Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Võsa, Urmo; Claringbould, Annique; Westra, Harm-Jan; Bonder, Marc Jan; Deelen, Patrick; Zeng, Biao; Kirsten, Holger; Saha, A; Kreuzhuber, Roman; Kasela, Silva; Pervjakova, Natalia; Alvaes, I; Favé, Marie-Julie; Agbessi, Mawussé; Christiansen, Mark; Jansen, Rick; Seppälä, Ilkka; Lin, Tong; Teumer, Alexander; Schramm, Katharina; Hemani, Gibran; Verlouw, Joost A M; Yaghootkar, Hanieh; Sönmez, Reyhan; Brown, Andrew A.; Kukushkina,; Kalnapenkis, Anette; Rüeger, Sina; Porcu, Eleonora; Kronberg-Guzman, Jaanika; Kettunen, Johannes; Powell, Joseph E.; B, Lee; Zhang, F; Arindrarto, Wibowo; Beutner, Frank; Brugge, Harm; Dmitreva, J; Elansary, Mahmoud; Fairfax, Benjamin P.; Georges, Michel; Bt, Heijmans; Kähönen, Mika; Kim, Yonggyun; Knight, Julian C.; Kovacs, Peter; Krohn, Knut; S, Li; Loeffler, Markus; Marigorta, Urko M.; H, Mei; Momozawa, Yukihide; Müller‐Nurasyid, Martina; Nauck, Matthias; Nivard, Michel G.; Penninx, Brenda W. J. H.; Pritchard, Jonathan K.; Raitakari, Olli T.; Rotzchke, Olaf; Ep, Slagboom; Cd, Stehouwer; Stümvoll, Michael; Sullivan, Patrick F.; Pa, Hoen; Thiery, Joachim; Tönjes, Anke; J, van Dongen; M, van Iterson; Veldink, Jan H.; Völker, Uwe; Wijmenga, Cisca; Swertz, Morris A.; Andiappan, Anand Kumar; Montgomery, Grant W.; Ripatti, Samuli; Perola, Markus; Kutalik, Zoltán; Dermitzakis, Emmanouil T.; Bergmann, Sven; Frayling, Timothy M.; Meurs, Joyce B. J. van; Prokisch, Holger; Ahsan, Habibul; Pierce, Brandon L.; Lehtimäki, Terho; Boomsma, Dorret I.; Bm, Psaty; Gharib, Sina A.; Awadalla, Philip; Milani, Lili; Ouwehand, Willem H.; Downes, Kate; Stegle, Oliver; Battle, Alexis; Yang, Jian; Visscher, Peter M.; Scholz, Markus; Gibson, Greg; Esko, Tõnu; Franke, Lude

doi:10.1101/447367

Cited by 489 publications

(768 citation statements)

References 99 publications

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“…(23,31) In addition, GWAS have identified associations between this allele and increased risk of childhood acute lymphoblastic leukemia in males, non-melanoma skin cancer, squamous cell carcinoma, cutaneous squamous cell carcinoma, basal cell carcinoma, actinic keratosis, and progressive supranuclear palsy(Figure 2b). (23) Both rs12203592 and rs62389423 risk alleles identified in our AML/MDS patients associated in eQTLGen consortium analyses with increased expression of IRF4, P=1.48x10-29 and P=1.4x10-22, respectively(21).…”

mentioning

confidence: 90%

“…To better understand the potential function of the variants identified by GWAS and ASSET analyses we annotated significant SNPs using publicly available data. eQTLGen, a consortium analyses of the relationship of SNPs to gene expression in 30,912 whole blood samples, was used to determine if significant and suggestive SNPs (p<5 x 10-6) were whole blood cis-eQTL, defined as allele specific association with gene expression (21). Genotype-Tissue Expression project (GTEx) was used to test for significant eQTLs in >70 additional tissues (22).…”

Section: Functional Annotation Of Genetic Variation Associated With Amentioning

confidence: 99%

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Genome-wide association analyses identify variants inIRF4associated with acute myeloid leukemia and myelodysplastic syndrome susceptibility

Wang

Clay-Gilmour

Karaesmen

et al. 2019

Preprint

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The role of common variants in susceptibility to acute myeloid leukemia (AML), and myelodysplastic syndrome (MDS), a group of rare clonal hematologic disorders characterized by dysplastic hematopoiesis and high mortality, remains unclear. These diseases may have a shared molecular basis and identifying germline loci associated with the risk for their development could be important. We performed AML and MDS genome-wide association studies (GWAS) in European Americans from the DISCOVeRY-BMT study population (2309 cases and 2814 controls). Association analysis based on subsets (ASSET) was used to conduct a summary statistics SNPbased analysis of MDS and AML subtypes. For each AML and MDS case and control we used PrediXcan to estimate the component of gene expression determined by their genetic profile and correlate this imputed gene expression level with risk of developing disease in a transcriptome-wide association study (TWAS). We identified an increased risk for de novo AML and MDS (OR=1.38, 95% CI, 1.26-1.51, Pmeta=2.8x10-12) in patients carrying the T allele at rs12203592 in Interferon Regulatory Factor 4 (IRF4), a transcription factor which regulates myeloid and lymphoid hematopoietic differentiation.Variants in this gene are recognized to confer increased susceptibility to B-cell malignancies. Rs12203592 is <80 bp from an IRF4 transcription start site. Our TWAS analyses showed increased IRF4 gene expression is associated with increased risk of de novo AML and MDS (OR=3.90, 95% CI, 2.36-6.44, Pmeta =1.0x10-7). The identification of IRF4 by both GWAS and TWAS contributes valuable insight into the limited evidence of common variants associated with AML and MDS susceptibility.

