Genomic prediction. Kevin Byskov, Ulrik Sander Nielsen and Gert Pedersen Aamand. Nordisk Avlsværdi Vurdering. Nordic Cattle Genetic Evaluation

Similar documents
What could the pig sector learn from the cattle sector. Nordic breeding evaluation in dairy cattle

Young Stock Survival Index Inclusion in NTM. Jørn Pedersen, Jukka Pösö, Jan-Åke Eriksson Ulrik S. Nielsen, Gert P. Aamand

Emma Carlén, Jørn Pedersen, Jukka Pösö, Jan-Åke Eriksson, Ulrik Sander Nielsen, Gert Pedersen Aamand

Single step genomic evaluations for the Nordic Red Dairy cattle test day data

Genomic selection and its potential to change cattle breeding

Youngstock Survival in Nordic Cattle Genetic Evaluation

Estimation of GEBVs Using Deregressed Individual Cow Breeding Values

Genomic selection applies to synthetic breeds

Genomic Postcard from Dairy Cattle Breeding GUDP Project. Søren Borchersen, Head R&D VikingGenetics

Genetic improvement: a major component of increased dairy farm profitability

João Dürr Interbull Centre Director. Animal identification and traceability Interbull s s and Interbeef s perspectives

ICAR meeting State of the art of genomic evaluation and international implications. Reinhard Reents. vit, IT solutions for Animal Prduction

Cow Reference Population - Benefit for Genomic Evaluation Systems - and Farmers

Cow Reference Population - Benefit for Genomic Evaluation Systems - and Farmers

NAV routine genetic evaluation of Dairy Cattle

Strategy for Applying Genome-Wide Selection in Dairy Cattle

NTM. Breeding for what truly matters. The NTM breeding goal is healthy, fertile, high producing cows the invisible cow. Elisabeth

1.1 Present improvement of breeding values for milking speed

Nordic NTM promotion

IT-Solutions for Animal Production. 3. März 2017 Seite 1

EuroGenomics: A successful European Partnership

Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. Nordic Cattle Genetic Evaluation, DK-8200 Aarhus N, Denmark

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

Establishment of a Single National Selection Index for Canada

Breeding for milkability How to use the new possibilities

Scandinavian co-operation

Managing genetic groups in single-step genomic evaluations applied on female fertility traits in Nordic Red Dairy cattle

Practical integration of genomic selection in dairy cattle breeding schemes

Genomic selection in cattle industry: achievements and impact

TWENTY YEARS OF GENETIC PROGRESS IN AUSTRALIAN HOLSTEINS

GENOMICS AND YOUR DAIRY HERD

Genomic Selection in Germany and Austria

19. WORLD SIMMENTAL FLECKVIEH CONGRESS. The robust Fleckvieh cow breeding for fitness and health

11/30/2018. Introduction to Genomic Selection OUTLINE. 1. What is different between pedigree based and genomic selection? 2.

Evidence of improved fertility arising from genetic selection: weightings and timescale required

ICAR Subcommittee Interbull

A management tool for breeders

Genomic Service Operation

The benefits of genotyping at farm level & the impact across the wider dairy herd in Ireland. Kevin Downing 27 th October, 2016

Genomic Selection in Dairy Cattle

Operation of a genomic selection service in Ireland

Strategy for applying genome-wide selection in dairy cattle

Section 9- Guidelines for Dairy Cattle Genetic Evaluation

Farm Management Decisions in the Era of Genomics

Use of genomic tests and sexed semen increase genetic level within herd

Application of MAS in French dairy cattle. Guillaume F., Fritz S., Boichard D., Druet T.

Should the Markers on X Chromosome be Used for Genomic Prediction?

The Irish Beef Genomics Scheme; Applying the latest DNA technology to address global challenges around GHG emissions and food security.

Genomic selection in the Australian sheep industry

Prediction of GEBV in Comparison with GMACE Value of Genotyped YYoung Foreign Bulls

Enhancing the Data Pipeline for Novel Traits in the Genomic Era: From farms to DHI to evaluation centres Dr. Filippo Miglior

Introduction. Development of national and international evaluations. Considerable genetic variation in health and fertility traits!

Breeding Objectives Indicate Value of Genomics for Beef Cattle M. D. MacNeil, Delta G

Simulation study on heterogeneous variance adjustment for observations with different measurement error variance

Use of data from electronic milking meters and perspective in use of other objective measures

Requirements for future recording systems

Big Data, Science and Cow Improvement: The Power of Information!

