of quantitative trait loci for susceptibility to mouse adenovirus type 1.

Adult SJL/J mice are highly susceptible to mouse adenovirus type 1 (MAV-1) infections, whereas other inbred strains, including BALB/cJ, are resistant (K. R. Spindler, L. Fang, M. L. Moore, C. C. Brown, G. N. Hirsch, and A. K. Kajon, J. Virol. 75:12039-12046, 2001). Using congenic mouse strains, we showed that the H-2(s) haplotype of SJL/J mice is not associated with susceptibility to MAV-1. Susceptibility of MAV-1-infected (BALB/cJ x SJL/J)F(1) mice was intermediate between that of SJL/J mice and that of BALB/cJ mice, indicating that susceptibility is a genetically controlled quantitative trait. We mapped genetic loci involved in mouse susceptibility to MAV-1 by analysis of 192 backcross progeny in a genome scan with 65 simple sequence length polymorphic markers. A major quantitative trait locus (QTL) was detected on chromosome 15 (Chr 15) with a highly significant logarithm of odds score of 21. The locus on Chr 15 alone accounts for 40% of the total trait variance between susceptible and resistant strains. QTL modeling of the data indicated that there are a number of other QTLs with small effects that together with the major QTL on Chr 15 account for 54% of the trait variance. Identification of the major QTL is the first step in characterizing host genes involved in susceptibility to MAV-1.

Adenoviridae↗
PubMed1998

Evaluation of a restricted likelihood ratio test for mapping quantitative trait loci with extreme discordant sib pairs.

Risch and Zhang recently proposed to use extreme discordant sib pairs for mapping quantitative trait loci. Here, it is shown that the set of genetically possible distributions of the number of marker alleles in such sib-pairs is described by two inequalities. Thus, a likelihood ratio test analogous to Holmans's possible triangle test for affected sib pairs can be defined. The performance of this test is compared to the mean test considered by Risch and Zhang. For most of the genetic models considered, the mean test is slightly more powerful than the restricted likelihood ratio test. However, for models with a rare recessive gene (or equivalently a common dominant gene), the restricted likelihood ratio test is much more powerful.

Alleles↗
PubMed2002

Complexities of cancer research: mouse genetic models.

Cancer susceptibility is a complex interaction of an individual's genetic composition and environmental exposures. Huge strides have been made in understanding cancer over the past 100 yr, from recognition of cancer as a genetic disease, to identification of specific carcinogens, isolation of oncogenes, and recognition of tumor suppressors. A tremendous amount of knowledge has accumulated about the etiology of cancer. Cancer genetics has played a significant role in these discoveries. Analysis of high-risk familial cancers has led to the discovery of new tumor suppressor genes and important cancer pathways. These families, however, represent only a small fraction of cancer in the general population. Most cancer is instead probably the result of an intricate interaction of polymorphic susceptibility genes with the sea of environmental exposures that humans experience. Although the central cadre of cancer genes is known, little is understood about the peripheral genes that likely comprise the polymorphic susceptibility loci. The challenge for cancer genetics is therefore to move forward from the mendelian genetics of the rare familial cancer syndromes into the field of quantitative trait loci, susceptibility factors, and modifier genes. By identifying the genes that modulate an individual's susceptibility to cancer after an environmental exposure, researchers will be able to gain important insights into human biology, cancer prevention, and cancer treatment. This article summarizes the current state of quantitative trait genetic analysis and the tools, both proven and theoretical, that may be used to unravel one of the great challenges in cancer genetics.

Animals↗
PubMed2001

Genetics, environment and cognitive abilities: review and work in progress towards a genome scan for quantitative trait locus associations using DNA pooling.

BACKGROUND: Multivariate genetic research indicates that genetic effects on diverse cognitive abilities are general rather than specific or modular. General cognitive ability (g), a key factor in learning and memory, is among the most heritable behavioural traits. AIMS: To give a brief overview of quantitative genetic research on g and to describe initial results from a programme of research that aims to identify genes responsible for the substantial heritability of general cognitive ability. METHOD: The research uses a new technique called DNA pooling, which combines DNA from individuals within a group and makes it feasible to screen thousands of DNA markers for a systematic scan of the genome for associations between DNA markers and g. Two independent samples of children with very high g scores and two control samples of children with average g scores were compared in a systematic scan of 147 markers on chromosome 4 and 66 markers on chromosome 22. RESULTS: Three replicated associations on chromosome 4 were identified using DNA pooling and confirmed using individual genotyping. CONCLUSIONS: These first results of the application of DNA pooling in systematic analysis of allelic association are encouraging.

Adult↗
PubMed2001

[Construction of a genetic map and location of quantitative trait loci for yield component traits in maize by SSR markers].

