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At least 325 records · Page 18Linked to original sources
PubMed2004

The influence of genes on the aging process of mice: a statistical assessment of the genetics of aging.

Genetic interventions that accelerate or retard aging in mice are crucial in advancing our knowledge over mammalian aging. Yet determining if a given intervention affects the aging process is not straightforward since, for instance, many disease-causing mutations may decrease life span without affecting aging. In this work, we employed the Gompertz model to determine whether several published interventions previously claimed to affect aging in mice do indeed alter the aging process. First, we constructed age-specific mortality tables for a number of mouse cohorts used in longevity experiments and calculated the rate at which mortality increases with age. Estimates of age-independent mortality were also calculated. We found no statistical evidence that GHRHR, IGF1R, INSR, PROP1, or TRX delay or that ATM + TERC, BubR1, klotho, LMNA, PRDX1, p53, WRN + TERC, or TOP3B accelerate mouse aging. Often, changes in the expression of these genes affected age-independent mortality and so they may prove useful to other aspects of medicine. We found statistical evidence that C/EBP, MSRA, SHC1, growth hormone, GHR, PIT1, and PolgA may influence aging in mice. These results were interpreted together with age-related physiological and pathological changes and provide novel insights regarding the role of several genes in the mammalian aging process.

Aging↗
PubMed1994

Maximum likelihood estimation of quantitative trait loci parameters with the aid of genetic markers using a standard statistical package.

Consistent parameter estimates of quantitative trait loci linked to genetic markers can be derived by maximum likelihood methodology. For many experimental designs of interest, parameter estimates and their standard errors can be obtained by program LE of BMDP, which uses the Newton-Raphson method of iteration. Program LE was tested on data simulated for a backcross between two inbred lines. A single quantitative trait locus linked to either one or two genetic markers was simulated. Convergence was rapid, and computing and programming time were insignificant. All parameter estimates were within the expected bounds. Many different designs can be readily analyzed.

Algorithms↗
PubMed2003

Multipoint and single point non-parametric linkage analysis with imperfect data.

We used simulation to explore the impact of common data imperfections (i.e., missing parents, genotyping error, map error, and missing genotypes) upon the performance of multipoint and single point linkage analysis in the analyses of linkage data from pairs of siblings affected with an idealized complex trait. The performance of single point and multipoint linkage was similar under an unrealistic best case scenario; however, when four data imperfections were combined, the performance of single point linkage analysis appeared to be superior to multipoint. The absence of parental genotypes in the presence of 1% genotype error led to marked degradation of linkage signal, particularly for multipoint analyses.

Computer Simulation↗
PubMed2001

Are partial mantel tests adequate?

Partial Mantel tests were designed to test for correlation among three matrices of pairwise distances. We show through an example that these tests may be inadequate, because the associated P-value is not indicative of the type I error.

Data Interpretation, Statistical↗
PubMed2002

Is the partial Mantel test inadequate?

A publication by Raufaste and Rousset (2001) questioned the effectiveness of the partial Mantel test, a nonparametric statistical test for association among three distance matrices. By repeated simulations, we calculated the cumulative density functions of the null-hypothesis probability of no correlation, within an explicit model of causal relationships. Results do not support the criticism: in conditions of moderate correlation between the independent matrices, the actual error rate is closely associated to the intended type-I error alpha.

Computer Simulation↗
PubMed2004

Percentiles of the null distribution of 2 maximum lod score tests.

We here consider the null distribution of the maximum lod score (LOD-M) obtained upon maximizing over transmission model parameters (penetrance values, dominance, and allele frequency) as well as the recombination fraction. Also considered is the lod score maximized over a fixed choice of genetic model parameters and recombination-fraction values set prior to the analysis (MMLS) as proposed by Hodge et al. The objective is to fit parametric distributions to MMLS and LOD-M. Our results are based on 3,600 simulations of samples of n = 100 nuclear families ascertained for having one affected member and at least one other sibling available for linkage analysis. Each null distribution is approximately a mixture p(2)(0) + (1 - p)(2)(v). The values of MMLS appear to fit the mixture 0.20(2)(0) + 0.80chi(2)(1.6). The mixture distribution 0.13(2)(0) + 0.87chi(2)(2.8). appears to describe the null distribution of LOD-M. From these results we derive a simple method for obtaining critical values of LOD-M and MMLS.

Alleles↗
PubMed2004

A spatial statistical model for landscape genetics.

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST<0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.

Animals↗
PubMed2002

Shared genetic risk of major depression, alcohol dependence, and marijuana dependence: contribution of antisocial personality disorder in men.

