The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
Abstract
Background
With the availability of high throughput genotyping, genomic selection, the evaluation of animals based on dense SNP genotyping, is receiving more and more attention. Several statistical methods have been suggested for genomic selection. Compared to traditional selection, genomic selection can be more accurate which can lead to higher efficiency in terms of time and cost. Herein we applied different genomic evaluation methods on the 14^{th} QTLMAS dataset.
Methods
Four different approaches were used for the estimation of EBV of animals for the Quantitative and the Binary Trait (QT and BT respectively). It included two Bayes B types of approaches (BB): using only SNP information (GBB) or SNP and Pedigree information (GPBB); and two genomic BLUP, GBLUP and GPBLUP. Traditional BLUP was also used only for comparison. When using BB methodology, the probability of SNP having an effect on the traits (which include a quantitative and a binary trait) were also estimated. We also performed “standard” QTL mapping approaches including linkage and association analyses to compare them with BB results as a potential QTL mapping tools.
Results
For QT, the best accuracy of EBV (correlation between EBVs and TBVs) for young animals, was obtained by BB methods (
Conclusions
Better accuracies were obtained using BB methods, compared to BLUP analyses. Compared to the traditional BLUP, the accuracy of the EBVs was improved about 70% and 50% using BB and GBLUP methods respectively. The benefit of genomic selection was the same for both the QT and BT. Models with and without polygenic effect led to similar accuracies in the estimation of breeding values. The BT and QT were genetically correlated (
Background
Genomic selection can be described as the use of highly dense genotyping in the evaluation of animals, to increase the accuracy of the estimated breeding values (EBV)
Regardless the approach used for genomic selection, their success would depend on the quality of the SNP map to capture the whole genetic variation, which would depend on several factors such as Linkage Disequilibrium (LD) between loci and the coverage of the whole genome. In order to safeguard against possible problems related to the quality of the SNP panel, the model can be modified to include an extra genetic effect which is explained by the pedigree information. A model combining both source of information may prove to be beneficial.
The aim of this study was to compare the results evaluations from using these two methods (a modified Bayes B and genomic BLUP) with and without polygenic in the model to evaluate animals in the QTLMAS dataset. We also compared BB results with “standard” association and linkage analyses, to assess its potential values for QTL mapping.
Methods
The data used is the simulated dataset distributed by the organisers of the QTLMAS workshop 2010. The population consists of 3226 individuals spanning 5 generations, of which the last 900 individuals have no phenotype for a quantitative and a binary trait (QT and BT). Genome is about 500 Mb long distributed in 5 chromosomes. All individuals have genotype for 10031 SNPs.
1. Genomic evaluation
a. Bayes B type models
Bayes B (BB) method was first described by Meuwissen et al.
where, y is the vector of phenotypes; µ is the population mean for the trait; n is total number of SNP, z_{i} is the vector of genotypes at SNP i;
The models were implemented using Gibbs sampling. The parameters π and
For the binary trait a liability threshold model was used.
In order to estimate the relative value of the genetic effect explained by the SNP or the pedigree information, the genetic variance explained by genomic information (SNP) was estimated. The simplest approach would have been to sum the variance individually explained by each SNP given their effects and frequencies, but this may be biased because it does not account for the LD between loci. In order to avoid this problem, an approximation based on the infinitesimal model theory was used
PEV stands for Prediction Error Variance, and
b. Genomic BLUP models
The genomic BLUP consists in using SNP genotype to estimate the relationship between individuals which later are used into the mixed model. Two genomic BLUP models, with and without polygenic effect, were fitted:
where, g is vector of random additive genetic effect explained by the SNP information and assumed to be normally distributed as
c. Model comparisons
The main criterion of comparison between the different genomic approaches was using the correlation between the total estimated breeding values (which includes the polygenic effect associated with the pedigree if added into the model) and the true breeding values (TBV). Alternatively, within each method we compared the model with and without polygenic effect using Bayes Factor (BF)
2. QTL mapping
Additionally to the estimated SNP effects, BB methods also estimate the probability of SNP having an effect on the trait, which can be used as a criterion for QTL mapping. In order to assess its potential in use, we compared these results with standard association and linkage analyses.
a. Association analyses
Association analyses were performed using the GRAMMAR approach
b. Linkage analyses
 Hafsib QTL mapping
Halfsib analyses (HSQTL) were performed as described by Haley et al.
 Variance component QTL mapping
As the population is distributed across several generations creating a complex pedigree structure, a QTL mapping based on a variance component approach (VCQTL) may perform better than the mapping based on half sibs regressions. Here, we performed this analysis for the quantitative trait. The method is based on a twostep approach
Results and discussion
Genomic evaluation
Correlations between TBV and EBVs estimated by the different methods were shown in Table
Correlation between true and estimated breeding values of unphenotyped individuals for the different genomic methods.
Methods
Quantitative Trait
Binary Trait
GBB
0.679
0.823
GPBB
0.678
0.824
GBLUP
0.604
0.714
GPBLUP
0.607
0.714
Traditional BLUP*
0.391
0.471
*Results from traditional frequentist BLUP were added for comparison.
Table
Heritability estimates for the quantitative trait using different genomic methods.
polygenic
SNP(genomic)
Total
BB
GPBB
16
40
56
GBB

