Large-Scale trans -eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation

Brynedal, Boel; Choi, Jin Kyeong; Raj, Towfique; Bjornson, Robert; Stranger, Barbara Elaine; Neale, Benjamin M.; Voight, Benjamin F.; Cotsapas, Chris

doi:10.1016/j.ajhg.2017.02.004

Cited by 81 publications

(84 citation statements)

References 48 publications

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“…Furthermore, variants far from the transcriptional start site (TSS) of a gene, trans-eQTL, explain more of the heritability of T2D than those near the gene, cis-eQTL, and there is mounting evidence for the importance of these variants in a variety of phenotypes (9)(10)(11). That trans-eQTL, which can influence hundreds of genes in humans (12), might have an impact on the phenotype is consistent with similar observations in model organisms (13). However, large-scale detection of trans-eQTL across populations and tissues (14) has only recently become feasible in humans, and our understanding of how multiple cis-and trans-eQTL influence gene expression and cellular functions in different tissues is incomplete.…”

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confidence: 99%

Exploring regulation in tissues with eQTL networks

Fagny

Paulson

Kuijjer

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.GTEx | expression quantitative trait locus | eQTL | bipartite networks | GWAS M ore than a decade after the sequencing of the human genome, our understanding of the relationship between genetic variation and complex traits remains limited. Genomewide association studies (GWASs), which look for association between common genetic variants and phenotypic traits, have resoundingly shown that complex phenotypes are influenced by many variants of relatively small effect size (1, 2), the overwhelming majority of which (∼93%) lie in noncoding regions of the genome (3, 4). Those single-nucleotide polymorphisms (SNPs) associated with complex traits are enriched for variants likely to affect gene expression, as measured by expression quantitative trait locus (eQTL) analysis (5), suggesting that they influence phenotypes through changes in gene regulation (6, 7). Identifying the regulatory role of these variants likely also depends on the tissues relevant to the phenotype. For example, eQTL identified in skeletal muscle and adipose tissues for type 2 diabetes (T2D) have been shown to explain a greater proportion of the disease heritability than those identified across tissues (8). Furthermore, variants far from the transcriptional start site (TSS) of a gene, trans-eQTL, explain more of the heritability of T2D than those near the gene, cis-eQTL, and there is mounting evidence for the importance of these variants in a variety of phenotypes (9-11). That trans-eQTL, which can influence hundreds of genes in humans (12), might have an impact on the phenotype is consistent with similar observations in model organisms (13). However, large-scale detection of trans-eQTL across populations and tissues (14) has only recently become feasible in humans, and our understanding of how multiple cis-and trans-eQTL influence gene expression and cellular functions in different tissues is incomplete.We performed a systems genetics analysis of the regulatory effects of common [minor allele frequency (MAF) >5%] variants in 13 tissues collected by the Genotype-Tissue Expression (GTEx) consortium. By constructing tissue-leve...

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confidence: 99%

Exploring regulation in tissues with eQTL networks

Fagny

Paulson

Kuijjer

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Based on the PhenoScanner database, we found that rs10830963 appeared to affect the expression levels of distant genes. Emerging evidence suggests that some SNPs affect the expression of distant genes and genes residing on other chromosomes (trans‐eQTLs) . Thus, it is biologically plausible that rs10830963 may affect the expression of genes and be involved in the pathogenesis of GDM.…”

Section: Discussionmentioning

confidence: 99%

“…Emerging evidence suggests that some SNPs affect the expression of distant genes and genes residing on other chromosomes (trans-eQTLs). 30,31 Thus, it is biologically plausible that rs10830963 may affect the expression of genes and be involved in the pathogenesis of GDM. However, functional studies are needed to confirm this.…”

Section: Discussionmentioning

confidence: 99%

Genetic predisposition to gestational glucose metabolism and gestational diabetes mellitus risk in a Chinese population

Xie

Zhang

Wen

et al. 2019

Journal of Diabetes

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Background Genome‐wide association studies (GWAS) have identified several genetic variants affecting gestational glucose metabolism. However, information regarding their known associations with gestational diabetes mellitus (GDM) risk remains scarce. Methods This study examined the associations of 12 gestational glucose metabolism‐related variants with GDM risk in a Chinese population (964 GDM cases, 1021 controls). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression analysis. Results Rs10830963 in melatonin receptor 1B (MTNR1B) was found to be associated with an increased risk of GDM, after adjusting for age, prepregnancy body mass index, parity, abnormal pregnancy history, and family history of diabetes (OR 1.20; 95% CI 1.05‐1.36; P = 0.007). Compared with women with a family history of diabetes, there was a significant association of rs7936247 with GDM risk among pregnant women without a family history of diabetes (OR 1.20; 95% CI 1.04‐1.38; P = 0.014; Pheterogeneity = 0.035). Further functional annotations showed that rs10830963 and rs7936247 fell in the functional elements of human pancreatic islets. Genotype‐phenotype associations indicated that the variants may contribute to GDM risk by affecting the expression of nearby or distant genes. Conclusions The findings of this study suggest that rs10830963 and rs7936247 may be markers for susceptibility to GDM in a Chinese population. Additional studies are warranted to validate our findings and clarify the underlying mechanism.

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“…New methods are developed to integrate multiple molecular phenotypes. 29,72,91,147 Human quantitative genetics is thus leaping into the area of systems biology. 148…”

Section: Caution and Limitations In Using Molqtlsmentioning

confidence: 99%

Genetic association of molecular traits: A help to identify causative variants in complex diseases

Vandiedonck

2018

Clinical Genetics

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In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.

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Large-Scale trans -eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation

Cited by 81 publications

References 48 publications

Exploring regulation in tissues with eQTL networks

Exploring regulation in tissues with eQTL networks

Genetic predisposition to gestational glucose metabolism and gestational diabetes mellitus risk in a Chinese population

Genetic association of molecular traits: A help to identify causative variants in complex diseases

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