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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

The genome-wide identification of significant eQTLs over six chromosomes (outer circle) in C. elegans by the block mixture model.The red line (inner circle) is the genome-wide critical threshold at the 5% significance level determined from permutation test. Significant eQTL spots, denoted by Roman letters with subscripts, were detected in chromosome II, III, IV, and X.
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f2: The genome-wide identification of significant eQTLs over six chromosomes (outer circle) in C. elegans by the block mixture model.The red line (inner circle) is the genome-wide critical threshold at the 5% significance level determined from permutation test. Significant eQTL spots, denoted by Roman letters with subscripts, were detected in chromosome II, III, IV, and X.

Mentions: The effect of an eQTL on gene clustering can be characterized by testing whether any one of the clusters has different mean values of gene expression between two genotypes at this eQTL bracketed by a pair of flanking SNPs. If such a difference is significant, then we claim that this eQTL is significantly associated with gene clustering. We implemented this testing procedure to scan all SNPs across six chromosomes of the C. elegans genome, obtaining a total of 52 clustering-related eQTLs, with 2, 2, 27, and 21 distributed on chromosome II, III, IV, and X, respectively (Fig. 2). Chromosome IV has three distinct eQTL spots, labeled as IV1, IV2, and IV3, of which IV2 and IV3 were also observed by a simple single marker/single gene association analysis in Rochman et al.’s (2010) original study whereas IV1 and IV2 observed by Chun and Keles’s multivariate sparse partial least squares regression (M-SPLS) regression14. Of the two eQTL spots detected on sex chromosome X from our approach, one, denoted as X1, was detected by the simple association analysis and the other, denoted as X2, detected by M-SPLS regression. The results from our approach cover different results detected by the two existing ones, respectively, suggesting that our approach is more general for eQTL mapping. A spot in chromosome II, labeled as II1, and a spot in chromosome III, labeled as III1, were detected only by our approaches, demonstrating its statistical power.


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)

The genome-wide identification of significant eQTLs over six chromosomes (outer circle) in C. elegans by the block mixture model.The red line (inner circle) is the genome-wide critical threshold at the 5% significance level determined from permutation test. Significant eQTL spots, denoted by Roman letters with subscripts, were detected in chromosome II, III, IV, and X.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: The genome-wide identification of significant eQTLs over six chromosomes (outer circle) in C. elegans by the block mixture model.The red line (inner circle) is the genome-wide critical threshold at the 5% significance level determined from permutation test. Significant eQTL spots, denoted by Roman letters with subscripts, were detected in chromosome II, III, IV, and X.
Mentions: The effect of an eQTL on gene clustering can be characterized by testing whether any one of the clusters has different mean values of gene expression between two genotypes at this eQTL bracketed by a pair of flanking SNPs. If such a difference is significant, then we claim that this eQTL is significantly associated with gene clustering. We implemented this testing procedure to scan all SNPs across six chromosomes of the C. elegans genome, obtaining a total of 52 clustering-related eQTLs, with 2, 2, 27, and 21 distributed on chromosome II, III, IV, and X, respectively (Fig. 2). Chromosome IV has three distinct eQTL spots, labeled as IV1, IV2, and IV3, of which IV2 and IV3 were also observed by a simple single marker/single gene association analysis in Rochman et al.’s (2010) original study whereas IV1 and IV2 observed by Chun and Keles’s multivariate sparse partial least squares regression (M-SPLS) regression14. Of the two eQTL spots detected on sex chromosome X from our approach, one, denoted as X1, was detected by the simple association analysis and the other, denoted as X2, detected by M-SPLS regression. The results from our approach cover different results detected by the two existing ones, respectively, suggesting that our approach is more general for eQTL mapping. A spot in chromosome II, labeled as II1, and a spot in chromosome III, labeled as III1, were detected only by our approaches, demonstrating its statistical power.

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