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14 th QTL-MAS Workshop, Poznań University of Life Sciences, Poland 2010Genome-wide scans for quantitative trait loci in experimental populations -issues of multiple testing and model selectionMałgorzata Bogdan 1∗1 Institute of Mathematics and Computer Science, Wrocław University of Technology, Poland∗ Presenting author: Małgorzata Bogdan, email: Malgorzata.Bogdan@pwr.wroc.plThere exist a variety of methods for locating multiple interacting quantitative trait inexperimental population. Most of these methods are based on linear models, relating traitvalues to the genotypes of potential QTL. The most difficult part in selecting the best linear isthe estimation of the number of QTL. This task could in principle be addressed by theapplication of one of the model selection criteria, like e.g. AIC or BIC. However, as observedin [1], BIC (and so AIC) has a strong tendency to overestimate QTL number. In [2] and [3] itis explained that this phenomenon is related to the well known issue of multiple testing. Also,in the Bayesian context, the overestimation can be explained by the undesired properties ofthe uniform prior on the class of possible models, implicitly used by BIC. To address theproblem of overestimation, some modified versions of BIC have been proposed. Two of theseversions, modified BIC (mBIC,[4]) and extended BIC (EBIC,[5]), are based on the Bayesianprinciples. BIC is extended by using different prior distributions on the class of possiblemodels. As shown in a series of papers, the priors used in mBIC and EBIC allow to overcomethe problem of overestimation and both these criteria have very good properties as applied forQTL detection.In this talk we will give a brief overview of the problems of high dimensional model selectionand their relationship to multiple testing. We will also present mBIC and EBIC and the resultsof their application to the problem of localizing QTL influencing the count data. If timepermits, we will also briefly discuss the application of model selection tools in the context ofGenome Wide Association Studies in human populations.[1] Broman K, Speed T. A model selection approach for the identification of quantitative trait loci inexperimental crosses. J. Roy. Stat. Soc. B., 2002, 64: 641--656.[2] Bogdan M,Ghosh JK,Żak-Szatkowska M. Selecting explanatory variables with the modified version ofthe Bayesian Information Criterion. QREI, 2008, 24:627--641.[3] Bogdan M,Frommlet F, Biecek P, Cheng R, Ghosh JK,Doerge RW. Extending the Modified BayesianInformation Criterion (mBIC) to dense markers and multiple interval mapping. Biometrics, 2008, 64: 1162--1169.[4] Bogdan M, Ghosh JK, Doerge RW. Modifying the Schwarz Bayesian Information Criterion to LocateMultiple Interacting Quantitative Trait Loci. Genetics, 2004, 167: 989-999.[5] Chen J, Chen Z. Extended Bayesian Information criteria for model selection with large model spaces.Biometrika, 2008, 95: 759--771.30
14 th QTL-MAS Workshop, Poznań University of Life Sciences, Poland 2010Genotype imputation for the prediction of genomic breeding values in nongenotypedand low-density genotyped individualsMatthew A. Cleveland 1∗ , John M. Hickey 2, , Brian P. Kinghorn 21Genus/PIC, 100 Bluegrass Commons Blvd. Suite 2200, Hendersonville, TN 37075, USA2School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia∗ Presenting author: Matthew Cleveland, email: matthew.cleveland@pic.comBackground. There is wide interest in calculating genomic breeding values (GEBVs) inlivestock using dense, genome-wide SNP data. The general framework for genomic selectionassumes all individuals are genotyped at high-density, which may not be true in practice.Methods to add additional genotypes for individuals not genotyped at high density have thepotential to increase GEBV accuracy with little or no additional cost. In this study a longhaplotype library was created using a long range phasing algorithm and used in combinationwith segregation analysis to impute dense genotypes for non-genotyped dams in the trainingdataset (S1) and for non-genotyped or low-density genotyped individuals in the predictiondataset (S2). Alternative low-density scenarios were evaluated for accuracy of imputedgenotypes and prediction of GEBVs.Results. In S1, females in the training population were not genotyped and predictionindividuals were either not genotyped or genotyped at low-density (evenly spaced at 2, 5 or10cM). The accuracy of imputed genotypes for training females did not change with theaddition of genotypes in the prediction set, as expected, whereas the number of correctlyimputed genotypes in the prediction set increased slightly. S2 assumed the complete trainingset was genotyped for all SNPs and the prediction set was not genotyped or genotyped at lowdensity.The number of correctly imputed genotypes increased with genotyping density in theprediction set. Genomic breeding values for the prediction set in each scenario werecorrelated with predicted GEBVs when all animals were genotyped for all SNPs to evaluatethe potential loss in accuracy with reduced genotyping. For both S1 and S2 the correlationswith the high-density GEBVs were similar when the prediction set was not genotyped andincreased with the addition of low-density genotypes, where the increase was larger for S2than S1.Conclusions. Genotype imputation using a long haplotype library and segregation analysishas promise for application in sparsely-genotyped pedigrees. The results of this study suggestthat dense genotypes can be imputed for selection candidates with some loss in GEBVaccuracy compared to the high-density case, but genotyping strategies for the training set maybe needed to maintain accuracy when using low-density SNP panels for genomic selection.31
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