Genoma

Nature Genetics volume 55, páginas 964–972 (2023)Cite este artigo

10 mil acessos

1 Citações

429 Altmétrico

Detalhes das métricas

A dissecção espontânea da artéria coronária (SCAD) é uma causa pouco estudada de infarto do miocárdio que afeta principalmente mulheres. Não se sabe até que ponto a SCAD é geneticamente distinta de outras doenças cardiovasculares, incluindo a doença arterial coronariana aterosclerótica (DAC). Aqui apresentamos uma meta-análise de associação genômica ampla (1.917 casos e 9.292 controles) identificando 16 loci de risco para SCAD. Anotações funcionais integrativas priorizaram genes que provavelmente serão regulados em células musculares lisas vasculares e fibroblastos arteriais e implicados na biologia da matriz extracelular. Um locus contendo o gene do fator tecidual F3, que está envolvido no início da cascata de coagulação sanguínea, parece ser específico para o risco de SCAD. Diversas variantes associadas têm associações diametralmente opostas com a DAC, sugerindo que processos biológicos partilhados contribuem para ambas as doenças, mas através de mecanismos diferentes. Também inferimos um papel causal para hipertensão arterial no SCAD. Nossas descobertas fornecem novos insights fisiopatológicos envolvendo integridade arterial e coagulação mediada por tecidos no SCAD e preparam o terreno para futuras terapêuticas e prevenções específicas.

A doença cardiovascular é a principal causa de morte em mulheres, mas os aspectos específicos do sexo sobre o risco de doença cardíaca e infarto agudo do miocárdio (IAM) permanecem pouco estudados1. A dissecção espontânea da artéria coronária (DCE) e a doença arterial coronariana aterosclerótica (DAC) são causas de síndromes coronarianas agudas que levam ao IAM2,3,4,5,6. No entanto, em contraste com a DAC, a SCAD afeta uma população mais jovem, predominantemente feminina7 e surge do desenvolvimento de um hematoma, levando à dissecção da túnica média coronária com a eventual formação de um falso lúmen, em vez de erosão ou ruptura da placa aterosclerótica8. A SCAD tem sido clinicamente associada à enxaqueca9 e arteriopatias extracoronárias, incluindo displasia fibromuscular (FMD)10,11,12,13. Entretanto, a aterosclerose coronariana coexistente é incomum8,14. Embora a base genética da DAC esteja cada vez mais bem estabelecida15, a fisiopatologia da SCAD permanece pouco compreendida4. A busca por mutações altamente penetrantes em vias candidatas ou por sequenciamento obteve um baixo rendimento, muitas vezes apontando para genes envolvidos em outras síndromes hereditárias clinicamente não diagnosticadas que se manifestam como SCAD16. Investigações anteriores sobre o impacto da variação genética comum no risco de SCAD descreveram cinco loci de risco confirmados17,18,19,20.

Neste artigo, realizamos uma meta-análise de estudos de associação genômica ampla (GWASs) compreendendo 1.917 casos de SCAD e 9.292 controles de ascendência europeia. Identificamos 16 loci de risco, incluindo 11 novos sinais de associação, demonstrando uma herdabilidade poligênica substancial para esta doença. É importante ressaltar que mostramos que vários loci de risco genético comuns para SCAD são compartilhados com DAC, mas têm um efeito direcionalmente oposto e uma contribuição genética diferente dos fatores de risco cardiovascular estabelecidos. Esses achados implicam a integridade arterial relacionada à biologia da matriz extracelular, ao tônus vascular e à coagulação tecidual na fisiopatologia da SCAD.

Realizamos uma meta-análise GWAS de oito estudos independentes de caso-controle (Figuras Complementares 1 e 2 e Tabela Suplementar 1). Dezesseis loci demonstraram sinais de associação significativos em todo o genoma com SCAD, entre os quais 11 foram recentemente descritos para esta doença (Tabela 1, Figura 1a, Tabela Suplementar 2 e Figura Complementar 3). Um locus no cromossomo 4 (AFAP1) foi recentemente relatado para SCAD no contexto da gravidez19 e agora foi confirmado como estando geralmente envolvido no SCAD (Tabela 1). As razões de probabilidade estimadas de loci associados variaram de 1,25 (intervalo de confiança (IC) de 95% = 1,16-1,35) em ZNF827 no cromossomo 4 a 2,04 (IC de 95% = 1,77-2,35) no cromossomo 21 próximo ao KCNE2 (Tabela 1). Relatamos evidências de poligenicidade substancial para SCAD com uma herdabilidade estimada baseada em polimorfismo de nucleotídeo único (SNP) acima de 0,70 (h2SNP = 0,71 ± 0,11 na escala de responsabilidade usando regressão de pontuação de desequilíbrio de ligação21 e h2SNP = 0,70 ± 0,12 usando SumHer22; Tabela Suplementar 3 ). O locus ECM1/ADAMTSL4 no cromossomo 1 foi responsável pela maior proporção de herdabilidade para SCAD em nosso conjunto de dados (h2 = 0,028), seguido pelo locus COL4A1/COL4A2, que continha dois sinais GWAS independentes (h2 = 0,022; Tabela Suplementar 4 e Suplementar Figura 4). No geral, estimamos que os 16 loci explicam ∼ 24% da herdabilidade total do SCAD baseada em SNP (Tabela Suplementar 4).

