S. Naderi1, R. R. Mota1, S. Vanderick1, N. Gengler1

Slides:

Advertisements

Similar presentations

2007 Paul VanRaden, Mel Tooker, and Nicolas Gengler Animal Improvement Programs Lab, Beltsville, MD, USA, and Gembloux Agricultural U., Belgium

Advertisements

John B. Cole Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville, MD

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Select Sires’

Use of Quantitative Trait Loci (QTL) in Dairy Sire Selection Fabio Monteiro de Rezende Universidade Federal Rural de Pernambuco (UFRPE) - Brazil.

How Genomics is changing Business and Services of Associations Dr. Josef Pott, Weser-Ems-Union eG, Germany.

Extension of Bayesian procedures to integrate and to blend multiple external information into genetic evaluations J. Vandenplas 1,2, N. Gengler 1 1 University.

2007 Paul VanRaden 1, Curt Van Tassell 2, George Wiggans 1, Tad Sonstegard 2, Jeff O’Connell 1, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4,

Mating Programs Including Genomic Relationships and Dominance Effects

Mating Programs Including Genomic Relationships and Dominance Effects Chuanyu Sun 1, Paul M. VanRaden 2, Jeff R. O'Connell 3 1 National Association of.

M. Dufrasne 1,2, V. Jaspart 3, J. Wavreille 4 et N. Gengler 1 1 University of Liège, Gembloux Agro Bio-Tech, Animal Science Unit - Gembloux 2 F.R.I.A.

Impacts of inclusion of foreign data in genomic evaluation of dairy cattle K. M. Olson 1, P. M. VanRaden 2, D. J. Null 2, and M. E. Tooker 2 1 National.

2007 J. B. Cole 1,*, P. M. VanRaden 1, J. R. O'Connell 3, C. P. Van Tassell 1,2, T. S. Sonstegard 2, R. D. Schnabel 4, J. F. Taylor 4, and G. R. Wiggans.

2007 Paul VanRaden Animal Improvement Programs Lab, Beltsville, MD 2011 Avoiding bias from genomic pre- selection in converting.

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD National Association.

2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics.

ASAS/ADSA 2001 Conference (1) 2001 Variance of effects of lactation stage within herd by herd yield N. Gengler 1,2, B. Auvray *,1, and G.R. Wiggans 3 1.

John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD March 2012 AIPL.

Bovine Genomics The Technology and its Applications Gerrit Kistemaker Chief Geneticist, Canadian Dairy Network (CDN) Many slides were created by.

2003 G.R. Wiggans,* P.M. VanRaden, and J.L. Edwards Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD

2007 Paul VanRaden and Mel Tooker Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA

REGRESSION MODEL y ijklm = BD i + b j A j + HYS k + b dstate D l + b sstate S l + b sd (S×SD m ) + b dherd F m + b sherd G m + e ijklm, y = ME milk yield,

2007 Paul VanRaden, Mel Tooker, Jan Wright, Chuanyu Sun, and Jana Hutchison Animal Improvement Programs Lab, Beltsville, MD National Association of Animal.

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk N. Gengler 1,2 and H. Soyeurt 1 1 Gembloux Agricultural University,

2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.

2008 ADSA-ASAS Joint Annual Meeting Indianapolis, July 7-11 Genetic Parameters of Saturated and Monounsaturated Fatty Acids Estimated by Test-Day Model.

2005 Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD An Example from Dairy.

T. A. Cooper and G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Council.

Adjustment of selection index coefficients and polygenic variance to improve regressions and reliability of genomic evaluations P. M. VanRaden, J. R. Wright*,

2007 Paul VanRaden and Jan Wright Animal Improvement Programs Lab, Beltsville, MD 2013 Measuring genomic pre-selection in theory.

2007 Paul VanRaden Animal Improvement Programs Lab, USDA, Beltsville, MD, USA Pete Sullivan Canadian Dairy Network, Guelph, ON, Canada

J. B. Cole * and P. M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD

Interbull Meeting – Dublin 2007 Genetic Parameters of Butter Hardness Estimated by Test-Day Model Hélène Soyeurt 1,2, F. Dehareng 3, C. Bertozzi 4 & N.

G.R. Wiggans* and P.M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD

2007 Paul VanRaden, Melvin Tooker*, George Wiggans Animal Improvement Programs Laboratory 2009 Can you believe those genomic.

2003 P.M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Genetic Evaluations.

J. B. Cole *, G. R. Wiggans, P. M. VanRaden, and R. H. Miller Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville,

Paul VanRaden and John Cole Animal Improvement Programs Laboratory Beltsville, MD, USA 2004 Planned Changes to Models and Trait Definitions.

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Select Sires’

April 2010 (1) Prediction of Breed Composition & Multibreed Genomic Evaluations K. M. Olson and P. M. VanRaden.

