Genomic selection in cattle industry: achievements and impact Dr. Fritz Schmitz-Hsu Senior Geneticist Swissgenetics CH-3052 Zollikofen Scientific Seminar WBFSH 2016 1
Source: https://i.ytimg.com/vi/cuvjxlhu79a/hqdefault.jpg Scientific Seminar WBFSH 2016 2
Structure Genomics: What is it? Application of genomics in cattle breeding Achievements and impact experiences Conclusions Scientific Seminar WBFSH 2016 3
Genomics: What is it? Scientific Seminar WBFSH 2016 4
Influence of the chromosome region on milk yield VanRaden (2008) Scientific Seminar WBFSH 2016 5
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 Scientific Seminar WBFSH 2016 6
The basics of genomic selection Markers are here differences in one single base pair = Single Nucleotide Polymorphism (SNP) Scientific Seminar WBFSH 2016 7
SNPs can be analyzed in the lab rather cheaply Scientific Seminar WBFSH 2016 8
Sample Blood Semen Hair bulbs Nasal swabs Ear tissue Scientific Seminar WBFSH 2016 9
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) Scientific Seminar WBFSH 2016 10
Genomic evaluation: Uses both traditional and genomic information Recordings of performance, type traits etc. Data on relationship Molecular genetic markers Statistical procedures Genomically enhanced Estimated Breeding Values GEBVs Scientific Seminar WBFSH 2016 11
Or simplified Traditionally estimated breeding value Direct Genomic Value (DGV) Genomically enhanced Estimated Breeding Value (GEBV) Scientific Seminar WBFSH 2016 12
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 Scientific Seminar WBFSH 2016 13
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 Scientific Seminar WBFSH 2016 14
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 Scientific Seminar WBFSH 2016 15
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 Scientific Seminar WBFSH 2016 16
Enormous increase of genotyped cattle No. of genotyped cattle in the US (Cole, 2016) 800 000 700 000 600 000 500 000 400 000 Imputed, Young Imputed, Old <50k, Young, Female <50k, Young, Male <50k, Old, Female <50k, Old, Male 50k, Young, Female 50k, Young, Male 50k, Old, Female 50k, Old, Male 300 000 200 000 100 000 0 Scientific Seminar WBFSH 2016 17
Genomic selection gives for young animals breeding values with much higher reliability Reliability increase compared to pedigree index (Cole, 2016, modified) Reliability gain (% points) Trait Bias* Reliability (%) 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 Scientific Seminar WBFSH 2016 18
What is a genotype worth? (Cole, 2016) Pedigree is equivalent to information on about 7 daughters For protein yield (h 2 =0.30), the SNP genotype provides information equivalent to an additional 34 daughters Scientific Seminar WBFSH 2016 19
What is a genotype worth? (Cole, 2016) And for daughter pregnancy rate (h 2 =0.04), SNP = 131 daughters Scientific Seminar WBFSH 2016 20
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 progenytested bulls for large scale marketing Scientific Seminar WBFSH 2016 21
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 genomically tested bull (Lay-off) (Marketing as progeny-tested bull) Scientific Seminar WBFSH 2016 22
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 Scientific Seminar WBFSH 2016 23
Trend to using younger bulls shorter generation interval García-Ruiz et al. (2016), US Holstein SB = Sire Bull; SC = Sire Cow; DB = Dam Bull; DC = Dam Cow Scientific Seminar WBFSH 2016 24
Average net merit ($) Genomic selection enables an increased genetic progress Cole (2015): 800 700 600 500 400 300 200 100 0-100 Average gain: $19.77/year Average gain: $52.00/year 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 Year entered AI Average gain: $85.60/year Scientific Seminar WBFSH 2016 25
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 Scientific Seminar WBFSH 2016 26
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 Scientific Seminar WBFSH 2016 27
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 animals are 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 Scientific Seminar WBFSH 2016 28
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 Scientific Seminar WBFSH 2016 29
Trends Race of reducing the generation interval even more more 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 Scientific Seminar WBFSH 2016 30
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 Scientific Seminar WBFSH 2016 31
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) Scientific Seminar WBFSH 2016 32
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 Scientific Seminar WBFSH 2016 33
Thank you for your attention! Questions? animalszooguru.blogspot.com 34