Genome-wide association study provides new insights into the genetic architecture and pathogenesis of heart failure

Shah, Sonia; Henry, Albert; Roselli, Carolina; H, Lin; Sveinbjörnsson, Garðar; Fatemifar, Ghazaleh; Åk, Hedman; Wilk, JB; Mp, Morley; Chaffin, Mark; Helgadóttir, Anna; Verweij, Niek; Dehghan, Abbas; Almgren, Peter; Andersson, C; Kg, Aragam; Ärnlöv, Johan; Jd, Backman; Ml, Biggs; Hl, Bloom; Brandimarto, Jeffrey; Mr, Brown; Buckbinder, Leonard; Dj, Carey; Di, Chasman; Chen, X; Chung, Jonathan; Chutkow, William A.; Jp, Cook; Ge, Delgado; Denaxas, Spiros; Doney, Alex S.F.; Dörr, Marcus; Sc., D.; Me, Dunn; Engström, Gunnar; Esko, Tõnu; Felix, SB; Finan, Chris; Ford, Ian; Ghanbari, Mohsen; Ghasemi, Sahar; Giedraitis,; Giulianini, Franco; Js, Gottdiener; Groß, Stefan; Df, Gudbjartsson; Gutmann, Rebecca; Haggerty, Christopher M.; P, van der Harst; Hyde, CL; Ingelsson, Erik; Jukema, J.W.; Kavousi, Maryam; Khaw, Kay Tee; Me, Kleber; Køber, Lars; Koekemoer, Andrea; Langenberg, Claudia; Lind, Lars; Cm, Lindgren; London, Barry; La, Lotta; Rc, Lovering; Luan, Jian; Magnusson, Patrik K. E.; Mahajan, Anubha; Kb, Margulies; März, Winfried; Melander, Olle; Ir, Mordi; Morgan, Thomas M.; Morris, AD; Ap, Morris; Ac, Morrison; Mw, Nagle; Cp, Nelson; Niessner, Alexander; Niiranen, Teemu J.; Ml, O’Donoghue; At, Owens; Cna., Palmer; Parry, Helen; Perola, Markus; Portilla‐Fernandez, Eliana; Bm, Psaty; Km, Rice; Pm, Ridker; Spr., Romaine; Ji, Rotter; Salo, Perttu; Salomaa,; J, van Setten; Aa, Shalaby; Dt, Smelser; Nl, Smith; Stender, Steen; Stott, DJ; Svensson, Per; Tammesoo, Mari‐Liis; Taylor, Kelly; Teder‐Laving, Maris; Teumer, Alexander; Þorgeirsson, Guðmundur; Þorsteinsdóttir, Unnur; Torp‐Pedersen, Christian; Trompet, Stella; Tyl, Benoît; Ag, Uitterlinden; Veluchamy, Abirami; Völker, Uwe; Aa, Voors; Wang, X; Wareham, NJ; Waterworth, Dawn; Pe, Weeke; Weiss, Raul; Kl, Wiggins; Xing, Heming; Lm, Yerges-Armstrong; Yu, Bo; Zannad, Faı̈ez; Zhao, Jing; Hemingway, Harry; Nj, Samani; Jj, McMurray; Yang, Jian; Visscher, Peter M.; Newton‐Cheh, Christopher; Mälarstig, Anders; Hólm, Hilma; Sa, Lubitz; Sattar, Naveed; Mv, Holmes; Tp, Cappola; Asselbergs, Folkert W.; Ad, Hingorani; Kuchenbaecker, Karoline; Pt, Ellinor; Lang, CC; Stefánsson, Kári; Jg, Smith; Vasan, Ramachandran S.; Di, Swerdlow; Rt, Lumbers

doi:10.1101/682013

Cited by 16 publications

(24 citation statements)

References 84 publications

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“…This ability to study CMs themselves is especially important given that GWAS studies for heart failure have revealed little of the genetic architecture of the disease. While pedigree analysis suggests that the heritability of heart failure is 26% -34% [68], two recent, large-scale GWAS identified only eleven [105] and three [6] genomic loci associated with heart failure. This could be due to a lack of quantitative measurements of heart failure, complex etiologies, or the significant environmental contributions to developing heart failure [97].…”

Section: Discussionmentioning

confidence: 99%

“…Unlike atrial fibrillation, GWAS for heart failure have not identified many association signals, likely due to the highly heterogeneous nature of the disease. Two recent meta-analyses identified 3 and 13 genes, respectively [6,105]. Three of the genes associated with heart failure had treatment cASE: FAM241A in 5 conditions (copper, dexamethasone, insulin, ca↵eine, and vitamin A); BAG3 in 5 conditions (dexamethasone, ca↵eine, vitamin A, nicotine, and aldosterone); and KLHL3 in triclosan.…”

Section: Biological and Clinical Significance Of Allele-specific Expression Across Cell Types And Treatmentsmentioning

confidence: 99%

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Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Findley

