Volume 6 Supplement 2
Proceedings of the 15th European workshop on QTL mapping and marker assisted selection (QTLMAS)
Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods
 Jian Zeng†^{1}Email author,
 Marcin Pszczola†^{2, 3},
 Anna Wolc^{1, 2},
 Tomasz Strabel^{2},
 Rohan L Fernando^{1},
 Dorian J Garrick^{1} and
 Jack CM Dekkers^{1}
DOI: 10.1186/175365616S2S7
© Zeng et al.; licensee BioMed Central Ltd. 2012
Published: 21 May 2012
Abstract
Background
The goal of this study was to apply Bayesian and GBLUP methods to predict genomic breeding values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15^{th} QTLMAS workshop.
Methods
Three methods with models considering dominance and epistasis inheritances were used to fit the data: (i) BayesB with a proportion π = 0.995 of SNPs assumed to have no effect, (ii) BayesCπ, where π is considered as unknown, and (iii) GBLUP, which directly fits animal genetic effects using a genomic relationship matrix.
Results
BayesB, BayesCπ and GBLUP with various fitted models detected 6, 5, and 4 out of 8 simulated QTL, respectively. All five additive QTL were detected by Bayesian methods. When two QTL were in either coupling or repulsion phase, GBLUP only detected one of them and missed the other. In addition, GBLUP yielded more false positives. One imprinted QTL was detected by BayesB and GBLUP despite that only additive gene action was assumed. This QTL was missed by BayesCπ. None of the methods found two simulated additivebyadditive epistatic QTL. Variance components estimation correctly detected no evidence for dominance geneaction. Bayesian methods predicted additive genetic merit more accurately than GBLUP, and similar accuracies were observed between BayesB and BayesCπ.
Conclusions
Bayesian methods and GBLUP mapped QTL to similar chromosome regions but Bayesian methods gave fewer false positives. Bayesian methods can be superior to GBLUP in GEBV prediction when genomic architecture is unknown.
Background
Bayesian methods and the genomic BLUP procedure (GBLUP) can be used for prediction of genomic estimated breeding values (GEBV) and quantitative trait loci (QTL) detection. BayesB generally performs slightly better than GBLUP, especially when nonadditive gene actions are involved [1]. Apart from Bayesian methods, GBLUP solutions can also be used to estimate marker effects [2]. The objectives of this study were 1) to identify the positions of QTL affecting the trait simulated for the 15th QTLMAS Workshop and estimate their effects using Bayesian methods and GBLUP, 2) to explore the genetic architecture of the trait, especially regarding presence of dominance and epistasis, and 3) to predict GEBV of the individuals without phenotypes.
Methods
Data
The simulated population included 20 sires, 10 dams per sire and 15 fullsib progeny per dam. The genome consisted of 5 chromosomes of 1 Morgan and 1,998 evenly spaced SNPs. Sources of information for analysis included 2 generations of pedigree, genotypes for all individuals and phenotypic records for 10 progeny per family. More detailed description of the dataset is available at [3].
Methods to predict GEBV
where X_{ ij } is the copy number of a given allele of animal i at SNP j, W_{ ij } is the dummy variable indicating whether the genotype for SNP j of animal i is heterozygous, a_{ j } (additive effect) is half the difference between homozygotes for SNP j, and d_{ j } (dominance effect) is the difference between heterozygote and the mean of homozygotes for SNP j. The priors for a_{ j } and d_{ j } were mixtures of normals as described in [5], with effect specific values for π (π_{ a } and π_{ d }) and variance σ^{2} (${\sigma}_{a}^{2}$and ${\sigma}_{d}^{2}$). Gibbs sampling was used to sample the posterior distribution of model parameters. SNP effects were estimated by the mean of the sampled values. GEBVs were predicted as the linear combination of the SNP substitution effects. GenSel [7] was used to implement the Bayesian methods.
In GBLUP the presence of dominance was investigated using a model with an additional random dominance effect (G2) for each animal. The variancecovariance matrix for this effect was created similar to the genomic relationship matrix G, except genotypes were coded as 1 for heterozygotes and 0 for both homozygotes. The third model (G3) had an additional random additivebyadditive epistatic effect for each animal, with G^{ 2 } as the variancecovariance matrix. GEBV were estimated using models G1 to G3 with variance components estimated using ASReml [8].
Methods to map QTL
where α is the vector of allele substitution effects, ${\sigma}_{\alpha}^{2}={\sigma}_{a}^{2}/2\sum {\mathsf{\text{p}}}_{\mathsf{\text{i}}}\left(1{\mathsf{\text{p}}}_{\mathsf{\text{i}}}\right)$ where ${\sigma}_{\mathsf{\text{a}}}^{2}$ is additive genetic variance, Z is the genotype matrix with dimensions equal to the number of individuals by the number of SNPs, and â is the vector of GEBV obtained from GBLUP. Given the estimated SNP effects, QTL were mapped to the positions where the SNP had visually significant effects on the trait.
Results
Estimated variance components
Estimated variance components and heritability (h^{2})
Methods  Genetic Variance Components  Residual  Total  h^{2}  

