Genetic relationships and trait comparisons between and within lines of local dual purpose cattle
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1 67 th Annual meeting of the European Association for Animal Production Belfast, 2016 Genetic relationships and trait comparisons between and within lines of local dual purpose cattle M. Jaeger, K. Brügemann, S. König Institute of Animal Breeding and Genetics University of Gießen, Germany 30 th of August 2016
2 Background The old dual purpose black and white cattle (DSN) belong to the lowland breeds Origin: northern Germany & the Netherlands Exchange of breeding animals (until19th century) between Dutch and German herds similar phenotypic and genotypic traits Widely distributed in Germany in 1930ies 50% of DSN cattle are kept in pasture based farms today good health, robustness, good fertility, adequate development potential with high forage and dry matter intake capacity RBB Rinderproduktion Berlin-Brandenburg GmbH Maria Jaeger EAAP 2016 Session 30 2
3 Aims of the Study Line comparisons between DSN and HF regarding functional traits (FPR, SCS) Thorough analysis of genetic structure of DSN population Genetic evaluations of functional traits, with detailed consideration of environmental effects regarding GxE interactions (multiple trait model) Maria Jaeger EAAP 2016 Session 30 3
4 Distribution of DSN Cattle Herds across Germany Testday records, Pedigree (2015) Calving years: Farms: - Holstein-Friesian (3599) - DSN (3688) Maria Jaeger EAAP 2016 Session 30 4
5 Line Comparisons of HF and DSN for Functional Traits Yijklmn = µ + FMi + YSj + G*Lactk + Al + 33 mm=11 ββ zz mm + eijklmn Y ijklmn = Vector of observation (FPR, SCS) μμ = Overall mean of population FM i = Fixed effect of farm and month YS j = Fixed effect of year and season (3 month) of test day records G*Lact k = Fixed effect of genotype (HF, DSN) and parity (1 st, 2 nd, 3 rd ) A l = Random cow effect 3 ββ mm=1 zz mm = Regression coefficients (² ) for days in milk; (z) covariates describing lactation curve (Legendre polynomials 3rd order) e ijklmn = Random error SAS Maria Jaeger EAAP 2016 Session 30 5
6 Least Square Means: Fat to Protein Ratio (FPR) Risk of Ketosis Risk of Acidosis Fat to Protein Ratio (SE d 0.01) for 3 lactations for DSN and HF cattle Maria Jaeger EAAP 2016 Session 30 6
7 Least Square Means: Somatic Cell Score (SCS) = Cells/ml Risk of subclinical Mastitis Kolibri 1894 Somatic Cell Score (SE d 0.05) over 3 lactations for DSN and HF cattle Maria Jaeger EAAP 2016 Session 30 7
8 Development of Inbreeding and Effective Population Size of DSN 2.13 Development of inbreeding FF: (per generation) Effective Populationsize Ne: 125 animals (= FF ) Average generation interval: 5.8 years ( ) Maria Jaeger EAAP 2016 Session 30 8
9 Average Relationship and Inbreeding Coefficients of Regions Av. Relationship between regions 0.7% 4% 1.4% CFC (Sargolzaei et al., 2006) Maria Jaeger EAAP 2016 Session 30 9
10 Environmental Herd Parameters according to Weigel and Rekaya (2000) Maria Jaeger EAAP 2016 Session 30 10
11 Average Relationship and Inbreeding Coefficients between Herd Parameters RBB Rinderproduktion Berlin- Brandenburg GmbH Maria Jaeger EAAP 2016 Session 30 11
12 Bivariate Model for Genetic Parameters of Production and Functional traits Bivariate model for all trait combinations: Y ijklmno = µ + G i + YS j + F k + Lact l +T m + Ca n + e ijklmno Y ijklmn = Vector of observation (SCC) μμ = Overall mean of population G i = Fixed effect of genotype (DSN, HF) YS j = Fixed effect of year and season (3 month) of test day records F k = Fixed effect of farm Lact l = Fixed effect of lactation (1 st, 2 nd, 3 rd ) T m = Additive genetic effect of animal Ca n = Calving age (covariable) e ijklmno = Random error DMU (Madsen & Jensen, 2000) Maria Jaeger EAAP 2016 Session 30 12
13 Genetic Parameters of SCC within Herd Parameters Additive genetic- (Ñ a2 ), residual- (Ñ e2 ) variances and heritabilities (h 2 ) for average herd SCC Herd parameter ϑ a 2 ϑ e 2 h 2 Group Group 1 Group 2 Group 1 Group 2 Group 1 Group 2 Ø Milk Ø SCC Herd size Ø Calving age Altitude of farm Latitude of farm DSN% SE for heritabilities d 0.02 Superior environment Better expression of true genetic potential Maria Jaeger EAAP 2016 Session 30 13
14 Analysis of Genotype by Environment Interactions Genetic correlations (rg) between groups within average herd parameters Herd parameters Traits Milk-kg Fat-% Protein-% SCC FPR Ø Milk Ø SCC Herd size Ø Calving age Altitude of farm Latitude of farm DSN% rg < 0.8 GxE interaction (Robertson, 1959) Maria Jaeger EAAP 2016 Session 30 14
15 Conclusion The balanced test design did not reveal differences for phenotypic line comparisons in terms of functional traits (FPR, SCS) Distinct relationships between different DSN herds apparent Genetic parameters are within the range of HF Better heritabilities and genetic variances of farms with greater herd sizes or higher production output GxE interactions for SCC and FPR Application of alternative models (RRM, multiple-trait herd cluster model) useful Maria Jaeger EAAP 2016 Session 30 15
16 Thank you for your attention! Biedermann The authors acknowledge the financial support for this project provided by transnational funding bodies, being partners of the FP7 ERA-net project, CORE Organic Plus, and the cofound from the European Commission.
17 Genetic Parameters of FPR within Herd Parameters Additive genetic- (Ñ a2 ), residual- (Ñ e2 ) variances and heritabilities (h 2 ) for average herd FPR Herd parameter ϑ 2 a ϑ 2 e h 2 Group Group 1 Group 2 Group 1 Group 2 Group 1 Group 2 Ø Milk Ø SCC Herd size Ø Calving age Altitude of farm Latitude of farm DSN% SE for heritabilities d 0.02 Maria Jaeger EAAP 2016 Session 30 17
18 Line Comparisons F-Test FPR Effect NumDF DenDF FValue ProbF ys 60 1,80E+05 8,48 <.0001 bmy ,80E+05 9,07 <.0001 breed*lanr_tt 5 1,80E+05 54,41 <.0001 lg1(breed*lanr_tt) 6 1,80E ,41 <.0001 lg2(breed*lanr_tt) 6 1,80E ,19 <.0001 lg3(breed*lanr_tt) 6 1,80E ,66 <.0001 SCS Effect NumDF DenDF FValue ProbF ys 60 1,80E+05 37,15 <.0001 bm 451 1,80E+05 17,37 <.0001 breed*lanr_tt 5 1,80E ,27 <.0001 lg1(breed*lanr_ 6 1,80E ,37 <.0001 lg2(breed*lanr_ 6 1,80E+05 37,92 <.0001 lg3(breed*lanr_ 6 1,80E ,25 <.0001 Maria Jaeger EAAP 2016 Session 30 18
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