Volume 4 Supplement 1
Proceedings of the 13th European workshop on QTL mapping and marker assisted selection
A twostep approach combining the Gompertz growth model with genomic selection for longitudinal data
 Ricardo PongWong^{1}Email author and
 Georgia Hadjipavlou^{1}Email author
DOI: 10.1186/175365614S1S4
© PongWong and Hadjipavlou; licensee BioMed Central Ltd. 2010
Published: 31 March 2010
Abstract
Background
We used the Gompertz growth curve to model a simulated longitudinal dataset provided by the QTLMAS2009 workshop and applied genomic evaluation to the derived model parameters and to a modelpredicted trait value.
Results
Prediction of phenotypic information from the Gompertz curve allowed us to obtain genomic breeding value estimates for a time point with no phenotypic records. Despite that the true model used to simulate the data was the logistic growth model, the Gompertz model provided a good fit of the data. Genomic breeding values calculated from predicted phenotypes were highly correlated with the breeding values obtained by directly using the respective observed phenotypes. The accuracies between the true and estimated breeding value at time 600 were above 0.93, even though t600 was outside the time range used when fitting the data. The analysis of the parameters of the Gompertz curve successfully discriminated regions with QTL affecting the asymptotic final value, but it was less successful in finding QTL affecting the other parameters of the logistic growth curve. In this study we estimated the proportion of SNPs affecting a given trait, in contrast with previously reported implementations of genomic selection in which this parameter was assumed to be known without error.
Conclusions
The twostep approach used to combine curve fitting and genomic selection on longitudinal data provided a simple way for combining these two complex tasks without any detrimental effect on breeding value estimation.
Background
A longitudinal trait is a composite of phenotypes recorded over time which have a complex genetic correlation structure. Different types of nonlinear functions have been used to model a timedependent trait and dissect its genetic components. For instance, the Gompertz model has been used for analysing the polygenic components [1] and growth QTL [2] for live weight in sheep. Genomic selection (GS) commonly refers to a new class of methods for genetic evaluation using very dense marker maps covering the entire genome [3]. The overall trend so far has been that GS increases the accuracy of the breeding values, especially for those individuals without phenotypic information.
The objective of this study was to estimate genomic breeding values for the trait at time 600 (t600), which resided outside the range of longitudinal yield data provided by the QTLMAS2009 workshop. We implemented a twostep procedure in which first the Gompertz function was fitted to the data for each individual and, then genomic selection was performed on the predicted phenotype at t600 and on the parameter estimates derived from the fitted Gompertz curve.
Methods
Data
The data provided by QTLMAS2009 is fully described in [4]. It consisted of 100 fullsib families, each with 20 offspring. Half of the offspring (training set) have both phenotype information of yield at 5 distinct time points (0, 132, 25, 397, 530) and genotype data on 453 SNP markers across 5 Morgans. The remaining offspring (candidate set) had only genotype information.
Procedure
To obtain genomic breeding values for t600, we used an approach composed by two independent steps: Firstly, a Gompertz growth curve was used to model the performance records across time, and to estimate the model descriptors (A, B, C) which best fit the phenotypes of each individual. Secondly, genomic evaluation was applied to obtain genomic estimated breeding values (GEBVs) for t600 using two different methods: I) estimating GEBVs for the model parameters (A, B, C; i.e. 3 GEBVs per individual) and using them to estimate the breeding value for t600 from the Gompertz function; II) predicting the phenotypes at t600 from evaluating the Gompertz function with the estimated parameters and later applying genomic selection on the predicted t600 phenotypes.
Growth model
The Gompertz equation is of the form: y(t) = Ae^{{e[Be(Ct)/A]}}, where y(t) is the yield at time t; A the final yield; B the maximum growth rate and C the age at maximum growth rate. The curve fitting was implemented using nonlinear regression in SAS [5]. The Gompertz function was fitted to each individual separately to estimate individual model parameters A, B, C. Subsequently, the fitted individual equations were used to predict the trait at t600 (or t600 GEBVs if using the parameter GEBVs).
Genomic evaluation
where y is the vector of phenotypes; b contains the fixed effects and X is its incidence matrix; α_{ i } is the allelic substitution effect for SNP i; g_{ i } is the vector of genotypes (1, 2 & 3 for genotypes 00, 10/01 and 11, respectively) for SNP i; and e the vector of residuals distributed N(0, ). The allelic substitution effects α for each SNP are assumed to be from a mixture distribution with probability π of having an effect on the trait and with probability (1 π) of not affecting the trait at all. If the SNP is affecting the trait, its allelic substitution is distributed N(0, ).
The implementation of the model was done using Gibbs sampling. The parameters , and π were also calculated in the analysis using flat priors. So far, the implementations of Bayes B reported in the literature have not estimated π, but assumed it was known without error.
For each analysis, a MCMC chain was run and the first 10000 cycles were discarded as burnin period. Following this, 10000 realisations were collected, each separated by 50 cycles between consecutive realisations. The posterior mean was used as the estimate for each parameter of interest.
Results and discussion
Growth model parameters
The Gompertz model provided a good fit of the data (see additional files 1 and 2) with the curve fitted for each individual being statistically significant. To further test how well the Gompertz curve fitted the phenotypic data, phenotypic values were predicted at all 5 time points for which observed phenotypic data was available. The Pearson and Spearman correlations between the true and predicted phenotypic values at t530 were above 0.99, with similar high correlations obtained for the other 4 time points. These high correlations remained when comparing the GEBVs calculated for both the true and predicted phenotypes.
Estimation of GEBVs for the parameters of the Gompertz curve
Estimation of GEBVs for the trait at a given time point
Comparison with the true model used to simulate the data
Pearson (lower diagonal) and Spearman (upper diagonal) correlations between true and estimated breeding values for t600 and the parameters used to simulate or analyse the data.^{1,2}
TBV  GEBV  

