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At least 19 recordsLinked to original sources
PubMed2004

Effects of censoring on parameter estimates and power in genetic modeling.

Genetic and environmental influences on variance in phenotypic traits may be estimated with normal theory Maximum Likelihood (ML). However, when the assumption of multivariate normality is not met, this method may result in biased parameter estimates and incorrect likelihood ratio tests. We simulated multivariate normal distributed twin data under the assumption of three different genetic models. Genetic model fitting was performed in six data sets: multivariate normal data, discrete uncensored data, censored data, square root transformed censored data, normal scores of censored data, and categorical data. Estimates were obtained with normal theory ML (data sets 1-5) and with categorical data analysis (data set 6). Statistical power was examined by fitting reduced models to the data. When fitting an ACE model to censored data, an unbiased estimate of the additive genetic effect was obtained. However, the common environmental effect was underestimated and the unique environmental effect was overestimated. Transformations did not remove this bias. When fitting an ADE model, the additive genetic effect was underestimated while the dominant and unique environmental effects were overestimated. In all models, the correct parameter estimates were recovered with categorical data analysis. However, with categorical data analysis, the statistical power decreased. The analysis of L-shaped distributed data with normal theory ML results in biased parameter estimates. Unbiased parameter estimates are obtained with categorical data analysis, but the power decreases.

Computer Simulation↗
PubMed2004

Mate searching in Caenorhabditis elegans: a genetic model for sex drive in a simple invertebrate.

Much of animal behavior is regulated to accomplish goals necessary for survival and reproduction. Little is known about the underlying motivational or drive states that are postulated to mediate such goal-directed behaviors. Here, we describe a mate-searching behavior of the Caenorhabditis elegans male that resembles the motivated behaviors of vertebrates. Adult C. elegans males, if isolated from mating partners, will leave the area of a food source and wander about their environment in an apparent search for a mate. When mating partners are present on the food source, males do not wander but remain with them. This behavior is sexually dimorphic for C. elegans and two additional male/hermaphrodite species studied; for these species, hermaphrodites leave food significantly slower than males. In contrast, for three male-female species examined, both males and females left food, in two cases with similar frequency, suggesting coordinate evolution of behavioral dimorphism with hermaphroditism. We use a quantitative behavioral assay to show that C. elegans male mate searching is regulated by signals from hermaphrodites and by physiological signals indicating nutritional and reproductive status. We identify genes in the serotonin, insulin, and sex determination pathways that affect the rate of mate searching. These genes may contribute to physiological and reproductive regulatory mechanisms. Our results establish C. elegans as a model genetic animal with a simple nervous system in which neural pathways leading to a motivated behavior may be genetically dissected.

Animals↗
PubMed1999

Testing for linkage under robust genetic models.

Robust genetic models are used to assess linkage between a quantitative trait and genetic variation at a specific locus using allele-sharing data. Little is known about the relative performance of different possible significance tests under these models. Under the robust variance components model approach there are several alternatives: standard Wald and likelihood ratio tests, a quasilikelihood Wald test, and a Monte Carlo test. This paper reports on the relative performance (significance level and power) of the robust sibling pair test and the different alternatives under the robust variance components model. Simulations show that (1) for a fixed sample size of nuclear families, the variance components model approach is more powerful than the robust sibling pair approach; (2) when the number of nuclear families is at least approximately 100 and heritability at the trait locus is moderate to high (>0.20) all tests based on the variance components model are equally effective; (3) when the number of nuclear families is less than approximately 100 or heritability at the trait locus is low (<0. 20), on balance, the Monte Carlo test provides the best power and is the most valid. The different testing procedures are applied to determine which are able to detect the known association between low density lipoprotein cholesterol and the common genotypes at the locus encoding apolipoprotein E. Results from this application show that the robust sibling pair method may be more effective in practice than that indicated by simulations.

Alleles↗
PubMed1998

QU-GENE: a simulation platform for quantitative analysis of genetic models.

