2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.

Slides:

Advertisements

Similar presentations

2007 Paul VanRaden, Mel Tooker, and Nicolas Gengler Animal Improvement Programs Lab, Beltsville, MD, USA, and Gembloux Agricultural U., Belgium

Advertisements

Genomic imputation and evaluation using 1074 high density Holstein genotypes P. M. VanRaden 1, D. J. Null 1 *, G.R. Wiggans 1, T.S. Sonstegard 2, E.E.

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD 2008 Genetic trends.

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Select Sires’

2007 Paul VanRaden and Jeff O’Connell Animal Improvement Programs Lab, Beltsville, MD U MD College of Medicine, Baltimore, MD

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2009 G.R. WiggansCroatian.

G.R. Wiggans 1, T.S. Sonstegard 1, P.M. VanRaden 1, L.K. Matukumalli 1,2, R.D. Schnabel 3, J.F. Taylor 3, J.P. Chesnais 4, F.S. Schenkel 5, and C.P. Van.

Wiggans, 2013RL meeting, Aug. 15 (1) Dr. George R. Wiggans, Acting Research Leader Bldg. 005, Room 306, BARC-West (main office);

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Georgetown Ag Systems.

2007 Paul VanRaden 1, Curt Van Tassell 2, George Wiggans 1, Tad Sonstegard 2, Jeff O’Connell 1, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4,

Mating Programs Including Genomic Relationships and Dominance Effects Chuanyu Sun 1, Paul M. VanRaden 2, Jeff R. O'Connell 3 1 National Association of.

2007 Paul VanRaden 1, George Wiggans 1, Curt Van Tassell 2, Tad Sonstegard 2, Jeff O’Connell 1, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4,

Changes in the use of young bulls K. M. Olson* 1, J. L. Hutchison 2, P. M. VanRaden 2, and H. D. Norman 2 1 National Association of Animal Breeders, Columbia,

Impacts of inclusion of foreign data in genomic evaluation of dairy cattle K. M. Olson 1, P. M. VanRaden 2, D. J. Null 2, and M. E. Tooker 2 1 National.

2007 J. B. Cole 1,*, P. M. VanRaden 1, J. R. O'Connell 3, C. P. Van Tassell 1,2, T. S. Sonstegard 2, R. D. Schnabel 4, J. F. Taylor 4, and G. R. Wiggans.

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD National Association.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD G.R. WiggansAg Discovery.

2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics.

Wiggans, th WCGALP (1) G.R. Wiggans*, T.A. Cooper, D.J. Null, and P.M. VanRaden Animal Genomics and Improvement Laboratory Agricultural Research.

2007 Paul VanRaden and Mel Tooker Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA

2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.

2007 Paul VanRaden, Mel Tooker, Jan Wright, Chuanyu Sun, and Jana Hutchison Animal Improvement Programs Lab, Beltsville, MD National Association of Animal.

2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2008 G.R. WiggansDHI-Provo.

John B. Cole, Ph.D. Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD, USA The U.S. genetic.

2005 Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD An Example from Dairy.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2009 G.R. WiggansGenomics:

T. A. Cooper and G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Council.

Adjustment of selection index coefficients and polygenic variance to improve regressions and reliability of genomic evaluations P. M. VanRaden, J. R. Wright*,

2007 Paul VanRaden, George Wiggans, Animal Improvement Programs Laboratory Curt Van Tassell, Tad Sonstegard, Bovine Functional Genomics Laboratory USDA.

2007 Paul VanRaden and Jan Wright Animal Improvement Programs Lab, Beltsville, MD 2013 Measuring genomic pre-selection in theory.

2007 Paul VanRaden Animal Improvement Programs Lab, USDA, Beltsville, MD, USA Pete Sullivan Canadian Dairy Network, Guelph, ON, Canada

Paul VanRaden, 1 Katie Olson, 2 Dan Null, 1 Mehdi Sargolzaei, 3 Marco Winters, 4 and Jan-Thijs van Kaam 5 1 Animal Improvement Programs Laboratory, ARS,

2007 Melvin Tooker Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD, USA

G.R. Wiggans 1, T.S. Sonstegard 1, P.M. VanRaden 1, L.K. Matukumalli 1,2, R.D. Schnabel 3, J.F. Taylor 3, F.S. Schenkel 4, and C.P. Van Tassell 1 1 Agricultural.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2009 G.R. WiggansCouncil.

