Single-step genomic evaluation for fertility in Nordic Red dairy cattle

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1 Single-step genomic evaluation for fertility in Nordic Red dairy cattle Kaarina Matilainen, Minna Koivula, Ismo Strandén, ert P. Aamand and Esa A. Mäntysaari

2 Background Female fertility genetic evaluations (BLUP) have been done in Nordic countries since 97 s Joint Nordic fertility evaluations have been done since 25 Nordic Cattle enetic Evaluations NAV The model was upgraded in 25 From sire to animal model From repeatability to multi-trait model for lactations Next step: enomic evaluation 2 Interbull Open Meeting 26, Matilainen et al.

3 Objectives of this presentation Single step genomic model (ssblup): Taking into account phenotypes, pedigree and genomic data simultaneously enetic groups may cause problems in the convergence of the genomic model QP-transformation for the full H - matrix (unified relationships) Single-step genomic evaluation may need a long solving time Algorithm for Proven and Young (APY) Janne Lehtinen / MTT:n arkisto 3 Interbull Open Meeting 26, Matilainen et al.

4 Traits NAV fertility evaluations 25 involve two different trait groups Model for trait group I contains correlated traits Heifer traits: non-return rate (NRR) length of service period (IFL) Cow traits for lactations - 3: non-return rate (NRR, NRR2, NRR3) interval from calving to first breeding (ICF, ICF2, ICF3) length of service period (IFL, IFL2, IFL3) 4 Interbull Open Meeting 26, Matilainen et al.

5 Data Janne Lehtinen / MTT:n arkisto RDC data in routine joint Nordic fertility evaluations in 26 Number of animals with observations: 4,226,636 Number of animals in the pedigree: 5,445,392 Number of genotyped animals: 33,969 enetic parameters: Estimated for the routine joint Nordic fertility evaluations in 25 Low heritabilities (.5-.4) High correlations among traits ( between lactations) 5 Interbull Open Meeting 26, Matilainen et al.

6 Relationships in genomic evaluation In ssblup the inverse of the relationship matrix is H A w A where A describes relationships based on the pedigree 22 A 22 describes pedigree based relationships for genotyped animals gives relationships based on genomic information and w = ( - w) + w A 22, where w is the weight for polygenic information (we used %) 6 Interbull Open Meeting 26, Matilainen et al.

7 QP-transformation for genomic model Let rows in matrix Q describe genetic group compositions for each animal Usually, A - is augmented to include groups as phanom parents (PP). This same transformation is blindly used in single-step: However, contributions to PP due to genomic relationships can be similarly accounted (Misztal et al., 23): where Q 2 is a submatrix of Q for the genotyped animals. 7 Interbull Open Meeting 26, Matilainen et al. A A Q Q A Q A Q A A A H ' ' w ppg w ) '( ) '( ) ( Q A Q A Q Q A A A H w w w w ppg

8 Algorithm for Proven and Young (APY) Increase in number of genotyped animals leads to computational challenges in forming, inverting and using the genotype based relationship matrix. APY approach (Misztal et al., 25): Divide to core (c) and young (y) animals w w Approximate inverse by w cc yc cy yy Here APY cc cccymyyyc yyyccc M 2,74 animals that had descendant(s) were selected to the core Weight for polygenic information w =. A - 22 is not formed explicitly cc M yy diag yy yc cc cy cc M cy yy M yy 8 Interbull Open Meeting 26

9 Analyses Four genomic evaluations were performed ssblup QP-transformation for pedigree based relationship matrix only ssblup QP ssblup QP_Inb ssblup QP_Inb_APY QP-transformation both for pedigree and genomic information based relationships Like previous plus inbreeding coefficients taken into account in A - Like previous but QP- transformation for APY approximated genomic information based relationship matrix Models were solved using MiX99, and iterative preconditioned conjugate gradient algorithm (PC) 9 Interbull Open Meeting 26, Matilainen et al.

