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Lecture 4: Basic Designs for Estimation of Genetic Parameters.

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1 Lecture 4: Basic Designs for Estimation of Genetic Parameters

2 Heritability Narrow vs. board sense Narrow sense: h 2 = V A /V P Board sense: H 2 = V G /V P Slope of midparent-offspring regression (sexual reproduction) Slope of a parent - cloned offspring regression (asexual reproduction) When one refers to heritability, the default is narrow-sense, h 2 h 2 is the measure of (easily) usable genetic variation under sexual reproduction

3 Why h 2 instead of h? Blame Sewall Wright, who used h to denote the correlation between phenotype and breeding value. Hence, h 2 is the total fraction of phenotypic variance due to breeding values Heritabilities are functions of populations Heritability measures the standing genetic variation of a population, A zero heritability DOES NOT imply that the trait is not genetically determined Heritability values only make sense in the content of the population for which it was measured.

4 Heritabilities are functions of the distribution of environmental values (i.e., the universe of E values) Decreasing V P increases h 2. Heritability values measured in one environment (or distribution of environments) may not be valid under another Measures of heritability for lab-reared individuals may be very different from heritability in nature

5 Heritability and the prediction of breeding values If P denotes an individual's phenotype, then best linear predictor of their breeding value A is The residual variance is also a function of h 2 : The larger the heritability, the tighter the distribution of true breeding values around the value h 2 (P -  P ) predicted by an individual’s phenotype.

6 Heritability and population divergence Heritability is a completely unreliable predictor of long-term response Measuring heritability values in two populations that show a difference in their means provides no information on whether the underlying difference is genetic

7 Sample heritabilities Peoplehshs Height0.65 Serum IG0.45 Pigs Back-fat0.70 Weight gain0.30 Litter size0.05 Fruit Flies Abdominal Bristles0.50 Body size0.40 Ovary size0.3 Egg production0.20 Traits more closely associated with fitness tend to have lower heritabilities

8 Estimation: One-way ANOVA Simple (balanced) full-sib design: N full-sib families, each with n offspring One-way ANOVA model: z ij =  + f i + w ij Trait value in sib j from family i Common MeanEffect for family i = deviation of mean of i from tsshe common mean Deviation of sib j from the family mean  2 f = between-family variance = variance among family means  2 w = within-family variance  2 P = Total phenotypic variance =  2 f +  2 w

9 Covariance between members of the same group equals the variance among (between) groups The variance among family effects equals the covariance between full sibs The within-family variance  2 w =  2 P -  2 f,

10 One-way Anova: N families with n sibs, T = Nn FactorDegrees of freedom, df Sums of Squares (SS) Mean sum of squares (MS) E[ MS ] Among-familyN-1SS f =SS f /(N-1)  2 w + n  2 f Within-familyT-NSS W =SS w /(T-N) 2w2w

11 Estimating the variance components: Since 2Var(f) is an upper bound for the additive variance

12 Assigning standard errors ( = square root of Var) Nice fact: Under normality, the (large-sample) variance for a mean-square is given by ( ( ) )

13 - - - Estimating heritability Hence, h 2 < 2 t FS An approximate large-sample standard error for h 2 is given by

14 Worked example FactorDfSSMSEMS Among-familes9SS f =  2 w + 5  2 f Within-families40SS w = 2w2w 10 full-sib families, each with 5 offspring are measured V A < 10 h 2 < 2 (5/25) = 0.4

15 Full sib-half sib design: Nested ANOVA Full-sibs Half-sibs

16 Estimation: Nested ANOVA Balanced full-sib / half-sib design: N males (sires) are crossed to M dams each of which has n offspring Nested ANOVA model: z ijk =  + s i + d ij + w ijk Value of the kth offspring from the kth dam for sire i Overall mean Effect of sire i = deviation of mean of i’s family from overall mean Effect of dam j of sire i = deviation of mean of dam j from sire and overall mean Within-family deviation of kth offspring from the mean of the ij-th family  2 s = between-sire variance = variance in sire family means  2 d = variance among dams within sires = variance of dam means for the same sire  2 w = within-family variance  2 T =  2 s +  2 d +  2 w

17 Nested Anova: N sires crossed to M dams, each with n sibs, T = NMn FactorDfSSMSEMS SiresN-1SS s = SS s /(N-1) Dams(Sires)N(M-1)SS s = SS d /(N[M-1]) Sibs(Dams)T-NMSS w = SS w /(T-NM)

18 Estimation of sire, dam, and family variances: Translating these into the desired variance components Var(Total) = Var(between FS families) + Var(Within FS) Var(Sires) = Cov(Paternal half-sibs)  2 w =  2 z - Cov(FS)

19 Summarizing, Expressing these in terms of the genetic and environmental variances,

20 4t PHS = h 2 h 2 < 2t FS Intraclass correlations and estimating heritability Note that 4t PHS = 2t FS implies no dominance or shared family environmental effects

21 Worked Example: N=10 sires, M = 3 dams, n = 10 sibs/dam FactorDfSSMSEMS Sires94, Dams(Sires)203, Within Dams2705,40020

22 Parent-offspring regression Single parent - offspring regression The expected slope of this regression is: Shared environmental values To avoid this term, typically regressions are male-offspring, as female-offspring more likely to share environmental values  Residual error variance (spread around expected values)

23   Midparent - offspring regression The expected slope of this regression is h 2, as  Residual error variance (spread around expected values) Hence, even when heritability is one, there is considerable spread of the true offspring values about their midparent values, with Var = V A /2, the segregation variance Key: Var(MP) = (1/2)Var(P)


25 Standard errors Single parent-offspring regression, N parents, each with n offspring Squared regression slope Total number of offspring Sib correlation Midparent-offspring regression, N sets of parents, each with n offspring Midparent-offspring variance half that of single parent-offspring variance

26 Estimating Heritability in Natural Populations Often, sibs are reared in a laboratory environment, making parent-offspring regressions and sib ANOVA problematic for estimating heritability A lower bound can be placed of heritability using parents from nature and their lab-reared offspring, Why is this a lower bound?   Covariance between breeding value in nature and BV in lab where is the additive genetic covariance between environments and hence  2 < 1

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