Implementation of dairy cattle breeding policy in Ethiopia some reflections on complementary strategies

Genetics Effective Use of New and Existing Methods

BLUP and Genomic Selection

2/22/2012. Impact of Genomics on Dairy Cattle Breeding. Basics of the DNA molecule. Genomic data revolutionize dairy cattle breeding

Genomic Selection in Beef Ireland Francis Kearney ICBF

Upgrading Dairy Cattle Evaluation System in Russian Federation

LowInputBreeds & Genomic breeding

What dairy farmers should know about genetic selection

. Open access under CC BY-NC-ND license.

Optimization of dairy cattle breeding programs using genomic selection

Using genotypes to construct phenotypes for dairy cattle breeding programs and beyond

Longhorn Cattle Performance Recording

GENETIC CONSIDERATIONS FOR HEIFER FERTILITY DR. HEATHER J. HUSON ROBERT & ANNE EVERETT ENDOWED PROFESSORSHIP OF DAIRY CATTLE GENETICS

6 Breeding your cows and heifers

Bull Selection Strategies Using Genomic Estimated Breeding Values

Genetics to meet pastoral farming requirements in the 2020 s a dairy perspective. Phil Beatson, R&D Manager CRV Ambreed

Improving Genetics in the Suckler Herd by Noirin McHugh & Mark McGee

Multi-breed Genomic Evaluations for 1 million Beef Cattle in Ireland. A.R. Cromie, R.D. Evans, J F Kearney, M. McClure, J. McCarthy and D.P.

Introduction to Animal Breeding & Genomics

Genomic predictions using 3K, 50K, 800K or sequence data within and across populations results and speculations

Single- step GBLUP using APY inverse for protein yield in US Holstein with a large number of genotyped animals

Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations

Angus BREEDPLAN GETTING STARTED

QTL Mapping, MAS, and Genomic Selection

Using Genomics to Improve the Genetic Potential and Management of Your Herd

New Limousin Genomic Breeding Values (GEBVS) Delivering s value for commercial producers

User Guide

CDCB tools for the improvement of the Jersey breed

Dairy Cattle Development in China

Traditional Genetic Improvement. Genetic variation is due to differences in DNA sequence. Adding DNA sequence data to traditional breeding.

Multi-breed beef genomics

Breeding briefs. A guide to genetic indexes in dairy cattle

Breeding for Tuberculosis and Liver Fluke Resistance. Siobhán Ring Irish Angus Meeting, 7 th February 2019

Alison Van Eenennaam, Ph.D.

EAAP : 29/08-02/ A L I M E N T A T I O N A G R I C U L T U R E E N V I R O N N E M E N T

New Zealand Hereford Selection Indexes

Including feed intake data from U.S. Holsteins in genomic prediction

Development of an Economic Breeding Index EBI for Ireland. Ross Evans (ICBF)

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

Applications of Genomics Introduction of genomics selection procedures in existing genetic improvement programmes

Genetic Analysis of Cow Survival in the Israeli Dairy Cattle Population

Simulation study on heterogeneous variance adjustment for observations with different measurement error variance

Estimation of variance components for Nordic red cattle test-day model: Bayesian Gibbs sampler vs. Monte Carlo EM REML

Transcription:

Genomic prediction Kevin Byskov, Ulrik Sander Nielsen and Gert Pedersen Aamand STØTTET AF mælkeafgiftsfonden

Present 2-step method (HOL), RDC,JER SNP and deregressed proof (DRP) Direct genomic values (DGV) Genomic enhanced breeding values (GEBV) 2...

Basic data: Deregressed proof Registered data (kg protein, mastitis diagnoses) Systematic effects (calving age, herd, ect.) EBV Adjusted phenotype Depends on reliability Deregressed proof (DRP) 3...

Deregressed proof (DRP) - genotyped animals Females: Own phenotypic deviation from parent average if animal should have the calculated EBV Bulls: Phenotypic deviation of daughters is used as a phenotype of the bull. Deviation from parent average of the bull if the bull should have the calculated EBV Each phenotype is only included once! 4...

Tested cows Females in reference group - information from 10 daughters of a bull with yield in 1. lactation Adjusted phenotype Not tested cows Adjusted phenotype Adjusted phenotype Adjusted phenotype 5...

DRP - use of pedigree information for tested animals Previous routine: Sire, MGS Now: Sire, Dam (Animal model) Advantage: DRP are closer to the true yield deviation of the animal 6...

