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Variance Components (variance + component)

Distribution by Scientific Domains
Life Sciences37%
Medical Sciences25%
Mathematics and Statistics18%
Earth and Environmental Science13%
2 Other Domains7%

Terms modified by Variance Components

  • variance component analysis
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  • variance component linkage analysis
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    Selected Abstracts

    EFFECTS OF GENETIC DRIFT ON VARIANCE COMPONENTS UNDER A GENERAL MODEL OF EPISTASIS

    EVOLUTION, Issue 10 2004
    N.H. Barton
    Abstract We analyze the changes in the mean and variance components of a quantitative trait caused by changes in allele frequencies, concentrating on the effects of genetic drift. We use a general representation of epistasis and dominance that allows an arbitrary relation between genotype and phenotype for any number of diallelic loci. We assume initial and final Hardy-Weinberg and linkage equilibrium in our analyses of drift-induced changes. Random drift generates transient linkage disequilibria that cause correlations between allele frequency fluctuations at different loci. However, we show that these have negligible effects, at least for interactions among small numbers of loci. Our analyses are based on diffusion approximations that summarize the effects of drift in terms of F, the inbreeding coefficient, interpreted as the expected proportional decrease in heterozygosity at each locus. For haploids, the variance of the trait mean after a population bottleneck is var(,z,) =where n is the number of loci contributing to the trait variance, VA(1)=VA is the additive genetic variance, and VA(k) is the kth-order additive epistatic variance. The expected additive genetic variance after the bottleneck, denoted (V*A), is closely related to var(,z,); (V*A) (1 ,F)Thus, epistasis inflates the expected additive variance above VA(1 ,F), the expectation under additivity. For haploids (and diploids without dominance), the expected value of every variance component is inflated by the existence of higher order interactions (e.g., third-order epistasis inflates (V*AA)). This is not true in general with diploidy, because dominance alone can reduce (V*A) below VA(1 ,F) (e.g., when dominant alleles are rare). Without dominance, diploidy produces simple expressions: var(,z,)==1 (2F) kVA(k) and (V*A) = (1 ,F)k(2F)k-1VA(k) With dominance (and even without epistasis), var(,z,)and (V*A) no longer depend solely on the variance components in the base population. For small F, the expected additive variance simplifies to (V*A)(1 ,F) VA+ 4FVAA+2FVD+2FCAD, where CAD is a sum of two terms describing covariances between additive effects and dominance and additive × dominance interactions. Whether population bottlenecks lead to expected increases in additive variance depends primarily on the ratio of nonadditive to additive genetic variance in the base population, but dominance precludes simple predictions based solely on variance components. We illustrate these results using a model in which genotypic values are drawn at random, allowing extreme and erratic epistatic interactions. Although our analyses clarify the conditions under which drift is expected to increase VA, we question the evolutionary importance of such increases. [source]

    Estimating the Generalized Concordance Correlation Coefficient through Variance Components

    BIOMETRICS, Issue 4 2003
    Josep L. Carrasco
    Summary. The intraclass correlation coefficient (ICC) and the concordance correlation coefficient (CCC) are two of the most popular measures of agreement for variables measured on a continuous scale. Here, we demonstrate that ICC and CCC are the same measure of agreement estimated in two ways: by the variance components procedure and by the moment method. We propose estimating the CCC using variance components of a mixed effects model, instead of the common method of moments. With the variance components approach, the CCC can easily be extended to more than two observers, and adjusted using confounding covariates, by incorporating them in the mixed model. A simulation study is carried out to compare the variance components approach with the moment method. The importance of adjusting by confounding covariates is illustrated with a case example. [source]

    Estimates of genetic parameters for conformation measures and scores in Finnhorse and Standardbred foals

    JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2010
    E. Schroderus
    Summary The aim of this study was to estimate genetic parameters for conformation measures and scores in the Finnhorse and the Standardbred foals presented in foal shows. Studied traits included height at withers and at croup, six subjectively evaluated conformation traits and overall grade. Data were from 10-year period (1995,2004) and consisted of 5821 Finnhorse foals (1,3 years old) with 7644 records and 2570 Standardbred foals (1,2 years old) with 2864 records. Variance components were estimated with REML , animal model using VCE4 program. The model included age class, year of judging, sex and region as fixed effects, and additive genetic, permanent environmental and residual as random effects. Estimates of heritability for measured traits were very high in both breeds (0.88,0.90). Estimates of heritability for conformation traits varied from 0.13 to 0.32 in the Finnhorse and from 0.06 to 0.47 in the Standardbred. In both breeds, estimates of heritability were lowest for hooves and movements at walk, and highest for type and body conformation among scored traits. Estimate of heritability for overall grade was in the Finnhorse 0.32 and in the Standardbred 0.34. Genetic correlations between overall grade and different conformation traits were 0.35,0.84 in the Finnhorse and 0.31,0.88 in the Standardbred. Thus, selection based on the overall grade would improve all studied characteristics. [source]

    Variance components due to direct genetic, maternal genetic and permanent environmental effect for growth and feed-efficiency traits in young male Japanese Black cattle

    JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2007
    M. A. Hoque
    Summary Variance components and genetic parameters were estimated using data recorded on 740 young male Japanese Black cattle during the period from 1971 to 2003. Traits studied were feed intake (FI), feed-conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG), metabolic body weight (MWT) at the mid-point of the test period and body weight (BWT) at the finish of the test (345 days). Data were analysed using three alternative animal models (direct, direct + maternal environmental, and direct + maternal genetic effects). Comparison of the log likelihood values has shown that the direct genetic effect was significant (p < 0.05) for all traits and that the maternal environmental effects were significant (p < 0.05) for MWT and BWT. The heritability estimates were 0.20 ± 0.12 for FI, 0.14 ± 0.10 for FCR, 0.33 ± 0.14 for RFI, 0.19 ± 0.12 for ADG, 0.30 ± 0.14 for MWT and 0.30 ± 0.13 for BWT. The maternal effects (maternal genetic and maternal environmental) were not important in feed-efficiency traits. The genetic correlation between RFI and ADG was stronger than the corresponding correlation between FCR and ADG. These results provide evidence that RFI should be included for genetic improvement in feed efficiency in Japanese Black breeding programmes. [source]

