Mapping Quantitative Trait Loci for Expression Abundance

Jia, Zhenyu; Xu, Shizhong

doi:10.1534/genetics.106.065599

Cited by 75 publications

(73 citation statements)

References 48 publications

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“…This can be modeled by assigning a different probability for each marker v kj ¼ v j with an hyper-prior on v j . This solution, first proposed by Jia and Xu (2007) with the conjugate prior p(v j ) ¼ Dirichlet(d 1j , d 2j ), assumes that this selection probability is the same for all the responses. However, whatever the sensible choice of the hyperparameters d 1j and…”

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confidence: 99%

“…This solution, first proposed by Jia and Xu (2007) with the conjugate prior p(v j ) ¼ Dirichlet(d 1j , d 2j ), assumes that this selection probability is the same for all the responses. However, whatever the sensible choice of the hyperparameters d 1j andthe posterior density greatly depends on the ratio between the number of transcripts associated to the marker j, q j, and the total number the transcripts in the eQTL experiment, q, sinceIn such formulation, the results are thus clearly influenced by the number of responses analyzed and sparsity of each kth regression cannot be controlled in the prior specification adopted for v kj of g kj.In this article we propose a novel way of specifying the selection probability v kj to synthetize the benefits of both approaches, and Jia and Xu (2007). We propose decomposing this probability into its marginal effects…”

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confidence: 99%

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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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confidence: 99%

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confidence: 99%

Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

Bottolo

Petretto

Blankenberg³

et al. 2011

Genetics

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“…Moreover, we assume that data has been centralized, Hierarchical modeling of cQTLs and eQTLs MJ Sillanpää and N Noykova that is, a j ¼ a 0 ¼ 0, and the residuals e i,j are uncorrelated even if centralization may induce dependence between residuals in practice (Qu and Xu, 2006;Jia and Xu, 2007). Here, the value of the indicator variable I j controls presence (I j ¼ 1) or absence (I j ¼ 0) of a regulatory effect for pair j.…”

Section: Expression Qtl Modelmentioning

confidence: 99%

“…Bhattacharjee and Sillanpää (2008) proposed the Bayesian cQTL model with the indicator variables (for model selection) to study stratified allele and expression effects to the phenotype using different clinical variables (for example, sex and onset) as stratifying factors. Recently, Jia and Xu (2007) presented a new Bayesian eQTL approach to simultaneously analyze hundreds of expression levels using a multiple marker model and model selection. Further studies are needed in this area, especially from the viewpoint of small sample size (number of individuals).…”

Section: Multiple Trait Analysismentioning

confidence: 99%

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Hierarchical modeling of clinical and expression quantitative trait loci

Sillanpää

Noykova

2008

Heredity

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Previous articles have presented clinical quantitative trait locus (cQTL) models, where the information provided by quantitative/qualitative phenotypes, molecular markers and gene expressions (transcription levels) were combined and analyzed simultaneously. Because of financial constraints, marker data may be available for much larger group of individuals than expression data. However, it is desirable to use all the available information. We therefore extend such approaches by presenting a reliable missing data model for the case when marker data is more complete (that is, has many fewer missing entries). In the suggested hierarchical model, an expression QTL (eQTL) model (which is essentially our missing data model) is part of the larger cQTL model and it represents a Bayesian model-based method for estimating cis-and trans-acting regulatory effects for multiple (typically hundreds of) expression phenotypes simultaneously. The modeling dependence between transcripts in the eQTL model is also considered. The method is based on presenting data in the form of marker gene pairs, for which the presence of regulatory effect (link) can be hypothesized. These marker gene pairs can be obtained from oligonucleotide arrays or created using information available on known pathways or previous eQTL/allelic expression studies. The estimation of the model parameters (such as presence/absence of regulation, eQTL/cQTL effects and proportion of eQTLs and cQTLs among the set of marker gene pairs) as well as the handling of missing data is performed using Markov Chain Monte Carlo (MCMC) sampling. The method is illustrated using both simulated and real data.

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