scholarly journals Analysis of Variance Components for Genetic Markers with Unphased Genotypes

2016 ◽  
Vol 7 ◽  
Author(s):  
Tao Wang
Keyword(s):  
Genetic Markers ◽  
Analysis Of Variance ◽  
Variance Components

scholarly journals Analysis of variance components in gene expression data

2004 ◽  
Vol 20 (9) ◽  
pp. 1436-1446 ◽  
Author(s):  
J. J. Chen ◽  
R. R. Delongchamp ◽  
C.-A. Tsai ◽  
H.-m. Hsueh ◽  
F. Sistare ◽  
...  
Keyword(s):  
Gene Expression ◽  
Analysis Of Variance ◽  
Gene Expression Data ◽  
Variance Components ◽  
Expression Data

The relationships between performance traits and the bGH/Alu I and Pit-1/Hinf I polymorphisms in Holstein cows

2017 ◽  
Author(s):  
Memis Ozdemir ◽  
Mehmet Topal ◽  
Vecihi Aksakal
Keyword(s):  
Linear Model ◽  
Genetic Markers ◽  
Analysis Of Variance ◽  
Genetic Selection ◽  
General Linear Model ◽  
Holstein Cows ◽  
Production Traits ◽  
Milk Traits ◽  
Reproduction Traits ◽  
General Linear Model Procedure

Progress in genetic selection in livestock can be increased by marker asisted selection. The identification of favorable genetic markers is one of the most important stages in marker-asisted selection. In this study, it was aimed to determine the effects of the bGH/AluI and Pit-1/HinfI polymorphisms on the production traits of organic reared cows. Genotyping was performed on total 245 Holstein cows, n=181 for Pit-1 gene and n=186 for bGH gene. Milk yields and some reproduction traits analyzed by analysis of variance using the general linear model procedure, and 703 production records from cows were used to. The results showed that neither the Pit-1/Hinf I nor bGH/Alu I polymorphisms affect the tested milk traits (p>0.05).

An Alternative Method for Estimating Variance Components in Generalizability Theory

1990 ◽  
Vol 66 (2) ◽  
pp. 379-386 ◽  
Author(s):  
George A. Marcoulides
Keyword(s):  
Maximum Likelihood ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Generalizability Theory ◽  
Simulated Data ◽  
Restricted Maximum Likelihood ◽  
Alternative Method ◽  
Sample Data

This study compares, using simulated data, two methods for estimating variance components in generalizability (G) studies. Traditionally variance components are estimated from an analysis of variance of sample data. The alternative method for estimating variance components is restricted maximum likelihood (REML). The results indicate that REML provides estimates for the components in the various designs that are closer to the true parameters than the estimates from analysis of variance.

A Note on inbreeding and genetic relationships among British tested pigs

1978 ◽  
Vol 27 (1) ◽  
pp. 125-128 ◽  
Author(s):  
C. Smith ◽  
C. H. C. Jordan ◽  
D. E. Steane ◽  
M. B. Sweeney
Keyword(s):  
Analysis Of Variance ◽  
Variance Components ◽  
Genetic Relationships ◽  
Large White ◽  
Genetic Contributions

Five samples from tested pig herds (Large White 1972, 1975, and 1976, British Landrace 1976 and Welsh 1976) were used to estimate the current rate of inbreeding in British pig testing herds. The annual rates of inbreeding (%) were estimated at 0·32, 0·19, 0·24, 0·33 and 0·34 respectively in the five samples. Overall average estimates of 0·49 to 0·52% per generation are similar to estimates from other pig populations reported in the literature. Coefficients of relationship within farms were calculated for various sib and non-sib groups and these were used to estimate the genetic contributions to the variance components in the analysis of variance of test records.

Correct and incorrect estimation of within-day and between-day variation.

1986 ◽  
Vol 32 (9) ◽  
pp. 1734-1737 ◽  
Author(s):  
M J Bookbinder ◽  
K J Panosian
Keyword(s):  
Statistical Theory ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Total Population ◽  
Total Variance ◽  
Population Variance ◽  
Biased Estimators ◽  
The Impact ◽  
Estimation Of Variance

Abstract Between-day variance is an ambiguous term representing either total variance or pure between-day variance. In either case, it is often incorrectly calculated even though analysis of variance (ANOVA) and other excellent methods of estimation are available. We used statistical theory to predict the magnitude of error expected from using several intuitive approaches to estimation of variance components. We also evaluated the impact of estimating the total population variance instead of pure between-day variance and the impact of using biased estimators. We found that estimates of variance components could be systematically biased by several hundred percent. On the basis of these results, we make recommendations to remove these biases and to standardize precision estimates.

scholarly journals Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data.

Genetics ◽  
1992 ◽  
Vol 131 (2) ◽  
pp. 479-491 ◽  
Author(s):  
L Excoffier ◽  
P E Smouse ◽  
J M Quattro
Keyword(s):  
Mitochondrial Dna ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Molecular Data ◽  
Human Mitochondrial Dna ◽  
Analysis Of Molecular Variance ◽  
Molecular Variance ◽  
Monte Carlo Studies ◽  
Different Types ◽  
Metric Distances

Abstract We present here a framework for the study of molecular variation within a single species. Information on DNA haplotype divergence is incorporated into an analysis of variance format, derived from a matrix of squared-distances among all pairs of haplotypes. This analysis of molecular variance (AMOVA) produces estimates of variance components and F-statistic analogs, designated here as phi-statistics, reflecting the correlation of haplotypic diversity at different levels of hierarchical subdivision. The method is flexible enough to accommodate several alternative input matrices, corresponding to different types of molecular data, as well as different types of evolutionary assumptions, without modifying the basic structure of the analysis. The significance of the variance components and phi-statistics is tested using a permutational approach, eliminating the normality assumption that is conventional for analysis of variance but inappropriate for molecular data. Application of AMOVA to human mitochondrial DNA haplotype data shows that population subdivisions are better resolved when some measure of molecular differences among haplotypes is introduced into the analysis. At the intraspecific level, however, the additional information provided by knowing the exact phylogenetic relations among haplotypes or by a nonlinear translation of restriction-site change into nucleotide diversity does not significantly modify the inferred population genetic structure. Monte Carlo studies show that site sampling does not fundamentally affect the significance of the molecular variance components. The AMOVA treatment is easily extended in several different directions and it constitutes a coherent and flexible framework for the statistical analysis of molecular data.

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