@article {261,
	title = {DNA methylation age is associated with an altered hemostatic profile in a multi-ethnic meta-analysis},
	journal = {Blood},
	year = {2018},
	abstract = {<p>Elevated epigenetic age is associated with an altered hemostatic factor profile and lower clotting time (aPTT).DNA methylation age is associated with mRNA levels of fibrinogen in multiple tissues. Many hemostatic factors are associated with age and age-related diseases, however much remains unknown about the biological mechanisms linking aging and hemostatic factors. DNA methylation is a novel means by which to assess epigenetic aging, which is a measure of age and the aging processes as determined by altered epigenetic states. We used a meta-analysis approach to examine the association between measures of epigenetic aging and hemostatic factors, as well as a clotting time measure. For fibrinogen, we used European and African-ancestry participants who were meta-analyzed separately and combined via a random effects meta-analysis. All other measures only included participants of European-ancestry. We found that 1-year higher extrinsic epigenetic age as compared to chronological age was associated with higher fibrinogen (0.004 g/L per year; 95\% CI: 0.001, 0.007; P = 0.01) and plasminogen activator inhibitor 1 (PAI-1; 0.13 U/mL per year; 95\% CI: 0.07, 0.20; P = 6.6x10-5) concentrations as well as lower activated partial thromboplastin time, a measure of clotting time. We replicated PAI-1 associations using an independent cohort. To further elucidate potential functional mechanisms we associated epigenetic aging with expression levels of the PAI-1 protein encoding gene (SERPINE1) and the three fibrinogen subunit-encoding genes (FGA, FGG, and FGB), in both peripheral blood and aorta intima-media samples. We observed associations between accelerated epigenetic aging and transcription of FGG in both tissues. Collectively, our results indicate that accelerated epigenetic aging is associated with a pro-coagulation hemostatic profile, and that epigenetic aging may regulate hemostasis in part via gene transcription.</p>
},
	issn = {0006-4971},
	doi = {10.1182/blood-2018-02-831347},
	url = {http://www.bloodjournal.org/content/early/2018/07/24/blood-2018-02-831347},
	author = {Ward-Caviness, Cavin K. and Huffman, Jennifer E. and Evertt, Karl and Germain, Marine and van Dongen, Jenny and Hill, W. David and Jhun, Min A. and Brody, Jennifer A. and Ghanbari, Mohsen and Du, Lei and Roetker, Nicholas S. and de Vries, Paul S. and Waldenberger, Melanie and Gieger, Christian and Wolf, Petra and Prokisch, Holger and Koenig, Wolfgang and O{\textquoteright}Donnell, Christopher J. and Levy, Daniel and Liu, Chunyu and Truong, Vinh and Wells, Philip S. and Tr{\'e}gou{\"e}t, David-Alexandre and Tang, Weihong and Morrison, Alanna C. and Boerwinkle, Eric and Wiggins, Kerri L. and McKnight, Barbara and Guo, Xiuqing and Psaty, Bruce M. and Sotoodenia, Nona and Dorret I. Boomsma and Gonneke Willemsen and Lannie Ligthart and Deary, Ian J. and Zhao, Wei and Ware, Erin B. and Kardia, Sharon L.R. and Joyce B.J. Van Meurs and Uitterlinden, Andre G. and Franco, Oscar H. and Eriksson, Per and Franco-Cereceda, Anders and Pankow, James S. and Johnson, Andrew D. and Gagnon, France and Morange, Pierre-Emmanuel and de Geus, Eco J.C. and Starr, John M. and Smith, Jennifer A. and Dehghan, Abbas and Bj{\"o}rck, Hanna M. and Smith, Nicholas L. and Peters, Annette}
}
@article {269,
	title = {Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits},
	journal = {bioRxiv},
	year = {2018},
	abstract = {<p>Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced \&rsquo;atlases\&rsquo; of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.</p>
},
	doi = {10.1101/305029},
	url = {https://www.biorxiv.org/content/early/2018/04/21/305029},
	author = {Grotzinger, Andrew D and Rhemtulla, Mijke and de Vlaming, Ronald and Ritchie, Stuart J. and Mallard, Travis T. and Hill, W. David and Ip, Hill F. and McIntosh, Andrew M. and Deary, Ian J. and Koellinger, Philipp D. and Harden, K. Paige and Michel G. Nivard and Tucker-Drob, Elliot M.}
}