MOTIVATION: Classical quantitative genetics theory makes a number of simplifying assumptions in order to develop mathematical expressions that describe the mean and variation (genetic and phenotypic) within and among populations, and to predict how these are expected to change under the influence of external forces. These assumptions are often necessary to render the development of many aspects of the theory mathematically tractable. The availability of high-speed computers today provides opportunity for the use of computer simulation methodology to investigate the implications of relaxing many of the assumptions that are commonly made. RESULTS: QU-GENE (QUantitative-GENEtics) was developed as a flexible computer simulation platform for the quantitative analysis of genetic models. Three features of the QU-GENE software that contribute to its flexibility are (i) the core E(N:K) genetic model, where E is the number of types of environment, N is the number of genes, K indicates the level of epistasis and the parentheses indicate that different N:K genetic models can be nested within types of environments, (ii) the use of a two-stage architecture that separates the definition of the genetic model and genotype-environment system from the detail of the individual simulation experiments and (iii) the use of a series of interactive graphical windows that monitor the progress of the simulation experiments. The E(N:K) framework enables the generation of families of genetic models that incorporate the effects of genotype-by-environment (G x E) interactions and epistasis. By the design of appropriate application modules, many different simulation experiments can be conducted for any genotype-environment system. The structure of the QU-GENE simulation software is explained and demonstrated by way of two examples. The first concentrates on some aspects of the influence of G x E interactions on response to selection in plant breeding, and the second considers the influence of multiple-peak epistasis on the evolution of a four-gene epistatic network. AVAILABILITY: QU-GENE is available over the Internet at (http://pig.ag.uq.edu.au/qu-gene/) CONTACT: m.cooper@mailbox.uq.edu. au

Computer Simulation↗
PubMed1994

Transgenic livestock as genetic models of human disease.

Genetic models of human disease can lead to new insights concerning disease aetiology or suggest novel therapeutic interventions. Livestock species, especially pigs, cows, sheep and horses, are often good animal models of human disease. However, genetic models in livestock species have included only the study of spontaneous mutations. Production of transgenic livestock is now possible but owing to a low efficiency of production, it is very expensive and its application limited. Anticipated application of improved technologies such as embryonic stem cells and homologous recombination will allow for increased sophistication of experimental design and wider use of genetically modified livestock. In all cases, the appropriate species for a genetic model of human disease should be the species which best models the physiology of the organ or system under consideration. In the future, livestock will play an increasingly more important role in biomedical research through the application of genetic engineering methodologies.

Acromegaly↗
PubMed1986

The implications and limitations of genetic models of alcoholism and other addictions.

The kind of clear-cut model of the genetic sources of alcoholism perceived by the public and presented in popular tracts does not accurately reflect the state of knowledge in this area. No persuasive genetic mechanism has been proposed to account for accumulated data about alcoholic behavior, social differences in alcoholism rates or the unfolding of the disease. Biological findings about the offspring of alcoholics have been inconsistent and grounds exist to challenge the notion of an enhanced genetic liability for alcoholism that has been accepted wisdom for the last decade. Genuine attempts to forge data and theory into genetic models have been limited to men alcoholics and to a minority of severely afflicted alcoholics with other special characteristics. However, several investigators dispute the idea of a special type of inherited alcoholism affecting only such groups. Even for these populations, balanced genetic models leave room for the substantial impact of environmental, social and individual factors (including personal values and intentions) so that drinking to excess can only be predicted within a complex, multivariate framework. The denial of this complexity in some quarters obscures what has been discovered through genetically oriented research and has dangerous consequences for prevention and treatment policies.

Alcohol Drinking↗
PubMed1988

A genetical model for vitiligo.

A genetical model is found to provide a good fit to family data on vitiligo. The model postulates that recessive alleles at a set of four unlinked diallelic loci are involved in the causation of the disorder. Under this multiple recessive homozygosis model, for normal X affected families ascertained through the affected parent, the expected segregation probability is .063; the estimated value is 0.53, which is not significantly different from the expected value. For normal X normal families ascertained through an affected offspring, the expected segregation probability is .037; the estimated value is .04.

