Limits...
A block mixture model to map eQTLs for gene clustering and networking.

Wang N, Gosik K, Li R, Lindsay B, Wu R - Sci Rep (2016)

Bottom Line: The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway.We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans.This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, University of Texas School of Public Health, Houston, TX 77030, USA.

ABSTRACT
To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework. The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway. We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans. This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression. The approach developed can also facilitate the genetic dissection of other dynamic processes, including development, physiology and disease progression in any organisms.

No MeSH data available.


Related in: MedlinePlus

ROC curves for hotspots detection using block mixture model (green line), MOM15 (brown line), and M-SPLS14 (purple star) from 500 simulated replicates.For M-SPLS, type I error and power were calculated conditionally on the penalized latent vector components.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4759821&req=5

f8: ROC curves for hotspots detection using block mixture model (green line), MOM15 (brown line), and M-SPLS14 (purple star) from 500 simulated replicates.For M-SPLS, type I error and power were calculated conditionally on the penalized latent vector components.

Mentions: We compared the performance of the new model with those of existing approaches, mixture over marker15 and multivariate sparse partial least squares regression14, through additional simulation. The new model shows much higher power of detecting a significant eQTL than existing approaches (Fig. 8).


A block mixture model to map eQTLs for gene clustering and networking.

Wang N, Gosik K, Li R, Lindsay B, Wu R - Sci Rep (2016)

ROC curves for hotspots detection using block mixture model (green line), MOM15 (brown line), and M-SPLS14 (purple star) from 500 simulated replicates.For M-SPLS, type I error and power were calculated conditionally on the penalized latent vector components.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4759821&req=5

f8: ROC curves for hotspots detection using block mixture model (green line), MOM15 (brown line), and M-SPLS14 (purple star) from 500 simulated replicates.For M-SPLS, type I error and power were calculated conditionally on the penalized latent vector components.
Mentions: We compared the performance of the new model with those of existing approaches, mixture over marker15 and multivariate sparse partial least squares regression14, through additional simulation. The new model shows much higher power of detecting a significant eQTL than existing approaches (Fig. 8).

Bottom Line: The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway.We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans.This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, University of Texas School of Public Health, Houston, TX 77030, USA.

ABSTRACT
To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework. The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway. We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans. This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression. The approach developed can also facilitate the genetic dissection of other dynamic processes, including development, physiology and disease progression in any organisms.

No MeSH data available.


Related in: MedlinePlus