Implementation of Genomic Selection in Pig Breeding Programs

Transcription

1 Implementation of Genomic Selection in Pig Breeding Programs Jack Dekkers Animal Breeding & Genetics Department of Animal Science Iowa State University Dekkers, J.C.M Use of high-density marker genotyping for genetic improvement of livestock by genomic selection CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 5, No. 037

2 Genomic selection Meuwissen et al Genetic Evaluation using high-density SNPs Phenotype Genotype for >50,000 SNPs Genotype for >50,000 SNPs Genotype for >50,000 SNPs Training data Statistical y i = µ + Σ α k g ik SNP k Estimate marker effects Predict analysis BV from marker genotypes at early age + e i Predict BV ^ from marker Estimates of SNPgenotypes effects α k at early age

3 The Promise of Genomic Selection Reduce requirement to obtain phenotypes on selection candidates and / or on close relatives in order to estimate EBV Traditional EBV Genomic Selection Estimates of marker effects from training population ^αk Animal

4 The Promise of Genomic Selection Increase accuracy of EBV at a young age Reduce generation intervals Increase accuracy for difficult traits Reproduction, longevity, meat quality Disease resistance Crossbred performance in field Reduce rates of inbreeding / generation Less emphasis on family information Select on animal s own genotypes (for markers)

5 Implementation of Genomic Selection in Pig Breeding Programs Outline Implementation of GS using small SNP panels Strategy Size of training data GS for commercial crossbred performance Potential benefits Training on crossbred / mixed populations Opportunities to redesign breeding programs A layer chicken example

6 Genomic Selection using Low-Density Panels Habier, Dekkers, Fernando. Genetics 2009 Original principle of Genomic Selection (GS) High-density (HD) SNP genotypes used for both Estimation of marker effects (training) Prediction of G-EBV for selection candidates Too costly for many species Need Low- (384) vs. High-density panel for routine implementation? <$40 vs. $200 per animal? Standard approach to developing Low-density panels: Select the best SNPs from the HD-panel Panels will be trait and population specific Proposed approach: use well-spaced Low-density SNP genotypes on selection candidates to impute / fill in missing HD SNP genotypes

7 Low-Density GS Approach Sire s rogeny i Dam d LD-GS LD-GS paternal maternal paternal maternal paternal maternal HD-GS EBV i = Σ (g m ik + g p ik ) SNP k Sum estimates of effects of maternal and paternal SNP alleles LD-GS EBV i = Σ (p md g m + p pd g m + p ms g p + p ps g p ) SNP k ik dk ik dk ik sk ik sk Prob. that i received dam s maternal vs paternal allele at SNP k

8 Accuracy of G-EBV based on High- vs Low- Density SNP genotyping Simulation (20 Replicates) 500 QTL Accuracy Training Generation HD

9 Accuracy of GS-EBV based on High- vs Low- Density SNP genotyping Simulation (20 Replicates) 500 QTL Accuracy Training Generation HD ELD-10

10 Accuracy of GS-EBV based on High- vs Low- Density SNP genotyping Simulation (20 Replicates) 500 QTL Accuracy Training Generation HD ELD-10 ELD-20

11 Accuracy of GS-EBV based on High- vs Low- Density SNP genotyping Simulation (20 Replicates) 500 QTL Accuracy With regenotyping of parents using HD panel HD ELD-10+ ELD-10 ELD-20+ ELD Training Generation

12 Evaluation of the ELD approach in a Layer Chicken Breeding Line A. Wolc et al. HD = 26,000 SNPs 5 generations of parents HDgenotyped Genomic training using own phenotypes on dams and phenotypes of ungenotyped progeny Haplotypes derived using fast rule-based approach ELD = 400 SNPs 210 progeny HD / ELD genotyped GEBV computed using 1. Observed HD genotypes 2. Imputed HD genotypes

13 1.00 Correlation between HD- and ELD-GEBV lps lpd eps epd eyw BW lyw lah eah SM EW3 CO3 lew lco eco eew Loss in accuracy < 5%

14 Genomic Selection using Evenly-spaced Low-Density SNPs Conclusions GS can be implemented by genotyping selection candidates for <400 SNPs spread across the genome Loss in accuracy limited: < 5 % - if parents re-genotyped Loss in accuracy ~ independent of # QTL and # traits Cost effectiveness depends on cost of Low- vs. High-density genotyping <$40?? <$200 ELD-genotyped individuals can also be used for training