BACKGROUND: Little is known about genetic factors that underlie the interrelationships among antisocial personality disorder (ASPD), major depression (MD), alcohol dependence (AD), and marijuana dependence (MJD). We examined the contribution of genetic effects associated with ASPD to the comorbidity of MD and substance use disorders. METHODS: The Vietnam Era Twin Registry is a general population registry of male veteran twins constructed from computerized Department of Defense files and other sources. A telephone diagnostic interview was administered to eligible twins from the Registry in 1992. Of 5150 twin pairs who served on active military duty during the Vietnam era, 3360 pairs (1868 monozygotic and 1492 dizygotic) in which both members completed the pertinent diagnostic interview sections were included. The main outcome measures were lifetime DSM-III-R ASPD, MD, AD, and MJD. RESULTS: Structural equation modeling was performed to estimate additive genetic, shared environmental, and nonshared environmental effects common and specific to each disorder. The heritability estimates for lifetime ASPD, MD, AD, and MJD were 69%, 40%, 56%, and 50%, respectively. Genetic effects on ASPD accounted for 38%, 50%, and 58% of the total genetic variance in risk for MD, AD, and MJD, respectively. After controlling for genetic effects on ASPD, the partial genetic correlations of MD with AD and with MJD were no longer statistically significant. Genetic effects specific to MD and AD and familial effects specific to MJD remained statistically significant. Nonshared environmental contributions to the comorbidity in these disorders were small. CONCLUSIONS: In this sample, the shared genetic risk between MD and both AD and MJD was largely explained by genetic effects on ASPD, which in turn was associated with increased risk of each of the other disorders.

Adult↗
PubMed1988

Genetic divergence in mandible form in relation to molecular divergence in inbred mouse strains.

Genetic divergence in the form of the mandible is examined in ten inbred strains of mice. Several univariate and multivariate genetic distance estimates are given for the morphological data and these estimates are compared to measures of genealogical and molecular divergence. Highly significant divergence occurs among the ten strains in all 11 mandible traits considered individually and simultaneously. Genealogical relationship among strains is highly correlated with genetic divergence in single locus molecular traits. However, the concordance between genealogical relationship and multivariate genetic divergence in morphology is much more complex. Whether there is a significant correlation between morphological divergence and genealogy depends upon the method of analysis and the particular genetic distance statistic being employed.

Animals↗
PubMed1996

Assessing genetic linkage and association with robust components of variance approaches.

Simulation studies are used to explore the properties of procedures for estimating components of variance and constructing test statistics in genetic linkage studies of quantitative traits. We evaluated the bias and median squared error of estimates of the linked additive genetic variance obtained by regression, maximum likelihood and quasilikelihood estimation procedures. The quasilikelihood and regression procedures provided unbiased estimates of the additive component of variance. Maximum likelihood procedures that assumed multivariate normality were biased for most sample sizes considered but had more precision for most generating models than regression or quasilikelihood methods did. Wald tests derived from quasilikelihood procedures had similar or greater power than Wald tests based upon estimators from maximum likelihood analyses. Quasilikelihood estimation may therefore be preferable whenever there is uncertainty about the generating distribution for the error variance, but the robustness of this approach is offset by its required computational complexity.

Age Factors↗
PubMed2004

Affected-sib-pair test for linkage based on constraints for identical-by-descent distributions corresponding to disease models with imprinting.

Holmans' possible triangle test for affected sib pairs has proven to be a powerful tool for linkage analysis. This test is a likelihood-ratio test for which maximization is restricted to the set of possible sharing probabilities. Here, we extend the possible triangle test to take into account genomic imprinting, which is also known as parent-of-origin effect. While the classical test without imprinting looks at whether affected sib pairs share 0, 1, or 2 alleles identical-by-descent, the likelihood-ratio test allowing for imprinting further distinguishes whether the sharing of exactly one allele is through the father or mother. Thus, if the disease gene is indeed subject to imprinting, the extended test presented here can take into account that affecteds will have inherited the mutant allele preferentially from one particular parent. We calculate the sharing probabilities at a marker locus linked to a disease susceptibility locus. Using our formulation, the constraints on these probabilities given by Dudoit and Speed ([1999] Statistics in Genetics; New York: Springer) can easily be verified. Next, we derive the asymptotic distribution of the restricted likelihood-ratio test statistic under the null hypothesis of no linkage, and give LOD-score criteria for various test sizes. We show, for various disease models, that the test allowing for imprinting has significantly higher power to detect linkage if imprinting is indeed present, at the cost of only a small reduction in power in case of no imprinting. Altogether, unlike many methods currently available, our novel model-free sib-pair test adequately models the epigenetic parent-of-origin effect, and will hopefully prove to be a useful tool for the genetic mapping of complex traits.

Alleles↗
PubMed2005

Bias correction with a single null marker for population stratification in candidate gene association studies.

Population stratification is a form of confounding by ethnicity that may cause bias to effect estimates and inflate test statistics in genetic association studies. Unlinked genetic markers have been used to adjust for test statistics, but their use in correcting biased effect estimates has not been addressed. We evaluated the potential of bias correction that could be achieved by a single null marker (M) in studies involving one candidate gene (G). When the distribution of M varied greatly across ethnicities, controlling for M in a logistic regression model substantially reduced biases on odds ratio estimates. When M had same distributions as G across ethnicities, biases were further reduced or eliminated by subtracting the regression coefficient of M from the coefficient of G in the model, which was fitted either with or without a multiplicative interaction term between M and G. Correction of bias due to population stratification depended specifically on the distributions of G and M, the difference between baseline disease risks across ethnicities, and whether G had an effect on disease risk or not. Our results suggested that marker choice and the specific treatment of that marker in analysis greatly influenced bias correction.

Bias↗