47
47
Traditional BLUP*
55

55
BLUP
GPBLUP
15
36
51
GBLUP

42
42
Traditional BLUP*
54

54
* Results for traditional BLUP are from the Bayesian and frequentist approaches when compared with BB and BLUP, respectively.
Heritability estimates for the Binary trait using the different genomic methods.
Polygenic
SNP
Total
BB*
GPBB
5
45
50
GBB

46
46
Traditional BLUP^{$}
43

43
BLUP*
GPBLUP
~0
36
36
GBLUP

36
36
Traditional BLUP^{$}
19

19
* Two different models (Logit link function or liability threshold) were used for BLUP and BB respectively (see text for more details) which may make the results between the two methodologies less comparable.
^{$} results for traditional BLUP are from the Bayesian and frequentist approaches when compared with BB and BLUP, respectively.
Correlation between EBV for both traits was around 0.58 across the four genomic selection methods (Figure
Plot of EBVs for the BT and QT using GBB method (r2 = 0.58).
Plot of EBVs for the BT and QT using GBB method (r2 = 0.58).
QTL mapping
BB estimated the probability of a given SNP having an effect on the trait. Locating single SNP or a cluster of linked SNP with a relatively large probability may be used as criteria for mapping QTL. Figures
Comparison of QTL mapping profiles: linkage analyses, association and genomic selection (GBB) for quantitative trait. Different colours mean different chromosomes (1 to 5). The scales on y axes from top to down are: LRT (Likelihood Ratio Test); F statistic, F statistic, probability (of having effect) and simulated substitution effect.
Comparison of QTL mapping profiles: linkage analyses, association and genomic selection (GBB) for quantitative trait. Different colours mean different chromosomes (1 to 5). The scales on y axes from top to down are: LRT (Likelihood Ratio Test); F statistic, F statistic, probability (of having effect) and simulated substitution effect.
Comparison of QTL mapping profiles: linkage analyses, association and genomic selection (GBB) for binary trait. Different colours mean different chromosomes (1 to 5). The scales on y axes from top to down are: F statistic, F statistic, probability (of having effect) and simulated substitution effect.
Comparison of QTL mapping profiles: linkage analyses, association and genomic selection (GBB) for binary trait. Different colours mean different chromosomes (1 to 5). The scales on y axes from top to down are: F statistic, F statistic, probability (of having effect) and simulated substitution effect.
The good performance of the BB, in terms of genomic evaluation of animals, was also reflected in the consistent QTL signals, obtained by the method, compared to the actual simulated QTLs, raising its potential value for QTL mapping.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JN and RPW carried out the analyses and drafted the manuscript. Both authors have read and contributed to the final text of the manuscript.
Acknowledgements
Authors warmly acknowledge Dr DirkJan de Koning for his comments and supports. JN acknowledge the SABRETRAIN project (EC Contract number MESTCT200520558), funded by the Marie Curie Host Fellowships for Early Stage Research Training. JN acknowledge also the ECfunded Integrated Project SABRE (EC contract number FOODCT200601625).
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