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POPULATION GENETICS

POPULATION GENETICS. Quantitative traits and evaluation of phenotypic variance. Heritability of quantitative traits Ing. Radovan KASARDA, PhD. Estimation of Heritability. Method „ parent – offspring“ h 2 OP similarity (kinship) between parents and offspring

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POPULATION GENETICS

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  1. POPULATION GENETICS Quantitative traits and evaluation of phenotypic variance. Heritabilityof quantitative traits Ing. Radovan KASARDA, PhD.

  2. Estimation of Heritability • Method „ parent – offspring“ h2OP • similarity (kinship) between parents and offspring • according to Wright (1920) relationship coefficient is0,5 • comparison of relatives between groups • h2 is twice of rXY resp. regress coefficient bYX.. Principle of calculation: To calculate rXY resp. bYX estimate semi-values A, B, C. variance of X variance of Y covariance between X and Y

  3. Estimation of Heritability • Method „ parent – offspring“ h2OP than: In case data are divided by fathers, calculate partial Ai, Bi, Ci separatelly for each father and sumarize it.

  4. Estimation of Heritability • Method „ parent – offspring“ h2OP With use of ANOVA:

  5. Method „half-sibs“ - ANOVA • half-sibs have one common parent (father) • use of genetic similarity between half-sibs (0,25) • universal method • Conditions of use: • groups of half-sibs in same conditions (min. 3 in group) • group based by father (min. 3 fathers) (p) • in group min. 3 measurements (ni) • i.e. min. 9 measurements in total (n)

  6. ANOVA – initial informations σ2e σ2g σ2e σ2g σ2e

  7. ANOVA – One Way h2=4.ri σ2P =σ2g + σ2e = 84,259; ri = 0,143; h2 = 0,57

  8. ANOVA – „full-sibs“ • in populations of multiparous animals (pigs, poultry,..) • high genetic similarity of full-sibs (0,5), whose should under equal conditions show equal phenotype • difficult for segmentation • in group – variability of environment • between groups – variability depended on diverse genetics of mothers • between classes – variability deperded on diverse genetics of fathers • σ2P =σ2F + σ2M + σ2E • two-factorial ANOVA is used • i = father; j = mother; k = progeny

  9. ANOVA – initial informations

  10. ANOVA – two factorial k1 – average # of daughter per mother k2 – average # of mother per father k3 – average # of daugh. per father

  11. ANOVA – two factorial

  12. ANOVA: Half-sibs • Exercise: Calculate heritability

  13. ANOVA: Full-sibs • Exercise: p =10 m = 130 n = 1955 MSF=391,3 k1=15,00 MSM=37,50 k2=15,40 MSE=6,40 k3=193,00 • Calculate: • σ2F=h2F= • σ2M=h2M= • σ2E= h2FM= • σ2P=

  14. Non-parametric estimates of heritability • very good elaborated • used if less information is known • low number of observation, ranking evaluation • could resuld in non-realistic estimates • only informative results of h2 • analyses of influence of genotypes on traits • Estimates by Young • estimates using Spearman correlation coeficients • estimates based on realised heritability

  15. Non-parametric estimates of heritability • Estimates by Young: above-average mothers below-average mothers above-average daughters below-average daughters • Estimates using Spearmann rank-order correlation coefficient: h2 = rS d – absolute difference between mother and daughter ranking • Calculate heritability:

  16. Non-parametric estimates of heritability • estimates based on realised heritability • by division of pop. on better (+) resp. worser (-) half O – offspring; P - parents • according to realized selection - realized genetic gain - selection difference (d=i.s) - difference of selected offspring from average of off. generation - difference of selected parents from average of parental generation

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