T600  Φ_{1}  Φ_{2}  Φ_{3}  t600_I  t600_II  A  B  C  
TBV t600  0.995  0.230  0.091  0.935  0.937  0.913  0.930  0.405  
TBV Φ _{ 1 }  0.997  0.285  0.160  0.931  0.937  0.928  0.925  0.465  
TBV Φ _{ 2 }  0.291  0.344  0.129  0.237  0.258  0.377  0.306  0.719  
TBV Φ _{ 3 }  0.098  0.157  0.108  0.082  0.112  0.213  0.029  0.463  
GEBV t600_I  0.942  0.941  0.316  0.079  0.990  0.968  0.979  0.402  
GEBV t600_II  0.947  0.949  0.332  0.116  0.990  0.969  0.981  0.437  
GEBV A  0.919  0.933  0.459  0.194  0.970  0.969  0.957  0.599  
GEBV B  0.938  0.940  0.396  0.034  0.983  0.983  0.971  0.454  
GEBV C  0.519  0.571  0.735  0.433  0.523  0.551  0.709  0.587 
Despite that the Gompertz curve was not the true model, its use provided very accurate GEBVs for t600. The correlation between the true breeding values and GEBVs are presented in Table 1 and additional file 4. Both methods of estimating GEBVs yielded similar accuracy. The Pearson and Spearman correlations between true and estimated breeding value with methods I and II for all individuals in the pedigree were above 0.93.
In this study, the proportion of SNPs affecting the trait, π, was estimated in the analysis. This contrasts with previously reported implementations of Bayes B where π was assumed to be known without error. The π values were slightly overestimated, partly due to the low linkage disequilibrium between SNPs (average r^{2} between consecutive SNP was 0.15) and also to the fact that the Gompertz function was not the true model. However, the success in estimating such an important parameter from the data itself, even when assuming a uniform prior, provides an improvement in genomic evaluation relative to assuming that π is known without error.
Conclusions
The twostep approach of growth model fitting and genomic selection on model parameters and on predicted phenotype appeared to be a simple and reliable strategy. Despite that the Gompertz curve was not the true model used to simulate the data, the correlations between true and estimated breeding values at t600 were very high (Pearson and Spearman correlations above 0.93). The approach of estimating GEBVs for phenotype at a time of interest using GEBVs of the three parameters and evaluating the Gompertz function could be beneficial when GEBVs are needed for different time points. In this study, the proportion of SNP affecting the trait was estimated from the data, contrasting with previous implementation of genomic selection where this proportion has been assumed to be known without error. The results from this study showed that separate implementation of the growth modelling process and genomic evaluation provided huge simplification of the methodology with no detrimental effect on the final results.
Acknowledgements
GH acknowledges the GENACT Project, funded by the Marie Curie Host Fellowships for Early Stage Research Training, as part of the 6^{th} Framework Programme of the European Commission. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/). The ECDF is partially supported by the eDIKT initiative. (http://www.edikt.org.uk).
This article has been published as part of BMC Proceedings Volume 4 Supplement 1, 2009: Proceedings of 13th European workshop on QTL mapping and marker assisted selection.
The full contents of the supplement are available online at http://www.biomedcentral.com/17536561/4?issue=S1.
List of abbreviations used
 QTL:

Quantitative Trait Locus
 SNP:

Single Nucleotide Polymorphism
 GS:

Genomic Selection
 GEBV:

Genomic Estimated Breeding value
 MCMC:

Monte Carlo Markov Chain
Declarations
Authors’ Affiliations
References
 Lambe NR, Navajas EA, Simm G, Bunger L: A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds. J Anim Sci. 2006, 84: 26422654. 10.2527/jas.2006041.View ArticlePubMedGoogle Scholar
 Hadjipavlou G, Bishop SC: Agedependent quantitative trait loci affecting growth traits in Scottish Blackface sheep. Anim Genet. 2009, 40: 165175. 10.1111/j.13652052.2008.01814.x.View ArticlePubMedGoogle Scholar
 Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genomewide dense marker maps. Genetics. 2001, 157: 18191829.PubMed CentralPubMedGoogle Scholar
 Coster A, Bastiaansen J, Calus M, Maliepaard C, Bink M: QTLMAS 2009: Simulated Dataset. BMC Proc. 2010, 4 (Suppl 1): S310.1186/175365614S1S3.PubMed CentralView ArticlePubMedGoogle Scholar
 SAS release 9.1. SAS Institute, Cary, NC. Ref Type: Computer Program
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