Age Factors↗
PubMed2000

Genetic models of vasopressin deficiency.

Animal models of genetic hormone deficiency are useful as models for physiological studies of hormone deficiency and hormone action. Structure-function studies of the specific underlying gene defect may help in understanding mechanisms regulating gene expression and secretion of the peptide product. Spontaneous genetic models of vasopressin deficiency, such as the Brattleboro rat and human familial diabetes insipidus, have facilitated many studies of vasopressin. However, the Brattleboro rat may not be an ideal model of genetic vasopressin deficiency and therefore could be less useful for studies of the central nervous system or as a background strain for the introduction of new vasopressin gene constructs. The human model is appropriately limited by the constraints of human studies, so that engineered animal models of specific diseases, such as familial neurohypophysial diabetes insipidus, are required. The recent development of a vasopressin-null mouse may provide insights into the various roles of vasopressin in the stress response, cardiovascular regulation and behaviour. Additionally, animals with a complete genetic deficiency of vasopressin can serve as a background strain for introduction of novel vasopressin gene constructs to enable sophisticated studies of the regulation of vasopressin expression and the intracellular processes required for appropriate secretion of vasopressin peptide. As advanced techniques of genetic manipulation become more reliable, conditional expression of vasopressin, regulated by time or body site will permit even more detailed studies in this field.

Animals↗
PubMed1998

Least squares interval mapping of quantitative trait loci under the infinitesimal genetic model in outbred populations.

Genetic marker and phenotypic data for a quantitative trait were simulated on 20 paternal half-sib families with 100 progeny to investigate properties of within-family-regression interval mapping of a postulated single quantitative trait locus (QTL) in a marker interval under the infinitesimal genetic model, which has been the basis of the application of quantitative genetics to genetic improvement programs, and to investigate use of the infinitesimal model as null hypothesis in testing for presence of a major QTL. Genetic effects on the marked chromosome were generated based on a major gene model, which simulated a central biallelic QTL, or based on 101 biallelic QTL of equal effect, which approximated the infinitesimal model. The marked chromosome contained 0, 3.3%, 13.3%, or 33.3% of genetic variance and heritability was 0.25 or 0.70. Under the polygenic model with 3.3% of genetic variance on the marked chromosome, which corresponds to the infinitesimal model for the bovine, significant QTL effects were found for individual families. Correlations between estimates of QTL effects and true chromosome substitution effects were 0.29 and 0.47 for heritabilities of 0.25 and 0.70 but up to 0.85 with 33.3% of polygenic variance on the marked chromosome. These results illustrate the potential of marker-assisted selection even under the infinitesimal genetic model. Power of tests for presence of QTL was substantially reduced when the polygenic model with 3.3% of genetic variance on the chromosome was used as a null hypothesis. The ability to determine whether genetic variance on a chromosome was contributed by a single QTL of major effect or a large number of QTL with minor effects, corresponding to the infinitesimal model, was limited.

Chromosome Mapping↗
PubMed2006

The search for cerebral biomarkers of Huntington's disease: a review of genetic models of age at onset prediction.

The mutation causing Huntington's disease is an expanded CAG trinucleotide repeat number beyond 35 in the 5' translated region of the gene. The mutation penetrance varies widely and depends on the CAG expansion length, the low pathological triplet range (36-41) showing a very low penetrance, possibly associated with late ages at onset. No research has so far yielded biomarkers for accurately predicting either age at onset or disease progression in at risk individuals. Specific markers able to follow-up mutation carrier subjects from the pre-symptomatic stages of life are crucial for testing experimental neuroprotective preventive therapies. Nevertheless, the factor accounting for the largest percentage of age at onset variation is the expanded repeat number within the gene. Over the years, this factor has helped in setting up models for genetically predicting age at onset. Once available for practical application in clinics, such models allowed phenotype-genotype correlations that were hitherto inconceivable. In this review, we discuss how these genetic models have been applied in clinical practice and comment on their potential value in searching for cerebral biomarkers of disease onset and severity and in designing trials of therapeutic drugs.

