THE EFFECT OF ADJUSTING FOR YEARLY SELECTION TRENDS ON VARIANCE COMPONENT ESTIMATES

1989 ◽  
Vol 69 (2) ◽  
pp. 487-490
Author(s):  
W. W. GEARHEART ◽  
M. E. DAVIS ◽  
W. R. HARVEY
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Variance Components ◽  
Variance Component ◽  
Error Variance ◽  
Component Selection ◽  
Variance Component Estimates ◽  
Selection Of

Computer-generated beef cattle data were used to investigate the effect of accounting for the yearly selection of parents on the bias and precision of sire and error variance component estimates. The adjustment for yearly selection reduced the biases of estimated sire variance components, but resulted in losses of precision of up to 25%. Key words: Beef cattle, variance component, selection

Evaluation of sugarcane morphological descriptors using variance component analysis

1997 ◽  
Vol 48 (6) ◽  
pp. 769 ◽  
Author(s):  
D. J. Gallacher
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Environmental Effect ◽  
Error Variance ◽  
Morphological Characters ◽  
Variance Component Analysis ◽  
Broad Sense Heritability ◽  
Morphological Descriptors ◽  
Quantitative Characters ◽  
High Level

Genotypic, environmental, interaction, and error variances were studied for each of 24 qualitative and 142 quantitative morphological characters. Data were collected in 3 character sets: vegetative macromorphological, inflorescence, and epidermal. Broad-sense heritability was estimated for quantitative characters, and variance components were estimated. Non-zero significance was determined for variance components of all characters. Many characters were identified as having a large genetic component to their variation. Optimising the descriptive power of vegetative macromorphology characters requires the use of test clones to quantify the environmental effect, and a high level of sampling to reduce error variance. Less work is required for inflorescence characters, which are mostly highly heritable. Some valuable epidermal characters were identified.

Sampling Errors of Variance Components in Small Sample Multifacet Generalizability Studies

1978 ◽  
Vol 3 (4) ◽  
pp. 319-346 ◽  
Author(s):  
Philip L. Smith
Keyword(s):  
Monte Carlo ◽  
Variance Components ◽  
Variance Component ◽  
Generalizability Theory ◽  
Small Sample ◽  
Least Square ◽  
Sampling Errors ◽  
Sample Stability ◽  
Sampling Behavior ◽  
Variance Component Estimates

The paper describes the small sample stability of least square estimates of variance components within the context of generalizability theory. Monte Carlo methods are used to generate data conforming to some selected multifacet generalizability designs to illustrate the sampling behavior of variance component estimates. Based on the findings, recommendations are made concerning the design of efficient small sample generalizability studies.

The Sensitivity of Variance Component Estimates to Underreporting: Method and Application to Substance Abuse Data

2007 ◽  
Vol 10 (5) ◽  
pp. 721-728 ◽  
Author(s):  
Paul W. Andrews ◽  
Kenneth S. Kendler ◽  
Nathan Gillespie ◽  
Michael C. Neale
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Illicit Drug ◽  
Sensitive Information ◽  
Twin Design ◽  
Psychological Constructs ◽  
Willingness To Disclose ◽  
Classical Twin Design ◽  
Illicit Drug Abuse ◽  
Variance Component Estimates

AbstractMany studies of human behavior and psychological constructs rely on subjects' willingness to disclose information about themselves. This is problematic for phenotypes that require the disclosure of sensitive information, such as sexual behavior or illicit drug use, which are likely to be underreported. We describe a method for evaluating how sensitive variance component estimates are to underreporting. The method involves estimating, by maximum likelihood, the original population proportions of the response classes, and adjusting them for a set of hypothesized underreporting parameters. If the true values of the underreporting parameters were known, the researcher could estimate the variance components based on these values. Usually, underreporting levels are not known with certainty. However, it is possible to assume a specific value for the underreporting rate, obtain response pattern proportions adjusted for this rate, and then to conduct the analyses on these revised estimates. By repeating the procedure across the range of plausible underreporting values, the researcher can assess how sensitive the variance component estimates are to variation in underreporting. We apply this method to a sample of male-male twin pairs who reported on themselves and their co-twins for illicit drug abuse and dependence (DAD). We show how underreporting influences estimates of additive genetic, common environment, and specific environment variance components (A, C, and E) obtained for DAD in a classical twin design.

