Applying different genomic evaluation approaches on QTLMAS2010 dataset
© Nadaf and Pong-Wong; licensee BioMed Central Ltd. 2011
Published: 27 May 2011
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 14th QTLMAS dataset.
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.
For QT, the best accuracy of EBV (correlation between EBVs and TBVs) for young animals, was obtained by BB methods (r = 0.68). Genomic BLUP estimations (GBLUP and GPBLUP) were less accurate (r = 0.60 and 0.61 respectively). Similar results were obtained for the BT: r were estimated at 0.82, 0.82, 0.71 and 0.70 for GPBB, GBB, GPBLUP and GBLUP respectively. Using traditional BLUP, r was at 0.39 and 0.47 for QT and BT respectively. The genetic correlation between the two traits (approximated by the correlation between EBVs for BT and QT using GBB method) was as high as 0.58.
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 (r=0.58) which suggested that bivariate analyses may be of advantages. Signal profile by GBB followed well the true QTL patterns, which was consistent with good estimation of EBVs by this method, suggesting its potential value for QTL mapping.
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) . Several statistical methods has been suggested and applied. Roughly speaking, they can be grouped into two categories. In the first group, the effects of all SNP in the map are jointly estimated, and then the EBV for each animal is calculated as the sum of all SNP effects, given their genotype. Meuwissen et al  compared several methods using this approach and the best performance was achieved when the model accounted for the fact that not all SNP in the map are affecting the trait (i.e. Bayes B, BB). This method also allows estimating the probability of a SNP having an effect on the trait, which can be used as a criterion for QTL mapping. In the second group, SNP genotype is used to better estimate the relationship among individuals . The benefit of this is that, such estimations can be later used in a standard Best Linear Unbiased prediction analysis (GBLUP) to calculate EBV. The advantages from using this approach are its speed and the availability of software, as the mixed model theory is well-established.
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.
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
where, y is the vector of phenotypes; µ is the population mean for the trait; n is total number of SNP, zi is the vector of genotypes at SNP i; β i indicates the allelic substitution effect for SNP i; and e is the vector of residuals. The allelic substitution effects β for each SNP were assumed to come from a mixture distribution, with probability of π to have a non-zero effect on the trait, , and with probability of (1- π) of not affecting the trait; a is the genetic addictive effect explained by pedigree information and assumed to be normally distributed, , where A is the additive relationship matrix calculated from pedigree information, is the variance of the effect explained by the pedigree. Z is the incidence matrix. Here, thereafter, the effect associated to the pedigree will be referred as the polygenic effects.
The models were implemented using Gibbs sampling. The parameters π and were estimated from data using flat priors. For each analysis, a MCMC chain was run and the first 10000 cycles were discarded as burn-in period. Following this, 50000 realisations were collected, each separated by 20 cycles between consecutive realisations (i.e. length of chain = 1,010,000 cycles). The posterior mean was used as the estimate for each parameter of interest.
For the binary trait a liability threshold model was used.
PEV stands for Prediction Error Variance, and r is the accuracy of estimates. The explained additive genetic variance ( ) was obtained using the above equations, for BB methods and the corresponding proportion of this variance (to the total variance) was reported.
b. Genomic BLUP models
where, g is vector of random additive genetic effect explained by the SNP information and assumed to be normally distributed as , where G is the realised relationship matrix calculated from SNP information , and is the variance of g. Both GBLUP models were implemented using ASREML , where the variance components were estimated from the data itself. For the binary trait, the logit link function was used .
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)  and likelihood ratio test (LRT) for the BB and GBLUP, respectively.
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 , which comprises two steps. First, phenotype were corrected for the polygenic effects and second, residuals were fitted against each SNP using additive model as implemented in GENABEL. The binary trait was treated as a quantitative trait.
b. Linkage analyses
- Haf-sib QTL mapping
Half-sib analyses (HS-QTL) were performed as described by Haley et al. , and implemented in the GridQTL . The analysis was based on studying the segregation of the paternal allele. The binary trait was treated as quantitative one.
