Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for … – Nature.com

Roth Gregory, A. et al. Global burden of cardiovascular diseases and risk factors, 19902019. J. Am. Coll. Cardiol. 76, 29823021 (2020).

Article CAS PubMed PubMed Central Google Scholar

Khera, A. V. & Kathiresan, S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat. Rev. Genet. 18, 331344 (2017).

Article CAS PubMed PubMed Central Google Scholar

Chen, Z. & Schunkert, H. Genetics of coronary artery disease in the post-GWAS era. J. Intern. Med. 290, 980992 (2021).

Article PubMed Google Scholar

Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 18031815 (2022).

Article CAS PubMed PubMed Central Google Scholar

Tcheandjieu, C. et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. 28, 16791692 (2022).

Article CAS PubMed PubMed Central Google Scholar

Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581594 (2013).

Article CAS PubMed Google Scholar

Plenge, R. M. Disciplined approach to drug discovery and early development. Sci. Transl. Med. 8, 349ps15 (2016).

Article PubMed Google Scholar

Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942948 (2021).

Article CAS PubMed Google Scholar

Do, R. et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102106 (2015).

Article CAS PubMed Google Scholar

Yao, K. et al. Exome sequencing identifies rare mutations of LDLR and QTRT1 conferring risk for early-onset coronary artery disease in Chinese. Natl Sci. Rev. 9, nwac102 (2022).

Article CAS PubMed PubMed Central Google Scholar

Khera, A. V. et al. Gene sequencing identifies perturbation in nitric oxide signaling as a nonlipid molecular subtype of coronary artery disease. Circ. Genom. Precis. Med. 15, e003598 (2022).

Article CAS PubMed PubMed Central Google Scholar

Martin, S. S. et al. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation 149, e347e913 (2024).

Article PubMed Google Scholar

Maddox, T. M. et al. Nonobstructive coronary artery disease and risk of myocardial infarction. JAMA 312, 17541763 (2014).

Article CAS PubMed PubMed Central Google Scholar

Park, D. W. et al. Extent, location, and clinical significance of non-infarct-related coronary artery disease among patients with ST-elevation myocardial infarction. JAMA 312, 20192027 (2014).

Article CAS PubMed Google Scholar

Forrest, I. S. et al. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. Lancet 401, 215225 (2023).

Article PubMed Google Scholar

Petrazzini, B. O. et al. Coronary risk estimation based on clinical data in electronic health records. J. Am. Coll. Cardiol. 79, 11551166 (2022).

Article PubMed PubMed Central Google Scholar

Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 10971103 (2021).

Article CAS PubMed Google Scholar

Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 21902191 (2010).

Article CAS PubMed PubMed Central Google Scholar

Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat. Genet. 48, 314317 (2016).

Article CAS PubMed Google Scholar

Zhou, W. et al. Efficiently controlling for casecontrol imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 13351341 (2018).

Article CAS PubMed PubMed Central Google Scholar

Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906908 (2018).

Article CAS PubMed PubMed Central Google Scholar

Nikpay, M. et al. A comprehensive 1,000 genomesbased genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 11211130 (2015).

Article CAS PubMed PubMed Central Google Scholar

Tarugi, P. et al. Molecular diagnosis of hypobetalipoproteinemia: an ENID review. Atherosclerosis 195, e19e27 (2007).

Article CAS PubMed Google Scholar

Ference, B. A. et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N. Engl. J. Med. 375, 21442153 (2016).

Article CAS PubMed Google Scholar

Schmidt, A. F. et al. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study. Lancet Diabetes Endocrinol. 5, 97105 (2017).

Article CAS PubMed PubMed Central Google Scholar

Lotta, L. A. et al. Association between low-density lipoprotein cholesterollowering genetic variants and risk of type 2 diabetes: a meta-analysis. JAMA 316, 13831391 (2016).

Article CAS PubMed PubMed Central Google Scholar

Benn, M., Nordestgaard, B. G., Grande, P., Schnohr, P. & Tybjrg-Hansen, A. PCSK9R46L, low-density lipoprotein cholesterol levels, and risk of ischemic heart disease: 3 independent studies and meta-analyses. J. Am. Coll. Cardiol. 55, 28332842 (2010).

Article CAS PubMed Google Scholar

Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311D1320 (2021).

Article CAS PubMed Google Scholar

Thomas, D. G., Wei, Y. & Tall, A. R. Lipid and metabolic syndrome traits in coronary artery disease: a Mendelian randomization study. J. Lipid Res. 62, 100044 (2021).

Article CAS PubMed PubMed Central Google Scholar

Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417425 (2015).

Article CAS PubMed PubMed Central Google Scholar

Schrodi, S. J. The impact of diagnostic code misclassification on optimizing the experimental design of genetic association studies. J. Healthc. Eng. 2017, 7653071 (2017).

Article PubMed PubMed Central Google Scholar

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203209 (2018).

Article CAS PubMed PubMed Central Google Scholar

Klarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49, 13921397 (2017).

Article CAS PubMed PubMed Central Google Scholar

Honigberg, M. C. et al. Premature menopause, clonal hematopoiesis, and coronary artery disease in postmenopausal women. Circulation 143, 410423 (2021).

Article PubMed Google Scholar

Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 12191224 (2018).

Article CAS PubMed PubMed Central Google Scholar

Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

Article PubMed PubMed Central Google Scholar

Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 113 (2010).

Article Google Scholar

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 13471358 (2019).

Article PubMed Google Scholar

Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 1822 (2002).

Google Scholar

Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 126 (2008).

Article Google Scholar

Grn, B., Kosmidis, I. & Zeileis, A. Extended beta regression in R: shaken, stirred, mixed, and partitioned. J. Stat. Softw. 48, 125 (2012).

Article Google Scholar

McCaw, Z. R., Lane, J. M., Saxena, R., Redline, S. & Lin, X. Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies. Biometrics 76, 12621272 (2020).

Article CAS PubMed PubMed Central Google Scholar

Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514518 (2019).

Article CAS PubMed PubMed Central Google Scholar

Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248249 (2010).

Article CAS PubMed PubMed Central Google Scholar

Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 38123814 (2003).

Article CAS PubMed PubMed Central Google Scholar

Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 15531561 (2009).

Article CAS PubMed PubMed Central Google Scholar

Schwarz, J. M., Cooper, D. N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361362 (2014).

Article CAS PubMed Google Scholar

Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence Kernel association test. Am. J. Hum. Genet. 89, 8293 (2011).

Article CAS PubMed PubMed Central Google Scholar

Liu, Y. et al. ACAT: a fast and powerful P value combination method for rare-variant analysis in sequencing studies. Am. J. Hum. Genet. 104, 410421 (2019).

Article CAS PubMed PubMed Central Google Scholar

McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

Article PubMed PubMed Central Google Scholar

See the rest here:
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for ... - Nature.com