Council on Dairy Cattle Breeding April 27, 2010 Interpretation of genomic breeding values from a unified, one-step national evaluation Research project.

2007 Paul VanRaden 1, Jeff O’Connell 2, George Wiggans 1, Kent Weigel 3 1 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2 University of Maryland.

2007 Paul VanRaden 1, Jeff O’Connell 2, George Wiggans 1, Kent Weigel 3 1 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2 University of Maryland.

2007 Paul VanRaden Animal Improvement Programs Lab, Beltsville, MD Iterative combination of national phenotype, genotype, pedigree,

2007 John Cole, Paul VanRaden, George Wiggans, and Melvin Kuhn Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD,

Paul VanRaden Animal Improvement Programs Laboratory Beltsville, MD, USA Inbreeding Adjustments and Effect on Genetic Trend.

Lactation Number Effects on the Genetic Variability of the Stearoyl Coenzyme-A Desaturase 9 Activity Estimated by Test-Day Model V. M.-R. Arnould 1, N.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Select Sires‘ Holstein.

G.R. Wiggans, T. A. Cooper* and P.M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD

2007 Paul VanRaden 1, Curt Van Tassell 2, George Wiggans 1, Tad Sonstegard 2, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4, Paul VanRaden 1, Curt.

2001 ASAS/ADSA 2001 Conference (1) Simultaneous accounting for heterogeneity of (co)variance components in genetic evaluation of type traits N. Gengler.

Strategies to Incorporate Genomic Prediction Into Population-Wide Genetic Evaluations Nicolas Gengler 1,2 & Paul VanRaden 3 1 Animal Science.

EAAP Meeting, Stavanger Estimation of genomic breeding values for traits with high and low heritability in Brown Swiss bulls M. Kramer 1, F. Biscarini.

My vision for dairy genomics

Validation of €uro-Star Replacement Index.

Y. Masuda1, I. Misztal1, P. M. VanRaden2, and T. J. Lawlor3

J. Vandenplas 1,2 and N. Gengler 1,2

Distribution and Location of Genetic Effects for Dairy Traits

Using recombination to maintain genetic diversity

Methods to compute reliabilities for genomic predictions of feed intake Paul VanRaden, Jana Hutchison, Bingjie Li, Erin Connor, and John Cole USDA, Agricultural.

Genomic Evaluations.

S. Vanderick1,2, F.G. Colinet1, A. Mineur1, R.R Mota1,

Correlations Among Measures of Dairy Cattle Fertility and Longevity

A National Sire Fertility Index

JANA OBŠTETER Janez JENKO John HICKEY Gregor GORJANC EAAP 2018

Use of a threshold animal model to estimate calving ease and stillbirth (co)variance components for US Holsteins.

Predictions of Breed Composition Using Holsteins, Jersey, and Brown Swiss Genotypes K. M. Olson and P. M. VanRaden.

Can you believe those genomic evaluations for young bulls?

Increased reliability of genetic evaluations for dairy cattle in the United States from use of genomic information Abstr.

Using Haplotypes in Breeding Programs

Development of Genomic GMACE

Presentation transcript:

S. Naderi1, R. R. Mota1, S. Vanderick1, N. Gengler1 Ideas for continuous genomic evaluation for newly genotyped Walloon Holstein females and males S. Naderi1, R. R. Mota1, S. Vanderick1, N. Gengler1 1 Gembloux Agro-Bio Tech, ULiège, B-5030 Gembloux, Belgium EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Current Situation Number of genotyped animals in the Walloon genomic evaluations~ 9,000 Single-step genomic Bayesian procedure (ssGBayes) Blending genomic, Walloon and MACE information Subtract Walloon information contributing to MACE Delay between genotyping animals and official GEBV Worst case scenario: 4 months EAAP 2018 Meeting Dubrovnik

Why preliminary genomic evaluations ? CulIing animals at calf-hood reducing rearing costs Time span reduction between DNA sampling and GEBV An official evaluation generating GEBV and GREL Processing time may increase EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Objectives Generate preliminary genomic evaluations that are: Simpler and faster: practical monthly/weekly evaluations Quite similar to current official routine evaluations EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Possible approches SNP effect methods using official routine evaluations as source  polygenic contributions ??? Genomic selection index methods  correct covariance structures “H” based? EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Decomposition of GEBV PA: parent average YD: yield deviation PC: progeny contribution PP: pedigree prediction based Young animals GEBVi ≈ w1PA + w2DGV (Lourenco et al., 2015) EAAP 2018 Meeting Dubrovnik

A- Group I: Animals without progeny GEBVi ≈ w1PAG + w2DGVi PAi = (GEBVs + GEBVd)/2 GEBV of young bulls without progeny used to derive Wi EAAP 2018 Meeting Dubrovnik