10 Comparisons between analyses Convergence Breeding values (for ICF2 and NRR3 shown as an example):. Annual EBV and EBV averages for males 2. Annual EBV and EBV correlations for both males and females 3. Comparisons between EBVs with and without APY EBV validation tests Janne Lehtinen / MTT:n arkisto Interbull Open Meeting 26, Matilainen et al.

11 Convergence Model PC rounds Time Time / round BLUP 2,42 5h 7s ssblup 6,282 22h 49s Interbull Open Meeting 26, Matilainen et al.

12 Convergence Model PC rounds Time Time / round BLUP 2,42 5h 7s ssblup 2,846 6,282 27h 22h 45s 49s ssblup QP 2,94 45h 55s ssblup QP_Inb 2,373 4h 62s 2 Interbull Open Meeting 26, Matilainen et al.

13 Convergence Model PC rounds Time Time / round BLUP 2,42 5h 7s ssblup 6,282 22h 49s ssblup QP 2,94 45h 55s ssblup QP_Inb 2,373 4h 62s ssblup QP_Inb_APY 2,573 34h 47s 3 Interbull Open Meeting 26, Matilainen et al.

14 Comparison of EBVs for ICF2 (Interval from calving to first breeding in second parity) After QP-transformation Annual EBV and EBV averages follows nicely each other. Annual EBV and EBV correlations were close to one for old animals but decreased somewhat for young animals. Annual averages Annual correlations Correlations between EBVs with and without APY were. and.998 for core and non-core animals, respectively. 4 Interbull Open Meeting 26, Matilainen et al.

15 Comparison of EBVs for NRR3 (non-return rate in third parity) After QP-transformation Annual EBV and EBV averages follows nicely each other. Annual EBV and EBV correlations were close to one for old animals but decreased somewhat for young animals. Annual averages Annual correlations Correlations between EBVs with and without APY were. and.999 for core and non-core animals, respectively. 5 Interbull Open Meeting 26, Matilainen et al.

16 Interbull EBV validation test Validation reliability (R 2 ) and regression coefficient (b ) from the regression of deregressed genetic predictions from the full data on EBV and EBV from the reduced data Observations from the latest 6 years were removed Validation group contained 75 genotyped bulls Bulls for which the effective record contribution: ERC > based on full data and ERC = based on reduced data Janne Lehtinen / MTT:n arkisto 6 Interbull Open Meeting 26, Matilainen et al.

17 Validation reliabilities R 2 for ssblup QP_Inb Trait EBV EBV EBV-EBV NRR IFL NRR ICF IFL NRR ICF IFL NRR ICF IFL NRR = Non-return rate IFL = Length of service period ICF = Interval from calving to first breeding = Heifer -3 = Parity 7 Interbull Open Meeting 26, Matilainen et al.

18 Regression coefficients b for ssblup QP_Inb Trait EBV EBV EBV-EBV NRR IFL NRR ICF IFL NRR ICF IFL NRR ICF IFL NRR = Non-return rate IFL = Length of service period ICF = Interval from calving to first breeding = Heifer -3 = Parity 8 Interbull Open Meeting 26, Matilainen et al.

19 Conclusions Single-step genomic evaluation for fertility in Nordic RDC was feasible. Janne Lehtinen / MTT:n arkisto Accounting for genetic groups also in genomic information via QP-transformation was necessary: Faster convergence More consistent genomic breeding values when compared with traditional breeding values Considering inbreeding coef in A - improved convergence greatly Model validation showed that ssblup improved the fertility evaluations, especially for cow traits. APY-algorithm reduced the solving time with no effect on solutions 9 Interbull Open Meeting 26, Matilainen et al.

20 Acknowledgements This work was part of the Nordic research project ENOMICS in HERDS Data: NAV Nordic Cattle enetic Evaluation Funding: MMM, NAV, Viking enetics, Faba and Valio 2 Interbull Open Meeting 26, Matilainen et al.

21 Thank you!

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