1% of cells in artery tissue24. The SCAD 95% credible set of causal SNPs and their linkage disequilibrium proxies were matched to random pools of neighboring SNPs using the GREGOR package43. Enrichment represents the ratio of the number of SCAD SNPs overlapping open chromatin regions over the average number of matched SNPs overlapping the same regions. P values were evaluated by binomial one-sided test, with greater enrichment as the alternative hypothesis43. The bottom dashed line represents significance (P < 0.05) after adjustment for 105 subclusters. Higher opacity is used to identify significant associations (adjusted P < 0.05). Bottom, composition of artery tissues relative to 105 single-cell subclusters, as determined by snATAC-seq in 30 adult tissues24. Only subclusters representing >1% of cells from either the aorta or tibial artery were represented. b, Representation of the SCAD TWAS z score for each prioritized gene in GWAS loci. The point shape indicates the tissue used in the TWAS association. The point color distinguishes genes located at different loci. The absence of a symbol indicates that the gene did not show significant heritability based on the eQTL data in the corresponding tissue. TWAS P values were calculated by two-tailed z test against a null distribution calculated by permutation for each gene or tissue44. Higher opacity is used to identify significant associations (Bonferroni adjusted P < 0.05), corresponding to a z score of >4.8 or <−4.8 (dashed gray lines)./p>90%)2,4. Using genetic association colocalization and genetic correlation, we genetically compared SCAD with CAD. We found that, among SCAD loci, several were known to associate with CAD. Disease association colocalization analyses showed that for six loci SCAD and CAD are likely to share the same causal variants with high posterior probabilities (posterior probability of the shared causal variant hypothesis (H4) = 84–100%), but all with opposite risk alleles (Fig. 3a and Supplementary Table 7). Genetic correlation confirmed a genome-wide negative correlation between SCAD and CAD (rg = −0.12 ± 0.04; P = 3.7 × 10−3) (Supplementary Table 10), including after conditioning SCAD GWAS results on systolic blood pressure (SBP) or diastolic blood pressure (DBP) GWAS results using the multitrait-based conditional and joint analysis (mtCOJO) tool31 (rgCAD/SBP = −0.19 ± 0.04 (P = 4.6 × 10−6); rgCAD/DBP = −0.19 ± 0.04 (P = 1.3 × 10−5)) (Supplementary Table 12 and Supplementary Fig. 11)./p> 0.7) with the lead SNP at each locus, based on information from European populations (1000 Genomes reference panel) queried using the ldproxy function of the LDlinkR package (version 1.1.2)49./p>1% of cells in at least one arterial tissue (T lymphocyte 1, CD8+, endothelial general 2, endothelial general 1, macrophage general, fibroblast general, vascular smooth muscle 2 or vascular smooth muscle 1) were extracted and grouped by annotated cell type as T lymphocytes, macrophages, fibroblasts, endothelial cells and VSMCs, respectively. Genome coverage was calculated using the bedtools (version 2.29.0) coverage function. We detected peaks from bedGraph output using the MACS2 bdgpeakcall function (Galaxy Version 2.1.1.20160309.0) on the Galaxy webserver52,53. All peak files were extended 100 base pairs upstream and downstream using the bedtools (version 2.29.0) slop function. We detected overlaps of SCAD potential functional variants with relevant genomic regions using the findOverlap function from the rtracklayer package (version 1.52.1)54. We used the Integrated Genome Browser (version 9.1.8) to visualize read density profiles and peak positions in the context of the human genome55./p>75% or if eQTL association was significant for SCAD lead SNPs and H4 was over 25%. TWASs were performed using the FUSION R/Python package44. Gene expression models were pre-computed from GTEx data (version 8 release) and were provided by the authors. Only genes with a heritability P < 0.01 were used in the analysis. Both tools used linkage disequilibrium information from the European panel of phase 3 of the 1000 Genomes Project. Bonferroni multiple testing correction was applied using the p.adjust function in R (version 4.1.0). Significant capture Hi-C hits in aorta tissue were provided as supplementary data by Jung et al.25. Genes associated with mouse cardiovascular phenotypes (code MP:0005385) were retrieved from the Mouse Genome Informatics database (www.informatics.jax.org)56. We also queried the DisGeNET database, using the disgenet2r package (version 0.99.2), for genes with reported evidence in human cardiovascular disease (code C14) with a score of >0.2, including “ALL” databases57. In the absence of a missense variant, colocalization and TWAS criteria were given a tenfold weight compared with other criteria. At each locus, we prioritized genes fulfilling the largest number of criteria. In cases where several candidates were retained, we prioritized genes that were most likely to have a function in arterial disease (for example, expression in arterial tissues or exclusion of pseudo-genes)./p>30% across 1,000 generated Bayesian networks starting from different random genes. Bayesian networks were combined for the top GWAS hits query, and mouse gene symbols were converted to their human orthologs. Bayesian networks were queried for the identified top GWAS hits to identify their first-degree network connections and to determine connections between their surrounding subnetwork nodes. The directions of edges were informed by prior knowledge, such as eQTLs and previously known regulatory relationships between genes. Subnetworks were annotated by top biological pathways representative of the subnetwork genes using Enrichr with a false discovery rate of <0.05./p> 0.9) and located within 500 kb from the SCAD lead SNP. COL4A1 and COL4A2 loci were separated by placing an equidistant border from SCAD lead SNPs for the inclusion of SNPs in the analysis. Signal colocalization was evaluated using the R coloc package (version 5.1.0) with default values as priors. We reported H4 coefficients indicating the probability of two signals sharing a common causal variant at each locus./p> 0.6 within a 10,000 kb window)./p>

blog