Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS

Manduchi, Elisabetta; Williams, Scott M.; Chesi, Alessandra; Johnson, Matthew E.; Wells, Andrew D.; Grant, Struan F.A.; Moore, Jason H.

doi:10.1007/s00439-018-1893-0

Cited by 7 publications

(4 citation statements)

References 61 publications

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“…In parallel with our study, recent research integrated epigenomics and TAD data and discovered T2DM-associated enhancer-promoter SNP pairs from imputed data where neither SNP achieved independent genome-wide significance [42]. Manduchi et al [42] reported that one enhancer-promoter SNP pair, rs7991210-rs3742250, was significantly associated with T2DM in pancreatic islets after main effect filtering (combined P=2.16×10 −9 ). In our data, we attempted to validate this epistasis effect of the SNP pair, rs7991210-rs3742250.…”

Section: Discussionsupporting

confidence: 72%

Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus

et al. 2021

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Background: Genetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM. Methods: We performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects. Results: We identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10 −10). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele "A" of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of "T" of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes. Conclusion: Genetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.

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Section: Discussionsupporting

confidence: 72%

Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus

et al. 2021

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“…Epigenomic information has previously been used to suggest causal variants for autoimmune diseases (Farh et al, 2015) and cancer (Han et al, 2015). Recently, an approach was proposed to identify pairs of potentially interacting variants, in active enhancers and gene promoters, respectively (Manduchi et al, 2018). These approaches make it possible to prioritize genetic markers and genes, but it can be difficult (in the absence of further experiments) to know whether the suggested markers and genes are correct.…”

Section: Discussionmentioning

confidence: 99%

Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC

et al. 2019

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We recently conducted a large association analysis to compare the genetic profiles between patients with psoriatic arthritis (PsA) and cutaneous-only psoriasis (PsC). Despite including over 7,000 genotyped patients, only the MHC achieved genome-wide significance. In this study, we hypothesized that appropriate epigenomic elements (H3K27ac marks for active enhancers) can guide us to reveal valuable information about the loci with suggestive evidence of association. Our aim is to investigate these loci and explore how they may lead to the development of PsA. We evaluated this potential by investigating the genes connected with these loci from the perspective of pharmacogenomics and gene expression. We illustrated that markers with suggestive evidence of association outside the MHC region are enriched in H3K27ac marks for osteoblast and chondrogenic differentiated cells; using pharmacogenomics resources, we showed that genes near these markers are targeted by existing drugs used to treat psoriatic arthritis. Significantly, six of the ten suggestive significant loci overlapping the regulatory elements encompass genes differentially expressed (FDR < 5%) in differentiated osteoblasts, including genes participating in the Wnt signaling such as RUNX1 , FUT8 , and CTNNAL1 . Our approach shows that epigenomic information can be used as cost-effective approach to enhance the inferences for GWAS results, especially in situations when few genome-wide significant loci are available. Our results also point the way to more directed investigations comparing the genetics of PsA and PsC.

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“…Similarly to (Manduchi et al, ), we operationally defined active enhancers, promoters, and exons in the relevant heart cellular context as described below, and used the union of these three types of regions as filter. When multiple peak files were available for the same mark, they were first merged.…”

Section: Methodsmentioning

confidence: 99%

A comparison of two workflows for regulome and transcriptome‐based prioritization of genetic variants associated with myocardial mass

Manduchi

Hemerich

Setten

et al. 2019

Genetic Epidemiology

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A typical task arising from main effect analyses in a Genome Wide Association Study (GWAS) is to identify single nucleotide polymorphisms (SNPs), in linkage disequilibrium with the observed signals, that are likely causal variants and the affected genes. The affected genes may not be those closest to associating SNPs.Functional genomics data from relevant tissues are believed to be helpful in selecting likely causal SNPs and interpreting implicated biological mechanisms, ultimately facilitating prevention and treatment in the case of a disease trait.These data are typically used post GWAS analyses to fine-map the statistically significant signals identified agnostically by testing all SNPs and applying a multiple testing correction. The number of tested SNPs is typically in the millions, so the multiple testing burden is high. Motivated by this, in this study we investigated an alternative workflow, which consists in utilizing the available functional genomics data as a first step to reduce the number of SNPs tested for association. We analyzed GWAS on electrocardiographic QRS duration using these two workflows. The alternative workflow identified more SNPs, including some residing in loci not discovered with the typical workflow.Moreover, the latter are corroborated by other reports on QRS duration. This indicates the potential value of incorporating functional genomics information at the onset in GWAS analyses. K E Y W O R D Sfunctional genomics, GWAS, left ventricular mass, SNP preselection *Elisabetta Manduchi and Daiane Hemerich contributed equally to this study.

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Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS

Cited by 7 publications

References 61 publications

Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus

Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus

Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC

A comparison of two workflows for regulome and transcriptome‐based prioritization of genetic variants associated with myocardial mass

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