Bias in Variance Components Due to Nonresponse in Twin Studies

2006 ◽  
Vol 9 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Annica Dominicus ◽  
Juni Palmgren ◽  
Nancy L. Pedersen
Keyword(s):  
Cognitive Abilities ◽  
Variance Components ◽  
Variance Component ◽  
Pair Correlation ◽  
Twin Studies ◽  
Likelihood Estimation ◽  
Genetic Influences ◽  
Perceptual Speed ◽  
Environmental Variance ◽  
Variance Component Estimates

AbstractIncomplete data on trait values may bias estimates of genetic and environmental variance components obtained from twin analyses. If the nonresponse mechanism is ‘ignorable’ then methods such as full information maximum likelihood estimation will produce consistent variance component estimates. If, however, nonresponse is ‘nonignorable’, then the situation is more complicated. We demonstrate that a within-pair correlation of nonresponse, possibly different for monozygotic (MZ) and dizygotic (DZ) twins, may well be compatible with ‘ignorability’. By means of Monte Carlo simulation, we assess the potential bias in variance component estimates for different types of nonresponse mechanisms. The simulation results guide the interpretation of analyses of data on perceptual speed from the Swedish Adoption/Twin Study of Aging. The results suggest that the dramatic decrease in genetic influences on perceptual speed observed after 13 years of follow-up is not attributable solely to dropout from the study, and thus support the hypothesis that genetic influences on some cognitive abilities decrease with age in late life.

Scope for genetic evaluation and selection of beef cattle for growth to weaning when birth dates are not recorded

1994 ◽  
Vol 45 (4) ◽  
pp. 819
Author(s):  
K Meyer ◽  
HU Graser
Keyword(s):  
Beef Cattle ◽  
Genetic Correlation ◽  
Western Australia ◽  
Variance Components ◽  
Genetic Parameters ◽  
Selection Criterion ◽  
Genetic Evaluation ◽  
Selection Experiment ◽  
Weaning Weight ◽  
Selection Of

Estimates of (co)variance components and genetic parameters were obtained for a preweaning weight, recorded between 2 and 5 months after birth, and the subsequent gain till weaning for two herds in a selection experiment in Western Australia. Analyses were carried out both accounting for age at weighing and assuming birth dates and thus ages were unknown. On adjusting for the interval between weighings, preweaning gain appeared to be independent of age and equally heritable to weaning weight. Estimates of the direct genetic correlation between preweaning gain and weaning weight (adjusted for age) were 0.9 or higher, while both genetic and permanent environmental maternal correlations were close to unity. Implications for the use of preweaning weight as an alternative selection criterion to weaning weight when birth dates are not recorded are discussed.

Variance estimate for the Allen activity index

1998 ◽  
Vol 25 (6) ◽  
pp. 643 ◽  
Author(s):  
Richard M. Engeman ◽  
Lee Allen ◽  
Gary O. Zerbe
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Activity Index ◽  
Likelihood Estimation ◽  
Mixed Linear Model ◽  
Variance Formula ◽  
Variance Estimate ◽  
Wildlife Populations ◽  
Variance Component Estimates ◽  
Restricted Maximum Likelihood Estimation

The Allen activity index, originally developed for monitoring dingo populations, is statistically described as a mixed linear model, from which a variance formula for the index is derived. The resulting formula requires input of variance component estimates, the estimation of which is accomplished using restricted maximum-likelihood estimation. An example is used to demonstrate the calculation of the variance components and their use in the variance formula. Application of the variance formula substantially enhances the quantitative practicality of this useful index of wildlife populations.

scholarly journals Genetic parameters estimates for gestation length in beef cattle

2005 ◽  
Vol 57 (6) ◽  
pp. 784-791 ◽  
Author(s):  
J.C.M.C. Rocha ◽  
H. Tonhati ◽  
M.M. Alencar ◽  
R.B. Lôbo
Keyword(s):  
Beef Cattle ◽  
Variance Components ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Heritability Estimate ◽  
Gestation Length ◽  
Phenotypic Variance ◽  
Fitting Model ◽  
Selection Of

