Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies

Hoggart, Clive; Whittaker, John C.; Iorio, Maria De; Balding, David J.

doi:10.1371/journal.pgen.1000130

Cited by 300 publications

(366 citation statements)

References 23 publications

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“…Studies have used values of at least 5-10 N e generations of random mating to initialize a genome linkage disequilibrium (LD) structure and have reported stable LD and heterozygosity (e.g., Meuwissen et al 2001;Habier et al 2007;Calus et al 2008;Daetwyler et al 2010b). Hoggart et al (2008) propose that 10-12 N e is sufficient to ensure that initial genome parameters have little influence on the final generation. During this period of random mating, genomes are randomly mutated and recombined.…”

Section: Simulation Of Genomesmentioning

confidence: 99%

“…While an extensive set of literature exists that describes the theoretical and practical strengths and weaknesses, as well as software implementing the coalescent-based methods [e.g., MaCS (Chen et al 2009) and MS (Hudson 2002)], many forward in time methods are perhaps more ad hoc and lack very solid theoretical reasoning for their details. However, there are some forward in time simulation programs that are well described in the literature, such as FREGENE (Hoggart et al 2008), simuPOP (Peng and Kimmel 2005), HaploSim (Coster and Bastiaansen 2009), quantiNemo (Neuenschwander et al 2008), and QMsim (Sargolzaei and Schenkel 2009). Others, such as AlphaDrop (Hickey and Gorjanc 2012), attempt to combine components of the coalescent [explicitly by using MaCS (Chen et al 2009)] with components of forward in time simulations, which allow for selection (most practical only for a relatively short number of recent generations).…”

Section: Simulation Of Genomesmentioning

confidence: 99%

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Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

Daetwyler¹,

et al. 2013

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The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare because data simulation varies, real or simulated data are not fully described, and not all relevant results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions difficult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits with fewer QTL variable selection did have some advantages. In the real data sets examined here all methods had very similar accuracies. We conclude that no single method can serve as a benchmark for genomic prediction. We recommend comparing accuracy and bias of new methods to results from genomic best linear prediction and a variable selection approach (e.g., BayesB), because, together, these methods are appropriate for a range of genetic architectures. An accompanying article in this issue provides a comprehensive review of genomic prediction methods and discusses a selection of topics related to application of genomic prediction in plants and animals. G ENOMIC information is transforming animal and plant breeding (e.g., Dekkers and Hospital 2002; Bernardo and Yu 2007;Goddard and Hayes 2009;Hayes et al. 2009a;Heffner et al. 2009;VanRaden et al. 2009a;Calus 2010;Crossa et al. 2010;Daetwyler et al. 2010a;Jannink et al. 2010;Wolc et al. 2011). Genomic selection can increase the rates of genetic gain through increased accuracy of estimated breedi...

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Section: Simulation Of Genomesmentioning

confidence: 99%

Section: Simulation Of Genomesmentioning

confidence: 99%

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

Daetwyler¹,

et al. 2013

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“…However, there is increasing evidence that a number of important traits and diseases are affected by a very large number of genes (McClellan and King 2010), as well as environmental factors. In this situation, a better false-positive and false-negative performance is achieved by analyzing all SNPs jointly (Hoggart et al 2008) using whole-genome random regression (WGRR) models, as in de los Campos et al (2010a) and Yang et al (2010). For a recent review of different linear models in the context of WGGR see de los Campos et al (2012).…”

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confidence: 99%

Inferences from Genomic Models in Stratified Populations

et al. 2012

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Unaccounted population stratification can lead to spurious associations in genome-wide association studies (GWAS) and in this context several methods have been proposed to deal with this problem. An alternative line of research uses whole-genome random regression (WGRR) models that fit all markers simultaneously. Important objectives in WGRR studies are to estimate the proportion of variance accounted for by the markers, the effect of individual markers, prediction of genetic values for complex traits, and prediction of genetic risk of diseases. Proposals to account for stratification in this context are unsatisfactory. Here we address this problem and describe a reparameterization of a WGRR model, based on an eigenvalue decomposition, for simultaneous inference of parameters and unobserved population structure. This allows estimation of genomic parameters with and without inclusion of markerderived eigenvectors that account for stratification. The method is illustrated with grain yield in wheat typed for 1279 genetic markers, and with height, HDL cholesterol and systolic blood pressure from the British 1958 cohort study typed for 1 million SNP genotypes. Both sets of data show signs of population structure but with different consequences on inferences. The method is compared to an advocated approach consisting of including eigenvectors as fixed-effect covariates in a WGRR model. We show that this approach, used in the context of WGRR models, is ill posed and illustrate the advantages of the proposed model. In summary, our method permits a unified approach to the study of population structure and inference of parameters, is computationally efficient, and is easy to implement.

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“…For example, minor homozygotes can be coded according to two different schemes: 1) having 2 minor alleles [1] or 2) having 0 major alleles [2]. In most analyses, the SNPs are treated as quantitative data because most statistical methods used rely upon quantitative measures [3][4][5]. Some multivariate approaches for SNPs include independent components analysis (ICA) [6], sparse reduced-rank regression (SRRR) [7], multivariate distance matrix regression (MDMR) [8,9], and PLS regression (PLSR) [10,11].…”

Section: Introductionmentioning

confidence: 99%

Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data

Beaton

Filbey

Abdi

2013

Springer Proceedings in Mathematics &Amp; Statistics

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We present an extension of PLS-called partial least squares correspondence analysis (PLSCA)-tailored for the analysis of nominal data. As the name indicates, PLSCA combines features of PLS (analyzing the information common to two tables) and correspondence analysis (CA, analyzing nominal data). We also present inferential techniques for PLSCA such as bootstrap, permutation, and χ 2 omnibus tests. We illustrate PLSCA with two nominal data tables that store (respectively) behavioral and genetics information.

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Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies

Cited by 300 publications

References 23 publications

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

Inferences from Genomic Models in Stratified Populations

Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data

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