Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses

Yi, Nengjun; Shriner, Daniel

doi:10.1038/sj.hdy.6801074

Cited by 60 publications

(37 citation statements)

References 63 publications

(101 reference statements)

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“…In QTL mapping, the fully Bayesian methods can easily treat missing genotypes and positions of QTL as unknowns and use model averaging to induce robustness and stability (Yi et al 2005(Yi et al , 2007aYi and Shriner 2008). We describe our method by allowing only observed markers as potential QTL and replacing missing marker genotypes by their conditional expectations.…”

Section: Discussionmentioning

confidence: 99%

“…These Bayesian models are computed using Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior distribution. Due to the recent development of MCMC algorithms and associated computer software, Bayesian methods have become increasingly popular in QTL mapping (Yiet al 2005(Yiet al , 2007aYandell et al 2007;Yi and Shriner 2008). The Bayesian MCMC approaches can provide comprehensive information, but they are computationally intensive in interacting-QTL analysis.…”

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confidence: 99%

“…The first assumes a two-component mixture distribution for each genetic effect, typically a normal distribution with known or unknown variance and a point mass at zero. This discrete prior allows each effect to have positive probability of dropping out of the model (Yi 2004;Yi and Shriner 2008). The second formulation takes continuous prior distributions for genetic effects that favor a sparse structure with many of the effects having values close to zero and few with large values (Meuwissen et al 2001;Xu 2003;Yi and Xu 2008).…”

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confidence: 99%

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Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

Banerjee

2009

Genetics

122

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We develop hierarchical generalized linear models and computationally efficient algorithms for genomewide analysis of quantitative trait loci (QTL) for various types of phenotypes in experimental crosses. The proposed models can fit a large number of effects, including covariates, main effects of numerous loci, and gene-gene (epistasis) and gene-environment (G 3 E) interactions. The key to the approach is the use of continuous prior distribution on coefficients that favors sparseness in the fitted model and facilitates computation. We develop a fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares for classical generalized linear models as implemented in the package R. We propose a model search strategy to build a parsimonious model. Our method takes advantage of the special correlation structure in QTL data. Simulation studies demonstrate reasonable power to detect true effects, while controlling the rate of false positives. We illustrate with three real data sets and compare our method to existing methods for multiple-QTL mapping. Our method has been implemented in our freely available package R/qtlbim (www.qtlbim.org), providing a valuable addition to our previous Markov chain Monte Carlo (MCMC) approach. MOST complex traits are influenced by interacting networks of multiple quantitative trait loci (QTL) and environmental factors (Carlborg and Haley 2004). Mapping QTL is to infer which genomic loci are strongly associated with the complex trait and to estimate genetic effects of these loci, i.e., main effects and gene-gene (epistasis) and gene-environment (G 3 E) interactions. Due to the multilocus nature of complex traits, it is desirable to simultaneously analyze multiple loci rather than one locus (or a few loci) at a time. However, QTL mapping studies usually genotype hundreds or thousands of genomic loci (markers), leading to numerous variables and a huge number of possible models, and the dependence of genotypes on a chromosome results in many correlated variables. Therefore, mapping multiple QTL requires sophisticated methods that can handle problems with high-dimensional correlated variables.The popular approaches to mapping multiple QTL are some form of variable selection. Such techniques involve identifying a subset of all possible genetic effects (a multiple-QTL model) that best explains the phenotypic variation. Classical variable selection methods use forward or stepwise search procedures and selection criteria such as Bayesian information criteria (BIC) or modified versions to find a multiple-QTL model (Kao et al. 1999;Broman and Speed 2002;Bogdan et al. 2004;Baierl et al. 2006). Bayesian methods proceed by setting up a likelihood function for observed data and prior distributions on unobserved quantities. Two types of prior distributions have been suggested for multiple-QTL mapping. The first assumes a two-component mixture distribution for each genetic effect, ty...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

Banerjee

2009

Genetics

122

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“…Compared with other approaches (for example, regression), the Bayesian approach has some advantages including flexibility in model selection and limited requirement of multiple testing (Yi et al, 2003). However, it also has some disadvantages in computational efficiency (Yi and Shriner, 2008) and repeatability of mapping results.…”

