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

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

2002 Paul M. VanRaden and Ashley H. Sanders Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
2007 Paul VanRaden and Jeff O’Connell Animal Improvement Programs Lab, Beltsville, MD U MD College of Medicine, Baltimore, MD
2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.
Extension of Bayesian procedures to integrate and to blend multiple external information into genetic evaluations J. Vandenplas 1,2, N. Gengler 1 1 University.
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
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.
2007 Paul VanRaden Animal Improvement Programs Lab, Beltsville, MD 2011 Avoiding bias from genomic pre- selection in converting.
2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics.
Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2013 Paul VanRaden University.
John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD March 2012 AIPL.
2003 G.R. Wiggans,* P.M. VanRaden, and J.L. Edwards Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
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.
John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD , USA The use and.
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 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2009 Mixing Different SNP Densities Mixing Different.
2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.
Cooper, 2014CDCB Meeting Aug. 5(1) T.A. Cooper, G.R. Wiggans and P.M. VanRaden Animal Genomics and Improvement Laboratory, Agricultural Research Service,
Paul VanRaden Animal Improvement Programs Laboratory Beltsville, MD, USA 2004 Genetic Base and Trait Definition Update.
2007 Paul VanRaden Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2009 Future Animal Improvement Programs Applied.
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.
2005 Paul VanRaden Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA Selection for.
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.
2005 Paul VanRaden, John Cole, Duane Norman, Mel Tooker, and Jan Wright Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville,
2007 Paul VanRaden Animal Improvement Programs Lab, USDA, Beltsville, MD, USA Pete Sullivan Canadian Dairy Network, Guelph, ON, Canada
Paul VanRaden and Melvin Tooker* Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD 2006.
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.
2007 Paul VanRaden Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD, USA
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (1) Dr. H. Duane Norman Interim Administrator Council on Dairy Cattle Breeding.
Paul VanRaden and John Cole Animal Improvement Programs Laboratory Beltsville, MD, USA 2004 Planned Changes to Models and Trait Definitions.
Adjustment of breeding values for past and future inbreeding Paul VanRaden*, Lori Smith Animal Improvement Programs Laboratory Agricultural Research Service,
P. M. VanRaden and T. A. Cooper * Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA
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.
2007 Paul VanRaden Animal Improvement Programs Lab, Beltsville, MD Iterative combination of national phenotype, genotype, pedigree,
Multi-trait, multi-breed conception rate evaluations P. M. VanRaden 1, J. R. Wright 1 *, C. Sun 2, J. L. Hutchison 1 and M. E. Tooker 1 1 Animal Genomics.
Multibreed Genomic Evaluation Using Purebred Dairy Cattle K. M. Olson* 1 and P. M. VanRaden 2 1 Department of Dairy Science Virginia Polytechnic and State.
2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.
2005 Paul VanRaden and Mel Tooker Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Genetic.
2006 Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Predicting Genetic.
2007 John Cole, Paul VanRaden, George Wiggans, and Melvin Kuhn Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD,
2006 Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD AIPL Contributions.
ADSA Indianapolis 2001 (1) 2001 A Global Scale for Ranking Dairy Bulls Using Blended National Rankings Rex L. Powell * Paul M. VanRaden Animal Improvement.
2001 ADSA Indianapolis 2001 (1) Heterosis and Breed Differences for Yield and Somatic Cell Scores of US Dairy Cattle in the 1990’s. PAUL VANRADEN Animal.
2007 Paul VanRaden Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA 2008 New.
Paul VanRaden Animal Improvement Programs Laboratory Beltsville, MD, USA Inbreeding Adjustments and Effect on Genetic Trend.
2007 Paul VanRaden Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA 2007 Genetic evaluation.
G.R. Wiggans, T. A. Cooper* and P.M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD
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.
Paul VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2014 Paul VanRaden Advancing.
2007 Paul VanRaden, Jan Wright, Gary Fok, and Mel Tooker Animal Genomics and Improvement Lab Agricultural Research Service, USDA Beltsville, MD, USA
G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD G.R. WiggansCouncil.
Y. Masuda1, I. Misztal1, P. M. VanRaden2, and T. J. Lawlor3
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.
Validation of genomic predictions and genomic reliability
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.
Development of Genomic GMACE
Presentation transcript:

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

Interbull annual meeting, Nantes, France, August 2013 (2) Paul VanRaden 2013  Traditional mixed models do not account for genomic selection Phenotypes only for animals with highest Mendelian sampling GBV differ from EBV for progeny, mates, parents, or herdmates  Multi-step methods may be biased  Single-step methods reduce bias In Theory

Interbull annual meeting, Nantes, France, August 2013 (3) Paul VanRaden Step Relationship Inverse Aguilar et al. (2010) H -1 = A 11 A 12 A 21 A 22 + G -1 - A = non-genotyped animals (60 million) 2 = genotyped animals (400,000)