G.R. Wiggans* and P.M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD

2007 Paul VanRaden, Melvin Tooker*, George Wiggans Animal Improvement Programs Laboratory 2009 Can you believe those genomic.

Wiggans, 2014ASAS-ADSA-CSAS Joint Annual Meeting (1) G.R. Wiggans* 1, T.A. Cooper 1, P.M. VanRaden 1, D.J. Null 1, J.L. Hutchison 1, O.M. Meland 2, M.E.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2008 AIPL Centennial.

2007 Paul VanRaden Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD, USA

Paul VanRaden and John Cole Animal Improvement Programs Laboratory Beltsville, MD, USA 2004 Planned Changes to Models and Trait Definitions.

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Select Sires’

2007 Paul VanRaden and Melvin Tooker* Animal Improvement Programs Laboratory 2010 Gains in reliability from combining subsets.

2007 Paul VanRaden 1, Jeff O’Connell 2, George Wiggans 1, Kent Weigel 3 1 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2 University of Maryland.

2007 Paul VanRaden 1, Jeff O’Connell 2, George Wiggans 1, Kent Weigel 3 1 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2 University of Maryland.

Duane Norman Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD NDHIA Board – 2009 (1)

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD G.R. WiggansSelect Sire.

2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.

G.R. Wiggans* 1, P.M. VanRaden 1, L.R. Bacheller 1, F.A. Ross, Jr. 1, M.E. Tooker 1, J.L. Hutchison 1, T.S. Sonstegard 2, and C.P. Van Tassell 1,2 1 Animal.

2007 John Cole, Paul VanRaden, George Wiggans, and Melvin Kuhn Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD,

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD G.R. WiggansADSA 18.

2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.

2007 Paul VanRaden Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA 2008 New.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Select Sires‘ Holstein.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2011 National Breeders.

2007 Paul VanRaden Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA 2007 Genetic evaluation.

John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Biological Insights.

2007 Paul VanRaden 1, Curt Van Tassell 2, George Wiggans 1, Tad Sonstegard 2, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4, Paul VanRaden 1, Curt.

G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2011 G.R. Wiggans DNA.

Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2014 Paul VanRaden Advancing.

Visualization of Results from Genomic Predictions

Distribution and Location of Genetic Effects for Dairy Traits

Methods to compute reliabilities for genomic predictions of feed intake Paul VanRaden, Jana Hutchison, Bingjie Li, Erin Connor, and John Cole USDA, Agricultural.

Genomic Evaluations.

Genomic Selection in Dairy Cattle

Can you believe those genomic evaluations for young bulls?

Percent of total breedings

Increased reliability of genetic evaluations for dairy cattle in the United States from use of genomic information Abstr.

Using Haplotypes in Breeding Programs

Presentation transcript:

2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional Genomics Laboratory USDA Agricultural Research Service, Beltsville, MD, USA Dairy Cattle Breeders Have Adopted Genomic Selection

Gordon Conference on Quantitative Genetics, Feb (2)Paul VanRaden 2009 How’s Your Genome?

Gordon Conference on Quantitative Genetics, Feb (3)Paul VanRaden 2009 Acknowledgments  Genotyping and DNA extraction: USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina  Computing: AIPL staff (Mel Tooker, Leigh Walton, Jay Megonigal)  Funding: National Research Initiative grants – , Agriculture Research Service Holstein and Jersey breed associations Contributors to Cooperative Dairy DNA Repository (CDDR)

Gordon Conference on Quantitative Genetics, Feb (4)Paul VanRaden 2009 CDDR Contributors  National Association of Animal Breeders (NAAB, Columbia, MO) ABS Global (DeForest, WI) Accelerated Genetics (Baraboo, WI) Alta (Balzac, AB, Canada) Genex (Shawano, WI) New Generation Genetics (Fort Atkinson, WI) Select Sires (Plain City, OH) Semex Alliance (Guelph, ON, Canada) Taurus-Service (Mehoopany, PA)