DRP - use of pedigree information for tested animals Now Previously Pedigree: 0,5 + 0,25 = 0,75 + genetic group Pedigree: 0,5 + 0,25 + 0,125 + 0,0625 = 0,9375 + genetic group 7...

Calculation of BV Tested cows and proven bulls with DRP DRP = μ + Animal + e Type of output BV depends on pedigree: Genomic pedigree DGV (genomic value) Traditional pedigree EBV (traditional value) 8

Pedigree on the basis of SNP Genomic pedigree: 0-1!Bull!Full Sib Traditional pedigree: 0,5 9...

Calculation of BV Tested heifers and young bulls without DRP Missing DRP = μ + Animal + e Type of BV depends on used pedigree: Genomic pedigree DGV Traditional pedigree Pedigree index 10...

Blending (GEBV) Combines DGV and EBV DGV: Pedigree (genomic), performance (DRP) EBV: Pedigree (traditional), performance (DRP) Combination BUT much overlap of information New method better to avoid double counting makes it possible to include females in reference group 11...

Reference population January 2015 Reference population Bulls Cows Holstein 27500 a) (8400) RDC 8150 b) 12700 Jersey 2450 c) 8550 a) Includes proven bulls from NLD, FRA, DEU, ESP b) Includes proven bulls from NOR c) Includes proven bulls from USA 12

Genomic information gives higher reliability for candidates! Reliability for 4 latest birth years of proven bulls compared to reliability on pedigree information Example: Validation reliability on DGV 45% Validation reliability on pedigree 20% Extra reliability 25% 13

Extra reliability in addition to pedigree information for RDC Reference population Bulls Bulls and cows Yield 0,13 0,18 Growth 0,13 0,14 Fertility 0,14 0,14 Birth 0,18 0,18 Calving 0,02 0,02 Udder health 0,17 0,23 Other diseases 0,14 0,14 Frame 0,24 0,29 Feet&Legs 0,24 0,33 Udder 0,23 0,30 Milking speed 0,17 0,22 Temperament 0,18 0,21 Longevity 0,07 0,07 Claw health 0,11 0,11 14

Extra reliability in addition to pedigree information for HOL Reference population Bulls Bulls and cows Yield 0,33 0,35 Growth 0,24 0,24 Fertility 0,32 0,32 Birth 0,31 0,31 Calving 0,22 0,22 Udder health 0,39 0,41 Other diseases 0,06 0,06 Frame 0,36 0,36 Feet&Legs - - Udder 0,52 0,52 Milking speed 0,46 0.46 Temperament 0,14 0.14 Longevity 0,21 0.21 Claw health (0,00) (0,00) 15

Extra reliability in addition to pedigree information for JER Reference population Bulls Bulls and cows Yield 0,16 0,22 Fertility 0,17 0,17 Birth 0,00 0,00 Calving 0,00 0,00 Udder health 0,09 0,16 Other diseases 0,00 0,00 Frame 0,19 0,30 Feet&Legs 0,05 0,13 Udder 0,26 0,29 Milking speed 0,15 0,34 Temperament 0,00 0,00 Longevity 0,11 0,11 16

Difference in reliability between breeds Reliability for RDC and Jersey still 12-15% point lower than HOL but higher than before! Expect increase in reliability when more genotyped RDC and Jersey cows are included in reference populationen 17

Correlations - previously (May 2014) and now (since August 2014) RDC Jersey Reference Genotyped animals group: Young bulls and Cows Young bulls and Cows heifers heifers Without cows 0.97-0.99 0.97-0.99 0.94-0.96 0.94-0.96 With cows 0.88-0.93 0.89-0.92 0.77-0.85 0.87-0.90 18

Genetic level Genetic level for young bulls and heifers has increased RDC: 4 index units for yield and NTM 0-2 index units for other traits with cows in the reference population 19

Blue = New, Red = previously 20

Summary What is improved? RDC/JER Previous routine New Pedigree Reference population Blending method Sire-Maternal grandsire Animal Model Important for candidates and cows only traits with strong trend Bulls Bulls and cows (8400 HOL), Developed by MTT (2010) Improved by MTT (2013/14) 12700 RDC, 8550 JER Better to avoid Double counting 21

Challenges Calculation of DRP Method sensitive for traits with low heritability when females are included in reference group or reference group is not uniform Causes instability in DGV Improved method is expected to be introduced in February 2015 Improvement of genomic prediction is a continuous process 22

