scholarly journals Effect of missing values in multi‐environmental trials on variance component estimates

Crop Science ◽  
2021 ◽  
Author(s):  
Jens Hartung ◽  
Hans‐Peter Piepho
Keyword(s):  
Variance Component ◽  
Missing Values ◽  
Variance Component Estimates

Effect of Missing Values on Variance Component Estimates in Multienvironment Trials

Crop Science ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 508-517
Author(s):  
Fernando Aguate ◽  
Jose Crossa ◽  
Mónica Balzarini
Keyword(s):  
Variance Component ◽  
Missing Values ◽  
Variance Component Estimates

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 Variance component estimates for alternative litter size traits in swine

2015 ◽  
Vol 93 (11) ◽  
pp. 5153-5163 ◽  
Author(s):  
A. M. Putz ◽  
F. Tiezzi ◽  
C. Maltecca ◽  
K. A. Gray ◽  
M. T. Knauer
Keyword(s):  
Litter Size ◽  
Variance Component ◽  
Variance Component Estimates

Visual Demerit and Microbiological Evaluation of Beef Carcasses: Methodology

1993 ◽  
Vol 56 (2) ◽  
pp. 114-119 ◽  
Author(s):  
KLAUS W. F. JERICHO ◽  
JOHN A. BRADLEY ◽  
VICTOR P. J. GANNON ◽  
GERALD C. KOZUB
Keyword(s):  
Variance Component ◽  
Most Probable Number ◽  
Inspection System ◽  
Bacterial Populations ◽  
Probable Number ◽  
Automated Method ◽  
Weak Association ◽  
Beef Carcasses ◽  
Good Repeatability ◽  
Variance Component Estimates

A repeatable, automated method was developed for estimating aerobic bacterial populations on surfaces of groups of beef carcasses. Ten sample cluster sites (CS) were identified by localizing visual demerits (Canadian Streamlined Inspection System) on 200 carcasses at one plant. Most probable number growth units per cm2 (MPNGU/cm2) on hydrophobic grid membrane filters (HGMF) were assessed by an automated HGMF interpreter for excision samples from the centers of these CS. Between-sample variation of more than 90% of the total log10 MPNGU/cm2 variance indicated good repeatability between HGMF of the same sample and interpretations of the same HGMF. Variance component estimates indicated that there was considerable variation in MPNGU/cm2 between carcasses and between paired adjacent samples for a CS. A statistically significant but weak association was found between the demerit scores of a CS and MPNGU at its center. The variance component estimates will be used to estimate the sample size required for future group-carcass evaluations.

scholarly journals How to deal with genotype uncertainty in variance component quantitative trait loci analyses

2011 ◽  
Vol 93 (5) ◽  
pp. 333-342 ◽  
Author(s):  
XIA SHEN ◽  
LARS RÖNNEGÅRD ◽  
ÖRJAN CARLBORG
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Component ◽  
Large Animal ◽  
Trait Loci ◽  
Sib Families ◽  
Variance Component Estimates ◽  
Variance Component Models ◽  
Component Models

SummaryDealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

Estimates of genetic parameters for ultrasound-measured carcass traits in sheep

2004 ◽  
Vol 84 (3) ◽  
pp. 361-365 ◽  
Author(s):  
T. L. Fernandes ◽  
J. W. Wilton ◽  
J. J. Tosh
Keyword(s):  
Genetic Improvement ◽  
Variance Component ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Backfat Thickness ◽  
Additive Genetic Effect ◽  
Multiple Trait ◽  
Breed Group ◽  
Variance Component Estimates

Data on ultrasound traits (loin depth, average backfat thickness, and loin width) were collected from lambs (n = 3483) across Ontario, born between 1997 and 1999. The data were analysed with a REML procedure in a multiple-trait mixed-animal model to obtain (co)variance component estimates. Analyses of all traits included the additive genetic effect of the lamb, sex of the lamb, contemporary group, and breed group effects. Weight or age was included as a covariate in two separate analyses. Estimates of direct additive heritabilities for loin depth, average backfat thickness, and loin width were 0.29, 0.29 and 0.26 respectively, with genetic correlations of -0.17 between loin depth and average backfat thickness, 0.43 between loin depth and loin width, and 0.23 between loin width and average backfat thickness for the weight constant analysis. When the data were analysed using age in the regression analysis, corresponding estimates of direct additive heritabilities were 0.38, 0.35 and 0.30, and genetic correlations between traits were all positive, 0.29 between loin depth and average backfat thickness, 0.61 between loin depth and loin width, and 0.44 between loin width and average backfat thickness. Results indicate that it is possible to make genetic improvement if selection is based on ultrasound information. Key words: Sheep, genetic parameters, heritability, ultrasound

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