    Genetic parameters of racing merit of thoroughbred horses in Poland

    JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2004
    M. Sobczynska
    Summary The study aimed at estimating variance components of racing ability traits in thoroughbred horses as a contribution to defining the breeding objective for this breed. Data collected were 12 143 placings at finish (square root) and 8641 earnings (log) won by 1414 horses running in 1693 races over the period of 1998,2001. Age of horses ranged from 2 to 5+ years, and the distances were from 1000 to 3200 m. Horses were from 11 state stables, from private breeders (one collective group), and from foreign breeding (another collective group within the factor ,breeder'). Variance components were estimated by the residual maximal likelihood (REML) method. Statistical analysis accounted for fixed effects of year, age, race, breeder (optional), sex, weight carried and distance, and for the random effects of rider, permanent environment, and animal additive genetics. Pedigrees were at least three generations deep. When breeder effect was excluded from the model, heritability coefficients were 0.12 and 0.18, repeatability 0.23 and 0.34 for earnings and placings at finish, respectively. Zusammenfassung Die vorliegende Studie befasst sich mit der Varianzkomponentenschätzung für Merkmale der Rennleistung von Vollblutpferden als einen Beitrag, um die Zuchtziele näher zu definieren. Die Daten stammen aus 12143 Platzierungen (Quadratwurzel) und 8641 Gewinnsummen (Logarithmus) von 1414 Pferden in 1693 Rennen aus den Jahren 1998 bis 2001. Das Alter der Pferde reichte von zwei bis neun Jahre, die Renndistanzen von 1000 bis 3200 m. Die Pferde stammten aus 11 Ställen, von Privatzüchtern (ein gemeinsamer Zusammenschluß) und aus fremder Zucht (eine weitere zusammengeschlossene Gruppe innerhalb der Gruppe ,,Züchter''). Die Varianzkomponenten wurden mit der REML-Methode geschätzt. Die statistischen Analysen berücksichtigten als fixe Effekte Jahr, Alter, Rasse, Züchter (optional), Geschlecht, zu tragendes Gewicht und Distanz, sowie als zufällige Effekte Reiter, permanente Umwelt und der additiv genetische Tiereffekt. Die Pedigrees umfassten wenigstens drei Generationen. Ohne den Effekt des Züchters im Modell wurden für Gewinnsummen und Platzierungen Heritabilitäten von 0,12 und 0,18 sowie Wiederholbarkeiten von 0,23 und 0,34 geschätzt. [source]

    Estimation of genetic parameters for canine hip dysplasia in the Swiss Newfoundland population

    JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2003
    E. Dietschi
    Summary Variance components and genetic parameters for hip dysplasia (HD) in a population of 1372 Newfoundlands were estimated using restricted maximum likelihood method applied to animal models comprising fixed effects of gender, screening expert and HD grading system. All models investigated included a random direct genetic effect, but differed for combinations of random maternal genetic effect, permanent maternal environmental effect and kennel effect. Although kennels had no effect on HD, the permanent maternal environmental effects, however were significant. The results for the maternal genetic effect were ambiguous. These results suggest a confounding of these three random effects. The model that included the fixed effects, the direct genetic effect and the permanent maternal environmental effect was the most parsimonious combined with an optimal fit. The heritability estimated with this model was 0.28 and the proportion of the permanent maternal environmental effect of the phenotypic variance was 0.10. The effects of gender and screening expert were significant but not the one of HD grading system. Zusammenfassung Schätzung genetischer Populationsparameter für die Hüftgelenksdysplasie bei den Neufundländern in der Schweiz In einer Hundepopulation von 1372 Neufundländern wurden für die Hüftgelenksdysplasie mit Hilfe der "restricted maximum likelihood method" Varianzkomponenten und genetische Parameter geschätzt, wobei ein Tiermodell zu Grunde gelegt wurde. Alle verwendeten Modelle enthielten einen zufälligen direkten genetischen Effekt und in unterschiedlichen Kombinationen einen zufälligen maternalen Effekt, einen zufälligen permanent maternalen Umwelteffekt und einen zufälligen Zwinger-Effekt. Als fixe Effekte wurden das Geschlecht, der Auswertungs-Experte und das HD-Einteilungssystem berücksichtigt. Der Zwinger hat keinen Einfluss auf die Hüftgelenksdysplasie, aber der permanent maternale Umwelteffekt ist signifikant. Der maternal genetische Effekt verhält sich je nach Modell unterschiedlich. Diese Resultate legen eine Vermengung dieser drei Effekte nahe. Die beste Anpassung an die Daten zeigt dasjenige Modell, das die fixen Effekte, den direkten genetischen Effekt und den permanent maternalen Umwelteffekt enthält. Die mit diesem Modell geschätzte Heritabilität beträgt 0,28 und der Anteil des permanent maternalen Umwelteffektes an der phänotypischen Varianz 0,10. Das Geschlecht und die Auswertungs-Experten sind signifikant, das HD-Einteilungssystem nicht. [source]