15 Possible Strategy for Implementation of GS within a Breed by ELD genotyping 1. Genotype at least 3 generations of parents with HD panel (60K) Use for genomic training 3 generations required for fast-rule haplotyping 2. Genotype selection candidates (progeny) with ELD panel Impute HD genotypes and compute G-EBV 3. Re-genotype selected sires (and selected dams) using HD panel 4. Retrain with new data on ELD and HD-genotyped animals

16 Possible Model for 1-step Genomic Evaluation for this data structure GBLUP Reduced Animal Model Wolc et al GSE (accepted) Combines data from genotyped individuals (incl. all parents) and their non-genotyped non-parent progeny y = Xb + (P + 1 / 2 QS + 1 / 2 QD)a + e Vector of all records Fixed effects a ~ N(0,Gσ a2 ) =1 if typed e i ~ N(0, σ e2 ) e i ~ N(0, σ e2 + 1 / 2 σ a2 ) =1 if not typed Connect progeny to genotyped Sires and Dams BV of genotyped individuals G = Genomic relationship matrix for genotyped individuals for ungenotyped progeny

17 GS requires large training populations Goddard and Hayes Nature Reviews: Genetics 10: 381 Expected Accuracy based on historic marker-qtl LD Accuracy of GEBV of the next generation within a breed is expected to be greater

18 Factors that contribute to the Accuracy of G-EBV Historic marker-qtl LD the original premise of GS Pedigree relationships captured by markers Deviations from pedigree relationships Within-family linkage / cosegregation information Cosegregation between markers and linked QTL from parents to progeny More important within breed and in short term

19 Implementation of Genomic Selection in Pig Breeding Programs Outline Implementation of GS using small SNP panels Strategy Size of training data GS for commercial crossbred performance Potential benefits Training on crossbred/mixed populations Opportunities to redesign breeding programs A layer chicken example

20 Current Pyramid Selection Programs Limitations: - limited selection for performance in the field - no selection for traits not recorded in nucleus - disease traits Sire line NUCLEUS herds Dam line High health environment Multiplier Multiplier r g < 1 Production herds Field environment

21 Field data on relatives Selection for Performance in Field Traditional Breeding Solution: Purebred data Collect phenotypes on relatives in field Sire line Multiplier Combined Crossbred-Purebred Selection Production herds Requirements / limitations: - Costly logistics - Pedigree-based phenotyping in field - Higher rates of inbreeding - family data vs. own phenotype

22 Selection for Performance in Field Possible Genomic Selection Solution: Genomic EBV Selection Genotype Sire line Estimate marker effects on crossbred performance in field Multiplier SNP effect estimates Genotype Phenotype Production herds

23 Genomic Selection for Field Performance Potential benefits (Dekkers 2007 JAS) Selection Genetic correlation (purebred commercial) = 0.7 Genomic EBV Genotype Sire line Multiplier SNP effect estimates Genotype Phenotype Production herds

24 Accuracy 0.8 Accuracy of selection for field performance by Crossbred Genomic Selection Dekkers JAS, P Pb + P Xbred 0.4 P Purebred Accuracy of G-EBV

25 Accuracy 0.8 Accuracy of selection for field performance by Crossbred Genomic Selection Dekkers JAS, GS Xb P Pb + P Xbred 0.4 P Purebred Accuracy of G-EBV

26 F (%) Impact on Inbreeding Crossbred Genomic Selection Dekkers JAS, P Pb + P Xbred P Purebred Accuracy of G-EBV

27 F (%) Impact on Inbreeding Crossbred Genomic Selection Dekkers JAS, P Pb + P Xbred P Purebred GS Xb Accuracy of G-EBV

28 GS training on Crossbred data 1. Genotype parents and train on crossbred progeny performance requires pedigree 2. Genotype crossbreds and train on own phenotype does not require pedigree NUCLEUS herds Sire line SNP effect estimates Dam line Multiplier Multiplier Genotype Phenotype Production herds

29 Genomic training in mixed / Xbred populations Toosi, Ibañez, Fernando, Dekkers, JAS 2010 GSE 2009 Founders 1000 generations N e = 500 Methods 1 M chromosome SNPs 100 QTL h 2 =0.3 Bayes-B Breed A 50 generations N e = 100 Breed B 50 generations N e = 100 Breed A Training data sets N=1000 Mix A+B Breed B 8 generations N e = 100 NRI Award Breed C C(AB) F1 AxB F2 AxB Breed B validation

