Genomic evaluation of dairy cattle health
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This document summarizes research on genomic evaluation of dairy cattle health traits. It discusses challenges in evaluating health traits including low heritability and inconsistent definitions. The researchers conducted single and multiple trait genetic and genomic analyses on health event data from over 1 million US dairy cow records. Heritability estimates for various health events ranged from 0.03 to 0.20. Genomic evaluation allowed more accurate prediction of sires' daughters' probabilities of different health events compared to traditional genetic evaluation. The multiple trait genomic analysis found moderate to strong genetic correlations between some health events.
Genomic evaluation of dairy cattle health
- 1. J. B. Cole1, K. L. Parker Gaddis2, J. S. Clay3, & C. Maltecca2 1Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350, USA 2Department of Animal Science North Carolina State University Raleigh, NC 27695-7621, USA 3Dairy Records Management Systems, Raleigh, NC, USA john.cole@ars.usda.gov Genomic evaluation of dairy cattle health
- 2. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (2) Cole et al. What are health and fitness traits? l Health and fitness traits do not generate revenue, but they are economically important because they impact other traits. l Examples: l Poor fertility increases direct and indirect costs (semen, estrus synchronization, etc.). l Susceptibility to disease results in decreased revenue and increased costs (veterinary care, withheld milk, etc.)
- 3. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (3) Cole et al. Trait Relative emphasis on traits in index (%) PD$ 1971 MFP$ 1976 CY$ 1984 NM$ 1994 NM$ 2000 NM$ 2003 NM$ 2006 NM$ 2010 Milk 52 27 –2 6 5 0 0 0 Fat 48 46 45 25 21 22 23 19 Protein … 27 53 43 36 33 23 16 PL … … … 20 14 11 17 22 SCS … … … –6 –9 –9 –9 –10 UDC … … … … 7 7 6 7 FLC … … … … 4 4 3 4 BDC … … … … –4 –3 –4 –6 DPR … … … … … 7 9 11 SCE … … … … … –2 … … DCE … … … … … –2 … … CA$ … … … … … … 6 5 Increased emphasis on functional traits
- 4. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (4) Cole et al. Challenges with health and fitness traits l Lack of information l Inconsistent trait definitions l No national database of phenotypes l Low heritabilities l Many records are needed for accurate evaluation l Rates of change in genetic improvement programs are low
- 5. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (5) Cole et al. What does “low heritability” mean? P = G + E The percentage of total variation attributable to genetics is small. • CA$: 0.07 • DPR: 0.04 • PL: 0.08 • SCS: 0.12 The percentage of total variation attributable to environmental factors is large: • Feeding/nutrition • Housing • Reproductive management
- 6. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (6) Cole et al. 0 0.5 1 1.5 2 2.5 3 1 2 3 4 5 6 7 8 9 10 11 12 2010 2011 2012 2013 Why are these traits important? M:FP = price of 1 kg of milk / price of 1 kg of a 16% protein ration Month Milk:FeedPriceRatio April 2013 Grain Costs Soybeans: $14.20/bu (€0.41/kg) Corn: $ 6.67/bu (€0.20/kg)
- 7. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (7) Cole et al. How does genetic selection work? ΔG = genetic gain each year reliability = how certain we are about our estimate of an animal’s genetic merit (genomics can é) selection intensity = how “picky” we are when making mating decisions (management can é) genetic variance = variation in the population due to genetics (we can’t really change this) generation interval = time between generations (genomics can ê)
- 8. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (8) Cole et al. Incidence of disease in on-farm data 0 10 20 30 40 Literature Incidences by Health Event Reported Literature Incidence CALC CYST DIAR DIGE DSAB DYST KETO LAME MAST METR RESP RETP The red asterisk indicates the mean ID/LIR from the data over all lactations. The box plots represent the ID/LIR based on literature estimates (figure from Parker Gaddis et al., 2012, J. Dairy Sci. 95:5422–5435).
- 9. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (9) Cole et al. Health event data for analysis Health event Records Cows Herd-years Cystic ovaries 222,937 131,194 3,369 Digestive disorders 156,520 97,430 1,780 Displaced abomasum 213,897 125,594 2,370 Ketosis 132,066 82,406 1,358 Lameness 233,392 144,382 3,191 Mastitis 274,890 164,630 3,859 Metritis 236,786 139,818 3,029 Reproductive disorders 253,272 151,315 3,360 Retained placenta 231,317 138,457 2,930
- 10. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (10) Cole et al. Genetic and genomic analyses Single-trait genetic Multiple-trait genetic Multiple-trait genomic MAST, METR, LAME, KETO, RETP, CYST, DSAB 1) MAST, METR, LAME, KETO 2) RETP. CYST, DSAB Fixed parity, year-season Random sire, herd-year Numerator relationship matrix, A Blended matrix, H ASReml THRGIBBS1F90 Genetic analyses included only pedigree and phenotypic data. Genomic analyses included genotypic, pedigree, and phenotypic data.