Tested females per country and birth year Year Holstein RDC Jersey DNK FIN SWE DNK FIN SWE DNK FIN SWE 2007 352 37 56 33 81 27 75 0 1 2008 641 58 75 66 166 33 118 0 0 2009 868 138 137 91 292 107 149 1 5 2010 1093 348 144 403 1842 1249 2175 1 42 2011 1547 1099 353 825 3556 1770 3925 6 87 2012 2153 1442 557 1098 3384 1908 4119 15 110 2013 3257 2045 1335 1360 2839 1920 2097 11 81 2014 1877 886 432 826 1001 561 990 7 37 Total 12371 6082 3184 4838 13230 7620 13742 41 375 HOL total : 21.637 Last year: 10.195 RDC total : 25.688 13.752 Jersey total : 14.158 8.446

Level of genomic tested Holstein November 2014 Bulls with HB Bulls with out HB Females Born Number NTM Number NTM Number NTM 2008 305 5,2 534-0,9 774 3,5 2009 302 8,3 818 1,4 1141 4,3 2010 250 13,6 898 5,2 1583 8,9 2011 200 18,5 1529 9,6 2916 9,9 2012 223 23,2 1954 13,0 3972 12,1 2013 190 28,7 2183 18,2 6083 15,6 2014 8 30,5 2106 22,0 1996 19,2

Level of genomic tested RDC November 2014 Bulls with HB Bulls without HB Females Born Number NTM Number NTM Number NTM 2008 258 2,0 60 2,7 263 5,4 2009 247 4,8 343 2,5 488 5,2 2010 256 10,6 736 5,6 2957 3,9 2011 293 13,6 1517 9,6 5935 6,2 2012 267 18,6 2070 11,9 5863 8,6 2013 248 22,1 2098 14,5 5055 12,9 2014 78 25,6 1481 18,7 974 17,3

Level of genomic tested Jersey November 2014 Bulls with HB Bulls without HB Females Born Number NTM Number NTM Number NTM 2008 47 2,8 33-1,6 204 5,4 2009 58 7,2 124 3,2 243 5,2 2010 72 9,2 179 3,0 2636 3,1 2011 73 10,5 325 5,2 4259 4,1 2012 58 12,9 370 7,8 4193 5,2 2013 66 15,9 381 10,2 2107 8,6 2014 345 12,8 565 10,8

Stated from practice First time a bull gets an EBV based on a progeny test it some times deviates/drops significantly from the GEBV but closes in on GEBV in subsequent runs and Top GEBV bulls tend to drop in level when daughters enter production

Current thresholds going from GEBV to EBV Trait Reliability EBV Yield, Beef 60% Type traits, temperament, milkability 15 daughters Longivity, calving traits direct 50% Mastitis, claw health, calving traits maternal, young stock survival 40% Fertility, other diseases 35% GEBV reliability are > than EBV reliability threshold

How well do the bulls meet expectations? HOL - Official GEBVs/EBVs for 2012-2014 Yield Udder health NTM Depending on trait 554-586 bulls Standard deviations of (EBV GEBV) Do the 1 st indices have larger STDVs

GEBV 1 year old genotype GEBV EBVref1 EBVref2 EBVref3 4-5 year old Genotype last evaluation before daughter information 5 year old daughter information 5 year old daughter information 5 year old daughter information

Expected results If a bull have GEBV NTM = 30 we do expect EBV NTM < 30! The regression βebv,gebv expected < 1 unless the bull has thousands of daughters. Why? Not Part-Whole, but to some degree independent data in the two evaluations. E(βEBV,GEBV) = r 2 EBV for independent tests

Expected results True BV GEBV

Expected results Not Part-Whole - βebv,gebv < 1 EBV 80 daughters True BV GEBV

Expected results Part-Whole - βebv,gebv = 1 EBV 1000 daughters True BV GEBV

HOL Yield

HOL Yield (EBV-GEBV)

HOL Udder health

HOL Udder health (EBV-GEBV)

HOL - NTM

HOL NTM (EBV-GEBV)

How well do the bulls meet expectations? RDC & JER changed genomic model in AUG14. Changed to AM and cows in reference GEBVs calculated for 4 birth years of progeny tested bulls. Bulls own EBVs and daughters EBVs not in reference population.