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mentioning

confidence: 90%

Section: Functional Annotation Of Genetic Variation Associated With Amentioning

confidence: 99%

Genome-wide association analyses identify variants inIRF4associated with acute myeloid leukemia and myelodysplastic syndrome susceptibility

Wang

Clay-Gilmour

Karaesmen

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…These studies have showed that the majority of eQTLs are located in cis, that is less than 1 Mb away of the gene they control, and that population‐specific eQTLs are driven by changes in allelic frequency rather than gene × gene or gene × environment interactions . While a few trans ‐ associations—involving genes located on different chromosomes—were detected, these associations were either weak or driven by cross mapping errors and changes in cellular composition . The strong potential of eQTL for biomedical applications has rapidly lead to extend this framework to multiple tissues or purified immune cell types, showing a strong tissue specificity of eQTLs.…”

Section: Genetic Control Of Transcriptional Activity Is Highly Stimulmentioning

confidence: 99%

“…59 While a few trans-associations-involving genes located on different chromosomes-were detected, these associations were either weak or driven by cross mapping errors 60 and changes in cellular composition. 61 The strong potential of eQTL for biomedical applications 62 has rapidly lead to extend this framework to multiple tissues 63 or purified immune cell types, 29,64 showing a strong tissue specificity of eQTLs. Furthermore, exposing human cells to immune and infectious challenges has allowed the identification of response eQTLs that are associated with changes in gene expression specifically upon immune stimulation ( Figure 1F,G).…”

Section: Genetic Control Of Transcriptional Activity Is Highly Stimmentioning

confidence: 99%

Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility

Rotival

2019

HLA

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Over the last 10 years, genome‐wide association studies (GWAS) have identified hundreds of susceptibility loci for autoimmune diseases. However, despite increasing power for the detection of both common and rare coding variants affecting disease susceptibility, a large fraction of disease heritability has remained unexplained. In addition, a majority of the identified loci are located in noncoding regions, and translation of disease‐associated loci into new biological insights on the etiology of immune disorders has been lagging. This highlights the need for a better understanding of noncoding variation and new strategies to identify causal genes at disease loci. In this review, I will first detail the molecular basis of gene expression and review the various mechanisms that contribute to alter gene activity at the transcriptional and post‐transcriptional level. I will then review the findings from 10 years of functional genomics studies regarding the genetics on gene expression, in particular in the context of infection. Finally, I will discuss the extent to which genetic variants that modulate gene expression at transcriptional and post‐transcriptional level contribute to disease susceptibility and present strategies to leverage this information for the identification of causal mechanisms at disease loci in the era of whole genome sequencing.

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“…To aid prioritisation of HiChIP target genes, we interrogated expression quantitative trait locus (eQTL) data from the largest study of whole blood gene expression [17] and TCGA endometrial tumors [18]. Using these data, we evaluated the overlap between endometrial cancer risk CVs and the top eQTL variants for each HiChIP target gene.…”

Section: Hichip Target Gene Expression Associates With Cvsmentioning

confidence: 99%

Analysis of promoter-associated chromatin interactions reveals biologically relevant candidate target genes at endometrial cancer risk loci

O’Mara

Spurdle

Glubb

2019

Preprint

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The identification of target genes at genome-wide association study (GWAS) loci is a major obstacle for GWAS follow-up. To identify candidate target genes at the 16 known endometrial cancer GWAS risk loci, we performed HiChIP chromatin looping analysis of endometrial cell lines. To enrich for enhancer-promoter interactions, a mechanism through which GWAS variation may target genes, we captured loops associated with H3K27Ac histone, characteristic of promoters and enhancers. Analysis of HiChIP loops contacting promoters revealed enrichment for endometrial cancer GWAS heritability and intersection with endometrial cancer risk variation identified 103 HiChIP target genes at 13 risk loci. Expression of four HiChIP target genes (SNX11, SRP14, HOXB2 and BCL11A) was associated with risk variation, providing further evidence for their regulation. Network analysis functionally prioritized a set of proteins that interact with those encoded by HiChIP target genes, and this set was enriched for pan-cancer and endometrial cancer drivers. Lastly, HiChIP target genes and prioritized interacting proteins were over-represented in pathways related to endometrial cancer development. In summary, we have generated the first global chromatin looping data from endometrial cells, enabling analysis of all known endometrial cancer risk loci and identifying biologically relevant candidate target genes.

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Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Cited by 489 publications

References 99 publications

Genome-wide association analyses identify variants inIRF4associated with acute myeloid leukemia and myelodysplastic syndrome susceptibility

Genome-wide association analyses identify variants inIRF4associated with acute myeloid leukemia and myelodysplastic syndrome susceptibility

Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility

Analysis of promoter-associated chromatin interactions reveals biologically relevant candidate target genes at endometrial cancer risk loci

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