A-Group II: Animals with progeny GEBVi ≈ w1PAG + w2DGVi +w3PAC + w4EBVi PAi = (EBVs + EBVd)/2 GEBV of ~3000 genotyped animals with progeny used to derive Wi EAAP 2018 Meeting Dubrovnik

Requirements of the method Combined Pedigree SNP effects (previous evaluation) Parent average Proper regression coefficients PAi = (GEBVs + GEBVd)/2 From BOTTOM to the TOP Fill the gaps with GEBV or EBV Running postGS (solution of previous evaluation) SNP sol DGV Running own code (Parents average all animals) Regression coefficient derivation Regular variance and covariance of vectors of PA ,DGV EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Milk Group/ Date Number of Animals Cor(R_GEBV, E_GEBV) Mean_R_GEBV Mean_E_GEBV Group I/ April 2017 60 0.92 762.79 698.04 August 2017 23 0.90 685.70 572.49 Group II/ 120 0.97 645.85 646.18 EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Fat Group/ Date Number of Animals Cor(R_GEBV, E_GEBV) Mean_R_GEBV Mean_E_GEBV Group I/ April 2017 60 0.95 32.57 32.77 August 2017 23 0.84 27.42 26.42 Group II/ 120 0.88 26.46 26.04 EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Protein Group/ Date Number of Animals Cor(R_GEBV, E_GEBV) Mean_R_GEBV Mean_E_GEBV Group I/ April 2017 60 0.93 25.15 24.86 August 2017 23 0.89 26.17 22.56 Group II/ 120 0.95 22.22 21.34 EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik SCS Group/ Date Number of Animals Cor(R_GEBV, E_GEBV) Mean_R_GEBV Mean_E_GEBV Group I/ April 2017 60 0.90 2.65 2.58 August 2017 23 0.88 2.66 2.62 Group II/ 120 0.93 2.68 2.67 EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Milk Fat Protein SCS EAAP 2018 Meeting Dubrovnik

Genomic selection index based methods Directly proportional: selection index “H” inversion difficulty However, as shown by many researchers (e.g. Gengler et al., 1997)  equivalent MME GEBV of routine evaluation as “data” and heritability close to unity Extending GEBV for new genotyped animals EAAP 2018 Meeting Dubrovnik

Extending GEBV for new genotyped animals (Ext_GEBV) New genotyped animals pedigree extraction Combining with routine pedigree GEBV from December 2017 routine evaluation as priors  GEBV of new genotyped animals (71 animals) EAAP 2018 Meeting Dubrovnik

Animals in routine (December) Overlapping and relationship of new genotyped animals with animal in routine evaluation New Genotyped animals Sire P.G.S Ancestors M.G.S. P. G.G.S. M.G.G.S. 71 46 (13) 31 (26) 20588 (8070) 47 (45) 133 (132) 193 (183) Animals in routine (December) New genotyped animals 0.026 0.041 New genotyped animals 0.070 EAAP 2018 Meeting Dubrovnik

Extending GEBV for new genotyped animals Trait cor(EXT, EST) Cor(R, EXT) Mean EXT Mean EST Mean R Milk 0.93 0.78 277.00 492.40 796.30 Fat 0.96 0.79 14.60 23.20 34.10 Protein 0.95 0.73 10.40 14.00 18.90 EAAP 2018 Meeting Dubrovnik

Accuracy of the preliminary evaluation could be improved Adding new genotyped animals parent average or missing (the correlation ~0.99, not shown results)  processing time may increase Adding a specific group of animals closely linked to routine population only a group of genotyped animals representing the dimensionality of the genomic information (proven and young) An official evaluation generating GEBV and GREL EAAP 2018 Meeting Dubrovnik

EAAP 2018 Meeting Dubrovnik Conclusion The correlation between preliminary and official evaluations was not as high as expected (specially for approach B): Small size of new genotyped animals Instability of SNP estimates The proportion of residual polygenic variance in total additive genetic variance (approach B) EAAP 2018 Meeting Dubrovnik

Thank you for your attention Acknowledgements Thank you for your attention EAAP 2018 Meeting Dubrovnik

Cor (R_GEBV, E_GEBV) for other traits (April 2017) Number of Animals Cor(R_GEBV, E_GEBV) BCS 60 0.86 Calving_ Ease 0.87 Longevity 0.89 Fertility 0.91 Stature 0.90 Chest width EAAP 2018 Meeting Dubrovnik

Regression (de-regression) coefficient used for Protein Data Genomic PA DGV Pedigree prediction based EBV Conventional PA Young bulls without progeny 0.62 0.72 -0.34 - EST_GEBV 0.6236 0.7223 -0.3449 EXT_GEBV 0.6673 0.4492 -0.3242 Animals with progeny 0.44 0.63 0.43 -0.50 Used to estimate GEBV for new genotyped animals Used to estimate GEBV for animals with Progeny EAAP 2018 Meeting Dubrovnik