Added value of whole-genome sequence data to genomic predictions in dairy cattle

Rianne van Binsbergen1,2, Mario Calus1, Chris Schrooten3, Fred van Eeuwijk2, Roel Veerkamp1, Marco Bink2

1 Animal Breeding & Genetics Centre, Wageningen UR (NL)2 Biometris, Wageningen UR (NL)3 CRV (cattle breeding company) , Arnhem (NL)

Genomic Prediction in agricultural species

Goddard & Hayes (2009) Nature Reviews Genetics 10:381

Reference population: 1) Estimate effects for each SNP (w)2) Generate a prediction equation that combines all

the marker genotypes with their effects to predict the breeding value of each individual

Each SNP represented by a variable (x), which takes the values 0 [A A] 1 [A B] 2 [B B]

Apply prediction equation to a group of individuals that have genotypes but not phenotypes Estimated genomic breeding values

Select the best individuals for breeding

Advantages:• Select at early age (before phenotypes available)• Save costs to phenotype candidates• Increase accuracy of predicted Breeding Values

One seminal paper on Genomic Prediction

Dense marker maps SNP markers at 1cM density

Prediction Accuracy Least Squares method: 0.32 Genomic BLUP method: 0.73 Bayesian methods(A,B): 0.85

Conclusion:“selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval”

Simulation Study

Another (seminal) paper on Genomic Prediction

“Only few SNPs were useful for predicting the trait [because they were in linkage disequilibrium (LD) with mutations causing variation in the trait] while many SNPs were not useful.”

Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps T. H. E. Meuwissen,* B. J. Hayes† and M. E. Goddard†,‡

Higher accuracy in genomic predictions since causal mutation is included (assumption) No dependency on LD Persistency across generations Genomic prediction across breeds

“In the case of whole-genome sequence data, the polymorphisms that are causing the genetic differences between the individuals are among those being analyzed.”

Genomic predictions from whole-genome sequence data

Tremendous increase in number of SNPs (more noise) Large (sequence) data are required

Solution Sequence core set of individuals (e.g. founders)

Impute whole-genome sequence genotypes of other individuals

Accuracy of imputation to whole-genome sequence data was generally high for imputation from 777K SNP panelVan Binsbergen, et al. Genet Sel Evol

2014 (in press)

This presentation: First results of genomic prediction with imputed whole-genome

sequence data for 5503 bulls with accurate phenotypes

Dataset: SNP genotypes & trait phenotypes

1000 bull genomes project

28M SNP genotypes

De-regressed progeny based proofs (DRP1) and associated effective daughter contributions (EDC2)

Somatic cell score (SCS)

Interval fist and last insemination (IFL)

Protein yield (PY)

1 VanRaden et al. 2009 (J Dairy Sci) 2 VanRaden and Wiggans 1991 (J Dairy Sci)

5503 Holstein Friesian bulls

777K SNP genotypes (Illumina BovineHD BeadChip)

5503 Holstein Friesian bulls

12M SNP genotypes MAF > 0.005Imputation accuracy > 0.05

Imputation - Beagle v4 software429 bulls

(multiple breeds)

Prediction reliability

Validation population Youngest bulls with EDC 0 Mainly sons of bulls in training population Mimics breeding practice

= squared correlation between original phenotype (DRP) and estimated genetic values (GEBV)

5503 Holstein Friesian bulls

777K SNP genotypes (Illumina BovineHD BeadChip)

5503 Holstein Friesian bulls

12M SNP genotypes MAF > 0.005Imputation accuracy > 0.05

training population

validation population

4322 old bulls

1181 young bulls

training population

validation population

4322 old bulls

1181 young bulls

differences?

Genomic prediction – 2 methods

GBLUP

Genome-enabled best linear unbiased prediction

Distribution QTL effects to be close to infinitesimal model (all SNPs equally small effect)

Build a genomic relationship matrix to model variance-covariance structure

BSSVS

Bayes stochastic search variable selection

Large number of SNPs with tiny (close to zero) and a few SNPs with moderate effects (=mixture of two Normal distributions)

Implementation via Markov chain Monte Carlo (MCMC)

simulation algorithms (computer intensive)

Calus M (2014). Right-hand-side updating for fast computing of genomic breeding values. Genetics Selection Evolution 46(1): 24.

