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A hierarchical bayesian approach to multi-trait clinical quantitative trait locus modeling.

Mutshinda CM, Noykova N, Sillanpää MJ - Front Genet (2012)

Bottom Line: Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits.A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision.The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki Helsinki, Finland.

ABSTRACT
Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits.

No MeSH data available.


Related in: MedlinePlus

Hierarchical directed acyclic graph (DAG) of the model structure. The pre-specified values (Model level I) or observed data (Model level IV) are given in boxes. Ellipses (Model levels II and III) refer to the unknown random variables, which are sampled. The solid arrows illustrate hierarchical dependencies, and the dotted arrows show deterministic dependences. The box given in bold indicates the multi-trait model structure.
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Figure 1: Hierarchical directed acyclic graph (DAG) of the model structure. The pre-specified values (Model level I) or observed data (Model level IV) are given in boxes. Ellipses (Model levels II and III) refer to the unknown random variables, which are sampled. The solid arrows illustrate hierarchical dependencies, and the dotted arrows show deterministic dependences. The box given in bold indicates the multi-trait model structure.

Mentions: Our HB multi-trait cQTL model comprises four hierarchical levels as graphically depicted in Figure 1. Note that the intermediate eQTL model, presented as a shadowed box in the figure, is exactly the same as the eQTL part of the single-trait cQTL model (Sillanpää and Noykova, 2008). A detailed description of each hierarchical level is given below.


A hierarchical bayesian approach to multi-trait clinical quantitative trait locus modeling.

Mutshinda CM, Noykova N, Sillanpää MJ - Front Genet (2012)

Hierarchical directed acyclic graph (DAG) of the model structure. The pre-specified values (Model level I) or observed data (Model level IV) are given in boxes. Ellipses (Model levels II and III) refer to the unknown random variables, which are sampled. The solid arrows illustrate hierarchical dependencies, and the dotted arrows show deterministic dependences. The box given in bold indicates the multi-trait model structure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368303&req=5

Figure 1: Hierarchical directed acyclic graph (DAG) of the model structure. The pre-specified values (Model level I) or observed data (Model level IV) are given in boxes. Ellipses (Model levels II and III) refer to the unknown random variables, which are sampled. The solid arrows illustrate hierarchical dependencies, and the dotted arrows show deterministic dependences. The box given in bold indicates the multi-trait model structure.
Mentions: Our HB multi-trait cQTL model comprises four hierarchical levels as graphically depicted in Figure 1. Note that the intermediate eQTL model, presented as a shadowed box in the figure, is exactly the same as the eQTL part of the single-trait cQTL model (Sillanpää and Noykova, 2008). A detailed description of each hierarchical level is given below.

Bottom Line: Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits.A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision.The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki Helsinki, Finland.

ABSTRACT
Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits.

No MeSH data available.


Related in: MedlinePlus