Gordon Conference on Quantitative Genetics, Feb (5)Paul VanRaden 2009 Genomics Timeline EventYear Dairy DNA repository began1992 Cattle genome sequenced ,000 SNP selectedMay 2007 Illumina SNP50 chip soldDec 2007 Prelim. genomic predictionsApr 2008 Official genomic predictionsJan 2009

Gordon Conference on Quantitative Genetics, Feb (6)Paul VanRaden 2009 SNP Edits and Counts Illumina SNP50 BeadChip58,336 Insufficient number of beads1,389 Unscorable SNP4,360 Monomorphic in Holsteins5,734 Minor allele frequency <5%6,145 Not in H-W equilibrium282 Highly correlated2,010 Used for genomic prediction38,416

Gordon Conference on Quantitative Genetics, Feb (7)Paul VanRaden 2009 Repeatability of Genotypes  2 laboratories genotyped the same 46 bulls About 1% missing genotypes per lab Mean of 98% SNP same (37,624 out of 38,416) – Range across animals of 20 to 2,244 SNP missing Mean of % SNP concordance (conflict <0.003%) Mean of 0.9 errors per 38,416 SNP – Range across animals of 0 to 7 SNP conflicts

Gordon Conference on Quantitative Genetics, Feb (8)Paul VanRaden 2009 Old Genetic Terms  Predicted transmitting ability and parent average PTA required progeny or own records PA included only parent data Genomics blurs the distinction  Reliability = Corr 2 (predicted, true TA) Reliability of PA could not exceed 50% because of Mendelian sampling Genomics can predict the other 50% Reliability limit at birth theoretically 99%

Gordon Conference on Quantitative Genetics, Feb (9)Paul VanRaden 2009 New Genetic Terms  Genomic vs. pedigree relationships Expected genes in common (A) Actual genes in common (G) Several formulas to compute G Wright’s (1922) correlation matrix or Henderson’s (1976) covariance matrix  Genomic vs. pedigree inbreeding Correlated by 0.68  Daughter merit vs. son merit (X vs. Y)

Gordon Conference on Quantitative Genetics, Feb (10)Paul VanRaden 2009 Differences in G and A G = genomic and A = pedigree relationships  Detected clones, identical twins, and duplicate samples  Detected incorrect DNA samples  Detected incorrect pedigrees  Identified correct source of DNA by genomic relationships with other animals

Gordon Conference on Quantitative Genetics, Feb (11)Paul VanRaden 2009 Genomic Evaluation Methods  Use Henderson’s mixed model  Replace A by G  Proposed by Nejati-Javaremi, Smith, Gibson, 1997 J. Anim Sci. 75:1738  Nonlinear regression, haplotyping or only slightly more accurate

Gordon Conference on Quantitative Genetics, Feb (12)Paul VanRaden 2009 Worldwide Dairy Genotyping as of January 2009 CountriesAnimals United States and Canada22,344 France8,500 Netherlands, New Zealand 1 6,000 New Zealand and Ireland4,500 Germany3,000 Australia2,000 Denmark, Finland, Sweden2,000 1 Using a customized Illumina 50K chip (different markers)

Gordon Conference on Quantitative Genetics, Feb (13)Paul VanRaden 2009 Phenotypes  26 traits plus the Net Merit index  The 6,184 bulls genotyped have >10 million phenotyped daughters (average 2,000 daughters per bull)  Most traits recorded uniformly across the world  Foreign data provided by Interbull

Gordon Conference on Quantitative Genetics, Feb (14)Paul VanRaden 2009 Genotyped Animals (n=22,344) In North America as of February 2009

Gordon Conference on Quantitative Genetics, Feb (15)Paul VanRaden 2009 Experimental Design - Update Holstein, Jersey, and Brown Swiss breeds HOLJERBSW Predictor: Bulls born <20004,4221, Cows with data Total5,3691, Predicted: Bulls born >20002, Data from 2004 used to predict independent data from 2009

Gordon Conference on Quantitative Genetics, Feb (16)Paul VanRaden 2009 Reliability Gain 1 by Breed Yield traits and NM$ of young bulls TraitHOJEBS Net merit2483 Milk2660 Fat32115 Protein2421 Fat % Protein % Gain above parent average reliability ~35%