    EFFECTS OF GENETIC DRIFT ON VARIANCE COMPONENTS UNDER A GENERAL MODEL OF EPISTASIS

    EVOLUTION, Issue 10 2004
    N.H. Barton
    Abstract We analyze the changes in the mean and variance components of a quantitative trait caused by changes in allele frequencies, concentrating on the effects of genetic drift. We use a general representation of epistasis and dominance that allows an arbitrary relation between genotype and phenotype for any number of diallelic loci. We assume initial and final Hardy-Weinberg and linkage equilibrium in our analyses of drift-induced changes. Random drift generates transient linkage disequilibria that cause correlations between allele frequency fluctuations at different loci. However, we show that these have negligible effects, at least for interactions among small numbers of loci. Our analyses are based on diffusion approximations that summarize the effects of drift in terms of F, the inbreeding coefficient, interpreted as the expected proportional decrease in heterozygosity at each locus. For haploids, the variance of the trait mean after a population bottleneck is var(,z,) =where n is the number of loci contributing to the trait variance, VA(1)=VA is the additive genetic variance, and VA(k) is the kth-order additive epistatic variance. The expected additive genetic variance after the bottleneck, denoted (V*A), is closely related to var(,z,); (V*A) (1 ,F)Thus, epistasis inflates the expected additive variance above VA(1 ,F), the expectation under additivity. For haploids (and diploids without dominance), the expected value of every variance component is inflated by the existence of higher order interactions (e.g., third-order epistasis inflates (V*AA)). This is not true in general with diploidy, because dominance alone can reduce (V*A) below VA(1 ,F) (e.g., when dominant alleles are rare). Without dominance, diploidy produces simple expressions: var(,z,)==1 (2F) kVA(k) and (V*A) = (1 ,F)k(2F)k-1VA(k) With dominance (and even without epistasis), var(,z,)and (V*A) no longer depend solely on the variance components in the base population. For small F, the expected additive variance simplifies to (V*A)(1 ,F) VA+ 4FVAA+2FVD+2FCAD, where CAD is a sum of two terms describing covariances between additive effects and dominance and additive × dominance interactions. Whether population bottlenecks lead to expected increases in additive variance depends primarily on the ratio of nonadditive to additive genetic variance in the base population, but dominance precludes simple predictions based solely on variance components. We illustrate these results using a model in which genotypic values are drawn at random, allowing extreme and erratic epistatic interactions. Although our analyses clarify the conditions under which drift is expected to increase VA, we question the evolutionary importance of such increases. [source]

    Parent-of-origin, imprinting, mitochondrial, and X-linked effects in traits related to alcohol dependence: Presentation Group 18 of Genetic Analysis Workshop 14

    GENETIC EPIDEMIOLOGY, Issue S1 2005
    Konstantin Strauch
    Abstract The participants of Presentation Group 18 of Genetic Analysis Workshop 14 analyzed the Collaborative Study on the Genetics of Alcoholism data set to investigate sex-specific effects for phenotypes related to alcohol dependence. In particular, the participants looked at imprinting (which is also known as parent-of-origin effect), differences between recombination fractions for the two sexes, and mitochondrial and X-chromosomal effects. Five of the seven groups employed newly developed or existing methods that take imprinting into account when testing for linkage, or test for imprinting itself. Single-marker and multipoint analyses were performed for microsatellite as well as single-nucleotide polymorphism markers, and several groups used a sex-specific genetic map in addition to a sex-averaged map. Evidence for paternal imprinting (i.e., maternal expression) was consistently obtained by at least two groups at genetic regions on chromosomes 10, 12, and 21 that possibly harbor genes responsible for alcoholism. Evidence for maternal imprinting (which is equivalent to paternal expression) was consistently found at a locus on chromosome 11. Two groups applied extensions of variance components analysis that model a mitochondrial or X-chromosomal effect to latent class variables and electrophysiological traits employed in the diagnosis of alcoholism. The analysis, without using genetic markers, revealed mitochondrial or X-chromosomal effects for several of these traits. Accounting for sex-specific environmental variances appeared to be crucial for the identification of an X-chromosomal factor. In linkage analysis using marker data, modeling a mitochondrial variance component increased the linkage signals obtained for autosomal loci. Genet. Epidemiol. 29(Suppl. 1):S125,S132, 2005. © 2005 Wiley-Liss, Inc. [source]

    Regression modelling of correlated data in ecology: subject-specific and population averaged response patterns

    JOURNAL OF APPLIED ECOLOGY, Issue 5 2009
    John Fieberg
    Summary 1.,Statistical methods that assume independence among observations result in optimistic estimates of uncertainty when applied to correlated data, which are ubiquitous in applied ecological research. Mixed effects models offer a potential solution and rely on the assumption that latent or unobserved characteristics of individuals (i.e. random effects) induce correlation among repeated measurements. However, careful consideration must be given to the interpretation of parameters when using a nonlinear link function (e.g. logit). Mixed model regression parameters reflect the change in the expected response within an individual associated with a change in that individual's covariates [i.e. a subject-specific (SS) interpretation], which may not address a relevant scientific question. In particular, a SS interpretation is not natural for covariates that do not vary within individuals (e.g. gender). 2.,An alternative approach combines the solution to an unbiased estimating equation with robust measures of uncertainty to make inferences regarding predictor,outcome relationships. Regression parameters describe changes in the average response among groups of individuals differing in their covariates [i.e. a population-averaged (PA) interpretation]. 3.,We compare these two approaches [mixed models and generalized estimating equations (GEE)] with illustrative examples from a 3-year study of mallard (Anas platyrhynchos) nest structures. We observe that PA and SS responses differ when modelling binary data, with PA parameters behaving like attenuated versions of SS parameters. Differences between SS and PA parameters increase with the size of among-subject heterogeneity captured by the random effects variance component. Lastly, we illustrate how PA inferences can be derived (post hoc) from fitted generalized and nonlinear-mixed models. 4.,Synthesis and applications. Mixed effects models and GEE offer two viable approaches to modelling correlated data. The preferred method should depend primarily on the research question (i.e. desired parameter interpretation), although operating characteristics of the associated estimation procedures should also be considered. Many applied questions in ecology, wildlife management and conservation biology (including the current illustrative examples) focus on population performance measures (e.g. mean survival or nest success rates) as a function of general landscape features, for which the PA model interpretation, not the more commonly used SS model interpretation may be more natural. [source]

