Whole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program

Author:

Seyerle Amanda A.12ORCID,Laurie Cecelia A.3ORCID,Coombes Brandon J.4ORCID,Jain Deepti3,Conomos Matthew P.3ORCID,Brody Jennifer5,Chen Ming-Huei6ORCID,Gogarten Stephanie M.3ORCID,Beutel Kathleen M.67ORCID,Gupta Namrata8ORCID,Heckbert Susan R.59ORCID,Jackson Rebecca D.10,Johnson Andrew D.6ORCID,Ko Darae11ORCID,Manson JoAnn E.12ORCID,McKnight Barbara3,Metcalf Ginger A.13ORCID,Morrison Alanna C.14ORCID,Reiner Alexander P.9ORCID,Sofer Tamar1516ORCID,Tang Weihong17ORCID,Wiggins Kerri L.5ORCID,Boerwinkle Eric14ORCID,de Andrade Mariza4ORCID,Gabriel Stacey B.8,Gibbs Richard A.13,Laurie Cathy C.3ORCID,Psaty Bruce M.591819ORCID,Vasan Ramachandran S.20ORCID,Rice Ken3ORCID,Kooperberg Charles21ORCID,Pankow James S.17ORCID,Smith Nicholas L.5922ORCID,Pankratz Nathan7ORCID,

Affiliation:

1. Division of Pharmaceutical Outcomes & Policy, Eshelman School of Pharmacy (A.A.S.), University of North Carolina at Chapel Hill.

2. Carolina Health Informatics Program (A.A.S.), University of North Carolina at Chapel Hill.

3. Department of Biostatistics (C.A.L., D.J., M.P.C., S.M.G., B.M., C.C.L., K.R.), University of Washington, Seattle.

4. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN (B.J.C., M.d.A.).

5. Cardiovascular Health Research Unit (J.B., S.R.H., K.L.W., B.M.P., N.L.S.), University of Washington, Seattle.

6. NHLB’s The Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Framingham, MA (M.-H.C., A.D.J.).

7. Department of Laboratory Medicine & Pathology, School of Medicine (K.M.B., N.P.), University of Minnesota, Minneapolis.

8. Broad Institute of MIT & Harvard, Cambridge, MA (N.G., S.B.G.).

9. Department of Epidemiology (S.R.H., A.P.R., B.M.P., N.L.S.), University of Washington, Seattle.

10. Division of Endocrinology, Diabetes & Metabolism, Ohio State University, Columbus (R.D.J.).

11. Cardiovascular Medicine Section, Boston University School of Medicine (D.K.).

12. Department of Epidemiology, TH Chan School of Public Health, Harvard University, Boston, MA (J.E.M.).

13. Human Genome Sequencing Center, Baylor College of Medicine (G.A.M., R.A.G.).

14. Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston (A.C.M., E.B.).

15. Division of Sleep & Circadian Disorders, Brigham and Women’s Hospital (T.S.).

16. Department of Medicine, Harvard Medical School, Boston, MA (T.S.).

17. Division of Epidemiology & Community Health (W.T., J.S.P.), University of Minnesota, Minneapolis.

18. Departments of Medicine & Health Services (B.M.P.), University of Washington, Seattle.

19. Kaiser Permanente Washington Health Rsrch Inst, Seattle, WA (B.M.P.).

20. Departments of Medicine& Epidemiology, Boston University, MA (R.S.V.).

21. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center (C.K.).

22. Seattle Epidemiologic Research & Information Center, VA Office of Research & Development, Seattle, WA (N.L.S.).

Abstract

Background: Risk for venous thromboembolism has a strong genetic component. Whole genome sequencing from the TOPMed program (Trans-Omics for Precision Medicine) allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies. Methods: The 3793 cases and 7834 controls (11.6% of cases were individuals of African, Hispanic/Latino, or Asian ancestry) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants). Results: Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only PROC (odds ratio, 6.2 for carriers of rare variants; P =7.4×10 −14 ) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at PROC (odds ratio, 3.8; P =1.6×10 −14 ), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: PROS1 became significant (minimum P =1.8×10 −6 with the secondary filter), while SERPINC1 did not (minimum P =4.4×10 −5 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, MS4A1 , became significant ( P =4.4×10 −7 using all missense variants with minor allele frequency <0.0005). Conclusions: Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel MS4A1 locus and to identify additional rare variation associated with venous thromboembolism.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

General Medicine

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