Robust MACE. H. Benhajali, Z. Liu, P. Sullivan, P.M. VanRaden, G.P. Aamand, G.de Jong, E. Mantysaari & V. Ducrocq

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1 Robust MACE H. Benhajali, Z. Liu, P. Sullivan, P.M. VanRaden, G.P. Aamand, G.de Jong, E. Mantysaari & V. Ducrocq

2 A bit of history... What is Robust MACE? =MACE (Multiple Across Country Evaluation) Country x Birth-Year effect instead of Country effect Inspired from Ducrocq et al., Benhajali et al., 213 showed that - Despite the trend validation tests, some discrepancies caused by G biases remain - Robust MACE can correct these discrepancies Benhajali et al., 214 tested the robustness of the Robust Mace model by simulating a systematic G bias for one or two countries

3 FRA bulls on FRA scale A bit of history... G_SCS Over-correction? MACE ROBUST MACE Benhajali et al., 214

4 Some other Unpublished results CC1_RMACE dlo_rmace ebvr ebvr2.5 ebvr ebvr2 ebvr: No bias, RMACE model ebvr2:+1%fra, RMACE model

5 Why is Robust MACE over-correcting the bias? Include the country*year effect in the de-regression model

6 Current study Change the de-regression procedure by including a country year effect in the model Repeat the study with 5 trait protein(pro), somatic cells(scs), stature(sta), longevity(dlo) and fertility(cc1)

7 DATA Data on Holstein breed from INTERBULL routine evaluation of December 215: 14 countries: AUS CAN DEU DFS ESP FRA GBR IRL ITA JPN NLD NZL POL USA 5 trait: SCS, dlo, sta, pro, cc1

8 BIAS Bias= (.5(BY bull 986)+.25(BY sire 986)+.25(BY dam 986))*stdg*B Different levels of bias: B= %, 1%, % Example: born in born in born in Bias= (,5*14 +,25*4+,25*1)*B*stdg= 1,5*B*stdg

9 BIAS Bias= (.5(BY bull 986)+.25(BY sire 986)+.25(BY dam 986))*stdg*B Different levels of bias: B= %, 1%, % Systematic biases were simulated in only one country (FRA) or two countries (FRA and NLD) 1. FRA_+1 2. NLD_

10 RUNS 6 Data sets 6 runs/trait 1. Regular data for all the countries 2. Regular data for all the other countries + FRA_+1 3. Regular data for all the other countries + FRA_+1+ NLD_ 3 MACE 3 R_MACE

11 NEW RESULTS

12 ΔG SCS: FRA BULLS/FRA scale MACE RMACE ebv ebv1 ebv2 -.5 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

13 ΔG SCS: NLD BULLS/NLD scale MACE RMACE BY ebv ebv1 ebv2 YB ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

14 ΔG STA: FRA BULLS/FRA scale 3 MACE 3 RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr3 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

15 ΔG STA: NLD BULLS/NLD scale MACE RMACE BY ebv ebv1 ebv2 BY ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

16 ΔG PRO: FRA BULLS/FRA scale MACE RMACE -8-8 ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

17 ΔG PRO: NLD BULLS/NLD scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

18 ΔG CC1: FRA BULLS/FRA scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

19 ΔG CC1: NLD BULLS/NLD scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

20 ΔG dlo: FRA BULLS/FRA scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

21 ΔG dlo: NLD BULLS/NLD scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

22 What about genetic groups?

23 ΔG SCS: GRP_DAM/FRA scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

24 ΔG PRO: GRP_DAM/FRA scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

25 ΔG STA: GRP_DAM/FRA scale MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

26 ΔG dlo: GRP_DAM/FRA scale.5 MACE RMACE ebv ebv1 ebv2 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

27 ΔG CC1: GRP_DAM/FRA scale 2.5 MACE 2.5 RMACE ebv ebv1 ebv2 1.5 ebvr ebvr1 ebvr2 ebv: No bias, MACE model ebv1:+1%fra, MACE model ebv2:+1%fra, %NLD, MACE model ebvr: No bias, RMACE model ebvr1:+1%fra, RMACE model ebvr2:+1%fra, %NLD, RMACE model

28 MACE v.s. RMACE current situation 1 Longevity country1_mace country2_mace country3_mace country4_mace country1_rmace country2_rmace country3_rmace country4_rmace

29 Next challenges Which model is better? Extend to all countries/breeds, effect on small populations Effect on correlations?

30 CONCLUSION ROBUST MACE Does not need any new data from countries Ability to detect (using the country*year solutions) and correct for the discrepancies on national genetic trends With more consistent G, It is expected to improve genetic correlations between countries (to be verified)

31 THANK YOU!

32 COUNTRY*YEAR SOLUTIONS.6 Scs/Regular data Country_1 Country_4 Country_3 Country_6 Country_7 Country_9 Country_ BY