scholarly journals SIMULATION OF QUANTITATIVE CHARACTERS BY GENES WITH BIOCHEMICALLY DEFINABLE ACTION. VI. MODIFICATIONS OF A SIMPLE MODEL

Genetics ◽  
1977 ◽  
Vol 85 (3) ◽  
pp. 557-572
Author(s):  
G Forkmann ◽  
W Seyffert
Keyword(s):  
Reference Point ◽  
Response Curve ◽  
Genetic Parameters ◽  
Linear Models ◽  
Parameter Estimates ◽  
Saturation Curve ◽  
Quantitative Characters ◽  
Nonallelic Interactions ◽  
Biological Situation ◽  
Estimation Of Genetic Parameters

ABSTRACT Investigations on metric characters of defined genotypes of Matthiola incana, and application of different linear models for the estimation of genetic parameters, indicate that the use of midparental value as a reference point results in parameter estimates that do not correspond to the actual biological situation. Use of the most recessive genotype as a reference point causes all of the contributions of single loci to be unidirectional and positive, and all the allelic and nonallelic interactions to be unidirectional and negative, in accord with our Model 2.2. The results indicate that the phenotypic response to allelic substitutions follows the characteristics of a saturation curve. The possibility is discussed that the saturation character results from regulating processes, whereas deviations of single measurements from the response curve, or response surface, reflect real interactions between allelic and nonallelic genes.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

Estimation of genetic parameters associated with frosty pod rot (Moniliophthora roreri) and cacao production in Mexico

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
José Luis Solís Bonilla ◽  
Uilson Vanderlei Lopes ◽  
Alfredo Zamarripa Colmenero ◽  
Biaani Beeu Martínez Valencia ◽  
Carlos Hugo Avendaño Arrazate ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Moniliophthora Roreri ◽  
Frosty Pod Rot ◽  
Estimation Of Genetic Parameters

Genetic parameter estimates for feeding pattern and production traits in pigs

1997 ◽  
Vol 1997 ◽  
pp. 31-31
Author(s):  
A.D. Hall ◽  
W.G. Hill ◽  
P.R. Bampton ◽  
A.J. Webb
Keyword(s):  
Feed Intake ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Feeding Pattern ◽  
Parameter Estimates ◽  
Group Housing ◽  
Production Traits ◽  
Production Environment ◽  
Feeding Patterns ◽  
Potential Benefits

Until recently, to enable accurate recording of feed intake, pigs were kept in individual pens. The advent of electronic feeders has allowed accurate records of feed intake and feeding patterns in group housing which is more similar to that found in the production environment. The objectives of this study were to estimate genetic parameters for these feeding pattern traits and their correlations with production traits to show potential benefits in selection.

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

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