Variance components were estimated for gestation length fitting the additive direct effect of calf, maternal genetic effect and sire effect as random effects. The statistical models also included the fixed effects of contemporary group, that included the date of breeding (AI), date of birth, and sex of calf, and the covariate age of dam at calving (linear and quadratic). Two different models were used, model 1 considering GL as a trait of the calf, and model 2 considering GL as a trait of the dam. The means of gestation length for the purebred animals were 294.55 days (males) and 293.34 days (females), while for the crossbred animals they were 292.49 days (males) and 292.55 days (females). Variance components for the purebred animals, fitting model 1, were 14.47, 72.78 and 57.31, for the additive genetic (sigma2a), total phenotypic (sigma2p) and residual (sigma2e) effects, respectively, with a heritability estimate of 0.21. For the crossbred animals, variance components for sigma2a, sigma2p, sigma2e were 90.40, 127.35 and 36.95, respectively, with a heritability of 0.71. Fitting model 2, the estimated variance components for the purebred animals were 12.78, 5.01, 74.84 and 57.05 for sigma2a , sire of calf (sigma ²asire), sigma2p, and sigma2e , respectively. The sire effect accounted for 0.07 (c²) of the phenotypic variance and the coefficient of repeatability was 0.17. For the crossbred animals, the variance components were 22.11 (sigma2a ), 22.97 (sigma ²asire ), 127.70 (sigma2p) and 82.61 (sigma2e), while c² was 0.18 and repeatability was 0.17. Therefore, regarding selection of beef cattle, it is suggested to use the heritability estimate obtained by model 1, where GL is considered as a trait of the calf.

GENETIC PARAMETERS OF THE MATERNAL GRANDSIRE CONTRIBUTION TO BEEF CATTLE WEANING WEIGHTS

1982 ◽  
Vol 62 (4) ◽  
pp. 1057-1062
Author(s):  
G. H. CROW ◽  
W. E. HOWELL
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Variance Components ◽  
Genetic Parameters ◽  
Mixed Model ◽  
Genetic Effects ◽  
Performance Data ◽  
Maternal Influence ◽  
Weaning Weight ◽  
Maternal Grandsire

Genetic aspects of maternal influence on weaning weights in beef cattle were examined using analyses within breed and parity of dam (first, second, third and fourth and greater parities) of Angus, Charolais and Hereford Record of Performance data. A mixed model which included herd-year and maternal grandsire (MGS) was used. The data were adjusted for calf sex within breed and parity of dam prior to analysis. The heritability of dam influence on her offspring weaning weight averaged 0.23 for first parity data of the three breeds. Heritability for second and third parities of the three breeds were lower than this but averaged 0.16 in parity four and greater. MGSs contributed significantly to variation in weaning weights. Their contribution, however, is a composite of both direct and maternal genetic effects. Key words: Beef cattle, weaning weight, maternal, variance components, heritability

Quantifying idiosyncratic and shared contributions to stimulus evaluations

2018 ◽  
Author(s):  
Joel Eduardo Martinez ◽  
Friederike Funk ◽  
Alexander Todorov
Keyword(s):  
Repeated Measures ◽  
Variance Component ◽  
Psychological Problem ◽  
Psychological Research ◽  
Large Variability ◽  
Four Dimensions ◽  
Stimulus Evaluation ◽  
Crossed Random Effects ◽  
Novel Objects ◽  
Variance Component Estimates

A fundamental psychological problem is identifying the idiosyncratic and shared contributions to stimulus evaluation. However, there is no established method for estimating these contributions and the existing methods have led to divergent estimates. Moreover, in many studies participants rate the stimuli only once, although at least two measurements are required to estimate idiosyncratic contributions. Here, participants rated faces or novel objects on four dimensions (beautiful, approachable, likeable, dangerous) for a total of ten blocks to better estimate the preferences of individual raters. First, we show that both intra-rater and inter-rater agreement – measures related to idiosyncratic and shared contributions, respectively – increase with repeated measures. Second, to find best practices, we compared estimates from correlation indices and variance component approaches on stimulus-generality, evaluation-generality, data preprocessing steps, and sensitivity to measurement error (a largely ignored issue). The correlation indices changed monotonically and nonlinearly with more repeated measures. Variance component analyses showed large variability in estimates from only two repeated measures, but stabilized with more measures. While there was general agreement among approaches, the correlation approach was problematic for certain stimulus types and evaluation dimensions. Our results suggest that variance component estimates are more reliable as long as one collects more than two repeated measures, which is not the current norm in psychological research, and can be implemented using mixed models with crossed random effects. Recommendations for analysis and interpretations are provided.

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

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