30 Accuracy of GS based on admixed training populations # markers on 1 M chrom.

31 Accuracy of GS based on admixed training populations # markers on 1 M chrom.

32 GS for Commercial Crossbred Performance Conclusions 1. Genomic Selection provides unique opportunities to improve crossbred performance in the field greater response less inbreeding 2. Genomic training can be on crossbred data without need for pedigree will require large data sets and possibly denser SNP panels

33 Implementation of Genomic Selection in Pig Breeding Programs Outline Implementation of GS using small SNP panels Strategy Size of training data GS for commercial crossbred performance Potential benefits Training on crossbred/mixed populations Opportunities to redesign breeding programs A layer chicken example

34 Redesign of Breeding Programs A Layer Chicken Example Jack Dekkers 1, Anna Wolc 1, Chris Stricker 2 Rohan Fernando 1, Dorian Garrick 1, Susan Lamont 1 Neil O Sullivan 3, Janet Fulton 3, Jesus Arango 3, Petek Settar 3 Andreas Kranis 4, Jim McKay 5, Kellie Watson 4, Alfons Koerhuis 4, Rudi Preisinger 6 1 Iowa State University 2 applied genetics network, 3 Hy-Line Int 4 Aviagen Ltd., 5 EW Group 6 Lohmann Tierzucht

35 Implementation of Genomic Selection in Layer Chickens Research Objective Evaluate and demonstrate the advantages and pitfalls of Genomic Selection in a commercial breeding population Research Questions / Goals In layer chickens, Genomic Selection can: increase response by halving the generation interval without increasing the rate of inbreeding per year in a breeding program comprising fewer individuals

36 Implementation A Commercial Breeding Line was split Traditional Genomic Selection Selection strategy Traditional Genomic Selection parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d a Complete phenotypes available at ~10 months of age b Equal selection of and maximizes response for given F c Traditional selection is after are phenotyped 12 mo. Traditional selection is limited by cost to rear and phenotype Male traditional selection is on sib data low accuracy high F

37 Expected Response and Inbreeding Based on simulation 4 3 Inbreeding Trad.Selection Strategy Traditional WGS 0.2 Parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 0.15 Response Year 0.05 Inbreeding 0

38 Expected Response and Inbreeding Based on simulation 4 3 Inbreeding WGS-0 Strategy Traditional WGS 0.2 Parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 0.15 Response 2 Inbreeding Trad.Selection Year Inbreeding

39 Expected Response and Inbreeding Based on simulation 4 3 Inbreeding WGS-0 Inbreeding Trad.Selection Strategy Traditional WGS 0.2 Parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 0.15 Response 2 Inbreeding WGS-retrain Year Inbreeding

40 Expected Response and Inbreeding Based on simulation 4 3 Response Trad.Selection Strategy Traditional WGS 0.2 Parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 0.15 Response Year 0.05 Inbreeding 0

41 Expected Response and Inbreeding Based on simulation 4 3 Response WGS-0 Strategy Traditional WGS 0.2 Parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 0.15 Response 2 Response Trad.Selection Year 0.05 Inbreeding 0

42 Expected Response and Inbreeding 4 Based on simulation Response WGS-0 Strategy Traditional WGS Parameters # candidates/gener. 1,000 3, # phenotyped a 3, # selected b 50 b Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 3 Response Trad.Selection 0.15 Response 2 Response WGS-retrain Year 0.05 Inbreeding 0

43 Initial results Training data Genotypes on 1630 sires and dams from last 5 generations Phenotypes: Own phenotype on dams (n = 913) Mean phenotype of fullsib progeny from each mating (n = 1211 families with ~8 progeny/family) % increase in accuracy of EBV at young age PD AH PS E3 EW YW CO C3 Trait

44 Conclusions Genomic Selection provides unique opportunities to enhance pig breeding programs by removing limitations on when, where, and on whom phenotypes are recorded With opportunities to: increase / maintaining response to selection reduce rates of inbreeding and / or generation intervals enhance selection for difficult traits (disease, field performance) Successful implementation requires: Large training data sets and continuous re-training Strategic use of low-density panels to reduce costs Redesign of breeding programs to reduce costs

45 College of Agriculture and Life Sciences Acknowledgements Rohan Fernando Dorian Garrick Honghua Zhao Ali Toosi David Habier Anna Wolc Chunkao Wang Noelia Ibañez