- 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 (VC-QTL) 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 two-step approach : At each position, first, a relationship matrix based on Identical-By-Descent (IBD) coefficients was estimated using a recursive method . Then a REML analysis was performed to calculate the variance components. Likelihood Ratio Test (LRT) was used as the test statistics to compare the model with QTL versus the one without QTL.
Results and discussion
Correlation between true and estimated breeding values of unphenotyped individuals for the different genomic methods.
Heritability estimates for the quantitative trait using different genomic methods.
Heritability estimates for the Binary trait using the different genomic methods.
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.
Authors warmly acknowledge Dr Dirk-Jan de Koning for his comments and supports. JN acknowledge the SABRETRAIN project (EC Contract number MEST-CT-2005-20558), funded by the Marie Curie Host Fellowships for Early Stage Research Training. JN acknowledge also the EC-funded Integrated Project SABRE (EC contract number FOOD-CT-2006-01625).
This article has been published as part of BMC Proceedings Volume 5 Supplement 3, 2011: Proceedings of the 14th QTL-MAS Workshop. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/5?issue=S3.
- Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157: 1819-1829.PubMed CentralPubMedGoogle Scholar
- Hayes BJ, Visscher PM, Goddard ME: Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Research. 2009, 91: 47-60. 10.1017/S0016672308009981.View ArticlePubMedGoogle Scholar
- Cameron N: Selection Indices and Prediction of Genetic Merit in Animal Breeding. 1997, CABI PublishingGoogle Scholar
- Amin N, van Duijn CM, Aulchenko YS: A Genomic Background Based Method for Association Analysis in Related Individuals. PLoS ONE. 2007, 2: 10.1371/journal.pone.0001274.Google Scholar
- Gilmour AR, Cullis BR, Welham SJ, Thompson R: ASReml Reference manual. NSW Department of Agriculture, Orange. NSW. Agriculture Biometric. 2000, 3: 210-Google Scholar
- Kass RE, Raftery AE: Bayes Factors. Journal of the American Statistical Association. 1995, 90: 773-795. 10.2307/2291091.View ArticleGoogle Scholar
- Aulchenko YS, De Koning DJ, Haley C: GRAMMAR: a fast and simple method for genome-wide pedigree-based quantitative trait loci association analysis. Genetics. 2007Google Scholar
- Aulchenko YS, Ripke S, Isaacs A, Van Duijn CM: GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007, 23: 1294-10.1093/bioinformatics/btm108.View ArticlePubMedGoogle Scholar
- Haley CS, Knott SA, Elsen JM: Mapping Quantitative Trait Loci in Crosses Between Outbred Lines Using Least Squares. Genetics. 1994, 136: 1195-1207.PubMed CentralPubMedGoogle Scholar
- Seaton G, Hernandez J, Grunchec JA, White I, Allen J, De Koning DJ, Wei W, Berry D, Haley C, Knott S: GridQTL: a grid portal for QTL mapping of compute intensive datasets. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production. 2006, 13-18.Google Scholar
- George AW, Visscher PM, Haley CS: Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach. Genetics. 2000, 156: 2081-PubMed CentralPubMedGoogle Scholar
- Pong-Wong R, George AW, Woolliams JA, Haley CS: A simple and rapid method for calculating identity-by-descent matrices using multiple markers. Genetics Selection, Evolution: GSE. 2001, 33: 453-10.1186/1297-9686-33-5-453.PubMed CentralView ArticleGoogle Scholar
- Hadjipavlou G, Hemani G, Leach R, Louro B, Nadaf J, Rowe S, de Koning D: Extensive QTL and association analyses of the QTLMAS2009 Data. BMC Proceedings. 2010, 4: S11-10.1186/1753-6561-4-s1-s11.PubMed CentralView ArticlePubMedGoogle Scholar
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