Systematic identification of trans eQTLs as putative drivers of known disease associations

Westra, Harm-Jan; Peters, Marjolein J.; Esko, Tõnu; Yaghootkar, Hanieh; Schurmann, Claudia; Kettunen, Johannes; Christiansen, Mark; Fairfax, Benjamin P.; Schramm, Katharina; Powell, Joseph E.; Zhernakova, Alexandra; Zhernakova, Daria V.; Veldink, Jan H.; Berg, Leonard H van den; Karjalainen, Juha; Withoff, Sebo; Uitterlinden, André G.; Hofman, Albert; Rivadeneira, Fernando; Hoen, Peter A C ‘t; Reinmaa, Eva; Fischer, Krista; Nelis, Mari; Milani, Lili; Melzer, David; Ferrucci, Luigi; Singleton, Andrew B.; Hernandez, Dena G.; Nalls, Michael A.; Homuth, Georg; Nauck, Matthias; Radke, Dörte; Völker, Uwe; Perola, Markus; Salomaa, Veikko; Brody, Jennifer A.; Suchy‐Dicey, Astrid; Gharib, Sina A.; Enquobahrie, Daniel A.; Lumley, Thomas; Montgomery, Grant W.; Makino, Seiko; Prokisch, Holger; Herder, Christian; Roden, Michael; Grallert, Harald; Meitinger, Thomas; Strauch, Konstantin; Li, Yang; Jansen, Ritsert C.; Visscher, Peter M.; Knight, Julian C.; Psaty, Bruce M.; Ripatti, Samuli; Teumer, Alexander; Frayling, Timothy M.; Metspalu, Andres; Meurs, Joyce B. J. van; Franke, Lude

doi:10.1038/ng.2756

Cited by 1,582 publications

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“…A lookup of the lead SNPs for cis expression quantitative trait loci (eQTL) was performed in the publicly available database of whole blood eQTL associations (Westra et al ., 2013). For the following SNPs, one or more cis eQTL associations were found: rs1065656 ( NUBP2 ), rs11977526 ( IGFBP3 ), rs2153960 ( FOXO3 ), rs509035 ( GHSR ), rs780093 ( GCKR ), rs934073 ( ASXL2 ), and rs978458 ( IGF1 ) (Table 2).…”

Section: Resultsmentioning

confidence: 99%

“…For each of the lead SNPs of the significant loci after final stage, significant cis eQTL associations in whole blood, lymphocytes, subcutaneous fat, muscle, and skin were looked up in the publically available association result databases (Grundberg et al ., 2012; Westra et al ., 2013). Association analysis of whole blood gene expression data with serum IGF‐I and IGFBP‐3 levels was conducted in 986 samples of the SHIP‐TREND cohort (Schurmann et al ., 2012).…”

Section: Methodsmentioning

confidence: 99%

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Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Teumer¹,

Qi²,

Nethander³

et al. 2016

Aging Cell

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SummaryThe growth hormone/insulin‐like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF‐related proteins including IGF‐I and IGF‐binding protein‐3 (IGFBP‐3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Through genomewide association study of up to 30 884 adults of European ancestry from 21 studies, we confirmed and extended the list of previously identified loci associated with circulating IGF‐I and IGFBP‐3 concentrations (IGF1, IGFBP3,GCKR,TNS3, GHSR, FOXO3, ASXL2, NUBP2/IGFALS, SORCS2, and CELSR2). Significant sex interactions, which were characterized by different genotype–phenotype associations between men and women, were found only for associations of IGFBP‐3 concentrations with SNPs at the loci IGFBP3 and SORCS2. Analyses of SNPs, gene expression, and protein levels suggested that interplay between IGFBP3 and genes within the NUBP2 locus (IGFALS and HAGH) may affect circulating IGF‐I and IGFBP‐3 concentrations. The IGF‐I‐decreasing allele of SNP rs934073, which is an eQTL of ASXL2, was associated with lower adiposity and higher likelihood of survival beyond 90 years. The known longevity‐associated variant rs2153960 (FOXO3) was observed to be a genomewide significant SNP for IGF‐I concentrations. Bioinformatics analysis suggested enrichment of putative regulatory elements among these IGF‐I‐ and IGFBP‐3‐associated loci, particularly of rs646776 at CELSR2. In conclusion, this study identified several loci associated with circulating IGF‐I and IGFBP‐3 concentrations and provides clues to the potential role of the IGF axis in mediating effects of known (FOXO3) and novel (ASXL2) longevity‐associated loci.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Teumer¹,