Interbull annual meeting, Nantes, France, August 2013 (4) Paul VanRaden 2013  Animal model EBV = w 1 PA + w 2 YD + w 3 PC (parent average, yield deviation, progeny contribution)  1-step genomic info (GI) model GBV = w 1 PA g + w 2 YD g + w 3 PC g + w 4 GI GI = ∑off-diagonal j of G -1 –A (GBV j ) divided by diagonal i of G -1 –A Numerator of w 4 in denominator of w Traditional and 1-Step Models

Interbull annual meeting, Nantes, France, August 2013 (5) Paul VanRaden 2013  Computed for 8,300 Brown Swiss  Diagonals of G and A 22 are similar G: mean F G = 3.98% and SD = 4.15% A 22 : mean F A = 3.95% and SD = 2.97%  Diagonals of G -1 larger than A Mean = 5.83 for G -1, 2.18 for A G -1, A and difference all highly correlated Diagonals of G -1 –A 22 -1

Interbull annual meeting, Nantes, France, August 2013 (6) Paul VanRaden 2013 GAG -1 A -1 G -1 - A -1 G A G A G -1 - A Correlations of Diagonals Genotyped Brown Swiss

Interbull annual meeting, Nantes, France, August 2013 (7) Paul VanRaden 2013  Phenotyped only animals with good Mendelian sampling genotypes  Discrete or overlapping generations Bias if discrete (Patry, Ducrocq 2011) OK with overlap (Nielsen et al, 2012) Large bias for dams (Liu et al, 2009)  Actual studies of pre-selection and genomic assortative mating needed Simulations of Pre-Selection Bias

Interbull annual meeting, Nantes, France, August 2013 (8) Paul VanRaden 2013  Test actual selection and mating  Quantify genomic pre-selection in: Mates of proven bulls (group 1) Mates of young bulls (group 2) Dams of young selected sons  Measure future bias because pre- selection has already occurred In Practice

Interbull annual meeting, Nantes, France, August 2013 (9) Paul VanRaden 2013 Percentage of Genotyped Mates Group 2 bulls ranked by NM$ Net Merit (Aug 2013 ) Percentage of mates genotyped Supersire Numero Uno S S I Robust Topaz Garrold Mogul

Interbull annual meeting, Nantes, France, August 2013 (10) Paul VanRaden 2013  Group 1 (proven bulls) Daughters with records Top 50, no dtrs in April 2010 Were mates pre-selected?  Group 2 (young bulls) Top 50, born 2009 and 2010 Study calves born in USA Will pre-selected mates cause bias? Mates of Group 1 and 2 Bulls

Interbull annual meeting, Nantes, France, August 2013 (11) Paul VanRaden 2013 TraitMeanSDMinMax NM$ Protein001 Prod Life.0.1 Dtr Preg Rate.0.1 SCS Final Score Udder Depth Group 1 Realized Mate Bias

Interbull annual meeting, Nantes, France, August 2013 (12) Paul VanRaden 2013 TraitMeanSDMinMax NM$89033 Protein0001 Prod Life Dtr Preg Rate SCS Final Score Udder Depth Group 2 Future Bias from Mates

Interbull annual meeting, Nantes, France, August 2013 (13) Paul VanRaden 2013  Dams with ≥ 1 sampled son born 2008 to 2012  Son selection differential = ∑(GPTA – PA) / # of sons sampled  Dam’s bias = 2 * sons’ selection differential * (DE from sampled sons) / (total conventional DE) DE = daughter equivalents or EDC Future Bias – Dams of Young Bulls

Interbull annual meeting, Nantes, France, August 2013 (14) Paul VanRaden 2013  29 sons genotyped, 6 selected, each will provide 5.4 DE  Son selection differential for milk = ∑(GPTA – PA) / 6 = 583 pounds  30 daughters, each provide 1.5 DE  8.3 DE from PA, 7.8 from records  Dam’s future bias = 2 * 583 * 6 * 5.4 / [ * * 1.5] = 404 Example Dam HOUSA

Interbull annual meeting, Nantes, France, August 2013 (15) Paul VanRaden 2013 TraitMeanSDMinMax NM$ Protein PL DPR SCS Expected Future Bias – Bull Dams

Interbull annual meeting, Nantes, France, August 2013 (16) Paul VanRaden 2013  Also from preferential treatment High-priced early daughters Lack of random sampling  Deregression removes some bias Example: dam gets credit only for own records and non-genotyped progeny, not genotyped sons Use matrix instead of simple one at a time deregression Potential Biases

Interbull annual meeting, Nantes, France, August 2013 (17) Paul VanRaden 2013 Conclusions  Evaluations should adjust for GBV instead of EBV of: Progeny, mates, contemporaries, and parents  Biases from pre-selection: Very small for recently proven bulls Moderate from mates top young bulls Will be large for dams of several highly selected sons, but deregression can remove some of the bias

Interbull annual meeting, Nantes, France, August 2013 (18) Paul VanRaden 2013  Phenotypes, genotypes, and pedigrees were provided by the Council on Dairy Cattle Breeding Acknowledgments