Gordon Conference on Quantitative Genetics, Feb (17)Paul VanRaden 2009 Reliability Gain by Breed Health and type traits of young bulls TraitHOJEBS Productive life3272 Somatic cell score23316 Dtr pregnancy rate287- Final score202- Udder depth37203 Foot angle2511- Trait average2913N/A

Gordon Conference on Quantitative Genetics, Feb (18)Paul VanRaden 2009 Value of Genotyping More Animals Actual and predicted gains for 27 traits and for Net Merit BullsReliability Gain PredictorPredictedNM$27 trait avg Cows:

Gordon Conference on Quantitative Genetics, Feb (19)Paul VanRaden 2009 Simulation Results World Holstein Population  40,360 older bulls to predict 9,850 younger bulls in Interbull file  50,000 or 100,000 SNP; 5,000 QTL  Reliability vs. parent average REL Genomic REL = corr 2 (EBV, true BV) 81% vs 30% observed using 50K 83% vs 30% observed using 100K

Gordon Conference on Quantitative Genetics, Feb (20)Paul VanRaden 2009 Marker Effects for Net Merit

Gordon Conference on Quantitative Genetics, Feb (21)Paul VanRaden 2009 Significance Tests are Stupid

Gordon Conference on Quantitative Genetics, Feb (22)Paul VanRaden 2009 Insignificant SNP Effects  Traditional selection on PA 50 : 50 chance of better chromosome  1 SNP with tiny effect : chance  38,416 SNPs with tiny effects 70 : 30 chance  Only test overall sum of effects!

Gordon Conference on Quantitative Genetics, Feb (23)Paul VanRaden 2009 X, Y, Pseudo-autosomal SNPs 487 SNPs 35 SNPs 0 SNPs 35 SNPs

Gordon Conference on Quantitative Genetics, Feb (24)Paul VanRaden 2009 Net Merit by Chromosome for O-Man Top bull, +$778 Lifetime Net Merit

Gordon Conference on Quantitative Genetics, Feb (25)Paul VanRaden 2009 Progeny Tested Bull O-Man  Semen sales ~200,000 units / year  Semen price $40 / unit  Income > $5 million / year  40,144 daughters already milking 29,811 in United States 1,963 in France, 1,895 in Denmark, 1,716 in Italy, 839 in Holland, etc.

Gordon Conference on Quantitative Genetics, Feb (26)Paul VanRaden 2009 O-Man Daughters vs. Average Cows Trait O-Man daughter Average Holstein Milk (gallons/day) Protein (lbs/day) Cell count (1000/ml) Productive life (mo) Pregnancy rate (%) Calving difficulty (%)3%8%

Gordon Conference on Quantitative Genetics, Feb (27)Paul VanRaden 2009 Genomic Tested Bulls Available Jan 2009 Age (yrs)ReliabilityNet Merit Freddie Al Russell Alan O-Man

Gordon Conference on Quantitative Genetics, Feb (28)Paul VanRaden 2009 Adoption of Genomic Testing US young bulls purchased by AI companies Birth Year Bulls Sampled Bulls Tested Genomic Tested % 2008* * * counts are incomplete

Gordon Conference on Quantitative Genetics, Feb (29)Paul VanRaden 2009 Genetic Progress  Assume 60% REL for net merit Sires mostly 1-3 instead of 6 years old Dams of sons mostly heifers with 60% REL instead of cows with phenotype and genotype (66% REL)  Progress could increase by >50% 0.37 vs genetic SD per year Reduce generation interval more than accuracy

Gordon Conference on Quantitative Genetics, Feb (30)Paul VanRaden 2009 Low Density SNP Chip  Choose 384 marker subset SNP that best predict net merit Parentage markers to be shared  Use for initial screening of cows 40% benefit of full set for 10% cost Could get larger benefits using haplotyping (Habier et al., 2008)

Gordon Conference on Quantitative Genetics, Feb (31)Paul VanRaden 2009 Conclusions  High accuracy requires very many genotypes and phenotypes  Most traits are very quantitative (few major genes)  Genomic reliability > traditional 30-40% with traditional parent average 60-70% using 8,100 genotyped Holsteins 81-83% from 40,000 simulated bulls