1. Selection and Application – Making More Profitable Holsteins (male selection) North American Perspective Marjorie Faust and Katie Olson USA

2. Making More Profitable Holsteins with Genomics  What is the current state of the science?  How is the technology being applied and used in the US?  What are the challenges and opportunities for genomics going forward to ensure that Holsteins can deliver on producers’ demands for production efficiency & value for money?

3. Making and Delivering More Profitable Holsteins  Tremendous progress has been achieved in the US through industry cooperation and collaboration, pre- genomics and now into the genomic era –Milk recording organizations – DHI, Milk Testing Labs, DPRCs –Genetic Evaluation Unit – USDA-AIPL –Cooperative Extension –Breed Associations –Dairy Breeders –Universities –AI Organizations

4. Genomic Evaluations in North America  Released publicly in 2009  Rapid adoption into breeding programs USDA-AIPL, 2012 >68,000 males >230,000 both sexes

5. Reliability for Genomic Evaluations in the US  Young sires have always been a good source of genetics and by 2012, genomics provide more accurate (but still imperfect) tools for identifying the best of the best TraitTraditionalGenomic Genomic- Traditional * Protein Yield3575+40 Productive Life, mo.2672+46 Somatic Cell Score3076+45 Daughter Pregnancy Rate, %2671+45 Type Final Score3275+42 Calving Ease3357+24 * Based on results from 44,950 Holstein young bulls.

6.  Availability of gPTAs –Updated monthly –If male and <2 years of age, lists are not publicly available –Lists for proven bulls 3x per year  How are genomic evaluations being used? –Females 25-30% of genotypes are 50k or larger chip Used for female culling and management Used for breeding purposes, including bull-dam selection –Males ~90% of genotypes are 50k or larger chip Use for screening is increasing Application of Genomic Evaluations in the US

7.  Young bulls as sires of sons –Pre-genomics ~10% or lower –By 2012, ~40% and growing –For bull calves born in 2011, sires are 23 months younger than for bull calves born in 2006  Bull dams –Pre-genomics: Dominated by 2-year old cows and older –Post-genomics: Many more heifer contracts More ownership of females by AI companies More contracts to heifers out of young sires More flushing of heifers, and IVF work with pre-puberty heifers By 2012, genomic estimates drive value in elite breeding programs – currently, the race is for the big numbers! Impact of Genomics on Bull Selection Decisions

8.  With the increase in the use of young bulls, there has been a concurrent increase in the number of sires of AI candidate bulls  Is this a true increase in sire families represented? Is the intent to find outcrosses or spread risk? Finding high ranking outcrosses still a challenge  Will semen sales also reflect an increase in sire families represented?  Does this represent a real trend that will continue? More Sires Represented in AI Candidate Bull Populations Number of Sires for Candidate Bulls Birth Year of Young Bulls USDA-AIPL, 2012 Proven Bulls Young Bulls

9.  30-50% of inseminations to young bulls represent traditional Progeny Test  Use of proven bulls has declined from ~70% to 50% of inseminations  First crop bull usage has declined and been replaced by young bulls  What will farmers demand? Where will young/proven bull usage stabilize? US Holstein Breeding Programs Using More Young Sires Total Inseminations Year of Insemination USDA-AIPL, 2012

10. Balancing the Economics of Increased Young Sire Usage  “Why would I pay $30 for older bulls when I can get high ranking genomic young bulls for $15 per dose?” (US Dairy Breeder comment, Oct 2012) – Semen price tends to be lower than proven bulls of equal genetic merit – Supply and demand should fix this but hasn’t done so in ~4 years  AI companies still working through strategies to produce young sire product – Young sires produce much lower volume of semen than proven bulls – Market life is shorter (no “branding” of bulls; faster turnover) – For same volume of semen sales, need more production stalls – More bulls required to produce product may reduce selection intensity  Acquisition cost per bull up ~50%-100% and climbing rapidly – More leases on extreme bulls (breeder retains ownership) – More bonus potential on extreme bulls  Production costs per dose of semen are considerably higher for young bulls

11.  By 2010-2011, essentially all major US AI organizations scaled back on progeny test programs in one form or another –Several companies significantly reduced number of bulls sampled –Others reduced bull numbers and doses of progeny test semen distributed per bull –Bull numbers appear to have rebounded recently, possibly to meet market demands for more doses of product?  Collection of Breed Association linear type data on young sire progeny has evolved with genomics (Holstein Pulse, Summer 2011 and 2012) –>20% drop in the number of young sire daughters evaluated –Fewer sires represented, but many more progeny for some bulls  AI companies still progeny testing bulls, but this may change –Potential reduction in amount of phenotypic data (type) –Decision will largely be driven by farmer demand for proven bulls Impact of Genomics on Progeny Test Programs

