Some Definitions Genotypic value: the phenotypic value a genotype would produce in the absence of environmental effects. A property of the diploid genotype. Not (fully) passed on to next generation. OR (working definition): The average phenotype of a genotype across all environments. Breeding value: Also termed „additive genotype“, or „additive genetic value“. A property of the haploid genotype. Passed on to next generation! The „variance in breeding values“ determines the response to selection and hence evolutionary change. OR (working definition): the mean genotypic value of an individual‘s offspring. From Modes of Gene-Action to Population Means Genotype Frequency Genotypic Value Mean value per genotype AA p2 2pq q2 +a d -a p 2a 2pqd Aa aa Population mean genotypic value (sum of products) -q2a G P p 2 a 2 pqd q 2 a a p q p q 2 pqd a p q 2 pqd From Modes of Gene-Action to Variances Additive Variance 2 VA 2 pqa d q p Note: The additive genetic variance is not equal to the variance of additive genetic effects, but partially contains variance due to dominance effects when p ≠ q! Dominance Variance VD 2 pqd 2 Note: The dominance genetic variance is purely based on variation from dominance effects! From Modes of Gene-Action to Variances VA 2 pqa d q p 2 VD 2 pqd 2 Pure Additivity (d=0) Complete Dominance (a=d) Genotypic Variance (VG) Additive Variance (VA) Complete Overdominance (a=0) Dominance Variance (VD) From Modes of Gene-Action to Variances Level: Population Variance components: VP = VA + VD + VI + VE Phenotypic Additive Dominance Interaction (epistatic) Environmental Genotypic The additive genetic variance (or variance in breeding values) is passed on preditably from parents to offspring. This component is therefore critical for predicting evolutionary change. Dominance variance is purely due to the deviation from the predictable genetic component, which arises from within-locus, between-allele interactions. It therefore decreases the predictability of the phenotype from one generation to the next. Heritability Non-additive Variance Broadsense VG VA VD VI h VP VA VD VI VE 2 Broad-sense heritability: „degree of genetic determination“ Narrowsense V VA h2 A VP VA VD VI VE Narrow-sense heritability: degree of parent-offspring inheritance /resemblance Heritability and evolutionary change The univariate „Breeders Equation“ R h2S Va S Vp µs,t+1 R µ0,t=µ0,t+1 R: response to selection S: selection differential; the difference between the mean trait value of selected individuals and the whole population before selection (under truncation selection; i.e. discontinuous variation in fitness); can also be estimated by the regression of fitness with the trait (under continuous variation in fitness). h2 1 µ0,t µs,t S Empirical Estimation of Variance Components and Heritability 1. Statistical background 2. Experimental design 3. Statistical models Approximation for continuous traits: the Normal (Gaussian Distribution) 0.7 Mean = 3, Var = 1 Mean = 3, Var = 0.36 Mean = 6, Var = 1 0.6 0.5 0.4 0.3 0.2 0.1 0 2 4 6 Density 8 10 1 e 2VP 0.5 P P 2 i 2VP The standard normal distribution is defined by two parameters, the mean and the variance Variance / Covariance Definitions Covariance(x,x) = Variance Covariance(x,y) = Covariance n Cov( x, x) y x x i 1 2 i n 1 n y 0 Cov( x, y ) 0 x x x x y i 1 i i y n 1 n: number of samples/individuals Regression Regression Definitions y a bx y a y bx Cov( X , Y ) b Var ( X ) b a: intercept b: slope a 0 0 x The regression line is the line through a cloud of datapoints that minimizes the sum of squared (vertical) deviations from the line Correlation Cov( X , Y ) r ( X , Y ) Var ( X )Var (Y ) 4-offspring mean pistil length 2-offspring mean pistil length Heritability, Regression and Correlation Slope=h2=0.79(0.12) r2=0.47 r=0.69 Slope=h2=0.74(0.09) r2=0.59 r=0.77 Slope of regression stays quite similar, the correlation becomes stronger Pistil: Stempel Midparent pistil length ANOVA Y (Analysis of Variance) Y1 Comparison of two groups Vmeans: Variance between group means n: number of groups SS: sum of squared deviations d.f.: degrees of freedom n-1: because 1 d.f. is „used up“ for estimating the grand mean ANOVA (Analysis of Variance) Comparison of multiple groups MS: Mean squares F: F-statistics, MS among devided by MS within P: statistical significance, calculated based on F-statistics Yi Y1 Y2 2. Experimental design in Quantitative Genetics 1. parent-offspring regression 2. cross-foster designs 3. breeding designs 4. artificial selection 5. pedigree analysis Not treated are: molecular approaches such as candidate gene, mutation screen, knock-out/down, etc. The genetic covariance between relatives Relationship r VA VD VI(A,A) Parent-offspring 1/2 1/2 0 1/4 Grandparent-grandchild 1/4 1/4 0 1/16 Great grandparent-great grandchild 1/8 1/8 0 1/64 Full sibs, dizygotic twins 1/2 1/2 1/4 1/4 Half-sibs (maternal or paternal) 1/4 1/4 0 1/16 Aunt(uncle)-niece(nephew) 1/4 1/4 0 1/16 First cousins 1/8 1/8 0 1/64 1 1 1 1 Monozygotic twins / Individual (Repeatability) r: coefficient of genetic relatedness Simply comparing individuals belonging to a certain category of relatives can be problematic to estimate components of genetic variation and heritabilities because these categories are usually confounded by nongenetic sources of variation. For example: Parents and their offspring may live in similar environments Siblings may live in similar environments, and receive similar amounts/qualities of resources from their parents Generally speaking: when the set of relatives that are compared also tends to live in similar environments (jargon: when there is a genotype x environment correlation), simple comparisons yield inflated heritability estimates The environment also includes pre-birth (maternal) effects, such as non-genetic influences during gestation and lactation (mammals) or in the egg (birds, ectotherms) and during incubation (birds) Offspring tarsus length Parent-offspring regression y=5.48+0.5x r2=0.453 Mid-parent tarsus length Mid-parent-offspring regression is a powerful way to estimate heritabilities. With single-parent-offspring regression (e.g., when the trait of only one parent is known), assortative mating for a trait under study between male and female parents lead to an increase in the variance between parents and, hence, can generate a biased estimation of heritability. Under assortative mating the heritability estimates become: Mid-parent * offspring regression: h2 = b Single-parent * offspring regression: h2 = 2 b / (1+r) r: correlation between phenotypic values of the two parents; b: slope of regression line
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