Section: Introductionmentioning

confidence: 99%

Controlling false positives in the mapping of epistatic QTL

et al. 2009

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This study addresses the poorly explored issue of the control of false positive rate (FPR) in the mapping of pair-wise epistatic quantitative trait loci (QTL). A nested test framework was developed to (1) allow pre-identified QTL to be used directly to detect epistasis in one-dimensional genome scans, (2) to detect novel epistatic QTL pairs in twodimensional genome scans and (3) to derive genome-wide thresholds through permutation and handle multiple testing. We used large-scale simulations to evaluate the performance of both the one-and two-dimensional approaches in mapping different forms and levels of epistasis and to generate profiles of FPR, power and accuracy to inform epistasis mapping studies. We showed that the nested test framework and genome-wide thresholds were essential to control FPR at the 5% level. The one-dimensional approach was generally more powerful than the two-dimensional approach in detecting QTL-associated epistasis and identified nearly all epistatic pairs detected from the two-dimensional approach. However, only the two-dimensional approach could detect epistatic QTL with weak main effects. Combining the two approaches allowed effective mapping of different forms of epistasis, whereas using the nested test framework kept the FPR under control. This approach provides a good search engine for high-throughput epistasis analyses.

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“…There is a vast literature on multi-mapping QTL (see the review by Yi and Shriner 2008); some of the models have been extended to the analysis of a small number of traits simultaneously (Banerjee et al 2008;Xu et al 2008). Several styles of approaches have been adopted ranging from adaptive shrinkage (Yi and Xu 2008;Sun et al 2010) to variable selection within a composite model space framework that sets an upper bound on the number of effects (Yi et al 2007).…”

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Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

Bottolo

Petretto

Blankenberg³

et al. 2011

Genetics

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High-throughput genomics allows genome-wide quantification of gene expression levels in tissues and cell types and, when combined with sequence variation data, permits the identification of genetic control points of expression (expression QTL or eQTL). Clusters of eQTL influenced by single genetic polymorphisms can inform on hotspots of regulation of pathways and networks, although very few hotspots have been robustly detected, replicated, or experimentally verified. Here we present a novel modeling strategy to estimate the propensity of a genetic marker to influence several expression traits at the same time, based on a hierarchical formulation of related regressions. We implement this hierarchical regression model in a Bayesian framework using a stochastic search algorithm, HESS, that efficiently probes sparse subsets of genetic markers in a high-dimensional data matrix to identify hotspots and to pinpoint the individual genetic effects (eQTL). Simulating complex regulatory scenarios, we demonstrate that our method outperforms current state-of-the-art approaches, in particular when the number of transcripts is large. We also illustrate the applicability of HESS to diverse real-case data sets, in mouse and human genetic settings, and show that it provides new insights into regulatory hotspots that were not detected by conventional methods. The results suggest that the combination of our modeling strategy and algorithmic implementation provides significant advantages for the identification of functional eQTL hotspots, revealing key regulators underlying pathways.T HE current focus of biological research has turned to high-throughput genomics, which encompasses largescale data generation and a variety of integrated approaches that combine two or more -omics of data sets. An important example of integrative genomics analysis is the investigation of the genetic regulation of transcription, also called expression quantitative trait locus (eQTL) or "genetical genomics" studies (Cookson et al. 2009;Majewski and Pastinen 2011). A typical eQTL analysis follows a natural structure of parallel regressions between the large set of q responses (i.e., expression phenotypes), and that of p explanatory variables (i.e., genetic markers, often single nucleotide polymorphism, SNPs), where p is typically much larger than the number of observations n.From a statistical point of view, the size and the complex multidimensional structure of eQTL data sets pose a significant challenge. Not only does one wish to detect the set of important genetic control points for each response (expression phenotype), including cis-and trans-acting control for the same transcript, but, ideally, one would wish to exploit the dependence between multiple expression phenotypes. This will facilitate the discovery of key regulatory markers, so-called hotspots (Breitling et al. 2008), i.e., genetic loci or single polymorphisms that influence a large number of transcripts. Identification of hotspots can inform on network and pathways, which are likel...

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Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses

Cited by 60 publications

References 63 publications

Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

Controlling false positives in the mapping of epistatic QTL

Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

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