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

Ubiquitous occurrence of eQTL by cluster interactions over all possible pairs of clusters.Green and red lines denote genotype by cluster interactions due to difference in the magnitude and direction of genetic effects, respectively, on a particular cluster pair. The thickness of the lines are proportional to the frequency of genotype by cluster interactions at 52 eQTLs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Ubiquitous occurrence of eQTL by cluster interactions over all possible pairs of clusters.Green and red lines denote genotype by cluster interactions due to difference in the magnitude and direction of genetic effects, respectively, on a particular cluster pair. The thickness of the lines are proportional to the frequency of genotype by cluster interactions at 52 eQTLs.

Mentions: Direction-varying interactions are more ubiquitous and stronger than magnitude-varying interactions. Figure 5 illustrates the numbers of eQTLs that display magnitude- and direction-varying genotype × cluster interactions over cluster pairs. Direction-varying interactions pervade cluster pairs, showing a considerable amount of genotypic variation in the differentiated expression of different clusters related to particular biological functions.


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)

Ubiquitous occurrence of eQTL by cluster interactions over all possible pairs of clusters.Green and red lines denote genotype by cluster interactions due to difference in the magnitude and direction of genetic effects, respectively, on a particular cluster pair. The thickness of the lines are proportional to the frequency of genotype by cluster interactions at 52 eQTLs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Ubiquitous occurrence of eQTL by cluster interactions over all possible pairs of clusters.Green and red lines denote genotype by cluster interactions due to difference in the magnitude and direction of genetic effects, respectively, on a particular cluster pair. The thickness of the lines are proportional to the frequency of genotype by cluster interactions at 52 eQTLs.
Mentions: Direction-varying interactions are more ubiquitous and stronger than magnitude-varying interactions. Figure 5 illustrates the numbers of eQTLs that display magnitude- and direction-varying genotype × cluster interactions over cluster pairs. Direction-varying interactions pervade cluster pairs, showing a considerable amount of genotypic variation in the differentiated expression of different clusters related to particular biological functions.

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