Titlebook: Quantitative Genetics; Shizhong Xu Textbook 2022 Springer Nature Switzerland AG 2022 Genetic correlation.Heritability.Linear models.Quanti

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Genetic Variances of Quantitative Traits,, the total genetic variance is partitioned into additive variance, dominance variance, additive by additive variance, additive by dominance variance and dominance by dominance variance. The average effect of gene substitution is geometrically proved to be the regression coefficient of the genotypic

Munificent

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Major Gene Detection and Segregation Analysis,t the genotype of the potential major gene is measurable across a sample. Three special statistical methods are introduced for major gene detection: (1) t-test for a segregating population with two genotypes, (2) F-test for a population with arbitrary number of genotypes, and (3) regression analysis

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Resemblance between Relatives,ariance of the quantitative trait under consideration. The covariance between all possible pairs of relatives are introduced, including relative pairs between a single offspring and a single parent, a single offspring and mid-parent (average of the two parents), mean offspring and a single parent, m

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Estimation of Heritability,the breeding value on the phenotypic value from the same individual. In plants, estimating heritability is based on two designs of experiment: (1) F. mating design and (2) multiple inbred lines or multiple hybrids. In animals, there are three typical statistical methods for estimating heritability:

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Identity-by-Descent and Coancestry Coefficient,atives are described: (1) path analysis and tabular method. The path analysis only applies to simple pedigrees while the tabular method can handle arbitrarily complicated pedigrees. Two software packages are used to calculate the kinship matrix of a pedigree: (1) kinship2 in R and (2) PROC INBREED i

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Multiple Traits and Genetic Correlation,and (2) linkage and/or linkage disequilibrium. Experimental designs for estimating genetic correlation are presented, including parent-offspring correlation, analysis of covariance from sib data and nested design of experiment for sire families containing both full-sibs and half-sibs. The CORR, GLM,

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Methods of Artificial Selection,ion), (2) family selection, (3) sib selection, (4) progeny testing, (5) within-family selection, (6) pedigree selection, and (7) combined selection. Pros and cons of each selection method are discussed. The chapter shows how to evaluate the efficiency of a selection method.