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John B. Cole
Animal Genomics and Improvement Laboratory
Agricultural Research Service, USDA
Beltsville, MD 20705-2350
john...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Why do we need new phenotypes?
● Ch...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Source: Miglior et al. (2012)
Selec...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
New phenotypes should add informati...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Cost of measurement vs. value to fa...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Some novel phenotypes studied recen...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
What do US dairy farmers want?
● Na...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
What can farmers do with novel trai...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Trait Bias*
Reliability
(%)
Reliabi...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Constructing phenotypes from genoty...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Genotypes are abundant
0
100000
200...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Example: Polled cattle
● Polled cat...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Prediction of gene content
● The de...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Addition of polled to the Net Merit...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Validation of Jersey polled gene co...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Reasons for variation in gene conte...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Gene content for polled in Jerseys
...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Jersey polled merit
Group N ρ
All a...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Validation of Holstein polled gene ...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Allele content for polled in Holste...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Holstein polled merit
Group N ρ
All...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Allele content for DGAT1 in Jerseys...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Name Chrome Location (Mbp) Carrier ...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Conclusions
• New technology is ena...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Acknowledgments
• Dan Null and Paul...
Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole
Questions?
http://gigaom.com/2012/0...
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Using genotypes to construct phenotypes for dairy cattle breeding programs and beyond

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Modern dairying uses sophisticated data collection systems to maximize farm profitability. This has traditionally included information on cows and their environments, and now commonly includes genotype information from high-density single nucleotide polymorphism (SNP) panels. The US national database alone contains genotypes for 924,543 bulls and cows as of March 23, 2015, and many other countries are also genotyping animals. As the data continue to grow, the prospect of using genotypes to construct phenotypes directly, instead of measuring phenotypes on animals, becomes more attractive. There are many applications for this genomic information other than the prediction of breeding values. A notable recent application is the use of haplotypes in combination with next-generation sequencing data to identify causal variants associated with recessives. The methodology for identifying recessive haplotypes by searching for a deficit of homozygotes was first used in combination with sequence data to identify the causal variant (APAF1) associated with the HH1 haplotype. The US currently tracks 24 recessive haplotypes in four cattle breeds, and thanks to the work of several teams around the world the causal variants for 17 of them are known. The haplotypes include lethal recessive conditions, such as brachyspina, as well as hair coat color and polledness. There is growing interest in the latter to improve animal welfare and increase economic efficiency, but the polled haplotype has a very low frequency (0.41%, 0.93%, and 2.22% in Brown Swiss, Holstein, and Jersey, respectively). Increasing haplotype frequency by index selection requires known status for all animals. Gene content (GC) for non-genotyped animals was computed using records from genotyped relatives. Prediction accuracy was checked by comparing polled status from recessive codes and animal names to GC for 1,615 non-genotyped Jerseys with known status. 97% (n = 675) of horned animals were correctly assigned GC near 0, and 3% (n = 19) were assigned GC near 1. Heterozygous polled animals had GC near 0 (52%, n = 474) and near 1 (47%; n = 433), although 3 animals were assigned a GC near 2. All homozygous polled animals (n = 11) were assigned GC near 2. Genotype information can also be combined with other data, such as milk spectral data, to predict phenotypes for traits that are expensive or difficult to measure directly. These data can be used for precision farm management, including early culling decisions, monitoring of animals at risk for health problems, and identification of efficient and inefficient cows. The most substantial challenge faced by many dairy managers will be the effective use of the new phenotypes that now are available.

