De novo variants in exomes of congenital heart disease patients identify risk genes and pathways

Bayrak, Çiğdem Sevim; Zhang, Peng; Tristani‐Firouzi, Martin; Gelb, Bruce D.; Itan, Yuval

doi:10.1186/s13073-019-0709-8

Cited by 49 publications

(37 citation statements)

References 89 publications

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“…There may also be clinical implications with respect to pulmonary hypertension in TOF 42 . IQGAP1 loss-of-function variants were identified in this and various other CHD cohorts 18, 31, 32 (including multiple de novo variants), but were also found in some parents of probands with autism. Besides its essential role in VEGF receptor signaling, IQGAP1 regulates and integrates other cellular processes, including neuronal functions 43 .…”

Section: Discussionmentioning

confidence: 59%

“…IQGAP1 loss-of-function variants have also been reported in several cohorts of TOF and other CHD 18, 31, 32 . In the TOF cohort analyzed here, there were three individuals with loss-of-function variants in IQGAP1 with significant enrichment compared to 1000 genomes control data (3/811 vs. 1/2,504; FET: p = 4.8E-2; gnomAD o/e LOF = 0.19), but at the non-significant trend level compared to control data comprising parents in an autism cohort (3/811 vs. 5/3,697; FET: p = 1.6E-1).…”

Section: Resultsmentioning

confidence: 88%

“…Likely pathogenic loss-of-function variants in FLT4 (VEGFR3), NOTCH1 , and JAG1 were among the most common findings, further confirming a major role for VEGF and Notch signaling in TOF. A dosage effect of VEGF/Notch signaling in outflow tract development and TOF has long been suggested from mouse models 39 , and has been recently substantiated by several human genetic studies 18, 20, 32 . We also provide further evidence that haploinsufficiency of KDR (encoding VEGF receptor 2) and, likely to a lesser extent IQGAP1 (VEGF receptor signaling), contribute to TOF risk.…”

Section: Discussionmentioning

confidence: 94%

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Clinical genetic risk variants inform a functional protein interaction network for tetralogy of Fallot

Reuter

Chaturvedi

Jobling

et al. 2021

Preprint

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Background: Tetralogy of Fallot (TOF), the most common cyanotic heart defect in newborns, has evidence of multiple genetic contributing factors. Identifying variants that are clinically relevant is essential to understand patient-specific disease susceptibility and outcomes, and could contribute to delineating pathomechanisms. Methods and Results: We used a clinically-driven strategy and current guidelines to re-analyze exome sequencing data from 811 probands with TOF, focused on identifying rare loss-of-function and other likely pathogenic variants in congenital heart disease (CHD) genes. In addition to confirming a major contribution of likely pathogenic variants in FLT4 (VEGFR3; n=14) and NOTCH1 (n=11), we identified 1-3 such variants in each of 21 other CHD genes, including ATRX, DLL4, EP300, GATA6, JAG1, NF1, PIK3CA, RAF1, RASA1, SMAD2, and TBX1. There were also three emerging CHD/TOF candidate genes with multiple loss-of-function variants in this cohort: KDR (n=4), IQGAP1 (n=3), and GDF1 (n=8). In total, these variants were identified in 64 probands (7.9%). Using the 26 composite genes in a STRING protein interaction enrichment analysis revealed a biologically relevant network (p-value 3.3e-16), with VEGFR2 (KDR) and NOTCH1 representing central nodes. Variants associated with arrhythmias/sudden death and/or heart failure indicated factors that could influence long-term outcomes. Conclusions: The results are relevant to precision medicine for TOF. They suggest considerable clinical yield from genome-wide sequencing, and further evidence for KDR as a CHD/TOF gene and VEGF and Notch signaling as mechanisms in human disease. Harnessing genetic heterogeneity of single gene defects could inform etiopathogenesis and help prioritize novel candidate genes for TOF.

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Section: Discussionmentioning

confidence: 59%

Section: Resultsmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 94%

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Clinical genetic risk variants inform a functional protein interaction network for tetralogy of Fallot

Reuter

Chaturvedi

Jobling

et al. 2021

Preprint

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“…(12). Indeed, one solution to the variant set characterization problem is to map SNPs to genes, e.g., based on predicted impact of a SNP on the function of a gene’s encoded protein, and then subject the resulting genes to the plethora of available gene set characterization methods (11), including enrichment tests (13,14) and pathway analysis tools (15). Of particular relevance to our work is the class of methods that test for associations between the study-based gene set (in this case, derived from the phenotype-related SNP set) and pre-determined gene sets from a compendium such as REACTOME (16), KEGG (17) or Gene Ontology (18).…”

Section: Introductionmentioning

confidence: 99%

VarSAn: Associating pathways with a set of genomic variants using network analysis

Xie

Kendzior

et al. 2020

Preprint

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There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports p-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn’s pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

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“…genes are highly expressed during critical stages of heart development. Unlike earlier studies7,31 which did not address the importance of expression changes over time, we evaluated the differential expression patterns of genes by comparing levels of expression in the heart, kidney and liver at different time points in development. This analysis allowed us to strengthen disease association for genes not falling under the high expression group and highlight the critical importance of all 21 genes independently of the genomic approach.…”

mentioning

confidence: 99%

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Audain

Wilsdon

Breckpot

et al. 2020

Preprint

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Congenital Heart Disease (CHD) affects approximately 7-9 children per 1000 live births.Numerous genetic studies have established a role for rare genomic variants at the copy number variation (CNV) and single nucleotide variant level. In particular, the role of de novo mutations (DNM) has been highlighted in syndromic and non-syndromic CHD. To identify novel haploinsufficient CHD disease genes we performed an integrative analysis of CNVs and DNMs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm (TAA). We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed mutation rate testing for DNMs identified in 2,489 parentoffspring trios. Our combined analysis revealed 21 genes which were significantly affected by rare genomic deletions and/or constrained non-synonymous de novo mutations in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in singletons and small cases series, or show new associations with CHD.In addition, a systems level analysis revealed shared contribution of CNV deletions and DNMs in CHD probands, affecting protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function.Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes.

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De novo variants in exomes of congenital heart disease patients identify risk genes and pathways

Cited by 49 publications

References 89 publications

Clinical genetic risk variants inform a functional protein interaction network for tetralogy of Fallot

Clinical genetic risk variants inform a functional protein interaction network for tetralogy of Fallot

VarSAn: Associating pathways with a set of genomic variants using network analysis

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

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