Genome-wide association mapping using single-step GBLUP!!!!
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1 Genome-wide association mapping using single-step GBLUP!!!! Ignacy'Misztal,'Joy'Wang'University!of!Georgia Ignacio'Aguilar,'INIA,!Uruguay! Andres'Legarra,!INRA,!France! Bill'Muir,!Purdue!University! Rohan'Fernando,!Iowa!State!!!'
2 Genome Wide Association Studies Large research interest in GWAS Current methods Classical single SNP analyses (e.g., Tassel, Wombat) BayesX - joint SNPs analysis (e.g., Gensel) Limitations in current methods Simple models Single trait Slow if not optimized Complicated if not all animals genotyped
3 Single-step GBLUP BLUP with combined pedigree-genomic relationship matrix (Aguilar et al., 2010; Christiansen et al., 2010) H = A 1 1 G A22 Works with any model, any number of traits, and combination of genotyped and ungenotyped animals Can ssgblup be adapted for GWAS?
4 Useful formulas Conversion from SNP effects To GEBV GEBV ˆ = a g Zuˆ SNP effects Genomic relationship matrix Estimate of SNP variance (Zhang et al., 2010) 2 ˆ σ G = ZDZ 'q 2 = uˆ 2 p (1 p u, i i i i ) Conversion from GEBV to SNP effects (VanRaden, 2008; Stranden and Garrick, 2010) û = qdz'g * 1 â g = DZ'[ZDZ'] 1 â g
5 Plots and accuracies in Zhang et al. (2010) simulation BayesB Acc 0.83 Weighted RRGBLUP Acc 0.75
6 GWAS under ssgblup 1. t=0; D (t) =I; â g 2. Compute by ssgblup 3. t=t+1; uˆ ( t) = λd ( t) Z' G 1 ( t) aˆ g Normalize 6. d (t+1),i = û i 2 2 p i (1 p i ) G D ( t + 1) = ZD( t+ 1) Z tr( D (0) ( t+ 1) = t tr( D( t+ 1) ) ' λ ) D * ( + 1) * Iteration on GEBV and SNP (SS/GEBV) 7. Loop to step 2 or 3 Iteration on SNP (SS/SNP)
7 Genet. Res., Camb. (2012), 94, pp f Cambridge University Press doi: /s Genome-wide association mapping including phenotypes from relatives without genotypes H. WANG 1 *, I. MISZTAL 1,I.AGUILAR 2,A.LEGARRA 3 AND W. M. MUIR 4 1 Department of Animal and Dairy Science, University of Georgia, Athens, GA , USA 2 Instituto Nacional de Investigacio n Agropecuaria, INIA Las Brujas, Canelones, Uruguay 3 INRA, UR631 Station d Ame lioration Ge ne tique des Animaux (SAGA), BP 52627, Castanet-Tolosan, France 4 Department of Animal Science, Purdue University, West Lafayette, IN , USA (Received 19 September 2011; revised 8 December 2011, and 9 March 2012; accepted 13 March 2012)
8 Comparisons by simulations (Wang et al., 2012) Data 15,800 individuals in 5 generations 1500 genotyped 3k SNP in 2 chromosomes Methods Classical GWAS - Wombat (Meyer & Tier, 2012) Degressed proofs BayesB - GenSel (Habier et al., 2011) Degressed proofs (c=0.1), 100k rounds ssgblup - iterations on SNP and on GEBV
9 MANHANTTAN PLOTS Wombat
10 BayesB (weighted DP)
11 ssgblup / SNP (it3)
12 ssgblup / GEBV (it3)
13 RESULTS (Simulated data : GEBVs)
14 Correlations between QTLs and clusters of SNP effects -ssgblup SS/SNP SS/GEBV
15 Correlations between QTLs and clusters of SNP effects BayesB & Wombat
16 Field data set Data Body weight in broiler chicken at 6 weeks N=275k ; N g =4500, 40K SNP (after edits) 6 generations Model for ssgblup: : fixed effects (sex, contemporary group) : maternal environment effects : animal effects
17 Models of Classical GWAS and BayesB: Classical GWAS:!!!!="# +$%+&'+( Wombat (Meyer & Tier, 2012) y : phenotypic records #!: sex, CG, and single snp marker BayesB:!=1)+&*+( GenSel (Habier et al., 2011) +: a vector of SNP markers y : NDP: non-weighted degressed proofs WDP: weighted degressed proofs (c=0.1) t = 51, 000 (first 1,000 as burn-in),=0.9
18 ssgblup iterations on SNP only Sliding window n=10 it1 it3 it5
19 ssgblup iterations on SNP and GEBV Sliding window n=10 it1 it3 it5
20 Sliding window n=10 Classical GWAS BayesB
21 Comparison of Three Methods: ssgblup Iterations on SNP (it5) Classical GWAS BayesB
22 Ranking of SNP regions in ssgblup during iteration SNP SNP+GEBV it1 it2 it3 it4 it5 it6 it7 it8 it2 it3 it4 it5 it6 it7 it Regions of 20 SNP
23 SS/SNP(3) chr Var SS/EBV(3) wombat BayesB % % % % % % % % % %
24 BayesB chr Var SS/SNP(3)SS/EBV(3) wombat % % % % % % % % % %
25 wombat chr Var SS/SNP(3) SS/EBV(3) BayesB % % % % % % % % % %
26 HapMap of Chromosome 6
27 Realized accuracies of ssgblup/gebv during iteration 35 Weights from training + validation 30 Weights from training population only Weights not normalized Round of iteration
28 R 2 in dairy 1400 genotypes (Lino et al., 2012) Milk Fat% Prot% BLUP ssgblup 1 ssgblup 2 ssgblup 3
29 ssgblup/snp for Heat Stress in Holsteins (Aguilar, 2011) Multiple-Trait Test-Day model, heat stress as random regression ~ 90 millions records, ~ 9 millions pedigrees ~ 3,800 genotyped bulls Computing time Complete evaluation ~ 16 h Regular effect -first parity Heat stress effect first parity
30 Workshop!on!genomic!selecBon!using!! singlecstep!methodology!! Athens,!May!26CJune!1!
31
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33 Renumbering! RENUMF90' CompuBng!of!extra!matrices! PreGSF90' BLUP!in!memory! BLUPF90' BLUP!!iteraBon!on!data! BLUP90IODF' CBLUP90IOD' Variance!component!esBmaBon! REMLF90'AIREMLF90' GIBBS2F90'THRGIBBS2F90''' Approximate!accuracies! ACCF90' PredicBons!via!SNP! PredGSF90' GEBV!to!SNP!conversions! GWAS! PostGSF90' Sample!analysis! POSTGIBBSF90'
34 Issues Alternative sampling of SNP variances (Sun et al., 2011) Significance testing Multiple trait models with large QTL/regions for some traits Maximum number of genotypes
35 CONCLUSIONS ssgblup for GWAS: Simple and Fast Applicable to any model ssgblup/snp Optimal if no large SNP effects Applicable to multiple traits ssgblup/gebv 1-2 rounds enough useful for more accurate GEBV if major SNP Large potential for research
36 Acknowledgements Cobb Vantress AFRI grants and from the USDA NIFA Daniela Lino for testing Dorian Garrick (GenSel) and Karin Meyer (Wombat)
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