Using 230 F2:3 lines derived from the hybrid of ND3138, a SSR genetic linkage map of maize (Zea mays L.) was constructed on which 80 markers distribute among 10 linkage groups and spanning maize genome about 2033.4 cM with average distance between markers being 25.42 cM. The 230 F2:3 lines were grown in a random design of one-row plot with three replication, and evaluated for ear length, bared tip, ear diameter, row number, kernel number row-1, one thousand grains weight, ear weight and grain weight plant-1. With interval mapping procedure, 30 QTLs were revealed for all traits, which distributed on 9 of 10 chromosomes of maize except chromosome 6 and accounted for from 9.5% to 55.3% of the total phenotypic variation. Genetic effects and gene action were also determined.

Chromosome Mapping↗
PubMed1995

Detection and positional cloning of blood pressure quantitative trait loci: is it possible? Identifying the genes for genetic hypertension.

Identification of the quantitative trait loci that influence blood pressure and cause genetic hypertension is a major challenge. Several genetically hypertensive rat strains exist and can be used to locate by linkage analysis broad chromosomal regions containing blood pressure quantitative trait loci. Such broad chromosomal regions, and the narrower subregions, can be moved among strains (ie, production of congenic strains and congenic substrains) to identify small chromosomal regions containing the blood pressure quantitative trait loci. However, ultimate positional cloning of the quantitative trait loci presents a major difficulty because the genetic variants involved are likely to result in subtle changes in function rather than the blatant loss of function characteristic of all mendelian disease genes discovered so far by positional cloning.

Animals↗
PubMed2004

Qualitative and quantitative PCR methods for detection of three lines of genetically modified potatoes.

Qualitative and quantitative polymerase chain reaction (PCR) methods have been developed for the detection of genetically modified (GM) potatoes. The combination of specific primers for amplification of the promoter region of Cry3A gene, potato leafroll virus replicase gene, and potato virus Y coat protein gene allows to identify each line of NewLeaf, NewLeaf Y, and NewLeaf Plus GM potatoes. Multiplex PCR method was also established for the simple and rapid detection of the three lines of GM potato in a mixture sample. For further quantitative detection, the realtime PCR method has been developed. This method features the use of a standard plasmid as a reference molecule. Standard plasmid contains both a specific region of the transgene Cry3A and an endogenous UDP-glucose pyrophosphorylase gene of the potato. The test samples containing 0.5, 1, 3, and 5% GM potatoes were quantified by this method. At the 3.0% level of each line of GM potato, the relative standard deviations ranged from 6.0 to 19.6%. This result shows that the above PCR methods are applicable to detect GM potatoes quantitatively as well as qualitatively.

Bacillus thuringiensis Toxins↗
PubMed2004

Maximizing genetic gain over multiple generations with quantitative trait locus selection and control of inbreeding.

Stochastic computer simulation was used to investigate the potential extra genetic gains obtained from gene-assisted selection (GAS) by combining 1) optimization of genetic contributions for maximizing gain, while restricting the rate of inbreeding with 2) optimization of the relative emphasis given to the QTL over generations. The genetic model assumed implied a mixed inheritance model in which a single quantitative trait locus (i.e., QTL) is segregating together with polygenes. When compared with standard GAS (i.e., fixed contributions and equal emphasis on the QTL and polygenic EBV), combined optimization of contributions of selection candidates and weights on the QTL across generations allowed substantial increases in gain at a fixed rate of inbreeding and avoided the conflict between short- and long-term responses in GAS schemes. Most of the increase of gain was produced by optimization of selection candidates' contributions. Optimization of the relative emphasis given to the QTL over generations had, however, a greater effect on avoiding the long-term loss usually observed in GAS schemes. Optimized contribution schemes led to lower gametic phase disequilibrium between the QTL and polygenes and to higher selection intensities both on the QTL and polygenes than with standard truncation selection with fixed contributions of selection candidates.

Alleles↗
PubMed1998

Empirical nonparametric bootstrap strategies in quantitative trait loci mapping: conditioning on the genetic model.

Several nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

Chromosome Mapping↗
PubMed2006

Shrinkage estimation method for mapping multiple quantitative trait loci.

In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.

Bayes Theorem↗
PubMed1990

Genetic transmission of major affective disorders: quantitative models and linkage analyses.

We comprehensively reviewed 2 types of studies aimed at specifying the mode of inheritance of major affective disorders: quantitative models and linkage analyses. Quantitative models attempt to represent the genetic mechanism responsible for the familial distribution of a disorder. Despite efforts to refine models by incorporating the bipolar-unipolar distinction or the sex effect, consistent support for a specific mode of transmission has not been found. Some mixed genetic models support single major locus inheritance, but transmission probabilities do not conform to Mendelian expectations. Linkage analysis is a more powerful technique used for testing the single gene hypothesis. Linkage results have also been inconsistent, showing moderate support for an X-linked variant of bipolar-related disorder and equivocal support for linkages to Chromosomes 6 and 11. However, relatively few genetic loci have been examined. Methodological factors, genetic heterogeneity, and phenotypic heterogeneity are discussed as potential explanations for inconsistent findings.

Bipolar Disorder↗