    Fine scale genetic population structure of the freshwater and Omono types of nine-spined stickleback Pungitius pungitius (L.) within the Omono River system, Japan

    JOURNAL OF FISH BIOLOGY, Issue 2006
    T. Tsuruta
    The fine scale geographic population structure of two types of nine-spined stickleback Pungitius pungitius (the widely distributed freshwater type and a local endemic, the Omono type) within the Omono River system, Japan, was investigated. A principal components analysis of allele frequencies and neighbour-joining tree for pair-wise FST values, based on 10 allozyme loci, revealed that the Omono type was comprised of four regional groups with relatively high genetic divergence. This grouping was also supported by hierarchical analysis of molecular variance (AMOVA) with a higher variance component among the regional groups, and by an exact test with significant genotypic differentiation for all sample pairs among the regional groups. Moreover, in the clustering of individuals using the Bayesian method, most of individuals in each regional group were assigned the corresponding cluster. On the other hand, there were less pronounced regional groups of the freshwater type, although AMOVA, exact test for genotypic differentiation and Bayesian analysis indicated genetic divergence between two sampling sites in lower reach of the Omono River and other sites. The results suggest that the Omono type represented an earlier colonization, with subsequent invasion of the freshwater type. [source]

    Sources of Phenotypic and Genetic Variation for Seawater Growth in Five North American Atlantic Salmon, Salmo salar, Stocks

    JOURNAL OF THE WORLD AQUACULTURE SOCIETY, Issue 3 2010
    William R. Wolters
    In 2003, pedigreed families were obtained from two St. John's River sources, Penobscot River, Gaspè, and landlocked salmon stocks. Eyed eggs were disinfected upon arrival, and incubated in separate hatching jars. Fry were transferred prior to first feeding into individual 0.1-m3 tanks receiving 8 L/min of oxygen-saturated freshwater from a recirculating biological filtration system. At approximately 30 d after the initiation of feeding, fish densities were equalized to 250 fish/tank, fed 5% of the tank's total biomass in 3,4 daily feedings. When the fish were approximately 40 g, approximately 30 fish from each family were pit tagged and stocked communally into three replicated 10-m3 smolt tanks. Approximately 1 mo prior to stocking into sea cages for performance evaluations, evaluations of serum chloride levels and gill Na+, K+ -ATPase activity were measured on subsamples from all stocks in freshwater and following seawater challenge. Smolts were stocked into sea cages in June 2005, harvested in February 2007, and evaluated for carcass weight, sex, and stage of sexual maturity. Data were analyzed by the mixed model ANOVA to determine the random effects of sire and dam (sire), and the fixed effects of sex, salmon stock, ploidy level, and replicate smolt tank on carcass weight with smolt weight as a covariate. Sire and dam variance components were significantly different from zero, and the fixed effects of salmon stock, sex × stock interaction, and smolt weight at stocking were significant (P < 0.05). There were no significant differences among sexes, replicate smolt tank, or ploidy level for carcass weight. Overall, St. John's River fish had the fastest growth with a carcass weight >4.1 kg compared with the slowest growth in landlocked fish at 1.7 kg. Grilsing was also highest in St. John's River fish (ca. 4,6%) and lowest in Penobscot River fish (0%). The sire heritability for carcass weight calculated from the sire variance component using the mixed model ANOVA or MTDFREML was 0.26 ± 0.14. Data were used to calculate breeding values on captive sibling adult brood fish, and a line selected for carcass weight was spawned in the fall of 2007, and eggs from these fish were released to industry. [source]

    Adolescent quality of life: A school-based cohort study in Western Australia

    PEDIATRICS INTERNATIONAL, Issue 6 2003
    Lynn B. Meuleners
    AbstractBackground:,Quality of life (QOL) has received increasing attention in pediatrics medicine recently. Improving QOL is the primary justification for many interventions, medications and therapies. The present study is a school-based longitudinal study which aims to investigate the factors affecting QOL of adolescents in Western Australia over a 6-month period. Methods:,A generic self-reported questionnaire was administered twice to participants from 20 schools in Perth at 6 months apart. In addition to QOL scores and physical health status, demographic and other information was also collected. For the cohort of 363 students who participated in the initial survey, 300 of them completed the second questionnaire. Results:,A significant change in QOL score between baseline and 6 months was observed. Results from fitting a hierarchical mixed regression model indicated that 55% of the variation in QOL was due to differences between individuals, and was significantly associated with age, control, opportunities and perceptions of physical health, while the remaining variance component could be attributed to within-individual changes. Improved control and opportunities appeared to have a significant positive impact on QOL, whereas increasing age and deterioration in physical health had the opposite effect. Conclusions:,The hierarchical regression analysis has enabled valid inferences to be made based on the observed longitudinal data. Perceptions of physical health, age, control and opportunities available are related to adolescent QOL. The findings have implications on evidence-based practices and childhood health issues. [source]

    Components of genetic variation for resistance of strawberry to Phytophthora cactorum estimated using segregating seedling populations and their parent genotypes