Adult↗
PubMed1985

Quantitative genetic models of sexual selection.

Quantitative genetic models of sexual selection have disproven some of the central tenets of both the handicap mechanism and the 'sexy son' hypothesis. These results suggest that the 'good genes' approach to sexual selection may generally lead to erroneous results. Runaway sexual selection seems possible under a wide variety of circumstances. Quantitative genetic models have revealed runaway processes for sexually selected attributes expressed in both sexes and for attributes of parental care. Furthermore, the runaway could occur simultaneously in a series of populations that straddle an environmental gradient. While the models support the feasibility of runaway processes, empirical studies are needed to evaluate whether runaways actually happen. Estimates of critical genetic parameters are particularly needed, as well as measures of natural and sexual selection acting on the same population. The models also show that sexual selection has tremendous potential to produce population differentiation, particularly in epigamic traits. Differentiation is promoted by indeterminancy of evolutionary outcome, transient differences among populations during the final slow approach to equilibrium, sampling drift among equilibrium populations, and the tendency of sexual selection to amplify geographic variation arising from spatial differences in natural selection. Recent work with two- and three-locus models of sexual selection has produced results that parallel the results of the polygenic models. Thus the feature of indeterminate equilibria (outcome dependent on initial conditions) is common to both types of model.

Animals↗
PubMed1987

Genetic models of alcohol dependence.

The use of genetically specified animal strains has proven historically to be a powerful tool to examine the genetic influence on alcohol dependence and withdrawal. Animal genetic models have successfully reproduced particular aspects of the syndrome, but have not completely mimicked alcoholism. Requiring complete mimicry of an animal genetic model is unwarranted and tends to distract analysis from the point. Since many rodent models of genetic sensitivity to several effects of ethanol now exist, the focus of our efforts should be concentrated in two areas. First, genetically specified lines may be analyzed in depth to reveal mechanisms of ethanol's actions on tissue which lead ultimately to the development of alcohol dependence. Second, attention should be paid to those traits which may predict alcohol dependence susceptibility, with particular emphasis on those traits which may be directly extrapolated to human populations. Studies with WSP and WSR mice, selected for alcohol withdrawal severity are discussed in this framework.

Alcoholism↗
PubMed2005

The choice of a genetic model in the meta-analysis of molecular association studies.

BACKGROUND: To evaluate gene-disease associations, genetic epidemiologists collect information on the disease risk in subjects with different genotypes (for a bi-allelic polymorphism: gg, Gg, GG). Meta-analyses of such studies usually reduce the problem to a single comparison, either by performing two separate pairwise comparisons or by assuming a specific underlying genetic model (recessive, co-dominant, dominant). A biological justification for the choice of the genetic model is seldom available. METHODS: We present a genetic model-free approach, which does not assume that the underlying genetic model is known in advance but still makes use of the information available on all genotypes. The approach uses OR(GG), the odds ratio between the homozygous genotypes, to capture the magnitude of the genetic effect, and lambda, the heterozygote log odds ratio as a proportion of the homozygote log odds ratio, to capture the genetic mode of inheritance. The analysis assumes that the same unknown genetic model, i.e. the same lambda, applies in all studies, and this is investigated graphically. The approach is illustrated using five examples of published meta-analyses. RESULTS: Analyses based on specific genetic models can produce misleading estimates of the odds ratios when an inappropriate model is assumed. The genetic model-free approach gives appropriately wider confidence intervals than genetic model-based analyses because it allows for uncertainty about the genetic model. In terms of assessment of model fit, it performs at least as well as a bivariate pairwise analysis in our examples. CONCLUSIONS: The genetic model-free approach offers a unified approach that efficiently estimates the genetic effect and the underlying genetic model. A bivariate pairwise analysis should be used if the assumption of a common genetic model across studies is in doubt.

