There are several components of the research program. Component 1: Platelet Biology, Reactivity and Genomics. Utilizing one of the largest human samples (FHS: Framingham Heart Study) with platelet reactivity we have conducted deeper genetic scans for contributing genes. These scans use new genetic maps with deeper coverage of rare variation. DNA genotyping of an additional diverse population sample, the FHS OMNI cohort, was supported allowing additional validation samples and gene coverage for platelet reactivity traits. Further support was provided for genotyping of the U.K.-based Caerphilly Study in Men cohort, a rich study repository of hemostatic factor and platelet reactivity trait data. New genetic studies in 2018-19 led by the lab expanded the SNP platforms and imputation used to study platelet traits to include TOPMed WGS studies (presented at ASHG 2018; ISTH 2019; in revision in Nature). GWAS studies in the Caerphilly study have been completed leading to a major new gene discovery with new functional experiments leading to a new platelet gene mechanism (new collaborators A.Bhan/T.Schlaeger Boston Childrens' Hospital) with a manuscript in submission late in this period. This gene has been casually linked to both venous and arterial cardiovascular disease now and could represent a novel drug target. The analysis of large populations for the genetics of PLT (platelet count) and MPV (mean platelet volume) has resulted in scores of novel loci, including in non-European populations (2 manuscripts in preparation at the time of this report). We have continued to support the analysis and inclusion of FHS venous thromboembolism (VTE cases) in large discovery consortia with 2 publications accepted in the last period. We have also contributed FHS data to 2 new clotting factor gene discovery efforts both published in the last period. One of these led to the growing insight that many VTE genes have known effects on platelet biology. A major initiative in the lab is large-scale platelet data collection in the FHS Gen3/Omni2 Exam 3. Deep data collection was started in April 2016 and data on >3,000 samples were completed in April 2019. These data are being carefully cleaned and integrated with other FHS datasets to fuel new platelet genetic and epidemiology projects in the coming years, with preliminary results already being presented at scientific meetings. New platelet function was initiated, led by the Lab, in 2018 in the Boston Puerto Rican Health Study in an effort to expand diverse genetic and epidemiological studies of platelet function. This study is ongoing and expected to complete in late 2019 or early 2020 and inform the field greatly on differences among populations with respect to platelet function. Component 2: Tissue-specific Gene Expression. A major cell- and tissue-specific database of genetic factors on gene expression (eQTLs) was maintained. This catalog was used to add information on genes to many disease and risk factor studies, primarily in the cardiovascular and metabolic disease domains. In 2019 we have utilized a publicly available platelet SNP and microarray dataset to re-impute genetic markers and expand knowledge of platelet eQTLs, as well how they are enriched for megkaryocyte epigenetic signatures. We are beginning plans for a larger scale platelet RNA-sequencing eQTL effort Component 3: Development and Application of Bioinformatics Resources. Beyond the eQTL database mentioned above, a large genome-wide association study (GWAS) results database GRASP continues to be updated, and an online NIH query site developed. The database is publicly downloadable and queryable at the URL: grasp.nhlbi.nih.gov/Overview.aspx. One of the main changes in the database is the posting of full GWAS summary statistics where available. There are now 144 studies with such full GWAS data posted for researchers worldwide to access and utilize, with several more coming. The database was widely used in addressing many research questions as evidenced by thousands of web hits and queries per month and 575+ citations to GRASP-related publications.

Project Start
Project End
Budget Start
Budget End
Support Year
7
Fiscal Year
2019
Total Cost
Indirect Cost
Name
National Heart, Lung, and Blood Institute
Department
Type
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Ward-Caviness, Cavin K; Huffman, Jennifer E; Everett, Karl et al. (2018) DNA methylation age is associated with an altered hemostatic profile in a multiethnic meta-analysis. Blood 132:1842-1850
Floyd, J S; Sitlani, C M; Avery, C L et al. (2018) Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group. Pharmacogenomics J 18:127-135
Eicher, John D; Lettre, Guillaume; Johnson, Andrew D (2018) The genetics of platelet count and volume in humans. Platelets 29:125-130
Yao, Chen; Chen, George; Song, Ci et al. (2018) Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat Commun 9:3268
Puurunen, Marja K; Hwang, Shih-Jen; Larson, Martin G et al. (2018) ADP Platelet Hyperreactivity Predicts Cardiovascular Disease in the FHS (Framingham Heart Study). J Am Heart Assoc 7:
Webb, Thomas R; Erdmann, Jeanette; Stirrups, Kathleen E et al. (2017) Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease. J Am Coll Cardiol 69:823-836
Chu, Audrey Y; Deng, Xuan; Fisher, Virginia A et al. (2017) Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation. Nat Genet 49:125-130
Eicher, John D; Chen, Ming-Huei; Pitsillides, Achilleas N et al. (2017) Whole exome sequencing in the Framingham Heart Study identifies rare variation in HYAL2 that influences platelet aggregation. Thromb Haemost 117:1083-1092
Zhu, Qiuyu Martin; Ko, Kyung Ae; Ture, Sara et al. (2017) Novel Thrombotic Function of a Human SNP in STXBP5 Revealed by CRISPR/Cas9 Gene Editing in Mice. Arterioscler Thromb Vasc Biol 37:264-270
Brody, Jennifer A; Morrison, Alanna C; Bis, Joshua C et al. (2017) Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology. Nat Genet 49:1560-1563

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