Additive  Epistasis  Dominance  
True Value  26.35  61.49  87.84  0.3  
BayesB  24.61      60.17  84.78  0.29 
BayesCπ  
AM  24.19      60.29  84.48  0.286 
DM  24.27    0.12  60.16  84.55  0.287 
GBLUP  
G1  22.09      59.8  81.89  0.269 
G2  22.19    0.51  59.34  82.03  0.27 
G3  22.09  6.20E06    59.8  81.89  0.269 
QTL mapping
Predictive accuracy of GEBV
Correlations among GEBV
Method  BayesB  BayesCπ  GBLUP 

BayesCπ  0.997  
GBLUP  0.918  0.897  
TBV  0.934  0.939  0.825 
Discussion
The simulated trait was affected by one QTL with major and seven with minor effects. Two QTL were interacting with each other (epistasis) and one was imprinted. All approaches detected the major QTL and three to six QTL with smaller effects. The Bayesian methods detected more simulated QTL regions and gave fewer false positives than GBLUP. GBLUP failed to find one of the two QTL that were close to each other. This confirms the finding of [8] that when the genetic architecture of the trait is complex, Bayesian methods are superior to GBLUP in QTL mapping.
The failure to detect the imprinted QTL for BayesCπ and the epistatic QTL for BayesB and BayesCπ reveals some drawbacks of basing QTL mapping solely on window variances. A 10SNP window may include too much noise, which results in shrinkage of the signals towards zero. Thus, the variance of the causative region may be underestimated. As shown in Figures 1 and 2, although some single SNP signals were shown for the imprinted and epistatic QTL on chromosome 4 and 5, the small window variances prevented these regions from being considered significant (Figure 3). For the major QTL, 10SNP windows may be too narrow to cover the entire causative region, which resulted in two QTL being identified. Moreover, if the parental origins of alleles were known, an additive model that fits substitution effects of the alleles specific to their parental origins, or a dominance model that fits dominance effects specific to the type of heterozygotes (01 or 10) is expected to capture the imprinting inheritance.
GEBV obtained using Bayesian and GBLUP analyses were highly correlated among each other, which agrees with [10]. In accord with earlier QTLMAS workshops [1, 11], Bayesian methods yielded higher accuracy of GEBV (0.930.94) than GBLUP (0.83). Because most SNP had no effects on the trait, including spurious SNP in the model introduced noise to GEBV and impaired the predictive accuracy. For highdensity SNP panels or DNA sequencing data, Bayesian models are considered more robust and the superiority over GBLUP is expected to increase.
Conclusions
Bayesian methods and GBLUP revealed the additive genetic attributes of the simulated trait. The number of indicated regions and their positions were in good agreement with the truth. Bayesian methods were superior to GBLUP in QTL mapping, with fewer false positives. The window variance is a plausible criterion to identify QTL using Bayesian methods, although some drawbacks exist. The mutual correlations among alternative methods were close to one but Bayesian methods yielded higher accuracy for GEBV than GBLUP.
Notes
List of abbreviations used
 QTL:

quantitative trait locus
 BLUP:

best linear unbiased prediction
 GBLUP:

BLUP with a realized relationship matrix
 TABLUP:

BLUP with a trait specific relationship matrix
 GEBV(s):

genomic estimated breeding value(s)
 TBV(s):

true breeding value(s)
 SNP:

single nucleotide polymorphism.
Declarations
Acknowledgements
Mario Calus provided software to calculate G. MP acknowledges financial support of GreenHouseMilk project and the Koepon Stichting (Arnhem, the Netherlands). The GreenHouseMilk project is financially supported by the European Commission under the Seventh Research Framework Programme, Grant Agreement KBBE238562. This publication represents the views of the authors, not the European Commission, and the Commission is not liable for any use that may be made of the information. Contribution in initial analysis of the simulated dataset of Maciej Szydłowski, Sebastian Mucha, Marek A. Wietrzykowski and Alicja Borowska (Department of Genetics and Animal Breeding, Poznan University of Life Science) is greatly appreciated.
This article has been published as part of BMC Proceedings Volume 6 Supplement 2, 2012: Proceedings of the 15th European workshop on QTL mapping and marker assisted selection (QTLMAS). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/supplements/6/S2.
Authors’ Affiliations
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