    PLANT PATHOLOGY, Issue 2 2008
    D. V. Shaw
    Strawberry (Fragaria × ananassa) seedlings from 50 bi-parental crosses among 20 elite genotypes were evaluated for resistance to Phytophthora cactorum after artificial inoculation. Plots of seedlings or runner plants were rated using a disease severity score and the percentage of stunted plants per plot. The distribution of cross means for percentages of plants with stunting was highly skewed; 79% of the inoculated seedlings showed some level of stunting compared to non-inoculated control seedlings, and all but one of the crosses had 50% or more stunted plants. Disease severity scores for the bi-parental crosses were normally distributed and expressed a range of variation not reflected by the percentage of visibly stunted plants. Factorial analysis based on seedling plot means demonstrated significant additive genetic variance for the disease severity score, and the additive genetic variance was 1·9 times greater than the estimated dominance variance. The cross-mean heritability was for the severity score. Estimates of the additive genetic variance component using the covariance of severity scores obtained from the seedling analysis and with severity scores for their parents evaluated in a commercial environment were similar, and 0·30, respectively. Most of the selection response obtained through genotypic selection would thus be transferred to segregating offspring. [source]

    Genetic structure of Eurasian cattle (Bos taurus) based on microsatellites: clarification for their breed classification,

    ANIMAL GENETICS, Issue 2 2010
    M.-H. Li
    Summary We pool three previously published data sets and present population genetic analyses of microsatellite variation in 48 Bos taurus cattle breeds from a wide range of geographical origins in Eurasia, mostly its northern territory. Bayesian model-based clustering reveals six distinct clusters: besides a single-population cluster of the Yakutian Cattle from Far Eastern Siberia and a cluster of breeds characteristic of an early origin, the other four major clusters largely correspond to previously defined morphological subgroups of Red Lowland, Lowland Black-Pied, Longhorned Dairy and North European Polled cattle breeds. The results highlighted past expansion events of the productive breeds such as Danish Red, Angeln, Holstein-Friesian and Ayrshire in northern and Eastern Europe. Based on genetic assignment of the breeds and the availability of breed information, we provide a preliminary classification of the five breeds that were to date undefined. Furthermore, in the analysis of molecular variance, despite some correspondence between geographical proximity and genetic similarity, the breed classification appears to be a better predictor of genetic structure in the cattle populations (the among-group variance component: breed classification, 2.47%, P < 0.001; geographical division, 0.77%, P < 0.001). [source]

    Mitochondrial DNA diversity and origins of South and Central American goats

    ANIMAL GENETICS, Issue 3 2009
    M. Amills
    Summary We have analysed the genetic diversity of South and Central American (SCA) goats by partially sequencing the mitochondrial control region of 93 individuals with a wide geographical distribution. Nucleotide and haplotype diversities reached values of 0.020 ± 0.00081 and 0.963 ± 0.0012 respectively. We have also observed a rather weak phylogeographic structure, with almost 69% of genetic variation included in the within-breed variance component. The topology of a median-joining network analysis including 286 European, Iberian, Atlantic and SCA mitochondrial sequences was very complex, with most of the haplotypes forming part of independent small clusters. SCA sequences showed a scattered distribution throughout the network, and clustering with Spanish and Portuguese sequences occurred only occasionally, not allowing the distinguishing of a clear Iberian signature. Conversely, we found a prominent cluster including Canarian, Chilean, Argentinian and Bolivian mitochondrial haplotypes. This result was independently confirmed by constructing a Bayesian phylogenetic tree (posterior probability of 0.97). Sharing of mitochondrial haplotypes by SCA and Canarian goats suggests that goat populations from the Atlantic archipelagos, where Spanish and Portuguese ships en route to the New World used to stow food and supplies, participated in the foundation of SCA caprine breeds. [source]

    Two-strata rotatability in split-plot central composite designs

    APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010
    Li Wang
    Abstract A rotatable design (Ann. Math. Stat. 1957; 28:195,241) for k factors is one such that the prediction variance is purely a function of distance from the design center. Of special interest in this paper is the rotatable central composite design (CCD), which most software packages use as the typical default choice for a second-order design. In many cases some factors are hard to change while others are easy to change, which creates a split-plot experiment. This paper establishes that the split-plot structure precludes the possibility of any second-order design being rotatable in the traditional sense. As an alternative this paper proposes the two-strata rotatable split-plot CCD, where the resulting prediction variance is a function of the whole plot (WP) distance and the subplot (SP) distance separately instead of the sum of them. The resulting design is rotatable in the WP space when the SP factors are held fixed, and vice versa. In the special case where the WP variance component is zero, the two-strata rotatable split-plot CCD becomes the standard rotatable CCD. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Multilevel Mixture Cure Models with Random Effects

    BIOMETRICAL JOURNAL, Issue 3 2009
    Xin Lai
    Abstract This paper extends the multilevel survival model by allowing the existence of cured fraction in the model. Random effects induced by the multilevel clustering structure are specified in the linear predictors in both hazard function and cured probability parts. Adopting the generalized linear mixed model (GLMM) approach to formulate the problem, parameter estimation is achieved by maximizing a best linear unbiased prediction (BLUP) type log-likelihood at the initial step of estimation, and is then extended to obtain residual maximum likelihood (REML) estimators of the variance component. The proposed multilevel mixture cure model is applied to analyze the (i) child survival study data with multilevel clustering and (ii) chronic granulomatous disease (CGD) data on recurrent infections as illustrations. A simulation study is carried out to evaluate the performance of the REML estimators and assess the accuracy of the standard error estimates. [source]

    Smoothing Spline-Based Score Tests for Proportional Hazards Models

    BIOMETRICS, Issue 3 2006
    Jiang Lin
    Summary We propose "score-type" tests for the proportional hazards assumption and for covariate effects in the Cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero. We show that the tests have a size close to the nominal level and good power against general alternatives, and we apply them to data from a cancer clinical trial. [source]

    Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of variance

    ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 9 2007
    Steve Gutreuter
    Abstract Predictors of the percentile lethal/effective concentration/dose are commonly used measures of efficacy and toxicity. Typically such quantal-response predictors (e.g., the exposure required to kill 50% of some population) are estimated from simple bioassays wherein organisms are exposed to a gradient of several concentrations of a single agent. The toxicity of an agent may be influenced by auxiliary covariates, however, and more complicated experimental designs may introduce multiple variance components. Prediction methods lag examples of those cases. A conventional two-stage approach consists of multiple bivariate predictions of, say, medial lethal concentration followed by regression of those predictions on the auxiliary covariates. We propose a more effective and parsimonious class of generalized nonlinear mixed-effects models for prediction of lethal/effective dose/concentration from auxiliary covariates. We demonstrate examples using data from a study regarding the effects of pH and additions of variable quantities 2',5'-dichloro-4'-nitrosalicylanilide (niclosamide) on the toxicity of 3-trifluoromethyl-4-nitrophenol to larval sea lamprey (Petromyzon marinus). The new models yielded unbiased predictions and root-mean-squared errors (RMSEs) of prediction for the exposure required to kill 50 and 99.9% of some population that were 29 to 82% smaller, respectively, than those from the conventional two-stage procedure. The model class is flexible and easily implemented using commonly available software. [source]

    Evaluation of reduced rank semiparametric models to assess excess of risk in cluster analysis

    ENVIRONMETRICS, Issue 4 2009
    Marco Geraci
    Abstract The existence of multiple environmental hazards is obviously a threat to human health and, from a statistical point of view, the modeling and the detection of disease clusters potentially related to those hazards offer challenging tasks. In this paper, we consider low rank thin plate spline (TPS) models within a semiparametric approach to focused clustering for small area health data. Both the distance from a putative source and a general, unspecified clustering process are modeled in the same fashion and they are entered log-additively in mixed Poisson-Normal models. Some issues related to the identification of the random effects arising from this approach are investigated. Under different simulated scenarios, we evaluate the proposed models using conditional Akaike's weights and tests for variance components, providing a comprehensive model selection methodology easy to implement. We examine observations of lung cancer deaths taken in Ohio between 1987 and 1988. These data were analyzed on several occasions to investigate the risk associated with a putative source in Hamilton county. In our analysis, we found a strong south-eastward spatial trend which is confounded with a significant radial distance effect decreasing between 0 and 150 km from the point source. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Growth curve models for stochastic modeling and analyzing of natural disinfection of wastewater

    ENVIRONMETRICS, Issue 8 2006
    Wolfgang Bischoff
    Abstract This work is motivated by a study on the natural disinfection of wastewater in marine environment for ocean outfall systems without chlorination. In the study of the disinfection on wastewater in marine environment two natural factors, consisting of light intensity and salinity, one controllable factor, the volumetric mixing ratio of seawater to wastewater, and one random effect factor, the existence of predators, were investigated. Our problem and data are modeled by a growth curve model with an unknown random parameter that can be described by a mixed model with the factors mentioned above as covariates. For our model we determine the optimal variance estimations. Finally, we apply our model with these optimal estimated variance components to the data obtained from the real experiments. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Sampling and analytical plus subsampling variance components for five soil indicators observed at regional scale

    EUROPEAN JOURNAL OF SOIL SCIENCE, Issue 5 2009
    B. G. Rawlins
    Summary When comparing soil baseline measurements with resampled values there are four main sources of error. These are: i) location (errors in relocating the sample site), ii) sampling errors (representing the site with a sample of material) iii) subsampling error (selecting material for analysis) and iv) analytical error (error in laboratory measurements). In general we cannot separate the subsampling and analytical sources of error (since we always analyse a different subsample of a specimen), so in this paper we combine these two sources into subsampling plus analytical error. More information is required on the relative magnitudes of location and sampling errors for the design of effective resampling strategies to monitor changes in soil indicators. Recently completed soil surveys of the UK with widely differing soils included a duplicate site and subsampling protocol to quantify ii), and the sum of iii) and iv) above. Sampling variances are estimated from measurements on duplicate samples , two samples collected on a support of side length 20 m separated by a short distance (21 m). Analytical and subsampling variances are estimated from analyses of two subsamples from each duplicate site. After accounting for variation caused by region, parent material class and land use, we undertook a nested analysis of data from 196 duplicate sites across three regions to estimate the relative magnitude of medium-scale (between sites), sampling and subsampling plus analytical variance components, for five topsoil indicators: total metal concentrations of copper (Cu), nickel (Ni) and zinc (Zn), soil pH and soil organic carbon (SOC) content. The variance components for each indicator diminish by about an order of magnitude from medium-scale, to sampling, to analytical plus subsampling. Each of the three fixed effects (parent material, land use and region) were statistically significant for each of the five indicators. The most effective way to minimise the overall uncertainty of our observations at sample sites is to reduce the sampling variance. [source]

    Spatial variation of ammonia volatilization from soil and its scale-dependent correlation with soil properties

    EUROPEAN JOURNAL OF SOIL SCIENCE, Issue 6 2008
    R. Corstanje
    Summary Quantitative predictions of ammonia volatilization from soil are useful to environmental managers and policy makers and empirical models have been used with some success. Spatial analysis of the soil properties and their relationship to the ammonia volatilization process is important as predictions will be required at disparate scales from the field to the catchment and beyond. These relationships are known to change across scales and this may affect the performance of an empirical model. This study is concerned with the variation of ammonia volatilization and some controlling soil properties: bulk density, volumetric water content, pH, CEC, soil pH buffer power, and urease activity, over distances of 2, 50, 500, and >2000 m. We sampled a 16 km × 16 km region in eastern England and analyzed the results by a nested analysis of (co)variance, from which variance components and correlations for each scale were obtained. The overall correlations between ammonia volatilization and the soil properties were generally weak: ,0.09 for bulk density, 0.04 for volumetric water content, ,0.22 for CEC, ,0.08 for urease activity, ,0.22 for pH and 0.18 for the soil pH buffer power. Variation in ammonia volatilization was scale-dependent, with substantial variance components at the 2- and 500-m scales. The results from the analysis of covariance show that the relationships between ammonia volatilization and soil properties are complex. At the >2000 m scale, ammonia volatilization was strongly correlated with pH (,0.82) and CEC (,0.55), which is probably the result of differences in parent material. We also observed weaker correlations at the 500-m scale with bulk density (,0.61), volumetric water content (0.48), urease activity (,0.42), pH (,0.55) and soil pH buffer power (0.38). Nested analysis showed that overall correlations may mask relationships at scales of interest and the effect of soil variables on these soil processes is scale-dependent. [source]