Monziani

Richards

et al. 2021

eLife

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Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing, and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Biological and Clinical Significance Of Allele-specific Expression Across Cell Types And Treatmentsmentioning

confidence: 99%

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Findley

Monziani

Richards

et al. 2021

eLife

View full text Add to dashboard Cite

show abstract

“…This ability to study CMs themselves is especially important given that GWAS studies for heart failure have revealed little of the genetic architecture of the disease. While pedigree analysis suggests that the heritability of heart failure is 26% -34% [65], two recent, large-scale GWAS identified only eleven [102] and three [6] genomic loci associated with heart failure. This could be due to a lack of quantitative measurements of heart failure, complex etiologies, or the significant environmental contributions to developing heart failure [94].…”

Section: Discussionmentioning

confidence: 99%

“…Unlike atrial fibrillation, GWAS for heart failure have not identified many association signals, likely due to the highly heterogeneous nature of the disease. Two recent meta-analyses identified 3 and 13 genes, respectively [6,102]. Three of the genes associated with heart failure had treatment cASE: FAM241A in 5 conditions (copper, dexamethasone, insulin, caffeine, and vitamin A); BAG3 in 5 conditions (dexamethasone, caffeine, vitamin A, nicotine, and aldosterone); and KLHL3 in triclosan.…”

Section: Biological and Clinical Significance Of Allele-specific Exprmentioning

confidence: 99%

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Findley

Monziani

Richards

et al. 2021

Preprint

View full text Add to dashboard Cite

Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing processes, and were also highly variable across cell types and treatments. For thousands of genes, we identified variable allelic expression across contexts, and characterized different types of gene-environment interactions. Many of these G×E genes are associated with complex traits. We characterized promoter functional and evolutionary features that distinguish genes with elevated allelic imbalance mean and variance. More than 47% of the genes with dynamic regulatory interactions were missed by GTEx, but we identified them using a suitable allelic imbalance study design. This indicates the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

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“…The data of LDL, HDL, TG, TC were from the meta-analysis by cristen J willer et al (17). HF data was derived from a large-scale GWAS meta-analysis involving 47,309 cases and 930,014 controls (18). AF data came from a meta-analysis of more than 500,000 participants (19).…”

Section: Data Sourcesmentioning

confidence: 99%

Genetic and Causal Relationship Between Coffee Intake and Cardiometabolic Risks: Cross-Phenotype Association And Mendelian Randomization Analysis

Wang¹,

Jia²,

Huang³

2020

Preprint

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Background: Many epidemiological studies have shown that there is a significant association between coffee intake and cardiometabolic diseases, which may be due to the common genetic structure or causal relationship. Methods: We used linkage disequilibrium score regression analysis to calculate the genetic correlation between coffee intake and 23 cardiometabolic traits (diseases), and then used cross-phenotype association analysis to identify the shared genetic loci for the trait pairs with significant genetic correlation. Besides, a bi-directional Mendelian Randomization analysis was used to explore the causal relationship between coffee intake and 23 cardiometabolic traits (diseases).Results: Coffee intake has a significant genetic correlation (after Bonferroni correction) with body mass index (BMI) (Rg = 0.3713, P-value = 4.13ⅹ10-64), body fat percentage (BF%) (Rg = 0.2810, P-value = 1.81ⅹ10-13), type 2 diabetes (T2D) (unadjusted for BMI) (Rg = 0.1189, P-value = 8.80ⅹ10-6), heart failure (HF) (Rg = 0.2626, P-value = 6.00ⅹ10-9), atrial fibrillation (AF) (Rg = 0.1007, P-value = 4.30ⅹ10-5). There are 203, 18, 86, 13, 38 independent shared loci between coffee intake and BMI, BF%, T2D, HF, AF, respectively, among which 22, 2, 23, 4,13 loci do not achieve genome-wide significance in single trait GWAS. Coffee intake has significant causal effect on BMI (b = 0.0717, P-value = 2.33ⅹ10-5), T2D (unadjusted for BMI, OR = 1.27, P-value = 1.46ⅹ10-7), and intracerebral haemorrhage (ICH) (all types ICH: OR = 1.86, P-value = 3.37ⅹ10-4; deep ICH: OR = 2.12, P-value = 2.93ⅹ10-4 ). And BMI (b = 0.3694, P-value=3.64ⅹ10-154), BF% (b = 0.5500, P-value = 1.68ⅹ10-4), T2D (adjusted for BMI, b = -0.0252, P-value = 4.83ⅹ10-6) and triglycerides (TG) (b = -0.1209, P-value = 4.56ⅹ10-15) have significant causal effect on coffee intake.Conclusions: Our study identified the shared genetic structure and causal relationship between coffee intake and several cardiometabolic traits (diseases), providing a new insight into the mechanism of coffee intake and cardiometabolic traits (diseases).

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Genome-wide association study provides new insights into the genetic architecture and pathogenesis of heart failure

Cited by 16 publications

References 84 publications

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Genetic and Causal Relationship Between Coffee Intake and Cardiometabolic Risks: Cross-Phenotype Association And Mendelian Randomization Analysis

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