Clustering expressed genes on the basis of their association with a quantitative phenotype

Cluster analyses of gene expression data are usually conducted based on their associations with the phenotype of a particular disease. Many disease traits have a clearly defined binary phenotype (presence or absence), so that genes can be clustered based on the differences of expression levels between the two contrasting phenotypic groups. For example, cluster analysis based on binary phenotype has been successfully used in tumour research. Some complex diseases have phenotypes that vary in a continuous manner and the method developed for a binary trait is not immediately applicable to a continuous trait. However, understanding the role of gene expression in these complex traits is of fundamental importance. Therefore, it is necessary to develop a new statistical method to cluster expressed genes based on their association with a quantitative trait phenotype. We developed a model-based clustering method to classify genes based on their association with a continuous phenotype. We used a linear model to describe the relationship between gene expression and the phenotypic value. The model effects of the linear model (linear regression coefficients) represent the strength of the association. We assumed that the model effects of each gene follow a mixture of several multivariate Gaussian distributions. Parameter estimation and cluster assignment were accomplished via an Expectation-Maximization (EM) algorithm. The method was verified by analysing two simulated datasets, and further demonstrated using real data generated in a microarray experiment for the study of gene expression associated with Alzheimer's disease.

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Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2017.05.002 ◽

2017 ◽

Vol 100 (6) ◽

pp. 985-986 ◽

Cited By ~ 10

Author(s):

Chen Yao ◽

Roby Joehanes ◽

Andrew D. Johnson ◽

Tianxiao Huan ◽

Chunyu Liu ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Regulation Of Gene Expression ◽

Trans Regulation

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Role of GSH in the modulation of NOS-2 expression in the weaned mammary gland

Biochemical Society Transactions ◽

10.1042/bst0331397 ◽

2005 ◽

Vol 33 (6) ◽

pp. 1397-1398

Author(s):

R. Zaragozá ◽

E.R. García-Trevijano ◽

V.J. Miralles ◽

M. Mata ◽

C. García ◽

...

Keyword(s):

Gene Expression ◽

Mammary Gland ◽

Mrna Expression ◽

Expression Patterns ◽

Microarray Experiment ◽

Oligonucleotide Microarray ◽

Lactating Mammary Gland ◽

Buthionine Sulphoximine

GSH delivery to the lactating mammary gland is essential for the maintenance of lactation as its decrease leads to apoptosis and involution of the mammary gland. In fact, it has already been demonstrated that some of the changes in gene expression found in the lactating mammary gland after forced weaning are reproduced in rats treated with buthionine sulphoximine to deplete GSH levels. An oligonucleotide microarray experiment would give us a better knowledge of the mRNA expression patterns during lactation and after weaning and the possible functions of GSH in the modulation of these events.

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Integrating gene expression with summary association statistics to identify susceptibility genes for 30 complex traits

10.1101/072967 ◽

2016 ◽

Cited By ~ 2

Author(s):

Nicholas Mancuso ◽

Huwenbo Shi ◽

Pagé Goddard ◽

Gleb Kichaev ◽

Alexander Gusev ◽

...

Keyword(s):

Gene Expression ◽

Genetic Correlation ◽

Complex Traits ◽

Association Studies ◽

Susceptibility Genes ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Causal Variants

AbstractAlthough genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. We leverage recently introduced methods to integrate gene expression measurements from 45 expression panels with summary GWAS data to perform 30 transcriptome-wide association studies (TWASs). We identify 1,196 susceptibility genes whose expression is associated with these traits; of these, 168 reside more than 0.5Mb away from any previously reported GWAS significant variant, thus providing new risk loci. Second, we find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, 8 are not found through genetic correlation at the SNP level. Third, we use bi-directional regression to find evidence for BMI causally influencing triglyceride levels, and triglyceride levels causally influencing LDL. Taken together, our results provide insights into the role of expression to susceptibility of complex traits and diseases.

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A quantile integral linear model to quantify genetic effects on phenotypic variability

10.1101/2021.04.14.439847 ◽

2021 ◽

Author(s):

Jiacheng Miao ◽

Yupei Lin ◽

Yuchang Wu ◽

Boyan Zheng ◽

Lauren L. Schmitz ◽

...