12. 4 Years into Genomic Era – Is there value in progeny data?  NM$ results for top 100 young and proven bulls from April 2010 Evaluation  No change in NM$ formula over this time period  For high ranking young bulls, PA & gPA overestimated daughter performance  Progeny and performance data continue to add necessary information

13.  High quality data are necessary for accurate, reliable evaluations. Current methodology requires unselected and unbia1sed data  As the industry continues to evolve, how do industry players share responsibility for ensuring that sufficient high quality data are collected? The Challenge of Collecting Data in the Genomics Era... “Fewer sires represented, but many more progeny for some bulls” (Holstein Pulse, Summer 2011 and 2012) Name Aug-09 TPI SM Aug-12 TPI SM HerdsDtrs Aug-09 PTAT Aug-12 PTATDtrs-Type PINE-TREE MARTHA SHOLTEN - ET221618022676312.931.13103 MS ATLEES SHT AFTERSHOCK - ET227018092444104.953.32105 GILLETTE JOB207517462403493.141.4293 BADGER ONESHOT - ET206917781682623.351.44214 LANGS-TWIN-B JANUARY - ET200418301242482.921.72135 DE-SU GILLESPY - ET227721571442274.152.68176 LADYS-MANOR RUBY D SHOUT - ET210719201112013.422.06159 A-L-H ANTONIO - ET19601665831683.551.7577 MOUNTFIELD MELVILLE - ET1869180258962.461.7268 HILROSE FREELANCE TACOMA - ET1611149959853.141.7242

14. Challenges and Opportunities of Genomics for Inbreeding  Who is related to who? Success story for genomics!  Who should be the parents of the next generation – balancing genetic progress and inbreeding? Jury is still out on the impact of genomics.  After selection, who should be mated to who? Genomic tools offer promise.

15.  Across time, more traits and a wider range of traits included –Many newer traits are more challenging for making genetic progress Broadening the Selection Goal for Holstein Profitability 1980s TPI ℠ 1990s TPI ℠ Today’s TPI℠ Production Non-Production

16. Opportunity Presented by Low Heritability Traits  Traits with low heritability have always presented a challenge for making more profitable Holsteins  Making measurable progress quickly is more difficult because it is challenging to identify the best parents on a genetic level

17. Challenge of Making Faster Genetic Progress  History teaches that even lowly heritable traits respond to intense selection on correlated traits – for example, genetic decline in fitness traits resulting from single trait selection on production traits  Are we measuring and monitoring all of the important/correlated traits?

18. Challenge and Opportunity of Genomics for New Traits  What traits need to be protected when selecting for traits like efficiency?  Think of genomics as providing a Formula 1 race car. Driving a Formula 1 race car can get us to the destination faster, but a crash at 150 miles/hour is considerably more painful than a crash at 20 miles/hour Efficiency Intake Production, fertility, growth, immunity, etc.

19.  As an industry, how do we share the responsibility of investing in the collection of high quality, unselected and unbiased data? –Necessary to recalibrate SNP effects (SNP key) –Minimize time gap between real data and selection candidates –Include data on families that are most relevant to the candidate group –Needed for selection traits and correlated traits which need to be protected  Methodology solutions needed in next steps of genomics developments –Improved comparability across age groups for both sexes Rankings within age groups do not appear to be biased, but more difficult to compare genetic rankings across age groups –Dampening or minimizing the impact of potential bias from female side –Avoiding bias which arises from genomic pre-selection –Need the help of genetic evaluation units and academia Challenges and Opportunities in the Genomic Era

20.  Genomics is providing more accurate (but still imperfect) tools for identifying the best of the best at a younger age  As genomic tools have been improved, more emphasis on younger and younger parents and grandparents of both sexes –Seeing use of young sires pre-release in IVF done with pre-puberty heifers out of young sires (3 generations removed from performance data) –For breeding programs, less concern with reliability than in the past  Genetic diversity continues to require attention for both sexes –Genomics can be both beneficial and detrimental for genetic diversity –Must protect against real risks of increasing inbreeding via genomics  Continued methodology improvements and developments needed to ensure that genomic technologies remain both accurate and useful Conclusions

21.  For AI companies, lots of cost going in, but not reflected in increased revenue (yet) –Semen price tends to be lower than proven bulls of equal genetic merit –Current option for AI companies is to cut cost. No PT. Less data  Driving the Formula 1 race car of genomics can get us to the destination faster, but a crash at 150 miles/hour is considerably more painful than a crash at 20 miles/hour –Progeny and performance data continue to add necessary information –The need to invest in phenotypic data is not shrinking, but growing and expanding  Producers demand production efficiency & value for money, and the industry shared responsibility is to make sure that Holsteins can deliver! Conclusions