- 11. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (11) Cole et al. Methods: Single-trait genetic analysis Estimate heritability for common health events occurring from 1996 to 2012 Similar editing applied US records Parities 1 to 5 Minimum/maximum constraints Lactations lasting up to 400 days Parity considered first versus later
- 12. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (12) Cole et al. Methods: Multiple-trait genomic analyses Multiple-trait threshold sire model using single-step methodology (Aguilar et al., 2011) THRGIBBS1F90 with genomic options Default genotype edits used − 50K SNP data available for 7,883 bulls − Final dataset included 37,525 SNP for 2,649 sires
- 13. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (13) Cole et al. Results: Single-trait genetic analyses 0 5 10 15 20 25 30 35 CYST DIGE DSAB KETO LAME MAST METR REPR RETP Lactational Incidence Rate for 10 best and worst sires’ daughters LactationalIncidenceRate(%) Health Event LIR for 10 worst sires’ daughters LIR for 10 best sires’ daughters
- 14. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (14) Cole et al. Results: Single-trait genetic analyses Health Event Heritability Standard Error Cystic ovaries 0.03 0.006 Digestive disorders 0.06 0.02 Displaced abomasum 0.20 0.02 Ketosis 0.07 0.01 Lameness 0.03 0.005 Mastitis 0.05 0.006 Metritis 0.06 0.007 Respiratory disorders 0.04 0.01 Reproductive disorders 0.03 0.006 Retained placenta 0.07 0.01
- 15. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (15) Cole et al. Results: Single-trait genetic analyses 0 50 100 150 200 250 300 350 CYST DIGE DSAB KETO LAME MAST METR REPR RETP Number of sires with reliability > 0.5 Health Event Numberofsires
- 16. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (16) Cole et al. Results: Single-trait genetic analyses Sire posterior mean of daughters’ probability to each disease
- 17. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (17) Cole et al. Results: Multiple-trait genetic analysis Mastitis Metritis Lameness Retained placenta Cystic ovaries Ketosis Displaced abomasum Mastitis 0.10 (0.09, 0.12) Metritis -0.30 (-0.45, -0.15) 0.04 (0.03, 0.05) Lameness -0.29 (-0.46, -0.11) 0.21 (0, 0.45) 0.019 (0.01,0.03) Retained placenta 0.01 (-0.14, 0.16) 0.78 (0.68, 0.88) -0.14 (-0.36, 0.07) 0.05 (0.03, 0.06) Cystic ovaries -0.09 (-0.29, 0.13) -0.17 (-0.37, 0.06) -0.19 (-0.40, -0.06) -0.12 (-0.34, 0.12) 0.026 (0.02, 0.03) Ketosis -0.28 (-0.47, -0.07) 0.45 (0.26, 0.64) 0.08 (-0.17, 0.34) 0.10 (-0.17, 0.35) -0.15 (-0.367, 0.13) 0.08 (0.05, 0.11) Displaced abomasum 0.005 (-0.15, 0.17) 0.44 (0.28, 0.60) -0.10 (-0.29, 0.09) 0.06 (-0.12, 0.25) -0.10 (-0.31, 0.10) 0.81 (0.70, 0.92) 0.13 (0.11, 0.16) Estimated heritabilities (95% HPD) on diagonal and estimated genetic correlations (95% HPD) below diagonal.
- 18. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (18) Cole et al. Results: Multiple-trait genomic analysis Mastitis Metritis Lameness Retained placenta Cystic ovaries Ketosis Displaced abomasum Mastitis 0.12 (0.10, 0.14) Metritis -0.36 (-0.53, -0.19) 0.04 (0.027, 0.043) Lameness 0.13 (-0.1, 0.34) 0.026 (0.015, 0.034) Retained placenta 0.04 (0.03, 0.05) Cystic ovaries -0.02 (-0.22, 0.16) 0.03 (0.01, 0.04) Ketosis -0.16 (-0.31, 0.01) 0.44 (0.26, 0.64) 0.08 (0.05, 0.10) Displaced abomasum 0.01 (-0.21, 0.16) -0.11 (-0.29, 0.13) 0.12 (0.09, 0.14) Estimated heritabilities (95% HPD) on diagonal and estimated genetic correlations (95% HPD) below diagonal.
- 19. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (19) Cole et al. Reliability with and without genomics Event EBV Reliability GEBV Reliability Gain Displaced abomasum 0.30 0.40 +0.10 Ketosis 0.28 0.35 +0.07 Lameness 0.28 0.37 +0.09 Mastitis 0.30 0.41 +0.11 Metritis 0.30 0.41 +0.11 Retained placenta 0.29 0.38 +0.09 Mean reliabilities of sire PTA computed with pedigree information and genomic information, and the gain in reliability from including genomics.
- 20. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (20) Cole et al. What do we do with these PTA? Focus on diseases that occur frequently enough to observe in most herds Put them into a selection index Apply selection for a long time There are no shortcuts Collect phenotypes on many daughters Repeated records of limited value
- 21. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (21) Cole et al. Conclusions The data stored in on-farm computer systems are useable for genetic evaluation We can compute PTA for bulls with many daughters Genomics improves reliabilities Multiple-trait analysis may help improve reliabilities
- 22. 2013 ICAR Health Data Conference, Aarhus, Denmark, 31 May 2013 (22) Cole et al. Questions? http://gigaom.com/2012/05/31/t-mobile-pits-its-math-against-verizons-the-loser-common-sense/shutterstock_76826245/