Cardiovascular Diseases↗
PubMed1995

Genetic models of human vascular disease.

The use of genetic models has greatly assisted investigations of the natural history, mechanisms, and potential therapy for human vascular disease. In the past, genetic models of vascular disease were obtained through serendipity and/or selective breeding to obtain inbred lines that express the phenotype of interest. This approach has yielded several valuable models of atherosclerosis and hypertension. In the past several years, the advent of molecular techniques has enabled investigators to produce additional novel genetic models of disease that have further enhanced the study of vascular biology and medicine. Transgenic techniques and the techniques of homologous recombination have allowed researchers to alter the genotype of an animal in a precise manner and to study the resultant change in phenotype. More recently, techniques of in vivo gene transfer have also accelerated and enhanced the development of novel models. The application of these methodologies has resulted in important breakthroughs in our understanding of the pathogenesis and treatment of vascular diseases. In this review, we compare and contrast these technologies along with examples of their use in the studies of vascular biology and medicine.

Animals↗
PubMed2002

Analysis of genetic effects of major genes and polygenes on quantitative traits I. Genetic model for diploid plants and animals.

A genetic model was proposed to simultaneously investigate genetic effects of both polygenes and several single genes for quantitative traits of diploid plants and animals. Mixed linear model approaches were employed for statistical analysis. Based on two mating designs, a full diallel cross and a modified diallel cross including F(2), Monte Carlo simulations were conducted to evaluate the unbiasedness and efficiency of the estimation of generalized least squares (GLS) and ordinary least squares (OLS) for fixed effects and of minimum norm quadratic unbiased estimation (MINQUE) and Henderson III for variance components. Estimates of MINQUE (1) were unbiased and efficient in both reduced and full genetic models. Henderson III could have a large bias when used to analyze the full genetic model. Simulation results also showed that GLS and OLS were good methods to estimate fixed effects in the genetic models. Data on Drosophila melanogaster from Gilbert were used as a worked example to demonstrate the parameter estimation.

Journal Article↗
PubMed2002

Analysis of genetic effects of major genes and polygenes on quantitative traits. II. Genetic models for seed traits of crops.

Genetic models for quantitative seed traits with effects of several major genes and polygenes, as well as their GE interaction, were proposed. Mixed linear model approaches were suggested for analyzing the genetic models. Monte Carlo simulations were conducted to evaluate unbiasedness and efficiency for estimating fixed effects and variance components of the embryo and the endosperm models, including effects of a major gene from an unbalanced modified diallel mating design with nine parents, respectively. Simulation results showed that estimates of generalized least squares (GLS) were unbiased and efficient, while those of ordinary least squares (OLS) were almost as good as GLS. Minimum norm quadratic unbiased estimation (MINQUE) could obtain unbiased estimates of the variance components. It was also suggested that precision of MINQUE estimation would be improved with augmentation of experimental size. Data from a modified diallel design in upland cotton ( Gossypium hirsutum L.) were used as a worked example to illustrate the parameter estimation.

Journal Article↗
PubMed1997

The use of likelihood-based confidence intervals in genetic models.

This article describes the computation and relative merits of likelihood-based confidence intervals, compared to other measures of error in parameter estimates. Likelihood-based confidence intervals have the advantage of being asymmetric, which is often the case with structural equation models for genetically informative studies. We show how the package Mx provides confidence intervals for parameters and functions of parameters in the context of a simple additive genetic, common, and specific environment threshold model for binary data. Previously published contingency tables for major depression in adult female twins are used for illustration. The support for the model shows a marked skew as the additive genetic parameter is systematically varied from zero to one. The impact of allowing different prevalence rates in MZ vs. DZ twins is explored by fitting a model with separate threshold parameters and comparing the confidence intervals. Despite the improvement in fit of the different prevalence model, the confidence intervals on all parameters broaden, owing to their covariance.

Adult↗