3 chains of 60,000 cycles (10,000 cycles burn-in)

Computation

GBLUP

●HPC – 1 node

●~ 3 hours

●~ 32 GB RAM

●HPC – 12 nodes

●~ 6 hours

●~ 600 GB RAM

BSSVS (per MCMC chain)

●Windows – 1 CPU

●~ 5 days

●~ 1.6 GB RAM

●HPC – 1 node

●~ 50 days

●~ 32 GB RAM

777K SNP

12M SNP

Windows 7 Enterprise desktop pc: 32 CPU – 8 GB RAM/CPU (clock speed 2.60 GHz)

HPC Linux cluster: Normal nodes – 64 GB/node (2.60 GHz); 2 fat nodes – 1 TB RAM/node (2.20 GHz)

3 chains of 60,000 cycles (10,000 cycles burn-in)

Results: Prediction Reliability

SCS IFL PY0.0

0.1

0.2

0.3

0.4

0.5

0.6

BovineHD GBLUPBovineHD BSSVSSequence GBLUPSequence BSSVS *R

eliab

ilit

y

* Based on 45,000 cycles

BSSVS: Average over 3 chains of 60,000 cycles (10,000 cycles burn-in)

Results: Prediction Reliability

SCS IFL PY0.0

0.1

0.2

0.3

0.4

0.5

0.6

BovineHD GBLUPBovineHD BSSVSSequence GBLUPSequence BSSVS *R

eliab

ilit

y

* Based on 45,000 cycles

BSSVS: Convergence & SNP effects

Sequence: 45,000 cycles

3 chains of 60,000 cycles (10,000 cycles burn-in)

Trace of variance of SNP effects Bayes Factor for SNP effects

777K SNP

12M SNP

Suitability of BSSVS model?

Large number of SNPs with tiny and a few SNPs with moderate effects

●Sequence data: Really large number of SNPs with tiny effects Captures too much signal?

Another Bayesian Prediction Model: Bayes-C●Large number of SNPs with NO effect and a few SNPs with moderate

effects

Concentrate on single chromosome (BTA 6)

777K SNP

12M SNP

BSSSVS Bayes-C

MCMC convergence

Concentrate on single chromosome (BTA 6)

777K SNP

12M SNP

   

Reliability estimates

BSSSVS Bayes-C

  BSSVS BayesCBovineHD 0.328 0.328Sequence  

0.324 0.325

Signal of QTL effects

Conclusions

Genomic prediction using sequence data becomes reality●However, sequence data requires intensive computation

Need for faster algorithmsUse of Sequence Data did not improve Prediction reliability

●Convergence issues with BSSVS Longer chains may yield better results

BSSVS slightly better compared to GBLUP Preliminary results BTA6 hint that Bayes-C method may work

better (than BSSVS) for sequence data

Next Steps: Did we bet on the wrong horse - named BSSVS?

Review choice of priors in BSSVS model.

Apply Bayes-C model to whole genome sequence data

Thanks!

1000 bull genomes project

(www.1000bullgenomes.com)

Acknowledgments

De-regressed proofs (DRP)

Effective daughter contribution (EDC)

𝐸𝐷𝐶𝐸𝐵𝑉=𝛼𝑅𝐸𝐿𝐸𝐵𝑉 / (1−𝑅𝐸𝐿𝐸𝐵𝑉 )(4−h2) /h2 Published reliability

of EBV

𝐸𝐷𝐶𝑝𝑟𝑜𝑔=𝐸𝐷𝐶𝐸𝐵𝑉−𝐸𝐷𝐶𝑃𝐴

Based on reliability of parents

VanRaden and Wiggans 1991 (J Dairy Sci)VanRaden et al. 2009 (J Dairy Sci)

Parent average

Estimated breeding value

Effective Daughter Contribution