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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

Heatmaps of 43 gene clusters who co-expression pattern varies depending on the genotype at an eQTL.Examples are derived from VI1 6461993 (A) and X1 16327274 (B), at each of which two homozygous genotypes each with the two same alleles were inherited from a parent, a laboratory strain (N2) or a wild isolate (CB4856).
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f6: Heatmaps of 43 gene clusters who co-expression pattern varies depending on the genotype at an eQTL.Examples are derived from VI1 6461993 (A) and X1 16327274 (B), at each of which two homozygous genotypes each with the two same alleles were inherited from a parent, a laboratory strain (N2) or a wild isolate (CB4856).

Mentions: We obtained 43 distinct clusters, but these clusters may have complex mutual relationships. Our approach allows us to test how an eQTL controls the structure of relationships among the clusters. Through hypothesis test (iv), we elucidate the difference in cluster relationships between different genotypes at a particular eQTL detected. Figure 6 shows two examples in which two eQTLs from spot VI1 and X1 affect the structure of clusters. Some clusters are close to each other in one genotype, but they tend to be far away in the alternative genotype at the same eQTL.


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)

Heatmaps of 43 gene clusters who co-expression pattern varies depending on the genotype at an eQTL.Examples are derived from VI1 6461993 (A) and X1 16327274 (B), at each of which two homozygous genotypes each with the two same alleles were inherited from a parent, a laboratory strain (N2) or a wild isolate (CB4856).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: Heatmaps of 43 gene clusters who co-expression pattern varies depending on the genotype at an eQTL.Examples are derived from VI1 6461993 (A) and X1 16327274 (B), at each of which two homozygous genotypes each with the two same alleles were inherited from a parent, a laboratory strain (N2) or a wild isolate (CB4856).
Mentions: We obtained 43 distinct clusters, but these clusters may have complex mutual relationships. Our approach allows us to test how an eQTL controls the structure of relationships among the clusters. Through hypothesis test (iv), we elucidate the difference in cluster relationships between different genotypes at a particular eQTL detected. Figure 6 shows two examples in which two eQTLs from spot VI1 and X1 affect the structure of clusters. Some clusters are close to each other in one genotype, but they tend to be far away in the alternative genotype at the same eQTL.

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