Keyword(s):

Linear Model ◽

Complex Traits ◽

Phenotypic Variability ◽

Population Level ◽

Predictive Performance ◽

Real Data ◽

Genetic Effects ◽

Environment Interaction ◽

Unified Framework ◽

Polygenic Scores

Detecting genetic variants associated with the variance of complex traits, i.e. variance quantitative trait loci (vQTL), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (N=375,791), QUAIL identified 11 novel vQTL for body mass index (BMI). Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Further, variance polygenic scores (vPGS) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

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Gut Microbiota Has a Widespread and Modifiable Effect on Host Gene Regulation

mSystems ◽

10.1128/msystems.00323-18 ◽

2019 ◽

Vol 4 (5) ◽

Cited By ~ 18

Author(s):

Allison L. Richards ◽

Amanda L. Muehlbauer ◽

Adnan Alazizi ◽

Michael B. Burns ◽

Anthony Findley ◽

...

Keyword(s):

Gene Expression ◽

Gene Regulation ◽

Gut Microbiota ◽

Complex Traits ◽

Chromatin Accessibility ◽

Host Gene ◽

Host Cells ◽

Host Gene Expression ◽

Host Genes

ABSTRACT Variation in gut microbiome is associated with wellness and disease in humans, and yet the molecular mechanisms by which this variation affects the host are not well understood. A likely mechanism is that of changing gene regulation in interfacing host epithelial cells. Here, we treated colonic epithelial cells with live microbiota from five healthy individuals and quantified induced changes in transcriptional regulation and chromatin accessibility in host cells. We identified over 5,000 host genes that change expression, including 588 distinct associations between specific taxa and host genes. The taxa with the strongest influence on gene expression alter the response of genes associated with complex traits. Using ATAC-seq, we showed that a subset of these changes in gene expression are associated with changes in host chromatin accessibility and transcription factor binding induced by exposure to gut microbiota. We then created a manipulated microbial community with titrated doses of Collinsella, demonstrating that manipulation of the composition of the microbiome under both natural and controlled conditions leads to distinct and predictable gene expression profiles in host cells. Taken together, our results suggest that specific microbes play an important role in regulating expression of individual host genes involved in human complex traits. The ability to fine-tune the expression of host genes by manipulating the microbiome suggests future therapeutic routes. IMPORTANCE The composition of the gut microbiome has been associated with various aspects of human health, but the mechanism of this interaction is still unclear. We utilized a cellular system to characterize the effect of the microbiome on human gene expression. We showed that some of these changes in expression may be mediated by changes in chromatin accessibility. Furthermore, we validate the role of a specific microbe and show that changes in its abundance can modify the host gene expression response. These results show an important role of gut microbiota in regulating host gene expression and suggest that manipulation of microbiome composition could be useful in future therapies.

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Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2017.02.003 ◽

2017 ◽

Vol 100 (4) ◽

pp. 571-580 ◽

Cited By ~ 46

Author(s):

Chen Yao ◽

Roby Joehanes ◽

Andrew D. Johnson ◽

Tianxiao Huan ◽

Chunyu Liu ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Regulation Of Gene Expression ◽

Trans Regulation

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Role of small nuclear RNAs in eukaryotic gene expression

Essays in Biochemistry ◽

10.1042/bse0540079 ◽

2013 ◽

Vol 54 ◽

pp. 79-90 ◽

Cited By ~ 34

Author(s):

Saba Valadkhan ◽

Lalith S. Gunawardane

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Rna Stability ◽

Splice Sites ◽

Branch Site ◽

Small Nuclear Rnas ◽

Eukaryotic Gene Expression ◽

Eukaryotic Gene ◽

Rna Biogenesis

Eukaryotic cells contain small, highly abundant, nuclear-localized non-coding RNAs [snRNAs (small nuclear RNAs)] which play important roles in splicing of introns from primary genomic transcripts. Through a combination of RNA–RNA and RNA–protein interactions, two of the snRNPs, U1 and U2, recognize the splice sites and the branch site of introns. A complex remodelling of RNA–RNA and protein-based interactions follows, resulting in the assembly of catalytically competent spliceosomes, in which the snRNAs and their bound proteins play central roles. This process involves formation of extensive base-pairing interactions between U2 and U6, U6 and the 5′ splice site, and U5 and the exonic sequences immediately adjacent to the 5′ and 3′ splice sites. Thus RNA–RNA interactions involving U2, U5 and U6 help position the reacting groups of the first and second steps of splicing. In addition, U6 is also thought to participate in formation of the spliceosomal active site. Furthermore, emerging evidence suggests additional roles for snRNAs in regulation of various aspects of RNA biogenesis, from transcription to polyadenylation and RNA stability. These snRNP-mediated regulatory roles probably serve to ensure the co-ordination of the different processes involved in biogenesis of RNAs and point to the central importance of snRNAs in eukaryotic gene expression.

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