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Machine learning to examine the genetic underpinnings of cardiac fibrosis at scale

We developed a machine learning model to quantify cardiac fibrosis (which is associated with cardiovascular disease) using cardiac MRI data from 41,505 UK Biobank participants. In the subsequent large-scale GWAS of cardiac fibrosis, we identified 11 independent genomic loci, 9 of which were implicated in in vitro cardiac fibroblast activation.

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Fig. 1: Multi-omic assessment of cardiac fibrosis.


  1. López, B. et al. Diffuse myocardial fibrosis: mechanisms, diagnosis and therapeutic approaches. Nat. Rev. Cardiol. 18, 479–498 (2021). A comprehensive review article on the mechanisms, diagnosis and therapeutic approaches for diffuse myocardial fibrosis.

    Article  PubMed  Google Scholar 

  2. Tam, V. et al. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20, 467–484 (2019). A review article that critically appraises the strengths and limitations of GWAS.

    Article  CAS  PubMed  Google Scholar 

  3. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018). A review article that describes the UK Biobank recruitment protocol and the deep phenotyping of study participants.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Diao, K. et al. Histologic validation of myocardial fibrosis measured by T1 mapping: a systematic review and meta-analysis. J. Cardiovasc. Magn. Reson. 18, 92 (2017). A meta-analysis providing evidence validating the use of non-invasive T1 mapping as a surrogate for histological assessment of myocardial fibrosis.

    Article  Google Scholar 

  5. Treibel, T. A. et al. Extracellular volume associates with outcomes more strongly than native or post-contrast myocardial T1. JACC Cardiovasc. Imaging 13, 44–54 (2020). This paper presents evidence supporting the use of extracellular volume fraction for MRI-based fibrosis quantification as a more sensitive marker with stronger associations with disease than T1 time.

    Article  PubMed  Google Scholar 

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This is a summary of: Nauffal, V. et al. Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nat. Genet. (2023).

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Machine learning to examine the genetic underpinnings of cardiac fibrosis at scale. Nat Genet 55, 736–737 (2023).

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