RDC - NTM βebv,gebv a little low Tend to overestimate high GEBV Bulls

JER - NTM

Conclusion Results generally looks as expected. Average EBV lower as not Part-Whole. βebv,gebv for RDC NTM looks a little low. Quite different population structure. Best GEBV group get best avg. EBV. Action Keep focus that top bulls get expected results

Conclusion Lower threshold r 2 EBV than GEBV r 2 Larger STDV for (EBVref1-GEBV) HOL yield Actions NAV will increase thresholds for r 2 EBV breedwise depending on GEBV r 2 Combine daughter information and genomic information (Part-Whole)

r2 of mean EBV 5-8 Genomic tested bulls has same expected change in EBV as one progeny tested bull 1,00 0,90 0,80 0,70 0,60 0,50 0,40 1 2 3 4 5 6 7 8 No genomic bulls in calculated mean EBV Focus less on single bulls and more on a group of bulls (5-8)

Implemented GEBV in 2014 Date February 2014 March 2014 May 2014 July/August 2014 August 2014 November 2014 Comment Adjustment Holstein longevity US Jersey bulls included in ref population Publication age decreased from 17 month to 10 month Cows in reference populations and revised blending method applied for RDC and Jersey. Results from GMACE routine evaluation published Cows in reference populations and revised blending method applied for Holstein yield traits

Implementation plan GEBV 2015 Date February 2015 Spring 2015 Spring 2015 Comment Cows in reference populations and revised blending method applied for Holstein remaining traits All type traits at each evaluation GEBV for females Official GEBV reliabilities Spring/summer 2015 Young stock survival 2015 Weekly evaluations 2015 Cows in ref for more traits fertility, claw health, longevity

More frequent genomic prediction Aim GEBV for bull calves available at a younger age than today 1. Efficient registration of animal and collection of DNA (farmer and VG) 2. More frequent and faster genotyping (GenoSkan) 3. More frequent genomic prediction (NAV) Room for improvement in all 3 steps!

Steps in routine genomic prediction Step 1. NAV pedigree file Information from all three countries only new animals needed 2. Check of pedigree Has to be solved in the future pedigree assignment program 3. Genotypes from GenoSkan 4. Imputing (FImpute) Check of pedigree done here as well can handle that part until step 2 is solved 5. GBLUP/SNP BLUP 6. Blending 7. Publication

More frequent genomic prediction Requirement for weekly evaluations More automatic Establish a simple warning/error detecting system Maybe need for more computer capacity or SNP BLUP (require GMACE test run sept 2015)

Work ongoing Kevin Byskov and Martha Bo Almskou in cooperation with Timmy Gregers Madsen, AU built stepwise a system up. Aim to introduce more frequent evaluation during 2015

Applied research focus 2015 One step (MTT) Simultaneously use of phenotypes and genotypes in evaluation More optimal use of data Use of extra SNPs added on LD (AU) Include information that some SNPs carry more information than other SNPs

(G)MACE MACE use national EBVs based on phenotypes (G) GMACE use national GEBVs based on genotypes 58

MACE versus GMACE Breeding values from MACE and GMACE are expressed on the same scale and comparable on each country scale All bulls included in GMACE are expressed on all participating countries scale also scales for countries only participating in MACE.

What has to be published from GMACE? All countries participating in MACE have to publish: International EBVs for all Interbull traits Interbull EBVs can be EBVs from MACE or GEBV from GMACE

Which countries participate in GMACE? December routine Australia, Belgium, Canada (not Semex bulls), Germany, DFS, France, England, Italy, Holland, Poland

Groups of countries (e.g. NLD, FRA, DEU and DFS) exchange genotypes for young AI bulls A bull with a genotype in more countries get an GEBV with the same reliability as national bulls on all country scales within the exchange group. (equivalent to a situation that a bull has daughters in all countries no conversion by use of genetic correlation) and A slightly higher reliability on 3rd country scales than if information from GMACE is coming from one country only.

Y-index - MACE and GMACE MACE bulls born after 2007 GMACE bulls born >2010 Australia 97,5 106,0 Canada 101,1 110,9 (without Semex) Germany 101,3 111,4 DFS 104,7 111,3 France 104,3 109,2 England 101,1 109,6 Italia 97,4 107,2 Holland 103,9 111,6

Udder health - MACE and GMACE MACE bulls born after 2007 GMACE bulls born >2010 Australia 96,9 - Canada 94,4 109,4 (without Semex) Germany 95,8 110,1 DFS 101,5 110,8 France 95,1 110,7 England 96,6 109,3 Italia 95,7 108,2 Holland 97,0 110,4

Interbull GMACE Focus on top lists Proportion of top bulls primary depending on average and number of tested bulls. Results see NAV interbull search tool