Added value of whole-genome sequence data to genomic predictions in dairy cattle
Rianne van Binsbergen 1,2 , Mario Calus 1 , Chris Schrooten 3 , Fred van Eeuwijk 2 , Roel Veerkamp 1 , Marco Bink 2
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
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
Apply prediction equation to a group of individuals that have genotypes but not phenotypes
 Estimated genomic breeding values
 Select the best individuals for breeding
Each SNP represented by a variable (x), which takes the values
0 [ A A ]
1 [ A B]
2 [B B]
Advantages:
• Select at early age (before phenotypes available)
• Save costs to phenotype candidates
• Increase accuracy of predicted Breeding Values
Goddard & Hayes (2009)
Nature Reviews Genetics 10:381

Simulation Study
 Dense marker maps
 SNP markers at 1cM density
 Prediction Accuracy



Least Squares method:
Genomic BLUP method:
Bayesian methods(A,B):
0.32
0.73
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”

Another (seminal) paper on Genomic Prediction
“In the case of whole-genome sequence data, the polymorphisms that are causing the genetic differences between the individuals are among those being analyzed.”
Higher accuracy in genomic predictions since causal mutation is included (assumption)

No dependency on LD

Persistency across generations

Genomic prediction across breeds
Prediction of Total Genetic Value
Using Genome-Wide Dense Marker
Maps
T. H. E. Meuwissen,* B. J. Hayes† and
M. E. Goddard†,‡
“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.”

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 panel
Van 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
5503 Holstein Friesian bulls
777K SNP genotypes
(Illumina BovineHD BeadChip)
Imputation - Beagle v4 software
1000 bull genomes project
28M SNP genotypes
429 bulls
(multiple breeds)
5503 Holstein Friesian bulls
12M SNP genotypes
MAF > 0.005
Imputation accuracy > 0.05
De-regressed progeny based proofs (DRP 1 ) and associated effective daughter contributions (EDC 2 )
 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)

= 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 > 0.005
Imputation accuracy > 0.05
training population
4322 old bulls validation population
1181 young bulls training population
4322 old bulls validation population
1181 young bulls
Validation population

Youngest bulls with EDC  0

Mainly sons of bulls in training population

Mimics breeding practice

GBLUP

Genome-enabled best linear unbiased prediction
BSSVS

Bayes stochastic search variable selection

Distribution QTL effects to be close to infinitesimal model (all
SNPs equally small effect)

Build a genomic relationship matrix to model variancecovariance structure
3 chains of 60,000 cycles
(10,000 cycles burn-in)

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.

GBLUP
777K
●
HPC – 1 node
SNP
12M
SNP
●
~ 3 hours
●
~ 32 GB RAM
●
HPC – 12 nodes
●
~ 6 hours
●
~ 600 GB RAM
3 chains of 60,000 cycles
(10,000 cycles burn-in)
BSSVS (per MCMC chain)
●
Windows – 1 CPU
●
~ 5 days
●
~ 1.6 GB RAM
●
HPC – 1 node
●
~ 50 days
●
~ 32 GB RAM
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)

0,6
0,5
0,4
0,3
0,2
0,1
0,0
SCS IFL
BSSVS: Average over 3 chains of 60,000 cycles
(10,000 cycles burn-in)
PY
BovineHD GBLUP
BovineHD BSSVS
Sequence GBLUP
Sequence BSSVS *
* Based on
45,000 cycles

0,6
0,5
0,4
0,3
0,2
0,1
0,0
SCS IFL PY
BovineHD GBLUP
BovineHD BSSVS
Sequence GBLUP
Sequence BSSVS *
* Based on
45,000 cycles

Trace of variance of SNP effects Bayes Factor for SNP effects
777K SNP
12M SNP
3 chains of 60,000 cycles
(10,000 cycles burn-in)
Sequence: 45,000 cycles


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)
MCMC convergence
BSSSVS
777K SNP
Bayes-C
12M SNP

Concentrate on single chromosome (BTA 6)
Signal of QTL effects
BSSSVS
777K SNP
Bayes-C
12M SNP
Reliability estimates
BSSVS
BovineHD 0.328
Sequence 0.324
BayesC
0.328
0.325


Genomic prediction using sequence data becomes reality
●
However, sequence data requires intensive computation

Need for faster algorithms

Use 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

1000 bull genomes project
(www.1000bullgenomes.com)

De-regressed proofs (DRP)
Effective daughter contribution (EDC)
𝐷𝑅𝑃 = 𝑃𝐴 + 𝐸𝐵𝑉 − 𝑃𝐴 ∗
𝐸𝐷𝐶
𝐸𝐵𝑉
𝐸𝐷𝐶 𝑝𝑟𝑜𝑔
Parent average
Effective Daughter
Estimated breeding value
Contribution
𝐸𝐷𝐶
𝐸𝐵𝑉
= 𝛼 𝑅𝐸𝐿
𝐸𝐵𝑉
/ 1 − 𝑅𝐸𝐿
𝐸𝐵𝑉
(4 − ℎ 2 )/ℎ 2 Published reliability of EBV
𝐸𝐷𝐶 𝑝𝑟𝑜𝑔
= 𝐸𝐷𝐶
𝐸𝐵𝑉
− 𝐸𝐷𝐶
𝑃𝐴
VanRaden et al. 2009 (J Dairy Sci)
Based on reliability of parents
𝑅𝐸𝐿 𝑠𝑖𝑟𝑒
+ 𝑅𝐸𝐿 𝑑𝑎𝑚
/4
VanRaden and Wiggans 1991 (J Dairy Sci)