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

  1. 1. John B. Cole Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 john.cole@ars.usda.gov 2015 Using genotypes to construct phenotypes for dairy cattle breeding programs and beyond
  2. 2. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Why do we need new phenotypes? ● Changes in production economics ● Technology enables collection of new phenotypes ● Better understanding of biology ● Recent review by Egger-Danner et al. (2015) in Animal
  3. 3. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Source: Miglior et al. (2012) Selection indices now include many traits 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Australia - APR Belgium (Walloon) - V€G Canada - LPI France - ISU Germany - RZG Great Britain - PLI Ireland - EBI Israel - PD11 Italy - PFT Japan - NTP Netherlands - NVI New Zealand - BW Nordic Countries - TMI South Africa - BVI Spain - ICO Switzerland - ISEL United States - NM$ United States - TPI Protein (kg) Fat (kg) Milk (kg) Type Longevity Udder Health Fertility Others
  4. 4. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole New phenotypes should add information low high Genetic correlation with existing traits lowhigh Phenotypiccorrelation withexistingtraits Novel phenotypes include some new information Novel phenotypes include much new information Novel phenotypes contain some new information Novel phenotypes contain little new information
  5. 5. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Cost of measurement vs. value to farmers low high Cost of measurement lowhigh Valueofphenotype (milk yield) (greenhouse gas emissions) (feed intake) (conformation)
  6. 6. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Some novel phenotypes studied recently ● Claw health (Van der Linde et al., 2010) ● Dairy cattle health (Parker Gaddis et al., 2013) ● Embryonic development (Cochran et al., 2013) ● Immune response (Thompson-Crispi et al., 2013) ● Methane production (de Haas et al., 2011) ● Milk fatty acid composition (Soyeurt et al., 2011) ● Persistency of lactation (Cole et al., 2009) ● Rectal temperature (Dikmen et al., 2013) ● Residual feed intake (Connor et al., 2013)
  7. 7. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole What do US dairy farmers want? ● National workshop in Tempe, AZ in February ● Producers, industry, academia, and government ● Farmers want new tools ● Additional traits (novel phenotypes) ● Better management tools ● Foot health and feed efficiency were of greatest interest
  8. 8. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole What can farmers do with novel traits? ● Put them into a selection index ● Correlated traits are helpful ● Apply selection for a long time ● There are no shortcuts ● Collect phenotypes on many daughters ● Repeated records of limited value ● Genomics can increase accuracy
  9. 9. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Trait Bias* Reliability (%) Reliability gain (% points) Milk (kg) −80.3 69.2 30.3 Fat (kg) −1.4 68.4 29.5 Protein (kg) −0.9 60.9 22.6 Fat (%) 0.0 93.7 54.8 Protein (%) 0.0 86.3 48.0 Productive life (mo) −0.7 73.7 41.6 Somatic cell score 0.0 64.9 29.3 Daughter pregnancy rate (%) 0.2 53.5 20.9 Sire calving ease 0.6 45.8 19.6 Daughter calving ease −1.8 44.2 22.4 Sire stillbirth rate 0.2 28.2 5.9 Daughter stillbirth rate 0.1 37.6 17.9 Holstein prediction accuracy *2013 deregressed value – 2009 genomic evaluation
  10. 10. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Constructing phenotypes from genotypes ● Prediction from correlated traits or phenotypes from reference herds ● Haplotypes can be used when causal variants are not known ● Causal variants can be used in place of markers ● Specific combining abilities can combine additive and dominance effects
  11. 11. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Genotypes are abundant 0 100000 200000 300000 400000 500000 600000 700000 800000 NumberofGenotypes Run Date 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
  12. 12. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Example: Polled cattle ● Polled cattle have improved welfare and increased economic value ● polled haplotypes have low frequencies: 0.41% in BS, 0.93% in HO, and 2.22% in JE ● Increasing haplotype frequency by index selection requires known status for all animals ● Estimate gene content (GC) for all non- genotyped animals.
  13. 13. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Prediction of gene content ● The densefreq.f90 program (VanRaden) was modified to use the methodology of Gengler et al. (2007) ● Information from all genotyped relatives used ● Gene content is real-valued and continuous in the interval [0,2].
  14. 14. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Addition of polled to the Net Merit index ● $11.79 (€10.85) and $10.73 (€9.87) for costs of dehorning and polled genetics, respectively (Widmar et al., 2013) ● Haplotype count multiplied by $22.52 (€20.72) for genotyped animals ● Gene content multiplied by $22.52 (€20.72) for non-genotyped animals ● Rank correlations with 2014 NM$ compared for bulls and cows
  15. 15. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Validation of Jersey polled gene content ● Polled status from recessive codes and animal names compared to GC for 1,615 non-genotyped JE with known status. ● 97% (n = 675) of pp animals correctly assigned GC near 0 ● Pp animals had GC near 0 (52%, n = 474) and near 1 (47%; n = 433) ● All PP animals (n = 11) assigned GC of ~2.
  16. 16. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Reasons for variation in gene content ● The expectation for GC is near 1 for heterozygotes ● GC can be <1 if many polled ancestors have unknown status or when pedigree is unknown ● In those cases GC may be set to twice the allele frequency, which is low for polled ● Some animals with -P in the name may actually be PP, not Pp
  17. 17. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Gene content for polled in Jerseys MAF = 2.5% pp Pp PP
  18. 18. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Jersey polled merit Group N ρ All animals 2,471,025 0.99997 All cows 2,436,439 0.99997 All bulls 34,586 0.99990 Young bulls (G status) 380 0.99787
  19. 19. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Validation of Holstein polled gene content ● Polled status from recessive codes and animal names compared to GC for 1,615 non-genotyped JE with known status. ● 97% (n = 675) of pp animals correctly assigned GC near 0 ● Pp animals had GC near 0 (52%, n = 474) and near 1 (47%; n = 433) ● All PP animals (n = 11) assigned GC of ~2.
  20. 20. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Allele content for polled in Holsteins MAF = 1.07% pp Pp PP
  21. 21. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Holstein polled merit Group N ρ All animals 29,010,457 0.99999 All cows 28,769,803 0.99999 All bulls 240,654 0.99994 Young bulls (G status) 1,607 0.99966
  22. 22. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Allele content for DGAT1 in Jerseys MAF = 47.9%
  23. 23. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Name Chrome Location (Mbp) Carrier Freq Earliest Known Ancestor HH1 5 62-68 4.5 Pawnee Farm Arlinda Chief HH2 1 93-98 4.6 Willowholme Mark Anthony HH3 8 92-97 4.7 Glendell Arlinda Chief, Gray View Skyliner HH4 1 1.2-1.3 0.37 Besne Buck HH5 9 92-94 2.22 Thornlea Texal Supreme JH1 15 11-16 23.4 Observer Chocolate Soldier BH1 7 42-47 14.0 West Lawn Stretch Improver BH2 19 10-12 7.78 Rancho Rustic My Design AH1 17 65.9-66.2 26.1 Selwood Betty’s Commander Other phenotypes may come from genotypes For a complete list, see: http://aipl.arsusda.gov/reference/recessive_haplotypes_ARR-G3.html.
  24. 24. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Conclusions • New technology is enabling the collection of novel phenotypes • Genotypes are now routinely available for young animals • High-density SNP genotypes can be used to construct phenotypes directly
  25. 25. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Acknowledgments • Dan Null and Paul VanRaden, AGIL • Chuanyu Sun, Sexing Technologies • AFRI grant 1245-31000-101-05, “Improving Fertility of Dairy Cattle Using Translational Genomics”
  26. 26. Final OptiMIR Scientific and Expert Meeting, Namur, Belgium, April 17, 2015 (‹#›) Cole Questions? http://gigaom.com/2012/05/31/t-mobile-pits-its-math-against-verizons-the-loser-common-sense/shutterstock_76826245/

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