genomic selection in cattle industry: achievements and … selection in cattle industry:...

Genomic selection in cattle industry: achievements and impact

Dr. Fritz Schmitz-HsuSenior Geneticist

Swissgenetics

CH-3052 Zollikofen

1Scientific Seminar WBFSH 2016

Scientific Seminar WBFSH 2016 2

Source: https://i.ytimg.com/vi/CUvJxLhu79A/hqdefault.jpg

https://i.ytimg.com/vi/CUvJxLhu79A/hqdefault.jpg

Structure

Genomics: What is it?

Application of genomics in cattle breeding

Achievements and impact experiences

Conclusions


Genomics: What is it?


Influence of the chromosome region on milk yield


VanRaden (2008)

What is the difference between marker and gene tests, and genomic selection?

Lab test on a specific location

Gene test (if the causal mutation is known and tested 100 % accuracy)

Marker test (if the causal mutation is not known, but a marker exists accuracy < 100 %)

Genomic selection: Analysis of many thousands of markers spread over the whole genome


The basics of genomic selection

Markers are here differences in one single base pair= Single Nucleotide Polymorphism (SNP)


SNPs can be analyzed in the lab rather cheaply


Sample

Blood

Semen

Hair bulbs

Nasal swabs

Ear tissue


Genomic evaluation combines several information sources

Marker information

+ estimation equations

Direct Genomic Value (DGV)

Pedigree Index

own performance (if available)

performance of progeny (if available)


Genomically

enhanced

Estimated

Breeding

Value

(GEBV)

http://upload.wikimedia.org/wikipedia/commons/8/81/ADN_animation.gif

http://upload.wikimedia.org/wikipedia/commons/8/81/ADN_animation.gif

Genomic evaluation: Uses both traditional and genomic information


Recordings of performance, type

traits etc.

Data on relationship

Statistical procedures

Genomically enhanced Estimated Breeding Values

GEBVs

Molecular genetic markers

Or simplified


Direct Genomic Value (DGV)

Traditionally estimated breeding value

Genomically enhanced Estimated Breeding Value (GEBV)

Fast adoption in dairy cattle breeding

2001 Theoretical framework by Meuwissen et al.

2006 Schaeffer presents model calculations showing the large potential of genomic selection

2008 - 2010 Genomic selection implemented in the principal countries


Why dairy cattle breeding got so fast into genomic selection

Artificial insemination very common concentration on relatively few bulls

Long generation interval and huge costs for developing a progeny tested bull

Well established performance recording and genetic evaluation in place

Industry and research organizations were willing and had the means to invest into this new technology

DNA of ancient key bulls was still available


Genomic selection in Switzerland

Joint development project of the Swiss breeding associations and the AI industry

For Brown Swiss, Red & White and Holstein

First Direct Genomic Values (DGV) published in Dec 2009

Genomically enhanced estimated breeding values (GEBV) are official since Dec 2010

Interbull validation passed in June 2011

For Original Braunvieh official since Aug 2015, for Simmental starting April 2017


Costs for genotyping dairy cattle in Switzerland

Low Density Chip ( 30'000 SNP): CHF / US $ 138.00

• CHF / US$ 60.00 for the lab

• CHF / US$ 78.00 for calculating the GEBVs

Medium Density Chip 150'000 SNP: CHF / US $ 185.00

• CHF / US$ 110.00 for the lab

• CHF / US$ 75.00 for calculating the GEBVs


Enormous increase of genotyped cattle

No. of genotyped cattle in the US (Cole, 2016)


0

100 000

200 000

300 000

400 000

500 000

600 000

700 000

800 000

…

Imputed, YoungImputed, Old<50k, Young, Female<50k, Young, Male<50k, Old, Female<50k, Old, Male50k, Young, Female50k, Young, Male50k, Old, Female50k, Old, Male

Genomic selection gives for young animals breeding values with much higher reliability

Reliability increase compared to pedigree index (Cole, 2016, modified)


Trait Bias* Reliability (%)Reliability gain

(% points)

