The DOI matches its Crossref record; 141 of the 141 references it cites were found in a registry or an index.
Genetics of Coronary Artery Disease
Abstract
Genetic factors contribute importantly to the risk of coronary artery disease (CAD), and in the past decade, there has been major progress in this area. The tools applied include genome-wide association studies encompassing >200 000 individuals complemented by bioinformatic approaches, including 1000 Genomes imputation, expression quantitative trait locus analyses, and interrogation of Encyclopedia of DNA Elements, Roadmap, and other data sets. close to 60 common SNPs (minor allele frequency>0.05) associated with CAD risk and reaching genome-wide significance ( P <5×10 −8 ) have been identified. Furthermore, a total of 202 independent signals in 109 loci have achieved a false discovery rate ( q <0.05) and together explain 28% of the estimated heritability of CAD. These data have been used successfully to create genetic risk scores that can improve risk prediction beyond conventional risk factors and identify those individuals who will benefit most from statin therapy. Such information also has important applications in clinical medicine and drug discovery by using a Mendelian randomization approach to interrogate the causal nature of many factors found to associate with CAD risk in epidemiological studies. In contrast to genome-wide association studies, whole-exome sequencing has provided valuable information directly relevant to genes with known roles in plasma lipoprotein metabolism but has, thus far, failed to identify other rare coding variants linked to CAD. Overall, recent studies have led to a broader understanding of the genetic architecture of CAD and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on CAD risk. Despite this success, there has been limited progress in understanding the function of the novel loci; the majority of which are in noncoding regions of the genome.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Non-coding RNAs in human disease
Esteller, M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3074 · Cited by 4,825
Ezetimibe Added to Statin Therapy after Acute Coronary Syndromes
Cannon, C. P., Blazing, M. A., Giugliano, R. P., McCagg, A., White, J. A., Theroux, P., Darius, H., Lewis, B. S., Ophuis, T. O., Jukema, J. W., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1410489 · Cited by 4,487
Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716
ATAC-seq: a method for assaying chromatin accessibility genome-wide
Buenrostro, J. D., Wu, B., Chang, H. Y., & Greenleaf, W. J. (2015) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2129s109 · Cited by 3,289
Sequence Variations in PCSK9, Low LDL, and Protection against Coronary Heart Disease
Cohen, J. C., Boerwinkle, E., Mosley, T. H., & Hobbs, H. H. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054013 · Cited by 3,181
Mutations in PCSK9 cause autosomal dominant hypercholesterolemia
Abifadel, M., Varret, M., Rabès, J.-P., Allard, D., Ouguerram, K., Devillers, M., Cruaud, C., Benjannet, S., Wickham, L., Erlich, D., et al. (2003) · Nature Genetics · doi:10.1038/ng1161 · Cited by 2,985
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447
Combinatorial patterns of histone acetylations and methylations in the human genome
Wang, Z., Zang, C., Rosenfeld, J. A., Schones, D. E., Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Peng, W., Zhang, M. Q., et al. (2008) · Nature Genetics · doi:10.1038/ng.154 · Cited by 2,331
Cited by
von Scheidt, M., Zhao, Y., de Aguiar Vallim, T. Q., Che, N., Wierer, M., Seldin, M. M., Franzén, O., Kurt, Z., Pang, S., Bongiovanni, D., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.050186 · Cited by 68
Song, Y., Choi, J.-E., Kwon, Y.-J., Chang, H.-J., Kim, J. O., Park, D.-H., Park, J.-M., Kim, S.-J., Lee, J. W., & Hong, K.-W. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02751-3 · Cited by 41
Aherrahrou, R., Reinberger, T., Hashmi, S., & Erdmann, J. (2024) · Cardiovascular Research · doi:10.1093/cvr/cvae161 · Cited by 25
Identification of the Transcription Factor ATF3 as a Direct and Indirect Regulator of the LDLR
Bauer, S., Eigenmann, J., Zhao, Y., Fleig, J., Hawe, J. S., Pan, C., Bongiovanni, D., Wengert, S., Ma, A., Lusis, A. J., et al. (2022) · Metabolites · doi:10.3390/metabo12090840 · Cited by 8
Metadata
- Authors
- McPherson, R., & Tybjaerg-Hansen, A.
- Venue
- Circulation Research
- Published
- 2016-02-19
- Volume
- 118
- Issue
- 4
- Pages
- 564-578
- ISSN
- 0009-7330 (ISSN-L)
- 1524-4571
- OpenAlex
- W2277439059
- Cited by
- 371
- References
- 141
- FWCI
- 31.07
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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