Qi²,

Nethander³

et al. 2016

Aging Cell

Self Cite

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“…8h). Given the greater tissue-specificity of trans -eQTLs, we note that heterogeneity in cellular composition of bulk tissue samples is one important confounder that may reduce power to detect trans -eQTLs, or even lead to false positive associations 6 . Despite the high tissue-specificity, we did observe a small number of tissue-shared trans -eQTLs, including rs7683255, which was moderately associated in trans with NUDT13 across most tested GTEx tissues with a consistent direction of effect (Extended Data Fig.…”

Section: Tissue-sharing and Specificity Of Eqtlsmentioning

confidence: 99%

“…Genetic variants associated with complex traits have been suggested to be enriched for trans -eQTLs 6,44–47 . Accordingly, we performed trans -eQTL mapping, restricting it to variants associated with a complex trait in a GWAS (Extended Data Fig.…”

Section: Expression Qtls and Complex Disease Associationsmentioning

confidence: 99%

Genetic effects on gene expression across human tissues

groups

Fund

Nhgri³

et al. 2017

Nature

3,677

2,412

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Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

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“…To identify eQTL effects, eQTL databases were analyzed (Borel et al., 2011; Dimas et al., 2009; Dixon et al., 2007; Fehrmann et al., 2011; Greenawalt et al., 2011; Grundberg et al., 2009; GTEx Consortium, 2015; Kim, Cho, Lee, & Webster, 2012; Kirsten et al., 2015; Mehta et al., 2013; Myers et al., 2007; Ramasamy et al., 2014; Schadt et al., 2008; Schröder et al., 2011; Veyrieras et al., 2008; Westra et al., 2013; Xia et al., 2012; Zeller et al., 2010). We only considered SNPs identified in brain or blood tissue and eQTLs had to be replicated in at least one study.…”

Section: Methodsmentioning

confidence: 99%

ATP2C2andDYX1C1are putative modulators of dyslexia‐related MMR

et al. 2017

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BackgroundDyslexia is a specific learning disorder affecting reading and spelling abilities. Its prevalence is ~5% in German‐speaking individuals. Although the etiology of dyslexia largely remains to be determined, comprehensive evidence supports deficient phonological processing as a major contributing factor. An important prerequisite for phonological processing is auditory discrimination and, thus, essential for acquiring reading and spelling skills. The event‐related potential Mismatch Response (MMR) is an indicator for auditory discrimination capabilities with dyslexics showing an altered late component of MMR in response to auditory input.MethodsIn this study, we comprehensively analyzed associations of dyslexia‐specific late MMRs with genetic variants previously reported to be associated with dyslexia‐related phenotypes in multiple studies comprising 25 independent single‐nucleotide polymorphisms (SNPs) within 10 genes.ResultsFirst, we demonstrated validity of these SNPs for dyslexia in our sample by showing that additional inclusion of a polygenic risk score improved prediction of impaired writing compared with a model that used MMR alone. Secondly, a multifactorial regression analysis was conducted to uncover the subset of the 25 SNPs that is associated with the dyslexia‐specific late component of MMR. In total, four independent SNPs within DYX1C1 and ATP2C2 were found to be associated with MMR stronger than expected from multiple testing. To explore potential pathomechanisms, we annotated these variants with functional data including tissue‐specific expression analysis and eQTLs.ConclusionOur findings corroborate the late component of MMR as a potential endophenotype for dyslexia and support tripartite relationships between dyslexia‐related SNPs, the late component of MMR and dyslexia.

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Systematic identification of trans eQTLs as putative drivers of known disease associations

Cited by 1,582 publications

References 60 publications

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Genetic effects on gene expression across human tissues

ATP2C2andDYX1C1are putative modulators of dyslexia‐related MMR

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