Milk (kg) −80 69 +30

Protein (%) 0.0 86 +48

Productive life (mo) −0.7 74 +42

Somatic cell score 0.0 65 +29

Daughter pregnancy rate (%) 0.2 54 +21

Sire calving ease 0.6 46 +20

Sire stillbirth rate 0.2 28 +6

Type traits -0.2 - +0.2 44 - 75 +13 - +38

*2013 deregressed value – 2009 genomic evaluation

What is a genotype worth?(Cole, 2016)


For protein yield (h2=0.30), the SNP genotype provides information equivalent to an additional 34 daughters

Pedigree is equivalent to information on about 7 daughters

What is a genotype worth?(Cole, 2016)


And for daughter pregnancy rate (h2=0.04), SNP = 131 daughters

The way to a new AI bull- before genomic selection

Year 0 1 2 3 4 5 6


Selecting parents planned mating

Buying 1 out of 2 - 3 bull calves

Rearing, producing ~5000 straws

Field test ~80 daughters

Lay-off Selecting 1 out of 6 - 10 progeny-tested bulls for large scale marketing

The way to a new AI bull- with genomic selection

Year 0 1 2 3 4 5 6


Selecting parents planned mating

Genotyping,buying 1 out of 10 - 20 bull calves

Rearing, producing >5000 straws

Marketing as genomicallytested bull

(Lay-off) (Marketing as progeny-tested bull)

Much more younger bulls are used in Artificial Insemination (AI)

No. of marketed AI Holstein bulls in the US (Cole, 2015)


Year entered AI Traditional progeny tested

Genomic marketed

All bulls

2008 1'768 170 1'938

2009 1'474 346 1'820

2010 1'388 393 1'781

2011 1'254 648 1'902

2012 1'239 706 1'945

2013 907 747 1'654

2014 661 792 1'453

Trend to using younger bulls shorter generation interval

García-Ruiz et al. (2016), US HolsteinSB = Sire Bull; SC = Sire Cow; DB = Dam Bull; DC = Dam Cow


Genomic selection enables an increased genetic progress

Cole (2015):


-100

0

100

200

300

400

500

600

700

800

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

Ave

rage

net

mer

it (

$)

Year entered AI

Average gain:$19.77/year



Other effects of genomic selection

Initially, in many countries only large AI companies invested into genomic selection

They kept the GEBVs of males for themselves

Concentration on a few companies, breeders lost information


Other effects of genomic selection

A large reference population of well-proven bulls from which the effects of the individual SNPs are computed, is essential

Genomic selection still only works for large breeds / populations

Multi-country consortiums sharing genotypes were formed

Several new genetic defects (recessives) affecting fertility were discovered


Hopes in genomic selection,and reality

Hope: New lines and families with large potential are discovered

Reality: Still a few top families dominate, but top animalsare quickly bypassed by even better ones

Hope: Reduced inbreeding thanks to broader selection base

Reality: With the focus on few animals, inbreeding still has to be monitored carefully, but genomics give more insight


Hopes in genomic selection,and reality

Hope: Gain in reliability of GEBVs by using more SNPs or even sequence data

Reality: Up to now only minor increases

Hope: SNP effects estimated in one breed can be successfully applied in other breeds

Reality: Does not work satisfactorily each breed needs an own reference population


Trends

Race of reducing the generation interval even moremore embryo transfer, ovum pick-up (OPU), in vitro

fertilization (IVF), semen sexing, genotyping embryos

GEBVs as a herd management tool which females to rear

Application in small breeds

Application of sequence data

Novel traits (health traits, feed efficiency, methane emission etc.)

Cows as an additional source for the reference population


Conclusions

Genomic selection is in dairy cattle breeding the most important technology since the introduction of artificial insemination

It accelerates very substantially the genetic progress due to reduced generation interval and increased reliability of the estimated breeding values

It works for all traits


Conclusions

Dairy cattle breeding was ideal for introducing this technology due to

well established performance recording

widely used artificial insemination

population structure

long generation interval

well organized breeding organizations and breeding programs

a favorable industry framework (close collaboration)


Conclusions

Genomic selection requires

the availability of enough clearly defined phenotypes

a large reference population of proven animals to estimate the SNP effects

a substantial investment to establish it

a continued genetic evaluation based on progeny performance


Thank you for your attention!

Questions?

34

animalszooguru.blogspot.com

genomic selection in cattle industry: achievements and … selection in cattle industry:...

Documents