    QUANTITATIVE GENETIC VARIATION IN POPULATIONS OF AMSINCKIA SPECTABILIS THAT DIFFER IN RATE OF SELF-FERTILIZATION

    EVOLUTION, Issue 5 2009
    Magdalena P. Bartkowska
    Self-fertilization is expected to reduce genetic diversity within populations and consequently to limit adaptability to changing environments. Little is known, however, about the way the evolution of self-fertilization changes the amount or pattern of the components of genetic variation in natural populations. In this study, a reciprocal North Carolina II design and maximum-likelihood methods were implemented to investigate the genetic basis of variation for 15 floral and vegetative traits in four populations of the annual plant Amsinckia spectabilis (Boraginaceae) differing in mating system. Six variance components were estimated according to Cockerham and Weir's "bio" model c. Compared to the three partially selfing populations, we found significantly lower levels of nuclear variance for several traits in the nearly completely self-fertilizing population. Furthermore, for 11 of 15 traits we did not detect nuclear variation to be significantly greater than zero. We also found high maternal variance in one of the partially selfing populations for several traits, and little dominance variance in any population. These results are in agreement with the evolutionary dead-end hypothesis for highly self-fertilizing taxa. [source]

    EFFECTS OF GENETIC DRIFT ON VARIANCE COMPONENTS UNDER A GENERAL MODEL OF EPISTASIS

    EVOLUTION, Issue 10 2004
    N.H. Barton
    Abstract We analyze the changes in the mean and variance components of a quantitative trait caused by changes in allele frequencies, concentrating on the effects of genetic drift. We use a general representation of epistasis and dominance that allows an arbitrary relation between genotype and phenotype for any number of diallelic loci. We assume initial and final Hardy-Weinberg and linkage equilibrium in our analyses of drift-induced changes. Random drift generates transient linkage disequilibria that cause correlations between allele frequency fluctuations at different loci. However, we show that these have negligible effects, at least for interactions among small numbers of loci. Our analyses are based on diffusion approximations that summarize the effects of drift in terms of F, the inbreeding coefficient, interpreted as the expected proportional decrease in heterozygosity at each locus. For haploids, the variance of the trait mean after a population bottleneck is var(,z,) =where n is the number of loci contributing to the trait variance, VA(1)=VA is the additive genetic variance, and VA(k) is the kth-order additive epistatic variance. The expected additive genetic variance after the bottleneck, denoted (V*A), is closely related to var(,z,); (V*A) (1 ,F)Thus, epistasis inflates the expected additive variance above VA(1 ,F), the expectation under additivity. For haploids (and diploids without dominance), the expected value of every variance component is inflated by the existence of higher order interactions (e.g., third-order epistasis inflates (V*AA)). This is not true in general with diploidy, because dominance alone can reduce (V*A) below VA(1 ,F) (e.g., when dominant alleles are rare). Without dominance, diploidy produces simple expressions: var(,z,)==1 (2F) kVA(k) and (V*A) = (1 ,F)k(2F)k-1VA(k) With dominance (and even without epistasis), var(,z,)and (V*A) no longer depend solely on the variance components in the base population. For small F, the expected additive variance simplifies to (V*A)(1 ,F) VA+ 4FVAA+2FVD+2FCAD, where CAD is a sum of two terms describing covariances between additive effects and dominance and additive × dominance interactions. Whether population bottlenecks lead to expected increases in additive variance depends primarily on the ratio of nonadditive to additive genetic variance in the base population, but dominance precludes simple predictions based solely on variance components. We illustrate these results using a model in which genotypic values are drawn at random, allowing extreme and erratic epistatic interactions. Although our analyses clarify the conditions under which drift is expected to increase VA, we question the evolutionary importance of such increases. [source]

    MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic component

    GENETIC EPIDEMIOLOGY, Issue 2 2007
    Yun Ju Sung
    Abstract We describe a new program lm_twoqtl, part of the MORGAN package, for parametric linkage analysis with a quantitative trait locus (QTL) model having one or two QTLs and a polygenic component, which models additional familial correlation from other unlinked QTLs. The program has no restriction on number of markers or complexity of pedigrees, facilitating use of more complex models with general pedigrees. This is the first available program that can handle a model with both two QTLs and a polygenic component. Competing programs use only simpler models: one QTL, one QTL plus a polygenic component, or variance components (VC). Use of simple models when they are incorrect, as for complex traits that are influenced by multiple genes, can bias estimates of QTL location or reduce power to detect linkage. We compute the likelihood with Markov Chain Monte Carlo (MCMC) realization of segregation indicators at the hypothesized QTL locations conditional on marker data, summation over phased multilocus genotypes of founders, and peeling of the polygenic component. Simulated examples, with various sized pedigrees, show that two-QTL analysis correctly identifies the location of both QTLs, even when they are closely linked, whereas other analyses, including the VC approach, fail to identify the location of QTLs with modest contribution. Our examples illustrate the advantage of parametric linkage analysis with two QTLs, which provides higher power for linkage detection and better localization than use of simpler models. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source]

