INTRODUCTION TO ANIMAL BREEDING. Lecture Nr 2. Genetics of quantitative (multifactorial) traits What is known about such traits How they are modeled

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1 INTRODUCTION TO ANIMAL BREEDING Lecture Nr 2 Genetics of quantitative (multifactorial) traits What is known about such traits How they are modeled Etienne Verrier INA Paris-Grignon, Animal Sciences Department Verrier@inapg.fr

2 Nature of variation, sources of variation Genotype and environment Components of the genetic value Ressemblance between relatives Summary

3 Specificities of quantitative traits 1. Continuous variation % % g/kg Protein content Dairy cows Litter size Sows Source: French protocol of performance recording on farm

4 Specificities of quantitative traits 2. Correlation between traits 60 Field data from the Alps Ibex Weight (kg) r = Chest measurement (cm) Source: Toïgo, 1998

5 0, , , Sources of continuity 2 loci 5 loci 10 loci Several loci Environmental factors Continuity Normal distribution Statistical analysis

6 Two sources of variation Phenotypic differences Genetic differences Génotypes diffents Major genes QTL Polygènes Environmental differences Milieux différents Controled environmental factors Residual environmental factors

7 Genetic sources of variation Polygenes Number of genes QTL Major gene Quantitative effect of the gene (proportion of variance explained by the gene)

8 Consequences of the number of genes Crossing experiment with chilli Photo: INRA - Avignon

9 Nature of variation, sources of variation Genotype and environment Components of the genetic value Ressemblance between relatives Summary

10 Characterisation of the genotype by its mean value Photo: INRA Not so easy to apply with animals But the general idea remains: Example with wheat (each variety = a single genotype) Value of a genotype = its (expected) mean value

11 Genotype and residual environment Performance P N c ~ µσ, 2 P If the distribution is not normal: - Mathematical transformation - Specific model: thresholds model for discrete traits (total) Genetic Value G= E Pgenotype E P G N0, c ~ σ 2 G Deviation of P from µ+g: E Environnemental (residual) value h c h b g h ~ N0, c σ 2 P= µ + G+ E E h

12 Means and variances P= µ + G+ E b g b g b g E P = µ ; E G = 0 ; E E = 0 b g b g b g var P = var G + var E P G E σ = σ + σ

13 Takin into account a controled environmental factor Category 1 Average effect m 1 Category 2 Average effect m 2 etc. P= µ + m+ G+ E The meaning of this + is to be questionned

14 Consequences of the additivity between genotype and environment P= µ + m+ G+ E 1) The change of environment (m) modifies the performance (P) of each animal by the same quantitity 2) The differences between genotypes (G) are the same in any environment

15 Genotype and environment The example of crossbreeding in beef cattle 850 g/j CH x AU AU x AU Good Average Bad G r a z i n g c o n d i t i o n s Daily growth (g/j) of calves during summer grazing in high altitude pastures AUxAU: Aubrac calves CHxAU: crossbred calves Charolais x Aubrac Source: Vissac (1971) Additivity Genotype + Environment Interaction Genotype x Environment

16 Nature of variation, sources of variation Genotype and environment Components of the genetic value Ressemblance between relatives Summary

17 How to characterise an allele The example of the naked neck gene Photo: INRA Genotype Na Na Na Plumage weight normal Resistance to heat = Average egg weight (g) under hot conditions Source: Mérat, 1986

18 How to characterise an allele The example of the naked neck gene In a given population, E(P) = µ, according to f(na) male Na Na x females? male?? x females? α Na = c h b g E PNa? E P Average effect of the Na allele

19 Components of G For a single locus G = α + α + δ paternal gene maternal gene paternel gene maternel gene G = A + D Generalisation to a large number of loci G = A + D A A N0, c ~ σ 2 h D N0, c ~ σ 2 h D b g b g b g var G = var A + var D G A D σ = σ + σ

20 Transmission from parent to offspring 1. A single parent known s? o EcGos,? h = 1 2 A s c h b g o NB. E D s,? = E D =0

21 Transmission from parent to offspring 2. Both parents known s d E 1 1 A s,d A A 2 2 c h = + o s d o 1 1 E Gos,d = As + Ad + E Dos,d 2 2 c h c h Selection Crossbreeding

22 Heritability: definition P= µ + G+ E = µ + A+ D+ E b g b g b g b g var P = var A + var D + var E P A D E σ = σ + σ + σ 2 0 h 1 h 2 2 A 2 P σ = = σ 2 σ A 2 A + 2 D + 2 E σ σ σ

23 Nature of variation, sources of variation Genotype and environment Components of the genetic value Ressemblance between relatives Summary

24 Analysis of the correlation between performances of relatives Sample of n sibs couples The correlation between the performances of relatives depends on: The kind of relatives The trait

25 The two sources of ressemblance between relatives Photo: E. Verrier Shared genes Shared environnement Covariance between their performances Photo: INRA

26 Genetic interpretation The example of paternal half-sibs families xxxx xxxxxxxxx x Sire # 1 Sire # 2 Sire # 3 P High correlation between half-sibs performances Large differences between families due to the effect of the different sires Large genetic differences High value of h²

27 Genetic interpretation The example of paternal half-sibs families Sire # 1 Sire # 2 Sire # 3 P Little correlation between half-sibs performances Small differences between families due to the effect of the different sires Small genetic differences Low value of h²

28 Covariance between (total) genetic values G = A + D Cov(G i, G j ) = Cov(A i, A j ) + Cov(D i, D j ) Dépends on the probability to share, in a given locus An allele A pair of alleles Probabilities for the genes to be identical by descent

29 Measurement of the kinship between individuals The coefficient of kinship (Φ) between two animals, i et j, is defined for an autosomal neutral locus, with no mutation Φ ij = Probability that a gene drawn at random in i and a gene drawn at random in j are identical by descent, i.e. came from the Mendelian duplication of the same ancestral gene i To see details j Φ i,j = 1/32 = 3.125%

30 Coefficient of kinship: Some usual values of the Coefficient Kind of relatives of kinship With no common ancestor 0 % With a single common grand-grand-parent 0.8 % With a single common grand-parent % Cousins (2 common grand parents) 6.25 % Double cousins (4 common grand-parents) 12.5 % Half sibs (a single common parent) Full sibss (2 common parents) Parent-offspring 25 %

31 Covariance between the components of the genetic values of relatives G = A + D d cov A, Ai= 2Φσ 2 i j ij A p m p m i j d i d i cov D, D = Φ ' Φ ' + Φ ' Φ ' σ 2 i j pp mm mp pm D d i d i d i i j i j i j cov G, G = cov A, A + cov D, D

32 Summary Quantitative traits are jointly governed by a few known genes and an unknown number of unknown genes known and unknown environmental factors Necessity of modeling the effects of these different sources of variation: P = µ + m + A + D + E A key-concept is the heritability of a trait: h 2 = var(a)/var(p) A key-issue is the ressemblance between relatives