Genomic analysis of dominance effects in milk production and conformation traits of Fleckvieh cattle

Transkript

1 13 Interbull Meeting, August 3-5, 13, Nantes, France Genomic analysis of dominance effects in milk production and conformation traits of Fleckvieh cattle J. Ertl 1, A. Legarra, Z. G. Vitezica 3, L. Varona 4, C. Edel 1, R. Emmerling 1, K.-U. Götz 1 1 Institut für Tierzucht, Bayerische Landesanstalt für Landwirtschaft, Poing-Grub, Germany UR 631 SAGA, Institut National de la Recherche Agronomique, Castanet-Tolosan, France 3 UMR 189 TANDEM, INRA/INPT-ENSAT/ENVT, Université de Toulouse, Castanet-Tolosan, France 4 Departamento de Anatomía, Embriología y Genetíca, Universidad de Zaragoza, Zaragoza, Spain Name-ITZ Ertl 3axxx

2 Dominance Biological dominance is largely captured by quantitative genetics (or statistical) additivity: pop. mean=a(p-q) + dpq; (Falconer & Mackay, 1997) Dominance variance: Up to ~5% of genetic variance Exploited in crosses V A =pq[a+d(q-p)]² Otherwise typically ignored in animal breeding: Procedures to estimate dominance effect in animals very inefficient (lack of information in pedigree) Use in assortative matings is complicated Selection acts on additivity, not on dominance

3 Renewed interest in dominance SNP chips: allow to see heterozygotes estimation of dominance it is easier to do assortative matings (=favorable combinations of SNP alleles)

4 Objectives of this study To evaluate: the importance of dominance in the genetic variation of Fleckvieh dual purpose cattle the extra accuracy in genomic predictions from the inclusion of dominance the benefit from dominance in assortative matings

5 Dataset 1996 Bavarian Fleckvieh cows Random sample of first-lactating cows (daughters of test bulls) HD genotypes Yield deviations (YD) in: Milk yield Fat yield Protein yield SCS Milkability Stature Udder score Udder depth Feet & legs

6 Estimation of variance components Three models were analyzed: Additive, pedigree σ A MA: y = Xb + Zu + e V(u)=A V(e)=F σ E Additive, genomic σ A MG: y = Xb + Zu + e V(u)=G V(e)=F σ E Dominance, genomic MGD: y = Xb + Zu + Zd + e σ A σ D V(u)=G V(d)=D V(e)=F σ E

7 Genomic relationship matrices G: genomic relationship matrix (VanRaden, 8) D: genomic dominance relationship matrix A 1 A 1 A 1 A A A W a -p 1-p -p W d -(1-p)² p(1-p) -p² G * = m p (1 p ) k = 1 WW a k a ' k D * = m 4 p (1 p ) k = 1 WW d d k k ' Vitezica, Varona, Legarra, submitted G =.95G* +.5A D =.95D* +.5I

8 Variance components (REML) 35 Milk yield 6 Fat yield 3 Protein yield Routine MA MG MGD Routine MA MG MGD Routine vara vard MA MGMGD

9 Variance components (REML) SCS Milkability,6,5,5,,4,3,15,,1,1,5 Routine MA MG MGD Routine MA MG MGD vara vard

10 Variance components (REML) Stature Feet & legs 7 1,6 6 1, , 1,8,6,4 1, Routine MA MG MGD Routine MA MG MGD vara vard Name-ITZ Ertl 3axxx

11 Variance components (REML) Udder score Udder depth 3,5,6 3,5,5 1,5 1,4,3,,5,1 Routine MA MG MGD Routine MA MG MGD vara vard

12 Estimation of variance components (Gibbs sampling) GIBBS1F9 (model MGD) on SNP level: GS3 (A. Legarra, A. Ricard, O. Filangi) Equivalent model with SNP effects (BLUP-SNP):,, y = Xb + Ta + Xδ + e σ a σ d σ E σ A = ( pq k k) σa + [ pq k k( qk pk) ] σd σ D ( pq k k) σ d =

13 REMLF9 / GIBBS1F9 / GS3 3 Milk yield VarA VarD 5 REMLF9 GIBBS1F9 GS3

14 Likelihood ratio test - log likelihood χ² P MG MGD Milk yield Fat yield Protein yield SCS Milkability <.1 Stature Feet and legs Udder score Udder depth likelihood(mg) χ = ln likelihood(mgd)

15 Variance components (REML) Consistent estimates between pedigree and genomic models Dominance variance did generally not reduce additive variance (exception: milkability) Little dominance variance in conformation traits 3% - 4% of genetic variance Considerable dominance variance in milk traits 8% - 41% of genetic variance Largest dominance variance in SCS 5% of genetic variance

16 Prediction of breeding values cross-validation 1 groups 9 groups reference, 1 group validation Prediction of breeding values and total genetic values (MA, MG, MGD) Predictive ability r(yd,û) r(yd,g) ˆ r(u,û) = r(yd,û)/h b(yd,û) h² from model MGD

17 r(u,û),6,5 No better prediction of breeding value with inclusion of dominance,4,3, MA MG MGD,1 MY FY PY SCS MI ST FL US UD

18 r(yd,û), ˆ r(yd,g),35,3,5,,15 r(yd,û) ^ r(yd,g),1,5 ˆ r(yd,g) slightly larger than r(yd,û) in some traits MY FY PY SCS MI ST FL US UD

19 b(yd,û) With inclusion of dominance effect: MY, FY, PY, SCS: slightly larger b(yd,û), not significant Milkability: much larger b(yd,û) Conformation traits: no difference in b(yd,û) 1,6 1,4 1, 1,8,6,4 MA MG MGD, MY FY PY SCS MI ST FL US UD

20 Prediction of total genetic value (GV) and breeding value (BV) of matings (1996 cows, 5 bulls) Estimation of additive (a k ) and dominant (δ k ) SNP effects (GS3, BLUP-SNP) σ σ [ pq( q p) ] D d σ a = ( pq) ( pq) A k k k k σ d σ = Mating between bull i and cow j (e.g. Toro & Varona, 1): GV = [ Pr ( AA) a + Pr ( Aa) δ Pr ( aa) a ] ij ijk k ijk k ijk k k k BVij = [ Prijk ( AA)( pk ) αk + Prijk ( Aa)(1 pk ) αk + Prijk ( aa)( pk ) αk ] α = a + δ ( q p ) k k k k k k k

21 Selection of matings on total genetic value (GV) or breeding value (BV) Milk yield Protein yield 1, 1, 1 1,8,8,6,6,4,4,, Selection on GV Selection on BV Selection on GV Selection on BV GV BV GV BV Name-ITZ Ertl 3axxx

22 Conclusions Estimates of dominance variation make sense with a few exceptions In general more dominance variation for production than for morphological traits Slight improvement in genomic predictions Potential to optimize offspring performance taking into account dominance effects in assortative matings

23 Thank you! This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed Synergistic plant and animal breeding (Grant ID 31558H). Name-ITZ Ertl 3axxx