    Sex differences in genetic and environmental determinants of pulse pressure

    GENETIC EPIDEMIOLOGY, Issue 5 2006
    Katrina J. Scurrah
    Abstract Pulse pressure (PP) is an independent risk factor for cardiovascular disease. PP rises with age, more so in women. We examined sex differences in the correlations and variance components of PP in adult subjects from 767 nuclear families, enriched with those containing twins, from the Victorian Family Heart Study. After adjusting for age, we found no significant differences in the means or variances of PP in males and females. Under the assumption of no sex differences, the proportions of variance due to shared genes, shared environment, and individual-specific environment were 20%, 23% and 57%, respectively. However, same-sex relative pairs had significantly higher correlations than opposite-sex pairs (P=0.005), implying the existence of sex-dependent effects. Extensions to the simple variance components model suggested three possible explanations for these differences: smaller genetic correlation between opposite-sex pairs (,G,MF=0.45, P=0.007); smaller environmental correlation between opposite-sex pairs (P=0.0003); or different environmental and genetic correlations obtained by estimating genetic, environmental, and individual variance components separately for males and females (not nested, Akaike's Information Criterion (AIC) smaller by 6.69). Under the last model, the genetic component of PP variance is greater for males (1.62 vs 0.33) while the environmental component is greater for females (1.84 vs 0), which would have implications for the planning of gene discovery studies, since heritability would be higher in males. However, the second (environmental) approach best fits the data according to the AIC. Genetic explanations for sex differences in phenotypic correlations may be misleading unless shared environmental factors are also considered. PP illustrates a phenotype in which sex dependency represents an important component of phenotypic determination that can be revealed by detailed variance components modelling. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source]

    Quantitative trait linkage analysis by generalized estimating equations: Unification of variance components and Haseman-Elston regression

    GENETIC EPIDEMIOLOGY, Issue 4 2004
    Wei-Min Chen
    Two of the major approaches for linkage analysis with quantitative traits in humans include variance components and Haseman-Elston regression. Previously, these were viewed as quite separate methods. We describe a general model, fit by use of generalized estimating equations (GEE), for which the variance components and Haseman-Elston methods (including many of the extensions to the original Haseman-Elston method) are special cases, corresponding to different choices for a working covariance matrix. We also show that the regression-based test of Sham et al. ([2002] Am. J. Hum. Genet. 71:238,253) is equivalent to a robust score statistic derived from our GEE approach. These results have several important implications. First, this work provides new insight regarding the connection between these methods. Second, asymptotic approximations for power and sample size allow clear comparisons regarding the relative efficiency of the different methods. Third, our general framework suggests important extensions to the Haseman-Elston approach which make more complete use of the data in extended pedigrees and allow a natural incorporation of environmental and other covariates. © 2004 Wiley-Liss, Inc. [source]

    Gamma regression improves Haseman-Elston and variance components linkage analysis for sib-pairs

    GENETIC EPIDEMIOLOGY, Issue 2 2004
    Mathew J. Barber
    Abstract Existing standard methods of linkage analysis for quantitative phenotypes rest on the assumptions of either ordinary least squares (Haseman and Elston [1972] Behav. Genet. 2:3,19; Sham and Purcell [2001] Am. J. Hum. Genet. 68:1527,1532) or phenotypic normality (Almasy and Blangero [1998] Am. J. Hum. Genet. 68:1198,1199; Kruglyak and Lander [1995] Am. J. Hum. Genet. 57:439,454). The limitations of both these methods lie in the specification of the error distribution in the respective regression analyses. In ordinary least squares regression, the residual distribution is misspecified as being independent of the mean level. Using variance components and assuming phenotypic normality, the dependency on the mean level is correctly specified, but the remaining residual coefficient of variation is constrained a priori. Here it is shown that these limitations can be addressed (for a sample of unselected sib-pairs) using a generalized linear model based on the gamma distribution, which can be readily implemented in any standard statistical software package. The generalized linear model approach can emulate variance components when phenotypic multivariate normality is assumed (Almasy and Blangero [1998] Am. J. Hum Genet. 68: 1198,1211) and is therefore more powerful than ordinary least squares, but has the added advantage of being robust to deviations from multivariate normality and provides (often overlooked) model-fit diagnostics for linkage analysis. Genet Epidemiol 26:97,107, 2004. © 2004 Wiley-Liss, Inc. [source]

    Robustness of inference on measured covariates to misspecification of genetic random effects in family studies

    GENETIC EPIDEMIOLOGY, Issue 1 2003
    Ruth M.Pfeiffer
    Abstract Family studies to identify disease-related genes frequently collect only families with multiple cases. It is often desirable to determine if risk factors that are known to influence disease risk in the general population also play a role in the study families. If so, these factors should be incorporated into the genetic analysis to control for confounding. Pfeiffer et al. [2001 Biometrika 88: 933,948] proposed a variance components or random effects model to account for common familial effects and for different genetic correlations among family members. After adjusting for ascertainment, they found maximum likelihood estimates of the measured exposure effects. Although it is appealing that this model accounts for genetic correlations as well as for the ascertainment of families, in order to perform an analysis one needs to specify the distribution of random genetic effects. The current work investigates the robustness of the proposed model with respect to various misspecifications of genetic random effects in simulations. When the true underlying genetic mechanism is polygenic with a small dominant component, or Mendelian with low allele frequency and penetrance, the effects of misspecification on the estimation of fixed effects in the model are negligible. The model is applied to data from a family study on nasopharyngeal carcinoma in Taiwan. Genet Epidemiol 24:14,23, 2003. © 2003 Wiley-Liss, Inc. [source]